Table 2.4 shows the results of the linear fixed-effects regressions by event and sex. Anticipation only plays a minor role in most events. It is only seen significant in the case of both male and female unemployment and female transition to the 5-day-working-week. Concerning impact and adaptation, our data produces interesting findings. Most events show substantial gender-specific impacts on life satisfaction. The estimates indicate full adaptation after some events, and more so for women. However, after others we see no or only partial habituation. Table 2.5 presents determinants of the observed intra-marriage gender happiness gap.

## **2.4.1 Marriage-Status-Related Events**

Columns (1) to (6) of Table 2.4 show the results of different changes in marital status on life satisfaction.3° Females experience a strong positive significant effect in the first year after marriage. Yet, this positive effect halves one year later and disappears completely after two years from marriage shifting women back to their baseline level of happiness. In comparison, males also experience the strongest positive effect in the year of marriage and this effect more than halves in the second year.

29 Further research should also include the effect of assets brought into marriage on the gender happiness differential. Traditional practice in Korea requests the groom's parents to provide the house for the new couple, while the bride's parents should provide the necessary house equipment. As the relative price of housing has been constantly on the rise, this usually means a higher share of assets brought into marriage by the groom.

30 Results are graphically displayed in Figure 2.1.

*Table 2. 4: Effect of Life and Labor Market Events on Life Satisfaction3~*

**No1es: Linear fixed-effects estimator. Other control variables incl1Kle 5yE".ar-age-cohorts, dummies for household ltf'.ad and spouse. ye,ars of schooling. household size, mnn~ of young (0-14 years) and old (15- 30 years) chtklren In the hoosehold. number of old females and males Jn die househokt, dununies fol' marital status. log per-capita household inco1ne, log percapita regional Income, housf' ownership, 14 rf'gional dummies, 10 year dunuuJts. dununl~ controlllng for nu·w.nt occupation status . .. ,,.,\_/'•\*/"' indicate a parameter estimalt> lsslgnlflcant at the 1%/SW/ 10'::lb level. Data: KLIPS 1998--2008.**

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However, they somehow manage to increase the positive effect again three years after marriage and are then able to sustain it in the long-run. The results suggest that men might benefit more from marriage than women.

Full results for the event "marriage" including all covariates can be seen in Table Al in the appendix. Here, the main findings are confirmed for the two ordered logit estimators (FF and BUC). The results provide additional evidence for the fact that life events are not randomly distributed (Stutzer and Frey, 2006). Comparing lead effects of simple ordered logit estimates in columns (1) and (5) with the fixed-effects estimates, it can be seen that among all never married individuals of the same age cohort those that eventually get married are already happier several years *before* marriage. Thus, happier personality traits are more likely to marry. 31 While this once more confirms the importance to control for fixed effects to avoid potential selection bias, the comparison of different estimators also indicates that the choice of the fixed-effects estimator does not change the main results.

Bearing in mind additional marital benefits for men, results for divorce and widowhood can be better understood. In general, results suggest that men suffer much more from marital dissolution. When getting divorced, women experience a short negative effect in the first year following divorce but quickly manage to recover. 32 Men experience a twice as strong negative initial effect, and when staying divorced never return to their marital level of happiness within the observed time frame. Results on widowhood also diverge between men and women. While widowed men show a transient negative effect and full adaptation thereafter, widowed women on average show no negative effect after their spouses pass away. They experience, if anything, small positive effects between the second and the fourth year after the event. 33

31 Full results of all events except marriage are not displayed in the paper because of space limitations. They can be obtained from the authors on request. Examining potential selection effects for the case of divorce, we find that unhappier female personality traits are more likely to get divorced, a finding that does not hold for men in our data.

32 Note that if the woman was a housewife, she is entitled to receive 30 percent of the wealth accumulated during marriage when divorce takes place within the first 10 years, and 40-50 percent thereafter. Wealth that was already formed before marriage goes to the partner who brought it into marriage. In the case of female widowhood, she is treated with a factor of 1.5, and each child of l. Thus the woman receives about 43 percent of total inheritance in the case of one daughter and one son.

33 Of course, the latter results of widowhood focus on the eldest generation and cannot be generalized for younger marriages. Gender inequality is likely to be highest in the eldest generation (see also Rudolf and Cho, 2011).

These results are particularly interesting when comparing them to the findings of Clark et al. (2008) and Clark and Georgellis (2010) for German and British women and men. In their analyses of more gender-equal societies, they do not find significant differences in adaptation to marriage-related events by sex. Both women and men adjust to the positive (negative) effects of marriage (widowhood) rather quickly and return to their baseline happiness within a similar time horizon than that of our paper. Only in the long run (5 or more years), they find significant negative effects for women. In the case of divorce, German and British women and men show strong negative effects before getting divorced and then increase their happiness gradually after. Here also, no specific gender effects could be traced out. Therefore, Korean results do significantly differ from what has been found so far in the literature.

In a further step we want to visualize the magnitude of the gender happiness gap. Thus, we assign monetary values to the effect, as has been done, for example, by Oswald and Powdthavee (2008) in the case of calculating compensation payments for disabled people. When looking at column (8) of Table Al in the appendix, we see that the average long-run happiness shift for men is around .15. This conservative calculation is almost double the effect of living in one's own house (.083), and it is equivalent to approximately a 300 percent increase in per-capita household income (assuming that we could raise only male per-capita household income). Since the mean of log annual household per-capita income in our sample is equivalent to 6.08 million Korean Won (KRW) in real 2005 value, an increase of 300 percent is equivalent to an increase of about 18.2 Million KRW. Applying a yearly average exchange rate of 1,024 KRW/\$US in 2005, this is equivalent to the happiness effect (exclusively for the husband) of raising per-capita yearly household income by approximately US\$ l 7,800.

#### **2.4.2 Labor Market Events**

Columns (7) to (12) of Table 2.4 show estimation results of effects of unemployment, first job entry, and the introduction of the 5-day-working-week on happiness. Unemployment and its long-run effects can only partially be analyzed with the help of Korean data. The dynamic nature of Korea's labor market and the near absence of public unemployment insurance both result in a very low number of long-term unemployed. Thus we had to reduce the number of lag categories in our model. Still, the results suggest partial adjustment for women already after more than one year of unemployment. Men seem to not adjust over the limited time horizon. Their negative satisfaction respond does not diminish in size after two or more years of continuous unemployment, it rather slightly increases. This is in line with Jang et al. (2009) who show that Korean men suffer much more from depressive symptoms than their female counterparts when unemployed, early retired or out-of-labor-force.

Entering one's first job does not show significant effects on happiness, once we control for income effects. If anything, one might notice that men have rather positive coefficients and women merely negative ones following first job entry. In the long-run we can see a negative effect for women who stay in their first job.34

Finally we look at what happens when individuals move from a 6-to-7-dayto a 5-day-working-week. Effects on life satisfaction are in general rather weak. The estimates suggest slight positive effects for men in the years after the reduction in working days. Women do not show significant effects after the reduction. Table A2 in the appendix examines this satisfaction response more in detail. It reports the results of the same model, but now additionally estimated with hours and job satisfaction as dependent variables.35 The results show that while women experience no effect on either of the three satisfaction measures, men show positive responses in both their satisfaction with working hours and their overall job satisfaction. The latter indicators show positive, significant effects starting 1 to 2 years before the actual reduction and then lasting in the long-run. Thus, we cannot reject non-adaptation to the observed working days reduction for men.36 It seems that men, who have on average higher labor force participation and work more days and hours than women, benefited more from the newly introduced two leisure days per week.

To sum up, the findings on labor market events show that men are both harder hit by unemployment and benefit more from a reduction in working days.

34 Direct gender discrimination in the Korean labor market manifests not only through significant gender wage differentials but also through unequal promotion chances (Lee et al., 2008).

35 The job satisfaction question is *"Overall, how satisfied or dissatisfied are you with your main job?",* while hours satisfaction is the answer given to the aspect *"Working hours"*  following the question *"How satisfied or dissatisfied are you with regard to your main job on the following aspects?".* Both variables use the same scale as life satisfaction, i.e. from 1 ("very dissatisfied") to 5 ("very satisfied") in our analysis.

36 Note that the result of no adaptation does not change after introducing working hours controls. Thus, the mere effect of shifting working time from six or seven days to only five days a week appears to have lasting positive effects.

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#### **2.4.3 Determinants of Intra-Marriage Gender Happiness Gap**

The finding of a significant intra-marriage gender happiness gap in the preceding subsections leads to the analysis of the determinants of this gap. The results are presented in Table 2.5. The female share in earnings has the expected negative significant effect on the husband-wife happiness gap. 37 When women increase their relative bargaining position in the household, they also increase their happiness *relative* to their husbands (column (1)). However, does this effect work through an increase of female happiness or through a decrease in male happiness? As the last three columns show, the latter is the case. When female shares in earnings rise, husband's happiness falls (column (4)). Since for the wife there is no effect (column (3)), the sum of husband's and wife's happiness is negatively affected by a relative increase in female earnings (column (2)). This result can be interpreted in two ways: On the one side it could be used as evidence supporting intra-household bargaining theories. Husband's happiness might have decreased because of a decrease in relative bargaining power. On the other side, it could also provide evidence for the gender identity hypothesis. He might suffer a negative utility effect due to his non-fulfillment of societal expectations. While it is not possible with our data to disentangle the relative importance of the two effects, we do believe that both effects play a role. Further support for the identity effect can be seen when comparing the effect that unemployment has on female versus male happiness. Husband's unemployment has not only a strong effect on male happiness, but also on female happiness. In fact, women are affected even stronger by male unemployment. Being confronted with wife's unemployment has a much smaller effect on the household's combined happiness. Thus, gendered role prescriptions are likely to influence utility. Another interesting finding is that women seem to compare themselves more with others than men,· as indicated by a much stronger negative effect of log regional per-capita income on women's happiness. Interestingly, men seem to prefer their wives not to stay at home as a housewife, but to work instead. Thus, while working women increase male happiness compared to housewives, a higher female earnings share is detrimental for male well-being. Women themselves seem to be indifferent between working and being a housewife.

37 Note that the female share in married couple earnings in our sample rose from 1998 (.17) to 1999 (.22), but then stagnated until 2008 (.23).

*Table 2.5: Intra-Marriage Happiness Inequality*

Notes: Linear fixed-effects estimation. Other control variables include 5-year-age-cohorts, 14 regional dummies and 10 year dummies.\*\*\*/\*\*/\* indicate a parameter estimate is significant at the 1 %/5%/10% level. Reference category for occupation status: Working 41-50 hours a week. Data: KLIPS 1998-2008.

## **2.5 Conclusion**

The main aim of this paper was to provide evidence for the baseline hypothesis from a society with relatively strong traditional gender roles. Korea presents an ideal case study, since compared to its economic and human development achievements, the country still ranks very low in terms of gender equity. Our analysis revealed that the inter-temporal nature of major life and labor market events matters. While partial and full adaptation can be seen to most events, anticipation only plays a minor role. We find important gender-related differences in both impact and adaptation to major events.  This can partly explain why women then return to their baseline happiness more quickly than men. Moreover, particularly the events related to a change in marital status provide evidence for a significant gender gap during marriage in Korea. While marriage has a strong and lasting positive effect on male happiness, female's happiness gain is limited to not more than the first two years after marriage. We find that the long-run male happiness shift due to marriage is equivalent of twice the effect of living in one's own house and to an increase of per-capita yearly household income by approximately US\$ l 7,800. Men suffer relatively more than women from divorce and widowhood. Female widows do not show any negative effects after their partner passes away. If anything, rather small positive effects can be observed. Examining potential determinants of the intra-marriage gender happiness gap closer revealed that low female earnings shares provide an important explanation for the happiness gap. Two theoretical explanations can be thought of. First, financial dependence lowers relative female bargaining power in the household, which might have impacts on the intra-marital happiness distribution. Second, if societal prescriptions are such that men should have the dominant earnings share, and if social comparison matters for utility, then higher female earnings might induce lower male happiness.

With respect to unemployment, our findings suggest partial adaptation for women but no adaptation for men to unemployment in the short-run. The reduction of working days and the move towards a 5-day working week shows positive effects only for men. Thus, labor market events also point towards the importance of gendered roles. Men's role as the main breadwinner might explain stronger male responses to labor market events.

Although gender roles are changing in Korea today, the fact that married women's share in couple earnings has stagnated between 1999 (.22) and 2008 (.23) indicates that past reforms have not led to significant improvements for married women yet. As traditional gender roles and a highly male-dominated labor market continue to be major obstacles to the pursuit of gender equity in happiness in Korea, reforms in this area should be continued and require critical evaluation. Future reforms should ensure equal pay and equal promotion chances for women, as well as the creation of family friendly job opportunities, particularly in high-skilled employment. In order to better separate effects of gender-specific impacts of the event from gender-specific adaptation patterns, further research is needed.

# **Essay 3**

# **Rural Reforms, Agricultural Productivity, and the Biological Standard of Living in South Korea, 1941-1974**

#### **Abstract**

This paper analyzes effects of the Republic of Korea's two major rural reforms in 1950 and 1962/63 on agricultural productivity and individual well-being. The 1950 Land Reform resulted in a large-scale redistribution of land while 'green revolution' -type reforms in 1962/63 pushed forward the application of modern agricultural technologies and improved rural infrastructure. This study' s findings indicate that both reforms had significant positive impacts on agricultural productivity. Using the link between final height outcomes and early childhood nutrition further allows an assessment of the interventions on the biological standard of living using adult height outcomes. Korean mean adult height grew by a remarkable 8.1-12 cm for women and 7 to 9.6 cm for men born between 1920 and 1987. Two thirds of this growth took place after the 1950 reform, and about 40-50 percent after the 1962/63 reforms. Structural break analyses of height trends reveal significant upward shifts in trend around the years of the reforms. While Korea can be considered a case of successful land reform, the years between the two major reforms can be considered as Korea's lost decade.

## **3.1 Introduction**

The World Development Report 2008 once again redirected the focus for development policy and poverty reduction efforts on the rural economy, accruing from the fact that the majority of the world's poor at the beginning of the 21st century still resides in rural areas. It argues that sustainable rural income growth and poverty reduction requires improvements in agricultural productivity, a general empirical finding in the literature (e.g. Datt and Ravallion, 1998b; Ravallion and Chen, 2007; Deininger, 2003; Grimm, Klasen and McKay, 2007; Minten and Barrett, 2008). It goes on to state that both an equitable land distribution and agricultural production technologies are key factors for attaining sustainable growth in the rural economy. Moreover, they can lay the basis for industrialization and equitable growth of the whole economy, as has been seen in d'l.e extraordinarily successful East Asian NICs.

In the short run, a more equal land distribution translates into both lower average farm size and higher rates of ownership. By redistributing land to the actual tillers, strong incentive effects can be set free. As long as feudal or semifeudal land structures pertain, with few landlords who own most of the land and many landless and land-poor wage laborers or tenants, cultivation is often less efficient and land productivity remains low. Usual contracts of hired farm labor or sharecropping are associated with lack of supervision and thus result in lack of incentive and static inefficiencies (Otsuka, Chuma and Hayami, 1992). 38 Moreover, empirical findings suggest that in low-productivity agricultural sectors, farm sizes are often inversely related to agricultural productivity (see e.g. Deolalikar, 1981; Fan Chan-Kang, 2005). In addition to efficiency considerations, ownership rights further enable farmers to access credit, and to raise investment in education and physical capital (e.g. Feder and Feeny, 1991; Deininger, 2003).

That initial asset inequality matters for long-run growth has been shown in the academic literature in several studies (Alesina and Rodrik, 1994; Persson and Tabellini, 1994; Deininger and Squire, 1998; Rodrik et al., 1995; Galor et al., 2009). Theoretical explanations put forward two main channels: On the one hand, credit rationing in the presence of indivisible, economically profitable investment opportunities in education or agricultural technology may lead to underinvestment of the asset-poor. On the other hand, political bargaining power of the asset-poor might be limited and thus also the ability or preference to participate in a democratic process. Therefore, creating low levels of asset inequality can foster broad-scale investment into human and physical capital not only on the individual but also on the government level, and therefore significantly enhance long-run growth. Rising overall human capital combined with low social pressure for redistributive policies furthermore facilitates efficient governance and political stability. For all the above mentioned reasons and the undeniable success of the East Asian low-initial-inequality growth examples, the formerly widely believed equity-efficiency trade-off which assumes that inequality is good for growth, is nowadays increasingly put into question.

Besides delivering lower growth, asset inequality does also lead to persistent income inequality even if the country manages to industrialize. In the process of development and structural change of an initially agriculture-based economy, traditional patterns of land holding are often translated into distributions of in-

38 Already in 1975, the World Bank stressed the desirability of owner-operated family farms and the importance of an egalitarian asset distribution as two of its three strategic principles for its land reform policy. Until today, these strategies have not been changed (Deininger and Binswanger, 1999).

dustrial productive assets including financial, physical and human capital. That is, in the absence of an initially equitable rural asset base, a limited number of initially wealthy families are those who will then easily maintain their relative political and economic predominance under the new circumstances perpetuating the country's unequal income distribution.

Therefore, a redistributive land reform at an early stage of development can be a crucial means to lay the basis for agricultural productivity gains in the short run while enhancing long-run equitable growth and poverty reduction prospects. Such government interventions can lead to both equity *and* efficiency gains. However, history has also taught us that implementing successful land reforms is not a simple task. First, success depends on earlier land structures, whether hacienda like systems prevailed with mainly wage contracts, or tenancy systems with sharecroppers used to run their own businesses. It has often shown advantageous if land was distributed to tenants rather than landless laborers, who might find it particularly difficult to become successful farmers. Second, most successful land reforms took place in times of grand political change and were government-led. In many cases, the class of rural landless and near landless found powerful allies in other sectors of society, and sometimes even from abroad (Barraclough, 1999). Third, land reforms might yield only limited success if land redistribution is not flanked by productivity enhancing complementary reforms. Such complementary reforms might include improvements in general public infrastructure, better access to credit, improved inputs like new seed varieties, increased supply of fertilizer, extension services, and agricultural R&D. An extensive empirical literature exists that focuses on the effects of agricultural technology adoption and productivity improvements during the Green Revolution. Strong positive effects on agricultural productivity are usually found from the increased use of fertilizer, improved seeds, pesticides, and related technical education ( e.g. Feder and Umali, 1993 ). Effective agricultural technology adoption requires government effort and can have pro-poor effects, thus reducing the relative income gap between the rich and the poor (De Janvry and Sadoulet, 2002; Minten and Barrett, 2008).39

South Korea represents an interesting case of land reform since land redistribution and 'green-revolution' -type reforms can be studied rather separately due to the fact that they were not enacted at the same time but the latter followed a decade after land redistributions had been completed. Since the government's

39 However, the so-called 'green-revolution' technologies do also have their negative sides, for example when exposure to pesticides like DDT lead to increased rates of breast cancer (Clapp et al., 2008) or when monoculture and an overuse of chemicals result in soil degradation (Singh, 2000).

objective of the 1950 reform was not the improvement of agricultural productivity but rather the restoration of political stability after turbulent years following liberation, it is possible to isolate relative effects of land redistribution and technology enhancing reforms in Korea (Pak, 1968; Jeon and Kim, 2000). The Korean case is particularly interesting since it represented one of the least developed countries in 1950 with a per-capita GDP level of around the average of the African continent and an almost complete agriculture-based economy. 40 During the following six decades it then went through its well-known growth miracle which lifted the country up to the level of high-income, high-technological OECD economies. In the meantime, Korea managed to hold income inequality at very low levels, with Gini coefficients of income always within the range of .3 to .4, initially rising but lately falling again (World Bank, 2004).

The aim of this paper is to shed new light on the still understudied effects of Korean rural reforms on agricultural productivity and individual living standards. Only few papers have estimated the impact of the reforms on the former, making use of macro data, which are scarce particularly during the 1940s and early 1950s. To the best of the author's knowledge no paper has yet analyzed effects of the reforms on individual well-being using national micro data. This paper takes advantage of the fact that current anthropometric data, or more precisely adult height data sets, can provide good proxies for Korean individual living standards during the midst of the 20th century for which no national-level survey data exists (Komlos, 1993; Komlos and Baten, 1998; Steckel, 1995; 2009; Deaton, 2008; Hoddinot et al., 2008). This paper tries to find if and to what extent land redistribution around 1950 and intensification reforms in 1962/63 positively affected agricultural productivity and individual well-being. The findings indicate that both the lowering of land inequality/increase in ownership cultivation and later technology-enhancing reforms had positive significant effects on productivity. Moreover, structural break analyses of time series of adult average height outcomes reveal positive shifts in trends around the years of the rural reforms. Thus, there seems to be evidence for improved food availability resulting from the reforms and an improved standard of living.

The paper is organized as follows: Section 3.2 gives the reader an overview over background, process and implications of the rural reforms in the Republic of Korea **(ROK).** Section 3 .3 then analyses the effect of the reforms on agricultural productivity while section 3.4 examines the effect on individual well-being using anthropometric data. Section 3.5 summarizes and concludes.

40 Maddison (2003) estimates a level of per-capita GDP for 1950 (in 1990 International Geary-Khamis dollar) for South Korea of \$854 while the average of 57 African countries is \$889.

## **3.2 Rural Reforms in 1950 and 1962/63**

South Korea's government-led land redistribution put into law by the Agricultural Land Reform Amendment Act (ALRAA) in March 1950 is considered in the literature one of the most successful cases of land reform. Before 1950, the Korean rural economy was characterized by a landlord estate system, where few land owners rented out their land to tenants in exchange for a fixed share of their harvest.41 By transferring large amounts of farm land from the rich to the poor as direct or indirect consequences of ALRAA, rural social relations changed and former landless and land-poor tenants were empowered both economically and politically (Oh, 2008). Equity and democratization gains resulting from the reform are viewed as one of the keys to the country's later economic miracle (World Bank, 2004; Rodrik et al., 1995; Boyce et al., 2007). Vested Japanese land had been distributed as early as 1948 under the US military government. After the formation of the ROK Government in August 1948, earlier discussed land reform ideas further progressed and led to voluntary land sales of many Korean landlords already before the actual Land Reform Law (ALRAA). According to the ALRAA scheme, excess land from Korean landlords was then purchased in order to provide it to former tenants and workers. A three hectare ceiling on land holding was established for each farm household. Excess land was distributed among tenants. Korean landlords received land securities in the beginning, and were later compensated in cash at the government's regulated rice price, which was much lower than the going market price of rice. In this way, the government managed to redistribute income from the rich to the poor while at the same time overcoming own capital shortages and enhancing investment

41 In the case of Korea the share rent was on average about 50 percent of "standard production", depending on the quality ofland it could be between 30 and 70 percent (Pak et al., 1966). "Standard production" meant that a tenant had to pay a fixed share of standard production each year rather than weighing the crop each year. This was partly due to the phenomenon of absent landlords, who preferred to live in the cities and thus were not able to continuously monitor farm production. De facto this was coming close to a fixed-rent contract.  Contract choice theory tells us that fixed-rent contracts are more efficient than sharecropping contracts in terms of providing maximal incentives. However, poor tenants might often prefer sharecropping arrangements if they are risk-averse and prefer to ensure themselves against bad harvest. In the case of Korea, an additional uncertainty that tenants faced was, as mentioned, the unpredictability of landlords' visits to evaluate "standard production". Thus, some tenants would even thin out the stand of newly headed out rice in good years and cook the heads in a stew. It was to their advantage to hold down rice yields to not encourage the landlord to raise the standard production figure (Morrow and Sherper, 1970).

into agricultural and industrial infrastructure (Jeon and Kim, 2000). Tenancy became legally prohibited. The reform which was implemented with the assistance of US military forces led to a sharp increase in ownership and reduction in average farm size. The percentage of tenants among all rural households decreased from 45.8 in 1949 to practically zero in 1952, only to increase slightly again afterwards (6.8% (1960), 9.7% (1970)). Average farm sizes decreased from 2.06 ha per rural household in 1950 to .88 ha in 1960 and .93 ha in 1970 (Fan and Chan-Kang, 2005). While land was fully redistributed by 1952 (Morrow and Sherper, 1970), compensation payments to landlords and tenants' payments to the government were practically finalized by the beginning of the 1960s.

As a result of the reform 1.29 million hectare ofland changed ownership. Of this total amount, 21 percent was confiscated land from Japanese landlords, 23 percent was land redistributed by the government through the ALRAA scheme, while as much as 55 percent was land targeted by the reform freely sold in the market in anticipation of ALRAA (KAERI, 1989, pp. 1030-31).

Korea had been under Japanese colonial rule and was then occupied after the Second World War by US troops who governed the country between 1945 and 1948. Several developments led to the Land Reform Law of 1950 in South Korea. First of all, the South Korean post-war government was anti-colonial and so it confiscated land from the Japanese. Second, rural landless and land-poor had already been organized in protests during colonial times. This facilitated implementation of the reform with a great deal of support and participation by the beneficiaries. Moreover, the weak government after 1945 in comparison with the preceding colonial administration led to a situation in which transaction costs for landlords to enforce tenant's payments saw a sudden sharp increase implying that tenant's rents were de facto reduced already as early as 1945.42 Third, especially the US occupants feared that North Korea with the support of the USSR could try to mobilize the class of rural poor in the South to overthrow the Southern government. And fourth, there was always competition between the North and the South with respect to the economic model chosen. Thus, political momentum and increased transaction costs for the rural oligarchy eventually led to large redistributions ofland around the years of the official reform in 1950.

42 For the same acreage of sharecropping land, a landlord could collect 1,500 suk of rice during the colonial period. This rent reduced to 100 suk right after August 1945, and 400-500 suk after 1946. One suk equals approximately 180.4 liters. Note that the U.S. military administration had set official rental rates for tenancy at one third of annual crop yields in October 1945, down from on average 50 percent before.

Even though redistribution of land in Korea succeeded in creating a more equal rural asset base, it failed in establishing a minimum size of land ownership. Due to geographic restriction ( most of the country consist of mountainous terrain which does not allow for agricultural production) and high population density, scarce land had to be distributed among a high number of farmers. Thus, a high degree of fractionalization of land was the natural consequence posing a natural limit to economies of scale production. In fact, after land reform in 1953, 41.2 percent of all farm households cultivated less than half a hectare, while 33.3 percent cultivated between .5 and l hectare of own land (Pak et al., 1966; Morrow and Sherper, 1970).43

The Korean War (1950-53) interfered with the land reform period, particularly with the post-reform period. As mentioned above, three quarters of redistributed land had already changed owners by the time that ALRAA was enacted in March 1950, i.e. three months before the invasion of North Korean forces into South Korea. Enforcement of ALRAA was only slightly delayed (MAFF, 1966). The war led to about one million casualties among the South Korean population.44 However, displacement of farmers from their farms is rather negligible since South Korea was merely occupied for about 3 months during 1950 before joint South Korean and US forces pushed back Northern troops. Thus, the major economic impact resulting from the war can be seen in the decline of farm population due to war casualties by about ten percent and through widespread destruction of industrial property and infrastructure. The latter effects led to the imposition of a land tax enacted in September 1951 in order to rebuild industrial structures. This levy on the rural population combined with forced rice sales to the government at below-market prices from February 1950 onwards is likely to lead to an underestimation of individual welfare changes after the land reform (Pak, 1966a).

This paper tries to show that the 1950 Land Reform brought about both equity and efficiency gains in the Korean economy. As Pak (1968) notices it succeeded in changing the rural social framework from a feudalistic two-class society into a much more equal one with nearly equality of opportunities. However, it might be the case that the redistributive change from tenancy to ownership alone might have only modest impacts on agricultural productivity ifland results

43 The 1965 Farmland Survey asked people for their preferred and present cropland sizes both including owned and rented land. Farm families with less than 1 hectare of land cultivated on average 0.56 ha and would have preferred a size of 1.2 ha. Families with more than 1 ha cultivated on average 1.6 ha and would have preferred on average 1.97 ha. It is also shown that larger households possessed on average more land but would have preferred also a much higher increase of land than smaller households (Pak, 1968).

44 Compare with Table 3.1 ahead.

to be highly fragmented and complementary policies remain absent. In fact, Korean agricultural productivity saw stronger growth after 1962 when Park Chunghee' s military government issued its first Five-Year Development Plan. It focused likewise on the increase of agricultural output and the creation of heavy industry (Pak, 1966b; Morrow and Sherper, 1970). The agricultural part of the plan which aimed at achieving Korea's food self-sufficiency through increased agricultural productivity, led to a rapid increase in the use of modem technologies and interlocked services such as increased supply of fertilizer, pesticides, improved seeds, improved irrigation, and access to credit and extension services. Japanese colonial officials had pushed modem technologies and agricultural services forward already in a first wave of agricultural reforms throughout the 1920s and early 1930s.45 However, the years after liberation had seen technology and service provision in Korea at a low with the Korean War (1950-53) and weak governments. Besides that the Five-Year Plan of 1962 issued several policies supporting small-scale farmers and the agricultural sector as a whole. Financial strain on the rural population was relaxed by a re-arrangement of terms of farm debt, provision of farm management funds, establishment of fair prices for farm products46 , consolidation of agricultural credit institutions, improvement of commodity loans on the farm crops, as well as the establishment of a bank for small business. Moreover, unjustly-acquired properties were confiscated by the government and the government took the control over the supply of commercial fertilizers (Pak, 1966b ).

## **3.3 Effect of Rural Reforms on Agricultural Productivity**

In order to identify the effect of the two waves of agricultural reforms on agricultural productivity and individual height outcomes this study proceeds as

45 Major institutions involved in this process were (1) the Korean Agricultural Associations (KAA) which was responsible for seeds, fertilizer and farm equipment and had 15,000 employees nation-wide, (2) the Federation of Financial Associations (FFA) which was responsible for credit supply, and (3) the national and local government administrative units which laid out exact agricultural programs and watched over their implementation (Morrow and Sherper, 1970). Besides that, an Agricultural Experiment Station had been installed under the Japanese in Suwon. A 1954 UN Report concludes that the interlocking services were simple, direct and enforced.

46 The law of crop control from February 1950 led to a two-price system in Korea which might have been considered 'unfair' by Korean farmers. Farmers were obliged to sell between IO and 24 percent of their output ( depending on farm size) to the government at below-market prices (approximately 50-60 percent of the going market price).

follows. This section presents the estimation of an agricultural productivity model using farm output per capita and per farm capita47 as the relevant productivity measures and including relevant controls to proxy for the rural reforms. Since agricultural production was almost completely devoted to domestic food consumption, this should be a valid proxy for Korean household's food availability around the time of the reforms. In section 4, height and agricultural productivity time series will be examined for the presence of structural breaks around the years of the reforms 1950 and 1963.

## **3.3.1 Historical Farm Data**

This study uses various macro sources to access national-level farm variables from 1941 onwards. Data on total farm production, farm households, farm population, and land inequality comes from Keidel (1981), while farm land comes from both Cheong (1975) and Keidel (1981). Tenancy rates are taken from Bank ofChosun (1948) and KAERI (1989) as cited in Jeon and Kim (2000). The fertilizer index is constructed using annual growth rates of chemical fertilizer production from UNKRA (1954) as reported in Morrow and Sherper (1970) for the period 1941 to 1952 and of chemical fertilizer sales from Keidel (1981) for the period 1952 to 1974. Irrigation data comes from various editions of the Ministry of Agriculture, Forestry and Fishery's (MAFF) Yearbooks of Agriculture as reported in Morrow and Sherper (1970). Data on farm credit employees is also taken from Morrow and Sherper (1970). GDP and population data are from Kim (2009) and CPI data from Kim and Park (2007). Note that all values until 1945 represent only the values for the provinces that nowadays form part of South Korea and not the North Korean part, the only exception being the share of irrigated paddy land.48

47 Farm output divided by farm population.

48 While some series are complete, missing values needed to be adjusted for in some cases.  For irrigation and land inequality, usually there is only one observation every five years. Here, too, linear interpolation was used to fill the gaps. Moreover, in the land inequality variable the years around the land reform were coded such that a linear decrease took place between 1947 and 1952 as observed in the qualitative literature.

#### **3.3.2 Land Reform and Trends of Agricultural Input and Output**

Korea's rural economy went through continuous change over the 20th century, as it developed from a traditional labor-intensive semi-feudal system at the end of the Joseon Dynasty to a post-modem, capital-intensive sector today. The main advances certainly took place during the 25 years after liberation in 1945. Table 3.1 shows important agricultural input and output variables from 1941 to 1974. Total farm output decreased during the first 3 years following liberation but then increased again from 1948 onwards, during the time when the government started the distribution of confiscated Japanese land and Korean landlords started selling their land. State-led land redistributions around 1950 totally changed rural social and economic composition, as can be seen by a large drop in tenancy rates and land inequality. While 58.5 percent of all farmers in 1941 were tenants, this number had dropped to practically zero around 1950. The redistribution brought down the Theil measure of land inequality from .925 in 1946 to .255 in 1955. Compared to the end of the Japanese occupation period, a strong increase in the share of irrigated paddy fields can be seen around the time of the land reform. Partly this can be assigned to government investments resulting from surpluses generated through the ALRAA scheme. At the same time, as theory suggests higher ownership leads to increased investment in the cultivated land. Thus, between 1945 and 1955 the share of irrigated paddy rose from about 20 to almost 50 percent. After the Korean War came to a halt in 1953, growth in agricultural inputs was rather slow throughout the 1950s which then also translated into slow growth of the whole agricultural sector. Tenancy rates as well as land inequality were slowly on the rise again, but remaining on relatively low levels. After the Park Chung-hee administration then took power and started the first Five-Year Development Plan, the application of modem agricultural technology inputs strongly increased, as can be seen in a 75 percent increase of fertilizer sales and a 263 percent increase in pesticide sales between 1962 and 1963. Interestingly, farm output did not increase directly, yet it did in the following years by about 20 to 40 percent. The reforms were flanked by an increase in the number of crop varieties, an increase in agricultural research and development, and a broader supply of farm credit. The number of farm credit employees had risen by 150 percent between 1958 and 1965. Population figures show how the beginning of massive rural-urban migration was supported by the agricultural sector: Rising agricultural productivity was setting free rural labor for the emerging industrial sector during the 1960s. Thus, while total population was strongly increasing, farm population as well as the number of farm households reached their maximum in 1967 and decline thereafter.

*Table 3. 1: Agricultural input and output summary statistics*

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**For varJous sources see Data section in 3.3. 1=assumed minimum values for tenancy rare and land inequality at the end of land redistribution In February 1952.**  I~

~

## **3.3.3 Econometric Model**

In order to assess the relative importance of individual production factors for total agricultural production a World Bank study by Ahn, Singh and Squire ( 1981) can be used. The authors analyze a simultaneous consumption and production model for 443 Korean farm households surveyed in 1970. Averaged over the households surveyed, total production costs split up into 28 percent variable input costs (fertilizer, irrigation, pesticides, seeds, salt), 66 percent labor costs (family plus hired labor), 2 percent draft animals costs, and 4 percent interest charges. Thus, in 1970 after modernization was launched, approximately two thirds of production costs account for labor and one third for variable inputs. In order to evaluate the effect of the two rural reforms on agricultural productivity, an agricultural production model of the following form will be estimated:

$$Q\_t = \alpha + \beta\_1 fland\_t + \beta\_2 fpop\_t + \beta\_3 fert\_t + \beta\_4 irrig\_t + \beta\_5 landinequ\_t + \beta\_6 k\omega ar\_t + \varepsilon\_t$$

*Qt* denotes agricultural production per capita or per farm capita in year *t, f landt*  and *f popt* denote total farm land and total farm population in *t.* The variables *f ertt* and *irrigt* represent an index of fertilizer use and the share of irrigated paddy land, respectively, where the former will be the main proxy for intensification reforms in 1963. Increased use of commercial fertilizers has been found as the most important factor in the reforms after 1962/63 in many studies (see e.g. Pak et al., 1966, p. 106).49 The land reform in 1950 enters the model through the variable *landinequt,* which will be measured in two alternative ways: (1) decreased land inequality as measured by a Theil index for land, and (2) increased land ownership as measured by the tenancy rate. 50 The variable *irrigt*  controls for the share of irrigated paddy land, while *kwart* controls for particular effects of the Korean War. 51

It can be safely argued that the rural reform variables are exogenous. Land redistribution had mainly political reasons strongly related to weak governments unable to sustain the old social and political order after the departure of the Japanese (Jeon and Kim, 2000). At the same time, land redistributions in the North put further pressure on the US military government in the South since they fac-

49 Morrow and Sherper ( 1970) argue that the breakdown of the KAA and limited resources of the FF A led to a general slack in the supply of farm inputs after liberation which then only recovered following the reforms in the early I 960s.

50 Since the tenancy rate time series is more complete, and the Theil land inequality measure requires substantial interpolation, the annual variation of the tenancy measure is likely to be more reliable.

51 For effects of war on heights see also Moradi (2005).

ing Communist tendencies among Southern farmers. Agricultural technology reforms in the 1960s were then designed by the Park Chung-bee administration in order to lay the foundation for future industrial growth allowing industrial sectors to absorb agricultural surplus labor. Therefore, it is plausible to assume one-way causality in both cases.

In order to estimate equation (3 .1) time series properties of all series have to be taken into account. Standard unit root tests (ADF, Phillips-Perron) indicate that all agricultural series in the model are integrated of order one. Thus, taking first differences and adding lagged values on the right hand side of the model allows the use of least squares estimation to consistently estimate an autoregressive distributed lag model (Greene, 2003) of the form:

$$d(Q)\_t = \sum\_{l=1}^{k} \gamma\_l d(Q)\_{t-l} + \sum\_{l=0}^{l} X\_{t-l}' \beta\_{t-l} + \varepsilon\_t \tag{3.2}$$

where *Xt* is a vector containing all explanatory variables from equation (3.1). Estimation of a very general model with *i* = 3 and *j* = 3 strongly decreases the number of degrees of freedom in the estimation. Thus, I apply Hendry's generalto-specific method and stepwise exclude right-hand side variables which show the lowest t-values. The results can be seen in Table 3.2. The dependent variable enters the final model with two lags. Farm population, farm land, fertilizer index, and the share of irrigated paddy only have a contemporaneous effect on agricultural output which is in line with the theory. Land inequality and tenancy rate however, do not have a direct effect on agricultural output, but one that is lagged three periods. Adaptation to ownership, initial uncertainty about land rights in the war period and thus resulting reduced investment and cultivation effort might be possible explanations. Columns (1) and (2) of Table 3.2 show the determinants of agricultural production per farm capita (farm output divided by farm population), while columns (3) and (4) show the models with agricultural production per capita (farm output divided by total population). Negative significant coefficients for the lagged dependent variables indicate a tendency towards mean reversion of the series. Farm land and share of irrigated paddy are positively linked with agricultural productivity, while the Korean War decreased farm output. The 1950 Land Reform seems to have increased agricultural productivity with a three years time lag: Both land inequality and tenancy rate show negative significant coefficients in all regressions. Moreover, results also suggest a positive effect of the 1962/63 reforms: The fertilizer index is positive and significant in all specifications. For all regressions, respective LM-tests indicate no presence of serial correlation in the residuals.

*Table 3.2: Determinants of Agricultural Output (1944-1974)*

Notes: Standards errors in parentheses. \*\*\* significant at I%, \*\* significant at 5%, \* significant at 10%. LM tests indicate no serial correlation in residuals. For various sources of national-level farm data see data section 3.3.

## **3.4 Effect of Rural Reforms on Individual Well-being 3.4.1 Body Height and Individual Welfare**

The method of the biological standard of living (BSoL) uses individual height data as a proxy for individual welfare during historical periods for which no household income or consumption data is available. The strong relationship between early childhood household economic conditions and heights has been frequently documented (Komlos, 1993; Komlos and Baten, 1998; Steckel, 1995; 2009; Hoddinot et al., 2008; Deaton, 2008). Final height outcomes are correlated with nutritional intake during the first three years of a person's life, and here particularly during the first 12 months after birth.52 Nutritional conditions in tum highly depend on relative food prices and a household's income situation. In the case of agricultural self-production, ownership of productive assets is crucial. Thus, welfare trends of a large enough sample of a particular population can be inferred from height data. In the context of the rural economy Kopczynski (2007) uses height data and finds stagnating mean heights for the birth cohorts 1882-1892 in the Kingdom of Poland resulting from an agrarian crisis whereas those born in the adjacent province of Galicia which carried out far-reaching agrarian reforms and closed the border for trade instead, continued to grow in mean height.

When working with height data it is important to deal appropriately with the issue of shrinking of old-age cohorts. While individuals usually reach their final height around the age of 20 to 30, shrinking usually starts from the age of 40 to 50 and then accelerates in later periods of life. Cline et al. (1989) as well as Chandler and Bock (1991) provide gender-specific formulas to adjust for shrinking. These formulas have been applied in the East Asian context by, among others, Pak et al. (2010) on Korean data, and Morgan (2008) on Chinese data. Both adjustment methods will be applied in the following. 53 Using height data, the problem of "age-heaping" might bias estimates when especially less educated individuals misreport their exact age and thus tend to report rounded years. This would then lead to biased sample mean height trends in the data. However, this does not present a problem for the used Korean data. Due to initially high literacy already in the first half of the 20th century as well as the cultural importance of the 12-year animal cycle of the Chinese zodiac, birth years are generally well known and thus age-heaping is not seen in the data set. 54

Korea in the 20th century has gone through one of the fastest anthropometric growth spurts ever seen in human history. While the second half of the century has been certainly more successful than the first half, even under Japanese colonial rule Korean average heights show a positive trend. Choi and Schwekendiek (2009), pooling various height series, estimate an increase of approximately 2 cm in average male adult heights during the colonial period from 1910 to 1945. While representing one of the darkest chapters in Korean history, the oc-

52 Of special importance is the availability of proteins. To a lesser degree, also vitamins A, Das well as minerals play a role for height (Silventoinen, 2003).

53 While Chandler and Bock's method only controls for old-age shrinking and thus should only be applied to those aged 40 and above, adjustment according to Cline et al. additionally corrects for not fully reached final height of the young age cohorts.

54 Besides that, Korea has a long history of detailed administrative record keeping. **Re**gressing heights on age-heeping dummies shows no problems with age-heeping in the data.

cupation period brought Korea important advances in education, health, and other basic infrastructure. The Japanese moreover modernized agricultural technologies in a first reform period during the 1920s and early 1930s. However, a gradual increase in forced rice collections from Korean farmers to sustain Japanese wars in the Pacific beginning with the Japanese invasion of Manchuria in 1931 and lasting until the end of World War II in 1945 led to a worsening of the nutritional situation in the later colonial years. 55

Individual height data used in this paper comes from Korea's National Health and Nutrition Surveys (KNHS) which have been collected regularly by the Korean Institute of Health and Social Affairs since 1995. Four nationally representative cross-sections collected in 1998, 2001, 2005 and 2007/08 have been published. The data offers for the purpose of this paper basic demographic and anthropometric information for between 3,051 and 5,333 randomly selected households per cross-section. KNHS data reports measured heights at the first decimal. Excluding ages below 20 and extreme heights below 120 cm and above 200 cm the final data set comprises 16,580 females and 12,705 males, respectively. The number of observations per five-year birth cohort can be seen in Table 3.3.

*Table 3.3: Observations per birth cohort (of variable height)*

Data: National Korean Health Surveys (KNHS) 1998, 2001, 2005, 2007/08;

55 For further evidence on development of average heights during the first half of the 20th century see e.g. Kimura (1993).

Figure 3.1 shows the trend in average height of Korean female and male adults from 1920 to 1987. Depending on the method of shrinking-adjustment, the trend lines show that average female heights increased over this 67-year time span by between 8.1 and 12 cm. Average male heights grew a little slower, but still by a strong 7 to 9.6 cm. This stronger female growth might reflect decreasing gender inequality in education and consumption which took place over the 20th century. It is remarkable that for both men and women about two thirds of this growth took place after the land reform in 1950, and even about 40-50 percent of the growth took place after 1962. Data quality might be compromised by survivor bias which has been found in past studies (Waaler, 1984; Steckel, 1995).

*Fig. 3.1: Korean female and male heights, 1920-1987. Data: National Korean Health Surveys 1998, 2001, 2005, 2007/08. Age-adjustments based on Cline et al. (1989) and Chandler and Bock (1991).*

According to these studies, there is a bias **in** older age cohorts in that taller people are on average more likely to survive a certain age than shorter people. Thus, estimating the mean of elder birth cohorts might lead to an upward bias in the estimated mean. This would then lead to an underestimation of growth over the whole period. However, as the bias would decrease in a *gradual* manner when moving to younger cohorts, this should not pose a problem for the following structural breaks analysis. The survivor bias is rather unlikely to produce abrupt breaks in the height series.56

## **3.4.2 Testing for Structural Breaks in Time Series**

Human body growth theory suggests that a person's final height outcome depends on both genetic and environmental factors. While the parent's heights play the major role in determining final height outcomes in the developed world, in nutrition and health constrained developing countries, environmental factors are still of high relative importance. The foremost environmental factor is a child's postnatal nutritional situation and thus this will be the basis of this study's main line of argumentation. In Korea, food expenditures had a very high share in total farm household expenditures during the time of this study. While the Engel coefficient, the share of total household expenditures spent on food, was as high as 73.6 percent in 1954, by 1964 it had slightly reduced but still represented 59 .3 percent (Pak et al., 1966). Second in rank is the postnatal disease environment of the newborn. 57 Geographic and climatic conditions play further roles in explaining final height outcomes but are more important in the analysis of large countries or cross-country height comparisons (Silventoinen, 2003; Komlos, 1993; Komlos and Baten, 1998).

In order to identify potential effects of the two rural reforms in 1950 and 1963 on average individual welfare, I will examine the height time series for women and men for potential structural breaks around the years of the reforms. Following the theory of the Biological Standard of Living and assuming nutritional improvements through the rural reforms, one should expect increases in intercepts or trends of final height outcomes of those born after the reforms.

Running standard unit root tests (ADF, Phillips-Perron) for the height series without allowing for structural breaks results in no-rejection of a unit root. However, as Perron ( 1989) points out, the power to reject the null of a unit root decreases if the stationarity alternative is true, but structural breaks are ignored.

56 One might argue that the survivor bias might produce breaks in height series in the case of the Korean War, if soldiers were more likely to be recruited among poorer parts of the population. However, this should then lead to a bias around the age cohorts 1930 to 1935, a period that is excluded from the following time series analysis.

57 Data on health indicators would also be interesting to study. However, there is a serious lack of data on health indicators between 1942 and 1955 for Korea. Since nutrition is a strong determinant of health, these two are likely to be highly correlated.

Thus he suggests a modification of the ADF test allowing for a known structural break. The break can be in the intercept, in the trend, or in both. Zivot and Andrews (1992) argue that break points (BP) might not be exogenous in some cases and thus selecting exogenous break points when the data is already known could lead to an over-rejection of the null hypothesis of a unit root. In the case of the Korean rural reforms, structural breaks could be determined exogenously as has been argued earlier. However, in order to check for validity of these breaks, first successive Zivot-Andrews(ZA)-tests are run in order to account for two endogenous breaks in the series. The ZA-model specification with shift only in trend for time series *Yt* can be written as follows:

$$
\Delta y\_t = \mu^A + \alpha^A \chi\_{t-1} + \beta^A t + \gamma^A DT\_t + \sum\_{l=1}^m \delta\_l^A \Delta \chi\_{t-j} + \varepsilon\_t \tag{3.3}
$$

The ZA-model with both shift in intercept and slope is then:

$$\mathbf{y}\_t = \boldsymbol{\mu}^{\mathcal{B}} + \boldsymbol{a}^{\mathcal{B}} \mathbf{y}\_{t-1} + \boldsymbol{\beta}^{\mathcal{B}} \mathbf{t} + \boldsymbol{\theta}^{\mathcal{B}} \mathbf{D} \mathbf{C}\_t + \boldsymbol{\gamma}^{\mathcal{B}} \mathbf{D} \mathbf{T}\_t + \sum\_{l=1}^{m} \delta\_l^{\mathcal{B}} \mathbf{A} \mathbf{y}\_{t-j} + \boldsymbol{\varepsilon}\_t \tag{3.4}$$

Here OT indicates an additional trend and DC a shift in the intercept such that

$$DT\_t = \begin{cases} t - BP \text{ } \text{\textquotedblleft} f \text{ } t > BP \\ 0 \text{ } otherwise \end{cases} \tag{3.5}$$

and

$$DC\_t = \begin{cases} 1 \text{ } if \ t > BP \\ 0 \text{ } otherwise \end{cases} \tag{3.6}$$

The model with only a change in intercept can be omitted since this does not seem to be relevant for the shape of this study's time series. The models in equation (3.3) and (3.4) on the series of heights for women and men are run separately for the two methods of shrinking-adjustments. Moreover, they are also run for agricultural productivity. Being interested in determining two break points, I estimate two successive ZA-tests on the series. The first test is estimated over the whole series. After obtaining the first break point *BP 1,* I then run the second test over the series [t1 *to (BP1* -1)] if lt1 - *BP11* > *IBP1* - *trl* or over *[(BP1* + 1) *to tr]* iflt1 - *BP11* < *IBP1* - *trl-*

Table 3.4 shows the results of the endogenous detection of the break points. Column (1) and (2) show results from the estimation of equation (3.3) for a mere shift in trend. While the first break in trends is detected between 1948 and 1957, the second break is determined between 1961 and 1965. This does support the existence of break points around the rural reforms. Columns (3) and (4) result from estimating equation (3.4) for shifts both in intercept and in trend.

#### *Table 3.4: Endogenous detection of structural breaks {successive Zivot-Andrews tests}*

Notes: Height data from National Korean Health Surveys 1998, 2001, 2005; adjustment based on Cline et al. (1989) and Chandler and Bock (1991). Fami data from Keidel (1981)

\*\*\* **significant at 1%, \*\* signiflcant at** 5%, \* **signlfkant at** 10%.

*Table 3.5: Regression estimations with exogenous structural breaks in 1950 and 1963. data for 1941-1974*

**Notes: Standards errors in parentheses.** ••• **significanl at 1** %, •• **significant at 5%, • significant at I 0%. Constants in height models insignificant and thus left out.**

Height data comes from National Korean Health Surveys 1998. 2001. 2005; adjustment based on Cline et al. (1989) and Chandler and Bock (1991). Farm data from Keidel (1981).  *w* ~ tTl

...,

...,

tT1

(")

..-;

0

...,

:;:0

C:

r' ~ ...,

0

Vl

z

< 6

C,

C:

>

t-< ~ tT1

t-<

t;'

l:D

tT1

2

Cl

Here, the first significant change in male heights and farm productivity happened between 1951 and 1954, while the model fails to detect a significant break of female heights during that period. The second break is detected between 1964 and 1971. Thus, except for the last column, results from this exercise support the hypothesis of structural breaks around 1950 and 1963.

In a further step the structural break points are assumed to be known and to be located in 1950 and 1963. Thus a model of the following form is estimated:

$$y\_t = \mathcal{C}0 + \beta\_1 D \mathcal{C}\_{add50,t} + \beta\_2 D \mathcal{C}\_{add63,t} + \beta\_3 T0 + \beta\_4 D T\_{add50,t} + \beta\_5 D T\_{add653,t} + \varepsilon\_t \tag{3.7}$$

where *Et= (f)Et-l* + *11t* follows an AR(l) process with idiosyncratic error term *11t·* In (3.7) *cO* and *TO* represent the general intercept and general linear trend, while *DCaddSO* and *DCadd63* are shifts in intercept and *DTaddSo* and *DTadd63* are shifts in trend which are defined as in equation (3.5). Equation (3.7) can be estimated for height and agricultural productivity series using least squares. Results are displayed in Table 3.5.

From column ( 1) in can be seen that farm output per capita experienced a structural change in 1950. Before 1950, agricultural productivity stagnated. Then in 1950 short-run dynamics probably related to the outbreak of the Korean War decreased agricultural productivity as can be seen in the significant downward move of the intercept. Yet, agricultural per-capita output entered a stable long-run growth after 1950. Looking at farm output per farm capita one can observe short-run decreases both in 1950 and 1963, yet only the latter shows up significant. The trend behavior indicates that productivity experienced an acceleration of growth rates both 1950 and 1963. This confirms findings in Table 3.2 that both rural reforms had positive long-run impacts on agricultural productivity.

Is there evidence for an improvement of the nutritional situation in the Korean population? Columns (3) to (6) try to answer this question. Here shifts **in**  intercept were left out since they turned out to be insignificant in all models. Female heights grew at a steady absolute rate already between 1941 and 1949 (.16 cm Cline-adjusted (.09 cm CB-adjusted)). This growth rate then continued after 1950 and eventually experienced an upward shift to .28 (.24) cm annual growth from 1963 onwards. Male heights did not grow in the pre-1950 period, yet reached an average annual growth of .16 cm from 1950 to 1962 which then further increased to an annual .21 (.24) cm between 1963 and 1974. Thus while male heights show a structural break in form of an upward shift in trend both in 1950 and 1963, women only show a break point in 1963.

*Fig 3.2: Estimation of trends in farm output and heights with structural breaks in 1950 and 1963, data for 1941-1974. Height data from National Korean Health Surveys 1998, 2001, 2005, 2007108. Adjustments based on Cline et al. (1989). Farm data from Keidel (1981).*

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Figure 3.2 graphically displays the results of columns (1) to (4) of Table 3.5.58 Actual series as well as fitted trend lines containing structural breaks in 1950 and 1963 are shown. Note that in the figure I exclude the AR-dynamic of the residuals included in the econometric model to illustrate more clearly break points and deviations of the time series from the estimated trend.

Strong negative effects around the Korean War as well as a significant upward trend can be observed in all four series.

## **3.5 Concluding Remarks**

The Korean Land Reform in 1950 is often taken as a textbook example for successful land reform (Ray, 1998). From an equity point of view massive land redistribution resulted in tremendous democratization of rural areas providing a more equal distribution of productive assets, income and political power within the Korean population. The resulting long-run effects for growth and development cannot be estimated high enough. It is remarkable, that even with a high fragmentation of land after the reform, increased ownership still led to significant efficiency and productivity gains as found in this paper. In a rural generation which struggled from war, a large influx of migration from urban areas, as well as bad governance until the early 1960s, increased ownership of land secured nutrition and stabilized individual welfare.

Without doubt, land reform effects could have been even stronger in 1950 if directly combined with rural technology and infrastructure reforms in the first place. However, Korean farmers had to wait more than a decade until in 1962/63 Green-Revolution type reforms were implemented by the Park Chung-bee administration leading to strong agricultural productivity growth in the following.

Individual welfare measured with the help of anthropometric data shows strong growth over the 20th century in Korea. Between the birth cohorts of 1920 to 1987 average adult heights grew by 8.1 to 12 cm for women and 7 to 9.6 cm for men, respectively. Two thirds of this growth took place after the land reform in 1950, and about 40-50 percent after the 1962/63 reforms. Structural break analyses of height trends reveal significant upward shifts in trend around the years of the reforms. Effects of the 1950 reform might even be underestimated due to the costs of the Korean War which were widely paid for by the farm population in form of additional 'reconstruction taxes' during the 1950s.

58 Results in columns (5) and (6) are very similar to those in (3) and (4) and are thus not displayed graphically.

Korean rural reforms thus not only increased equity, but also efficiency of the rural economy and the economy as a whole. The Korean case might hold important lessons for agricultural-based economies still marked with high rural asset inequality and low productivity in less developed economies in Africa, Latin America and Asia. The design of rural reforms in a holistic way that combines both land redistribution and productivity enhancing policies should be given particular importance.

# **Essay 4**

# **Cash Crop Choice and Income Dynamics in Rural Areas: Evidence for Post-Crisis Indonesia**

#### **Abstract**

In this paper we investigate the factors affecting income levels and income growth in rural Indonesia following the crisis of 1997/98. In particular, we investigate the relative roles of non-farm incomes and productivity improvements achieved via changes in crops versus improvements on the same crops on income dynamics. Drawing on unique household panel data from Central Sulawesi and using advanced panel econometric methods, we find that local innovations related to the adoption and intensification of new cash crop varieties, more specifically the shift from coffee to cocoa production, can explain a substantial part of the observed post-crisis developments. Causal estimates of the effect of growing cocoa suggest that households were on average able to achieve about 14 percent higher income levels during the post-crisis period compared to the planting of other crops most notably coffee. Also, our results demonstrate the importance of engagement in non-farm activities for household income growth. Comparative analyses using a nationally representative survey suggest that similar processes are at play in other parts of Indonesia.

Based on joint work with Jan Priebe and Stephan Klasen.

#### **4.1 Introduction**

In the years 1997 /98 Indonesia experienced a major economic, financial and political crisis. Within one year real GDP per capita fell by about 15 percent and real wages in the urban formal sector declined by 40 percent in 1998 (Frankenberg et al., 2003). Although urban areas were hit hardest during the crisis in economic terms, rural areas which represent approximately 60 percent of the population and 80 percent of the poor in Indonesia were severely affected too. Moreover, droughts and fires associated with El Nifio in 1997/98 depressed agricultural output in many parts of the country and thereby exacerbated the situation of rural households. Since then the recovery of the Indonesian economy has been comparatively stable with annual GDP growth rates of about *5* percent between 1999 and 2010. Poverty rates at the national level declined substantially from 24.2 to 13.3 percent between 1998 and 2010 and in rural Indonesia from 25.7 to 16.6 percent (BPS, 2010).

However, despite poverty in Indonesia being largely a rural phenomenon, little is known about the underlying factors that determine rural incomes and that have contributed to the observed income growth in the post-crisis period.

Clearly, agricultural output and household incomes in Indonesia are likely to have increased as a result of forest conversion and land use changes related to the planting of rubber or palm oil across the country (Sunderlin et al., 2001; Dewi et al., 2005). However, no empirical study currently exists that investigates to what extent increases in agricultural productivity, in contrast to increases in the area under cultivation, have contributed to the observed growth in agricultural incomes. Improvement in agricultural productivity has been found to be crucial to raise rural incomes in many other parts of the world (Datt and Ravallian, 1998a; Fan et al., 2008; Dercon et al., 2009). However, nearly all of these studies have focused on the analysis of the impact of large public interventions such as cash transfer programs, provision of infrastructure, land titling policies or the provision of improved seeds on improvements in agricultural productivity and the resulting growth in incomes.

In contrast to these studies and given the absence of large public interventions in rural areas in the post-crisis period we focus our analysis on the impact of local innovation, more precisely the shift of cropping patterns among cash crops, as an explanatory factor for the observed income growth in rural Indonesia. For our analysis we make use of the large restructuring efforts across Indonesian rural households to switch from coffee to cocoa production. Coffee had been Indonesia's major export crop until the mid 1980s. However, due to falling world market prices for coffee during the 1990s it had been replaced by cocoa in many areas of the country. As a consequence, by the early 2000s Indonesia has become the second largest cocoa producer in the world after Cote d' lvoire (FAOSTAT, 2011).

Furthermore, we investigate whether income growth has been influenced by more longer-term trends away from agriculture. Particularly, engagement in high-productivity, non-agricultural activities has been found to be conducive towards income growth, especially in the presence of poor physical infrastructure and human capital constraints (Datt and Ravallion, 1998b; Ravallion and Datt, 2002; Lanjouw and Lanjouw, 2001; Micevska and Rahut, 2008).

This article's principal objective is to therefore examine the sources of income growth in rural Indonesia. Notably, the following research questions are of paramount interest to us: (a) What have been the main sources of observed income growth in the post-crisis period in rural Indonesia? (b) What has been the relative importance of productivity improvements of the same crops versus

shifts to more lucrative crops in explaining agricultural productivity improvements? (c) How has income diversification, in particular into the non-agricultural sector, helped households to increase incomes?

Several contributions set this article apart from others in the literature. First, we use a unique data set based on a household panel survey (STORMA) collected at three different points in time (2001, 2004, 2006). To the best of our knowledge these are the most detailed surveys conducted to investigate the livelihoods of rural households in Indonesia. Second, this article is the first to investigate panel-based household income dynamics related to cropping patterns and the role of income diversification for Indonesia. In particular, the panel structure allows us to address endogeneity issues so that we are able to derive causal estimates for our key variables of interest. Third, in contrast to other studies that use small scale rural household surveys, we directly compare our findings to those obtained from analyzing the national socio-economic household survey (SUSENAS), which is used to calculate official poverty rates in Indonesia. Therefore, we are able to separate between effects that hold for all of rural Indonesia and those that might be particular to the study area.

Controlling for endogeneity issues and using a large set of control variables, our analysis reveals that household incomes increased substantially in the postcrisis period. We show that the growth in household incomes can be primarily attributed to increases in the value of agricultural production (both in terms of output and yields) which is caused largely by shifts in cropping patterns related to cash crop production, namely the switch from coffee to cocoa, and much less to an increase in the efficiency of agricultural production for a particular crop; poorer households have particularly benefited from these increases in agricultural self-employment incomes. In addition, in the context of the nationwide economic recovery, the growth in agricultural incomes was complemented by steady increases in non-agricultural incomes which have become the principal source of income for a rising number of households, with richer households benefitting particularly from these opportunities. These results seem to be robust to different econometric specifications. Furthermore, when comparing our results to SUSENAS we obtain very similar findings when extending our analysis to all of rural Indonesia except the heavily populated island of Java.

The remainder of this article is organized as follows. Section 4.2 briefly discusses the relevant literature on cropping patterns and the determinants of income dynamics in rural Indonesia. Section 4.3 presents details about the data sets and main variables used. Moreover, this section provides the descriptive analysis of the data. In section 4.4 the empirical identification strategy is outlined and causal effects from panel regressions are presented and discussed. Section 4.5 analyzes the results from a comparison of our data with national household data from SUSENAS while section 4.6 summarizes and concludes.

## **4.2 Literature Review**

Most rural households in Indonesia are engaged in small-scale farming activities deriving a substantial share of their income from the cultivation of subsistence crops such as rice and cash crops. However, the cropping pattern has changed significantly over time due to government interventions, trade patterns and world market prices (Rosegrant et al. 1998).

In the 1960s and 1970s Indonesia's primary focus was to become selfsufficient on rice which was finally achieved in the mid 1980s. To accomplish this objective the government provided farmers with new rice varieties and substantial subsidies on agricultural input factors that aimed at increasing the production of rice. In addition, the producer price of rice was controlled to ensure attractive conditions to producers and stable prices for consumers (Timmer, 2007). Since the end of the 1970s Indonesia has complemented its rice policy by promoting the adoption and intensification of cash crops such as rubber, sugar, coffee, tea and particularly palm oil in order to increase earnings from non-oil exports (Barbier, 1989).

Since the mid 1980s Indonesia experienced strong growth in the production of its major cash crops. Likewise, the area under cultivation has significantly increased over this period mostly due to deforestation (Sunderlin et al., 2001). One of the most important cash crops over the last three decades has been coffee. In the mid 1980s coffee was the main export crop from Indonesia. Only in the late 1980s coffee was gradually replaced by palm oil and rubber as major export crop of the country. In the wake of the economic crisis in 1997/1998 which coincided with a substantial decline in world coffee prices farmers started to either plant cocoa on new plots or to gradually switch from coffee production into the production of cocoa (Sunderlin et al., 2001). While the ratio of cocoa to coffee exports, all in volume (tons), value (US\$), and area under cultivation was about 1/10 in 1986 and 2/5 in 1996, cocoa has replaced coffee as the third major export crop by the early 2000s (F AOSTAT, 2011 ). At the same time Indonesia has become the world's second largest producer of cocoa after Cote d'Ivoire.

Therefore, in contrast to experience from other main coffee exporting countries such as Uganda (Bussolo et al., 2007; Hill, 2010), rural households in Indonesia did not only respond to the coffee price shock of the late 1990s by replacing old coffee trees with new coffee trees, as observed in Uganda, but as well by starting to produce and intensifying the production of cocoa. One reason for the ready adoption of cocoa was the already prevailing knowledge on cocoa production and its distribution channels in some areas of the countries so that the widely documented risk of adopting new crop varieties (Ashraf et al., 2009; Dercon, 1996; Fafchamps, 1992) was mitigated by already existing experience in other parts of the country.

Interestingly, despite the importance of income from agriculture, either through wage or self-employment or through the production of subsistence and cash crops, for the majority of rural households, no study currently exists that evaluates the importance of agricultural income and its different sources on the observed income growth in rural areas over recent years. In contrast, several descriptive studies recently emerged that analyze the role of the non-agricultural sector on incomes of rural Indonesian households (Dewi et al., 2005; World Bank, 2006; Suryahadi et al., 2009; Gibson and Olivia, 2010). Consistent with findings from Latin American (Lanjouw and Lanjouw, 2001) and various Southeast Asian countries (Estudillo et al., 2006; Cherdchuchai and Otsuka, 2006; Nargis and Hossain, 2006) these studies confirm the growing importance of non-agricultural activities as a means for rural households to generate income. Moreover, most of these studies point to a common set of obstacles for the development of non-agricultural activities related to poor infrastructure such as bad roads or unstable electric power supply. However, all of these studies only present descriptive evidence on the importance of non-farm activities and therefore require caution in the interpretation of its results.

In this paper we will examine the respective roles of crop switching, productivity improvements on existing crops, and non-farm incomes on rural income growth in Indonesia. By using panel data and explicitly controlling for endogeneity, we are able to move beyond the existing more descriptive analyses.

#### **4.3 Data and Setting**

The data comes from three household surveys generated as part of the so-called STORMA project and conducted in the second half of 2001, 2004, and 2006 in the rural areas of the province of Central Sulawesi ( CS). Compared to most other provinces in Indonesia CS shows a relatively low GDP per capita level which is partly attributable to its low level of urbanization and industrialization. During the economic crisis of 1998 the province was hit hard but was not as much affected as most other provinces (Ravalli on and Lokshin, 2007). CS itself is largely agrarian, based on traditional farming methods and terraced slopes. Most production comes from ownership-cultivation on small farms with an average size of two hectare. The main staple crop in the area is rice while the main cash crop

in the 1990s was coffee. At the end of the 1990s the majority of rural households, due to the decline of world coffee prices, began switching to the production of cocoa.

The study area comprises about 110 villages in four sub-districts (kecamatan). Out of these 110 villages 12 were chosen randomly for the inclusion into the household surveys. The sample size in each village was determined with respect to the share of the village population in the overall population. 59 In 2001, 294 households in 12 villages were interviewed. Due to financial and technical problems, only 258 households were interviewed in the 2004 round. In the 2006 round still 271 of the original 294 households could be re-interviewed. Since we are primarily interested in income dynamics, we restrict the analysis to those households that were interviewed in all three rounds which gives a total number of 257 households per round.60 The surveys themselves provide detailed information on agricultural and non-agricultural activities, demographic status, asset and land holdings, and further socio-economic household and individual characteristics.

An important geographical feature of the study area is its close proximity to the rain forest area of the Lore Lindu National Park. Although enforcement of the borders of the park is occasionally difficult in practice, little deforestation of rain forest areas was observed in the study area during that time period (Schwarze et al., 2009). This circumstance provides us with the empirical advantage that our causal estimates will not be confounded by income effects that result from economies of scale in the production process due to the increase in the land area used for agricultural production.

#### **4.3.1 Variables of Interest**

In the subsequent analyses we distinguish between four types of income sources following Barrett and Aboud (2001) who classify income sources according to sectors (agriculture and non-agriculture) and employment status (wage and selfemployment). Concerning the construction of a measure of agricultural selfemployed income, we add the implicit income from subsistence production imputed at local prices to the value of crops and animal products marketed in the last year. From the total value of agricultural production, we subtract the costs of seed, fertilizer, livestock, repairs of machinery, hired labor, and the like. Agri-

59 A detailed description of the sampling procedure is provided in Zeller et al. (2002).

60 The comparison of characteristics between households that could not be interviewed again and those that remained in the sample between the first and third round showed that no statistically significant differences exist.

cultural and non-agricultural wage incomes include payments in kind, while non-agricultural self-employed income is net of business costs, such as expenditures on raw materials, energy, hired labor, and equipment maintenance. Based on the amount of income received from these four income categories, we classify households into five types. If a household's income from one of these four categories exceeds 50 percent of total household income, a household is classified as agricultural self-employed, agricultural wage, non-agricultural selfemployed or non-agricultural wage, respectively. In case a household does not receive an income of more than 50 percent from one of the four sources, the household is classified as mixed.

The level of education of a household can be measured and incorporated in different ways. Since cultural factors in Indonesia often lead to the situation that the oldest person in the household will be considered the head, we follow Basu et al. (2001) to take the highest educational level of an adult in working age, as the educational information most relevant for a household. This way we circumvent the problem that some of the household heads do not contribute to the income generating process of the household anymore.

In most studies, the area of land a household owns is included in the analysis. Instead we use the area of arable land a household uses for agricultural production since this is the relevant measure for land being an input into the household's production process. Also, the land variable excludes the area dedicated to the housing area of the household since this land cannot be used for agricultural production. In addition, we construct a variable referring to the area of agricultural land devoted to the production of cocoa and one variable referring to the area of land under coffee production. Both, the cocoa and the coffee variables are meant to capture the ability of households to diversify into more economically rewarding agricultural activities compared to subsistence agriculture. In the multivariate analysis all three variables are included whereby the inclusion of the area of agriculturally suitable land has in this context the additional role to control for mere size effects in the cocoa and coffee variables.

Clearly, the wealth of households determines their ability to invest, to obtain access to the formal credit market, and to participate in high-productive nonagricultural activities. We include the value of assets a household owns as a proxy for household wealth. The variable comprises productive, consumer and financial assets. Taking sample size limitations into account we decided to focus on this aggregate measure instead of incorporating asset variables for each of the three components.

In our empirical analysis we further control for locational characteristics. Ease of access to infrastructure and proximity to markets is proxied by travel time of households to the next paved road. Given the hilly terrain of the region

and the sometimes poor condition of roads, mileage is not an appropriate measure. Instead we rely on time measured in minutes. In line with a recent study from Dercon et al. (2009) who find a positive causal effect of improvements in access to roads and agricultural extension services on consumption growth in rural Ethiopia we include a control variable that indicates whether a household was visited by an extension officer during the year preceding the survey. However, in contrast to Dercon et al. (2009) we do not observe any major expansion in road networks, changes in the quality of roads or an expansion in agricultural extension services. Since travel time to the next paved road and access to extension services remains rather constant over time we do not expect these variables to be responsible for the observed growth in incomes over time. Furthermore, we control for whether a household has access to electricity or not. Interregional disparities are captured by grouping villages into the four sub-districts (kecamatan) they belong to and using kecamatan-fixed effects.61

#### **4.3.2 Descriptive Analysis**

In 2001, the first year of our panel data, recovery from the crisis of 1997 /98 was already under way in rural Sulawesi. Furthermore, income growth continued substantially between 2001 and 2006 as depicted in Table 4.1 below.

*Table 4.1: Summary Statistics (Means) on STORMA households*

61 A detailed overview on the definition and coding of variables is provided in Table Al in the appendix.

#### 4.3 DATA AND SETTING 113

All monetary values are real in Indonesian Rupiahs with base year 2001 and use regional CPis provided by BPS. Incomes are monthly. Standard deviations in parentheses. Local land units are measured in are. One are is equal to I 00m2•

While in 2001 monthly per capita household income was at 95,076 Rupiah, it increased about 25 percent to 119,586 Rupiah in real terms in 2006. Nonetheless, income growth was not continuous during this period. From 2001 to 2004 households experienced even a decline in per capita income attributable to the restructuring of cropping patterns. In the context of the economic crisis and strong declines in world coffee prices in the late 1990s, households in the STORMA region gradually switched their main cash crop production from coffee to cocoa. In 2004 households were still in the middle of this transformation process. In particular, cocoa trees had not reached full maturity for production in most cases. Consequently, income from agricultural self-employment rose only moderately during this period while income from agricultural wage labor from 2001 to 2004 fell. After 2004 agricultural production increased significantly and in 2006 both agricultural self-employment and agricultural wage incomes show peak values for the whole study period.

The shift to cocoa was highly rewarding for rural households. As Table 4.2 shows, households harvest more physical output per area (kg/are62) with cocoa compared to coffee, while at the same time mean farm gate prices per kg of cocoa are clearly above those for coffee. These two things together lead to cocoa yields (in value terms) that are about 90 percent above those from coffee.63

*Table 4.2: Cocoa and coffee production*

Monetary values are real Indonesian Rupiahs with base year 2001 and use the provincial CPI for Palu provided by BPS. Output, price and yields are median values per year based on all farmers active in the particular crop. Local land units are measured in are. One are is equal to 100m2.

Moreover, Table 4.2 demonstrates that increases in real incomes from cocoa between 2001 and 2006 are primarily due to increases in the scale of production

62 An are comprises 100 square meters, i.e. 100 ares make up one hectare.

63 The true difference in terms of outputs is likely to be even larger since only productive coffee plants were still left on the plots, while cocoa plants were sometimes not yet ready for full production.

(area under cultivation and output per are). In 2001, 115 out of the 257 households were engaged in cocoa cultivation while in 2006 already 174 households derived agricultural self-employed income from cocoa. Accordingly the average area of agricultural land devoted to cocoa cultivation increased by about 60 percent. In addition, it is important to note that the growth in the area of cocoa mirrors the reduction in the area devoted to coffee production. Hence, we do not observe much change in the average area devoted to the production of cash crops or in the total area of arable land. Accordingly, we observe no difference in the area devoted to rice which is the main perennial crop in the study area. Furthermore, output of cocoa per are increased due to the circumstance that more cocoa trees reached its production stage. While price effects partly explain the increase in income from cocoa in the period 2001-2004, the price difference of cocoa between 2001 and 2006 is rather small and therefore does not explain much of the observed increase in cocoa income.

One can decompose the increase in incomes of farmers working on perennial crops into gains due to shifts from coffee to cocoa and increases in yields. Such decomposition suggests that about half of the increase in incomes is due to the shift of farmers from coffee to cocoa, while the other half is due to increases in yields in both coffee and cocoa. But the latter is likely to greatly overestimate the impact of improved agricultural productivity in the two crops. In the case of cocoa, much of the increase in yields is, as discussed above, due to the fact that households planted these cocoa trees in the early 2000s and they only started to yield the returns by the third round in 2006. Similarly, it is likely that the much reduced acreage in coffee was limited to the most productive plots so that the increase in yields is actually not primarily due to increases in agricultural productivity. Moreover, this development seems not to be caused by improvements in the agricultural production technology. As shown in Table 4.1 the values of all three of our proxies (share of rice fields with technical irrigation systems, expenditures on fertilizer/pesticides and access to agricultural extension services) remain largely unchanged over the entire period. Thus this descriptive evidence suggests that the primary driver of income growth for farmers with perennial crops was due to the shift to cocoa.

A closer look at the composition of incomes from agricultural self-employment reveals that rural households derive incomes mainly from crops with a minor part coming from other sources like livestock and gathering. 64 Moreover, households derive incomes from perennial and annual crops rather equally.

64 The decline in incomes from gathering follows from the improvement in economic conditions. Gathering forest products like rattan is time-intensive and dangerous. It is only done by households in times of greatest needs.

While the income from annual crops, like rice and maize, reflects household preferences for food security, it becomes clear from Table 4.3 that particularly the growth in incomes from perennial crops helps in explaining the growth of agricultural self-employment income with cocoa constituting about 85 percent of perennial crop income in 2004 and 2006 (2001: 58 percent).

*Table 4.3: Agricultural Diversification* - *Mean Incomes of Seif-employment*

All values are monthly in per-capita terms and real Indonesian Rupiahs with base year 2001. Provincial CPis for Palu were provided by BPS.

In contrast to agricultural incomes, non-agricultural incomes do not seem to have been affected much by the shift from coffee to cocoa and grew steadily in accordance with the growing national economy of the post-crisis period. As shown in Table 4.1 non-agricultural self-employed income nearly doubled between 2001 and 2006 and non-agricultural wage income increased by about 50 percent in the same period. In this context, non-agricultural income has become the principal income source for several households in the region. The income source transition matrix in Table 4.4 shows that the number of households who receive more than half of their income from non-agricultural activities rose from 41 to 54 between 2001 and 2006. Meanwhile, the number of households that generate most of their income from agriculture decreased from 209 to 189 . 65

65 Non-agricultural self-employment in the STORMA region consists mainly of small trading shops, restaurants (warung) and small-scale handicrafts. Wage employment in the non-agricultural sector is available in terms of work in the construction and public sector.

*Table 4.4: Income Sector Transition Matrix*

Incomes are monthly, real Indonesian Rupiahs with base year 2001 and provincial CPI for Palu.

Engagement in non-agricultural activities proved to be strongly beneficial. Table 4.4 shows that already in 2001 households with mainly non-agricultural self-employed incomes were best off, followed by non-agricultural wage, agricultural self-employed and agricultural wage households. Moreover, the income gap between non-agricultural and agricultural households further broadened in the post-crisis period, when non-agricultural self-employed households' mean incomes rose by 23.8 percent, non-agricultural wage households' incomes by 43.5 percent, agricultural self-employed households' incomes by 18.1 percent and agricultural wage households' incomes by 16.6 percent.

Although engagement in non-agricultural activities seems to be highly rewarding in order to raise incomes of rural households, gaining access to highproductivity non-agricultural income sources strongly depends on a household's income and wealth situation. Dividing the 2001 household per capita incomes into quintiles, Table 4.5 shows that in particular households situated in the upper two quintiles receive incomes from non-agricultural sources.

*Table 4.5: Income Quintile Statistics*

Note: Quintile classification is based on 2001 household per-capita income distribution. Quintile I refers to the poorest quintile.

While the number of households engaged in some sort of non-agricultural activity increased across quintiles from 2001 to 2006, the share of income derived from these sources is much higher for richer households and only increased for households in the richest three quintiles. In contrast, given the increase of average household incomes across all five quintiles, the share of agricultural self-employed income increased remarkably for poorer households, despite a higher number of poor households being engaged in non-agricultural activities. Thus, the principal source of income growth observed between 2001 and 2006 differs between initially poorer and richer households. Income growth among poor households can be primarily attributed to increases in agricultural self-employed income due to increases in crop output, shifting cultivation patterns and favorable price developments, while richer households in addition seem to have benefited from strong increases in non-agricultural incomes.

## **4.4 Model and Results**

The primary focus of this paper is to investigate the impact of agricultural production, in particular the decision to grow cocoa or coffee, and engagement in non-agricultural activities, more precisely non-farm self-employment, on income levels and the growth of incomes in the post-crisis period. To achieve this we adopt a regression framework in which we control for other factors that might determine incomes in the region.

In order to understand the determinants of incomes across households *i*  ( *i* = l, ... , N) and time *t* ( *t* = 1, ... , T) we adopt an econometric framework that links household per-capita income (Yit) to a source of income levels that is common to all households (8). Furthermore, incomes might be determined by a household specific set of variables (Xie) such as demographic characteristics, access to infrastructure, wealth level of households, cropping patterns or the economic sector of employment. The main focus of our analysis is on the effect of non-farm self-employment *(NSE)* and the planting of cocoa *(Cocoa)* and coffee *(Coffee).* Although our data set collects very detailed information of a variety of household characteristics there might be additional factors that influence incomes which are specific to each household but that are largely unobserved to the researcher such as ability or motivation (0i)- The econometric specification of this model is represented in equation (4.1) in which we assume an error term that is household specific and time-varying.

$$\ln(\chi\_{\rm tt}) = \delta + X\_{\rm tt}'\beta + \chi\_1 NSE\_{\rm tt} + \chi\_2 Cooca\_{\rm tt} + \chi\_3 Cofee\_{\rm tt} + \theta\_l + u\_{\rm tt} \tag{4.1}$$

Since it is very likely that the unobserved characteristics *0i* are correlated with at least some of our control characteristics *Xi* and more importantly with our main variables of interest, *NSEit, Cocoait* and *Coffeeit,* the coefficients y1 , y2 and y3 might be biased. In order to address this endogeneity problem we make use of our panel structure and implement a Fixed-Effects specification that helps to eliminate the effect of0i on the other coefficients so that y1 , y2 and y3 can be estimated consistently (Baltagi, 2008).

Secondly, we want to assess the impact of NSE, Cocoa and Coffee on percapita income growth for the post-crisis period. A natural way of doing so is an empirical growth model that allows for transitional dynamics (Mankiw, 1992). In this model growth rates for household i [ln(Yit) - In CYit-i)] are related to initial levels of income In CYit-i)- Similar to the static model above we assume that there is source of growth common to all households which we again denote by *8.* Other sources of growth from *t-1* to tare exogenous levels of our socioeconomic control variables X and our main variables NSE, Cocoa and Coffee, each observed at *t-1.* As in model (1) we allow the household specific unobserved effect *0i* to be correlated with the other regressors.

Mindful of the numerous reasons why one should be careful in applying this framework given the theoretical and empirical assumptions implied by this model (Temple, 1999) our basic model is

$$\begin{array}{l} \ln(\mathbf{y}\_{lt}) - \ln(\mathbf{y}\_{lt-1}) = a\ln(\mathbf{y}\_{lt-1}) + \delta + X\_{lt}'\beta + \gamma\_1 NSE\_{lt} + \gamma\_2 Covoa\_{lt} + \\ \gamma\_3 \mathcal{C}offee\_{lt} + \theta\_l + u\_{it} \end{array} \tag{4.2}$$

In contrast to our model in equation (4.1) using a Fixed Effects approach will provide an inconsistent estimate ofy1 , y2 and y3 from equation (4.2) due to the inclusion ofln *(Yit-i)* as regressor. To address the discussed econometric problems we use a Generalized Methods of Moments (GMM) estimator with a twostep Windmeijer (2005) correction so that our estimates ofy1 , y2 and y3 are both consistent and efficient (Blundell and Bond, 1998).

Before estimating equations ( 4.1) and ( 4.2) there are further empirical issues that require consideration. While we addressed endogeneity issues arising from X, NSE, Cocoa or Coffee being potentially correlated with the householdspecific unobserved effect *0i,* there might be further endogeneity problems. In particular our main variables of interest, NSE, Cocoa and Coffee, might be correlated with the household specific and time-varying error component *uit* which might lead to biased causal estimates ofy1 , y2 and y3 . Such a correlation might stem from issues of reversed causality between the dependent variable and the NSE, Cocoa or Coffee variable. For instance, in our context it is likely that those households that achieve a high level of or growth in per-capita incomes are more likely to have the financial resources to be able to diversify into lucrative nonfarm self-employment opportunities. Likewise richer households might be better able to cope with periods of lower agricultural incomes and higher income risks when transiting from the production of coffee to cocoa.

To address the issue of simultaneous causality we adopt an Instrumental Variable (IV) approach to estimate equations (4.1) and (4.2). To instrument NSE, Cocoa and Coffee we use lagged values of these variables as instruments. Estimation of equation (4.1) is done by 2SLS. In the GMM framework we include in addition the lagged differences of these values as recommended in Blundell and Bond (1998). Since the values of the endogenous variables are only providing moderate variation over time, we include additional instruments that we believe are exogenous to the income generating process.

In the case of non-farm self-employment we make use of an important geographical feature of our study area that can be used to construct a suitable instrument for the panel regressions. All study villages are situated in one of the two valleys (Palolo and Kulawi valley) which extend up to 200km south of the

provincial capital Palu. Both valleys are connected with paved roads to Palu and for each of the villages Palu is by far the nearest city in the area. Palu itself comprises roughly 250,000 inhabitants and contains the main port in CS which is used to import commodities (durables) and to export cocoa and coffee primarily to the USA. The distance to Palu matters for rural households mainly in that Palu offers a variety of non-agricultural employment opportunities. Therefore, households residing closer to the provincial capital are more likely to find or start non-agricultural activities. Thus, distance to Palu proxies the strength of spill-over effects from urban to rural areas. As found in Suryahadi et al. (2009) such spill-over effects are most likely to occur in the rural service sector which in our case is part of the non-agricultural sector. Once we control for asset possession, education, demographic and location characteristics, as well as sector choice in our income regressions, we do not expect that distance to Palu (measured in travel time) exercises any direct influence on household incomes. Furthermore, it is noteworthy that in our case distance to Palu varies on the household level since households usually first need to reach the nearest paved road (which varies from household to household even within a village) in order to get to the main road to Palu.

With respect to our Cocoa and Coffee variables we use the lagged village level prices of cocoa and coffee as additional instruments. Obviously, the decision to grow cocoa depends on the price of cocoa as well as on the price differential between cocoa and coffee. The prices of these two crops can reasonably be assumed to be exogenous to the households in our study region since production of these crops in Central Sulawesi is too small to have an effect on the world market price of these products. Although we have household level data on farm gate prices for cocoa and coffee we instrument by the village level price in order to reduce the presence of measurement error in the price data.

#### **4.4.1 Basic Results**

Table 4.6 presents the results of our panel regressions ( 4.1 ), the determinants of per capita income levels, and ( 4.2), the determinants of per-capita income changes. The first and third column provide results for the case that NSE, Cocoa and Coffee are assumed to be exogenous determinants of the dependent variable while the second and fourth columns show the IV estimates when NSE, Cocoa and Coffee are instrumented as outlined above. All regressions include a common set of control variables X that are related to demographic characteristics (age and age2 of the household head, gender of the head, household size, number of men in the household and dependency ratio), engagement in a particular economic sector, household wealth variables (wealth in durable consumption and production assets, area of arable land) and infrastructure variables (access to electricity, distance to the next paved road, visit with an extension officer over the last year).

*Table 4. 6: Determinants of income and income growth*

All monetary values were included in real terms with base year 2001. Further controls inelude sub-district and time dummies. A common intercept is included. Significance levels: \*\*\*/\*\*/\* denote 0.01, 0.05 and 0.1. Robust t-statistics used.

Moreover, time and sub-district dummies were included.66

66 In the discussion of our results we focus on the role of NSE, cocoa and coffee on the levels and growth of household per-capita income. The direction, magnitude and significance levels of the control variables, X, are in line with what would be expected from economic theory or what is known from other empirical studies. In particular, the effects of the demographic variables, and the lagged dependent variable are similar to

The results show that a household's engagement in non-farm self-employment leads to increases in income levels of approximately 139 percent67 ( column 1) compared to the reference category of agricultural wage employment. Likewise, non-farm self-employment seems to lead to higher income growth of about 69 percent (column 3) compared to the reference category. Both coefficients are statistically significant at a one-percent level. When controlling for the potential endogeneity of NSE using an IV approach we find that the results do not change much. After instrumenting the effect of NSE on per-capita income levels declines slightly to 132 percent while in the growth model we see that the effect of NSE on income growth is about 60 percent. Thus, in both cases we observe that engagement in non-farm self-employment seems to generate strong payoffs in terms of per-capita income levels and income growth during the post-crisis period. Furthermore, the benefits seem to be similar for non-farm wage employment which also generates a high income premium. However, both forms of non-farm employment generate a sizable pay-off compared to agricultural selfemployment.

With respect to the effect of cropping patterns on household incomes we find that growing cocoa boosts incomes by about 0.2 percent per are (column 1). Given that households had on average about 70 are under cocoa cultivation across 2001 and 2006 this effect implies that on average households were able to achieve approximately 14 percent higher incomes due to the cultivation of cocoa, holding everything else constant. Moreover, ceteris paribus, an average household having planted *all* its two hectares with cocoa in 2006 would have had an about 40 percent higher income level. Similarly, households growing cocoa were experiencing higher income growth throughout the period 2001 - 2006 of a similar magnitude (column 3). The results in columns 2 and 4 are nearly identical to those in columns 1 and 3. Therefore, even in the IV set up we still find a substantial economic and statistically significant effect of cocoa on household incomes. In contrast to the growing of cocoa we find a positive but statistically insignificant effect of planting coffee.

those found in other studies (e.g. Woolard and Klasen, 2005; Fields et al., 2003). The insignificant education coefficient is also not unusual. In fact,the sign of the education coefficient on rural incomes have even been found to be negative in some cases, e.g. Adams (1995) on the value of wheat, sugarcane, and rice production in Pakistan or Rosegrant and Evenson (1992) on total factor productivity in India. In our case, the correlation of the education variable with non-agricultural activities and the value of assets, is likely to cause the coefficient to be statistically insignificant.

67 Because the value ofy1 is relatively large the correct interpretation of the coefficient of a dummy variable in the log-normal model is obtained after using the exponential transformation of ( *exp(0.87)-1* )\* l 00= 139 percent.

#### **4.4.2 Robustness Checks**

Table 4.7 shows the results of a series of robustness checks designed to assess whether changes in model specification, estimation, or sample affect the core results. Since the coefficient on our coffee variable is very close to zero and not statistically distinguishable from zero in all of these specifications we decided to not report the coefficient of coffee here in this table (but it is available on request). Row (0) repeats the results from our preferred specification (Table 4.6, columns 2 and 4). Row (1) explores how sensitive results are to the influence of outliers. In particular, the GMM estimates might be vulnerable to the influence of outliers because the optimal weighting matrix that underpins them is a function of fourth moments (Baum et al., 2003). We address this concern by trimming our sample such that we drop the top and bottom 1 percent of observations of income growth in the period 2001 - 2006.

*Table 4. 7: Selected robustness checks on basic results*

The Basic results specification (0) repeats the results from columns (II) and (IV) from Table 4.5. Specifications (1) to (9) are reported. against specification (0). This means that each row reports results applying exactly only one change compared to the basic specification. Significance levels:\*\*\*/\*\*/\* denote 0.01, 0.05 and 0.1.

In row (2) we test whether our results are robust to the inclusion of additional covariates. More specifically, we include the share of irrigated rice fields, the usage of pesticides, taking part in formal or informal credit groups and reception of remittances. Again the obtained results are very similar to those from the main specification. Similarly, results do not change much when we use village fixed effects instead of sub-district fixed effects (row (3)).

Next, we consider the impact of treating the value of durable consumptive and productive assets a household possess as endogenous to the income generating process (row (4)). Similar to the instrumentation of NSE and cocoa we use lagged values of the variable. In addition, we use an external instrument which measures the value of assets brought into marriage of the household head by husband and spouse. When we do so, results from the first stage F statistic indicate that we have strong instruments. Likewise, the p-value from the Hansen J test indicates that instruments used in the model fulfill the necessary exclusion restrictions. Again, the results do not change in a substantial way. Row (5) reports results when we run a sort of reduced form regression by excluding the value of assets from the regressions. As before, the main results seem to be robust to a change in the specification.

Row (6) reports results when taking into account that the three infrastructure variables, access to electricity, distance to next paved road and access to agricultural extension officers might be endogenous. We instrument by using lagged values of these variables. In contrast, in row (7) we adopt again a reduced form regression framework by excluding all three variables compared to the basic specification. In both cases the results seem not be sensitive to the potential endogeneity of these control variables.

To estimate equation (4.1) we adopted a household fixed effects framework that allows the covariates to be correlated with the unobserved effect *0i.* However, if *0i* are uncorrelated with NSE or cocoa then a Random Effects (RE) specification might provide more efficient estimates since it draws both on withinand between-household variation. Results for estimating equation (4.1) with a RE estimator while still instrumenting NSE and cocoa (Row (8)) gives roughly the same results as with the FE approach and therefore do not change the main conclusions of the paper. Moreover, the specific Hausman test suggests that the random effects assumption is not valid. A further alternative to estimate equation (4.1) when allowing *0i* to be correlated with NSE or cocoa is to eliminate *0i*  by taking first differences (FD) instead of demeaning each variable by its household specific period average as implied by the FE approach. In a static model such as in equation ( 4.1) FE is typically preferred over FD since FE can have less bias as *N* goes to infinity. Moreover, the FE estimator is more efficient under the assumption that the error term *uit* is serially uncorrelated. However, if the uitfollows a random walk the FD estimator is more efficient (Wooldridge, 2006). As before the results (row (9)) do not change much compared to the main specification when estimation is done by using an FD estimator. 68

68 As further robustness check we run the regressions when using adult equivalent incomes instead of per capita incomes. We used adult equivalents scales as provided in

## **4.5 Extension**

In a last step we explore to what extent the findings obtained from our study region can be generalized to a larger geographical setting covering substantial parts of rural Indonesia. To do this we compare our results to those obtained from large national household surveys called SUSENAS. SUSENAS is conducted by the Indonesian National Statistical Office (BPS) and covers more than I 00,000 households annually. Moreover, SUSENAS is Indonesia's principal source for official poverty, consumption, income, and education statistics. For our analysis we make use of the 2002 and 2005 rounds of SUSENAS which are the latest two rounds for which a detailed income and consumption module is available.

Unfortunately, SUSENAS does not capture information on several of the covariates that we used in the previous analysis, e.g. infrastructure variables, value of household assets, and detailed income data from agricultural sources. In particular, SUSENAS does not contain data on type of crops planted, quantity harvested and respective output prices, but simply asks households about their income from agriculture in the respective year. Likewise, the SUSENAS 2002 and 2005 rounds are cross-sectional data so that we cannot apply any of the previous panel techniques. Given this restriction our analysis rests on comparing descriptive but not causal estimates between our study region and other areas of Indonesia. More specifically, we rely on simple cross-sectional OLS regressions using the same set of explanatory variables on our and the SUSENAS data.

In a first step we assess whether households from the study region are similar to households in other regions in Indonesia. In order to compare households we need to guarantee that variables are measured in the same or similar way. The main difference between variables that are available in STORMA and SU-SENAS is found to be in the total household income data. Total household income in SUSENAS contains imputations for rent and housing. Since the exact imputation procedure has not been published by BPS and moreover such an imputation can easily lead to merely adding additional noise to the income variable we subtract this imputed income from the total household income variable in SUSENAS.

(Deaton and Zaidi, 2002). Again our results on NSE and cocoa didn't change much compared to the presented specifications. Given that we include a variety of demographic controls in our main specification the results should in general not be affected very strongly by using per-capita income instead of some sort of adult equivalence scale.

In addition, we decided to drop households in the three highest income deciles from the SUSENAS data set. This procedure is motivated by two aspects. Villages in the STORMA area are comparatively small and are situated in rather remote areas. These villages therefore are far from becoming classified as urban areas within the next decades. In contrast, households in SUSENAS classified as rural are sometimes on the edge of becoming classified as urban as soon as the next census will provide BPS with a new sampling frame. Since urban areas in Indonesia are much richer than rural areas, we expect more populous villages to be richer than villages with a small number of inhabitants. Therefore, we would expect that most of the richer households in the rural SUSENAS sample are located in larger villages. In addition, when comparing demographic and socioeconomic characteristics of the households in STORMA and SUSENAS we find that the samples for the two different data sources compare very well, when restricting the SUSENAS sample to households within the 1-7 deciles (See Table *A2* for the descriptive statistics).69

Moreover, we find that STORMA households compare favorably with rural households in Indonesia except to those residing on Java. Rural Javanese households tend to have lower household sizes, lower educational attainments, but a much higher share of total income coming from non-agricultural sources than the rest of rural Indonesia which might be due to the much higher degree of urbanization and the higher population density on the island. Moreover, agricultural self-employment is much less important on Java compared to the rest of Indonesia. In contrast, agricultural wage labor on Java seems to play a much more important role than in the rest of rural Indonesia, which might be due to larger farm sizes and the existence of large agricultural estates. In consequence, we drop households from rural Java from the subsequent analysis. However, the later results are robust to including Java in the analysis.

Bearing in mind the limitations of the final SUSENAS data set we estimate OLS regressions with household per capita income in logs as dependent variable. The regression coefficients can be interpreted similar to those in a simple Mincerian earnings regression. Table 4.8 shows the respective results for the 2001 round of our data set and the 2002 round for different regional aggregates using SUSENAS. Results for our 2004 and 2006 rounds as well as results from the SUSENAS 2005 round are provided in Table A3 in the appendix.

69 Our decision was derived from comparisons based on per-capita income, engagement in the four economic sectors and household size. However, the main results are robust to using the full sample from SUSENAS or choosing different cut-off points (60, 80 or 90 percent). Furthermore, Table A2 presents statistics for the 2001 STORMA and the 2002 SUSENAS round. Descriptive results for the other survey rounds lead to the same conclusions. These additional statistics can be obtained from the authors upon request.

*Table 4.8: Regional OLS Regressions* - *Comparison*

Comparing OLS estimates of SUSENAS with STORMA shows that the coefficients on most covariates are very similar. Key determinants of the income generating process are in both data sets a subset of the household characteristics, in particular household size and composition and the education levels70 , all of which are statistically significant and take signs as expected from economic theory. Most importantly, we observe that in other areas of Indonesia households that are predominantly engaged in the non-farm sector seem to do much better than households who derive most of their incomes from the agricultural sector. This observation is in line with our previous findings. Therefore, it seems

70 Since common findings suggests that better educated households are wealthier and that wealthier households obtain higher education levels, we think that the large magnitude in the education coefficient rather reflects the incorporation of direct and indirect effects of education on per-capita income levels. Including the value of assets as control variable in the STORMA regressions strongly reduced the magnitude of the education coefficient. Therefore, provided the same or at least similar underlying income generating process between STORMA and SUSENAS, results from SUSENAS for higher regional aggregates therefore seem to overstate the direct effect of education on income.

as if roughly the same functional relationship seems to exist in most of rural Indonesia except Java. 71