rafmacalaba/datause-encoder-data huggingface dataset:
considered. In addition, one Gender Equality Strategy of the World Bank is to expand women’s access to credit. According to Fletschner (2008), an efficiency-based argument could support the idea of enhancing women’s access to credit. Cohen (2010) identifies four additional components to consider in the multidimensional poverty assessment tool for rural households, including access to credit.

There are two main channels through which access to credit may affect a household’s wellbeing. The first one is related to the opportunity for households to alleviate their capital constraints and to develop income-generating activities. The second channel is by increasing households’ abilities to face risks, including strategies that involve consumption smoothing (Diagne and Zeller, 2001). According to Becchetti and Conzo (2013), the credit access effects go beyond the mere change in current income since they also involve a significant improvement in life satisfaction. By addressing the issue of financial ethics, Hudon (2009) argues that to proclaim credit access as a human right is not necessarily a proper decision. In fact, even though there is agreement that credit access may reduce poverty, especially when it is directly used to improve development outcomes, it could, by contrast, induce perverse effects such as indebtedness. Thus, in some cases, women who borrow money may experience a reduction in welfare (Ngo and Wahhaj, 2012). Positive effects can be observed when certain initial conditions hold, including investments in productive activities and large household expenses. Moreover, having access to formal credit without necessarily borrowing is likely to result in positive and significant marginal effects on household income (Diagne and Zeller, 2001). This argues in favor of considering access to credit as an input for welfare. Data from the World Development Indicators show that the proportion of women who possess an account at a formal financial institution represents 10.8% and 9.2% respectively in Burkina Faso and Togo, which remains very low compared with the 21.5% recorded for the whole of Sub-Saharan Africa.

# **3 Methodology and data**

12
31 Table A.7: Regressions for fixed investment with interaction terms, unbalanced 5-year average panel, 1980–2009<sup>_†_</sup>

||**A.I1**|**A.I2**|**A.I3**|**A.I4**|**A.I5**|
|---|---|---|---|---|---|
|Lagged investment|0.600<br>(0.11)<sup>_∗∗∗_</sup>|0.620<br>(0.11)<sup>_∗∗∗_</sup>|0.421<br>(0.08)<sup>_∗∗∗_</sup>|0.356<br>(0.09)<sup>_∗∗∗_</sup>|0.403<br>(0.10)<sup>_∗∗∗_</sup>|
|Output|0.414|0.393|0.572|0.629|0.592|
||(0.12)<sup>_∗∗∗_</sup>|(0.11)<sup>_∗∗∗_</sup>|(0.10)<sup>_∗∗∗_</sup>|(0.10)<sup>_∗∗∗_</sup>|(0.12)<sup>_∗∗∗_</sup>|
|Output growth|1.078|1.068|1.723|1.175|1.374|
||(0.29)<sup>_∗∗∗_</sup>|(0.29)<sup>_∗∗∗_</sup>|(0.30)<sup>_∗∗∗_</sup>|(0.23)<sup>_∗∗∗_</sup>|(0.33)<sup>_∗∗∗_</sup>|
|Cost of capital|0.768|1.095|0.306|1.278|1.555|
||(0.88)|(0.87)|(0.93)|(1.29)|(1.31)|
|Trade openness|0.158|0.185|0.056|0.086|0.087|
||(0.16)|(0.16)|(0.13)|(0.12)|(0.11)|
|Financial openness|-0.156|-0.146|-0.075|-0.103|-0.123|
||(0.06)<sup>_∗∗_</sup>|(0.06)<sup>_∗∗_</sup>|(0.08)|(0.06)*|(0.07)<sup>_∗_</sup>|
|Financial|0.017|0.028|0.034|0.102|0.097|
|development|(0.11)|(0.11)|(0.12)|(0.16)|(0.15)|
|Financial|||0.052|0.166|0.084|
|structure|||(0.14)|(0.24)|(0.23)|
|Fin. dev. _×_||||-0.153|-0.057|
|fn. struc.||||(0.22)|(0.21)|
|Institutional|-1.158|-1.152|-1.396|0.162|0.136|
|quality|(0.60)<sup>_∗_</sup>|(0.60)<sup>_∗_</sup>|(0.91)<sup>_∗_</sup>|(0.08)<sup>_∗∗_</sup>|(0.10)|
|Institutional|-0.934|-0.935|-1.221||0.098|
|structure|(0.48)<sup>_∗_</sup>|(0.47)<sup>_∗∗_</sup>|(0.75)||(0.09)|
|Inst. qual. _×_|0.692|0.697|0.869|||
|inst. struc.|(0.34)<sup>_∗∗_</sup>|(0.33)<sup>_∗∗_</sup>|(0.51)<sup>_∗_</sup>|||
|Business||-0.069|-0.055||-0.099|
|environment||(0.16)|(0.18)||(0.20)|
|Wald _χ_<sup>2</sup>|18,421<sup>_∗∗∗_</sup>|18,225<sup>_∗∗∗_</sup>|9,250<sup>_∗∗∗_</sup>|10,117<sup>_∗∗∗_</sup>|13,206<sup>_∗∗∗_</sup>|
|Hansen J|29.435|28.634|30.755|28.874|29.156|
|AR(2) z|-1.927<sup>_∗_</sup>|-1.853<sup>_∗_</sup>|-1.003|0.165|0.105|
|Instruments|45|46|47|41|44|
|N (countries)|321 (105)|321 (105)|229 (82)|236 (82)|236 (82)|

> _†_ All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. Period fixed effects and a constant term were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,

> _∗∗_ indicates significance at 5 percent level, and _∗∗∗_ indicates significance at 1 percent level.
First, we use the data on regulations and focus on the change in the last period. We regress the decadal average per capita GDP growth in the 1990s (measured at PPP in constant 1985 dollars)<sup>15</sup> on the initial value of log real GDP per capita at PPP (at constant 1985 dollars) at the beginning of the decade, the change over the previous decade in the log of average trade to GDP (or the log of average trade to GDP at PPP)<sup>16</sup> , the change in the average inflation rate, the change in the investment ratio, and the interactive term between the change in trade and the regulation dummy variable.<sup>17</sup> In this section, because the sample is smaller and to keep the cutoff point similar to that from the cross-section regression, we report results for the one-third most regulated countries; the results are similar if we the one-quarter most regulated countries, though standard errors are slightly larger. We also try including a measure of the change in institutional environment. We use the average rule of law index from ICRG<sup>18</sup> as an institutional measure. This measure reflects upon the security of property rights, risks of political instability and the stability of laws respectively. We also use the change in the number of revolutions and change in the fraction of the population killed in civil or international wars as alternative measures of institutional quality.<sup>19</sup> In Table 5, estimation results are reported for the 1990s decadal regressions, using growth in trade/GDP as an independent variable. Columns 1-6 report results using OLS with a correction for heteroskedasticity. An increase in trade significantly raises growth. However, in

> 15 The data here are not from the Summers-Heston dataset but taken from the Dollar and Kraay dataset. Dollar and Kraay use data on real per capita GDP at PPP at constant 1985 dollars from the Summers and Heston Penn World Tables Version 5.6, after extending it to the 1990s using constant price local currency growth rates.

> 16 Trade/GDP is constructed by deflating the sum of exports and imports at constant 1995 dollars by the cumulative growth of US GDP deflator from 1985-1995 and dividing by GDP at PPP in constant 1985 dollars.

> 17 We also include change in government spending to GDP and change in the black market premium as additional measures of macro stability, but neither was ever significant, they tended to be close to zero and changed sign depending on specification, so we do not include them in the reported regressions.

> 18 This is the Law and Order rating from ICRG and is different from the rule of law index of Kaufmann, Kraay, and Zoido-Lobaton (2002).

> 19 The investment ratio is investment/GDP both at constant 1985$, at PPP; the inflation measure is

> ln (1+inflation/100),  inflation rate measured as annual percent change in CPI if available, otherwise annual percent change in GDP deflator. All data in the decadal analysis are from the Dollar and Kraay dataset.

20
countries and averages across country groupings. The import response for each product depends on the height of the tariff at the HS6-level and on the corresponding price elasticity for that product. Price elasticity estimates are from Kee et al. (2008). The price elasticity estimates are applied to average import data for 2010-11 at the HS 6-digit level and the reduction in protection is carried out only for tariffs on the APEC list (and also on the WTO list for the classifications by income group). Estimates for each Davos country are obtained by adding up estimates at the product level. For those at the region level, to save space, only group averages are reported.<sup>11</sup> Because the import response is the result of applying the price elasticity estimate to the tariff rate, one could obtain low import response for a country (group of countries) with high tariffs if the corresponding price elasticity is low. Despite substantial differences in prices elasticities across country groupings, with higher average price elasticity estimates for highincome countries (as one would expect, since substitutes to imports are higher for lowprotection HIC group), the estimates show a monotonic relation between average protection and import response. Taking the WTO list where disparities in protection are greatest across groups, the LIC group with the highest average protection (8.9%) shows the highest average import response (7.9%) to a cut in half of tariffs. This estimate is however barely greater than the one for the UMIC group, whose average protection rate is 30 percent lower than the one of the LIC group.

The estimated magnitudes are small. An average increase in imports ranges between 2-4 percent for a 50% reduction on the APEC list and between 2-8 percent for the WTO list; estimates that are far from hinting at a possible import surge. For the Davos group, only China, Korea, and New-Zealand could expect import increases in the 7-10 % range in an ambitious scenario where tariffs are completely eliminated. However these estimates do not account for any supply response that would entail a reduction in output and employment requiring adjustment assistance. Still, the estimates are obtained by applying the MFN tariffs on all imports while imports under free-trade agreements should have been omitted from the estimates.

> 11 The discussion paper version gives the formula for calculating the estimates and figure 2 reports the distribution of elasticities by income group.

15
diseases. Poor households are not only vulnerable to air pollution related health impacts, they are also vulnerable to accidental and occupational health impacts. This is because much of the workforce is in coal mining, oil and gas drilling, oil tanker driving, and coal hauling in power plants comes from low-income households. In many developing countries, casual workers in mining, power plants and hydropower dams do not have proper health and safety measures and insurance plans.

Health impacts from the energy supply chain, particularly from household air pollution, disproportionally fall on women and children. About half of the global population and most of them in developing countries, use traditional biomass, the main source of household air pollution, for cooking. Women and small children (under age 5 years) are the ones who are most exposed to household air pollution and its health consequences. This implies that clean cooking either through switching to cleaner fuels or to low emitting more fuel-efficient biomass stoves should be a primary energy policy issue in the developing world.

Nuclear power traditionally has had a bad perception among the general public. Many countries do not allow nuclear power because of the perception problem. From the human health impact angle, however, nuclear power is not the worst. Historically, it has one of the lowest impacts on human health compared to other sources of energy. Nuclear power has lower accidental and occupational health impacts compared to other source of energy, particularly coal. Moreover, it does not produce air pollution, which is the main channel of health risks in the energy supply chains. Policy makers should consider a social cost approach that accounts for both environmental and human health impacts while making the choice of fuels to meet their energy needs, particularly electrical energy.

# **References**

Adetona, O., Z. Li, A. Sjödin, L.C. Romanoff, M. Aguilar-Villalobos, L.L. Needham, D.B. Hall, B.E. Cassidy and L.P. Naeher, 2013. Biomonitoring of polycyclic aromatic hydrocarbon exposure in pregnant women in Trujillo, Peru—Comparison of different fuel types used for cooking. Environment International, Vol. 53, pp. 1-8.

26
home; the percentage of students whose parents incentivize them to study, to do the homework, to read, to not miss classes, and that talk about what happens in the school; the percentage of teachers with tenure; whether there is a lack of textbooks in the school; the quality of the textbooks; the number of enrollments in the school; and whether the students are allocated into their classes at the beginning of the school year based on their previous academic performance.<sup>14</sup> The sample used in this analysis consists of 795 schools with 118,804 fifth-grade students in the treatment group and 2,568 schools with 239,778 students in the comparison group (Table 2).

# **4.0.2 Triple DiD on the sample of state and locally-managed schools**

Because the DiD design considers only 13 treated municipalities and these are considerably larger than most municipalities in the comparison group, one may wonder whether some unobserved time-varying factors could bias the estimates through the selection mechanism. For instance, it could be that the municipalities decided to postpone school return because they had more resources and better infrastructure to put in place mitigating policies later on; or because these municipalities registered the higher incidence of H1N1 cases, it is more likely that parents, teachers and students of these municipalities were more concerned with the risks of contamination and this concern affect their both teachers and students’ attendance rates. As long as these factors could affect learning, the DiD estimates would be biased.

To overcome this identification threat, we leverage a within-municipality variation. We consider the municipalities with at least one state and one locally-managed school and split them into two groups. Let _G_ = 1 denote the 112 municipalities where state-managed schools were closed and locally-managed schools remained open, and _G_ = 0 the ten municipalities where state and municipals schools were closed (Table 2). We assume that idiosyncratic shocks at the municipal level affect students and schools (either state or locally managed) similarly.

Due to the differences in the learning trajectories between state and locally-managed schools preschool shutdown, a simple comparison of state and locally-managed schools in _G_ = 1 would likely

> 14Table A.3, Table A.4, Table A.5, Table A.6, Table A.7, Table A.8, Table A.9 present the descriptive statistics for these covariates. The model does not include variables that might have been affected by the shutdowns, such as absenteeism of students and teachers, principal managerial skills, student motivation, and teacher motivation. By including these variables, it would be more likely to underestimate the effects of the shutdowns.

12
should have no direct effect on a static input like chemical fertilizer.

# 5 Conclusion

Although committment and holdup problems lie at the core of incomplete contract theory, their quantitative signi fi cance has rarely been assessed directly. This paper fi nds striking evidence of a holdup threat in a setting where theory suggests it should be prevalent, namely where investment is cooperative (in our case, the tenant’s investment bene fi ts the landlord). In rural Pakistan, speci fi c investment is lower on leased land than on owned land cultivated by the same household, even after accounting for the potentially confounding effects of asymmetric information in the leasing market. Further corroboration is found in the fact that land taken on fi xed rent receives as little, if not less, investment than land taken on a share basis, a result that is inconsistent with an explanation based on static moral hazard.

We also fi nd, after ruling out numerous alternative interpretations of the evidence, that security of tenure, and hence the incentive to invest, varies considerably across tenancies. This means that the mere potential for holdup does not necessarily lead to an unravelling of all investment. What is the mechanism for restraining ex-post opportunism? Our data show important differences across landlords in their propensity to retain their tenants, suggesting that some landlords might value reputation more than others. One can imagine an equilibrium in which ‘good’ landlords, who can credibly commit to not appropriate their tenants’ investment returns, coexist with ‘bad’ landlords on whose land tenants never invest. From the policy perspective, the fi nding that the degree of commitment varies across tenancies means that there is scope for effective tenancy reform. Put differently, if tenure insecurity arises merely from inherent differences in tenant mobility, not from landlord behavior, then legislation binding landlords to long term contracts would do little to encourage investment.

Finally, it is worth asking about the economic signi fi cance of underinvestment on tenanted land. Of course, the full extent of the dynamic inefficiency cannot be assessed without accounting for all the different types of land-speci fi c investment. This paper focuses on just a single investment, albeit an important noncontractible one, the use of farmyard manure. Nevertheless, it is still instructive to ask, based on our estimates, how yields would be affected by giving land ownership rights to tenants. Fortunately, we have access to results from a large number of agricultural experiments in India compiled by 24
birth from every individual observed during the 2001 Nepal Population Census and calculate yearly district birth rates. As reported in Figure 6, there is no dierence in birth rates between the districts experiencing above and below the median conict intensity (months of war). Table A.4 reports regression results for the same test and we see no dierence in periods long before and just before the start of the conict.

# 5.3 Impact on Fertility, Education, Employment and Wealth

Estimates in Table 7 show the impact of conict on reproductive health and fertility using the baseline specication. Early childhood exposure to conict has no signicant impact on probability of miscarriage or of stillbirth among women in age cohorts 9 to 15 and 4 to 8 in 1996. Women experiencing conict during their entire growth period, however, are signicantly more like to have had a stillbirth or miscarriage: each additional month of conict exposure increases the risk of stillbirth by 0.03 and of miscarriage by 0.06 percentage respectively. Valente (2015) nds similar results that pregnancies that were exposed to Nepal's conict were more likely to result in a miscarriage. There is no signicant impact on the probability of abortion. Women exposed to conict during their early growing periods have signicantly more live births, 0.024 and 0.013 more births per month of exposure among cohorts aged 0 to 3 and 4 to 8 in 1996 respectively. The impact of conict on the number of infant deaths is zero (column 5).

Impacts of early childhood exposure to conict on other fertility outcomes are presented in Table 8. The conict has statistically no impact on the likeliness of contraceptive use (column 1), number of women's sex partners, age at rst sexual intercourse and age at rst birth. There are also no signicant dierences between women in control and treatment groups on their age at rst cohabitation with their domestic partner and number of marriages and unions. Additionally, there is no dierence in the smoking or chewing tobacco habits of women in treatment groups and control group.

Table 9 shows impact on women's human capital accumulation, employment and wealth. While there is no signicant impact on years of education, women in age cohort 0 to 3 are signicantly less likely to have completed a school leaving certicate (SLC). The lack of results in years of schooling are consistent with Valente (2014) and Pivovarova and Swee (2015). Both examine the conict's impact on human capital accumulation. Similarly, there is no impact on the probability 21
Second, I find some evidence that growth in average household survey incomes is correlated with several of the usual determinants of growth from the empirical growth literature, including institutional quality, openness to international trade, and size of government. Although the evidence documented here for the correlates of growth in household survey incomes is not especially compelling, I argue that this likely reflects the limited country coverage and presence of measurement error in the household survey data on which this paper is based.

Third, I find relatively little evidence that poverty-reducing patterns of growth in relative incomes are significantly correlated with a set of explanatory variables that the empirical growth literature has identified as significant determinants of growth in per capita GDP. The same is true for a number of other variables, that while not generally significant for growth, have been suggested in the literature as potentially reducing inequality.

Taken together, these results underscore the importance of growth in average incomes for poverty reduction. This in turn suggests that a policy package focusing on known determinants of growth in average incomes, such as the protection of property rights, stable macroeconomic policies and openness to international trade should be at the heart of pro-poor growth strategies. Moreover, the absence of compelling evidence that these factors are systematically correlated with the changes in income distribution that matter most for poverty reduction suggests that there are no obvious tradeoffs – policies that lead to growth in average incomes are unlikely to systematically result in adverse effects on poverty through their effects on relative incomes.

This does not mean that growth in average incomes is sufficient for poverty reduction. Rather, the results presented here suggest that cross-country evidence is unlikely to be very informative about the policies and institutions that are likely to lead to poverty-reducing patterns of growth in relative incomes. This suggests that more microlevel and case-study research may be useful in shedding light on the determinants of poverty-reducing distributional change.

This paper is related to a growing empirical literature on growth, inequality, and poverty. Most immediately, this paper builds on Dollar and Kraay (2002). In that paper, 2