rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Expanding Social Insurance Coverage**

# **3.1 Key Programs and Targeted Populations**

After first developing an overall framework for social insurance programs, recent policy initiatives have emphasized implementation and extension of social insurance to groups not previously covered, including the informal and flexibly employed, the self-employed, and rural migrant workers. In recent years, basic health insurance has been introduced for urban residents, and a new urban residents’ pension program is currently being piloted for national roll-out by the end of 2012.<sup>14</sup> Key social insurance programs and the types of persons they target are shown in Table 1. Individuals are grouped into the following categories:<sup>15</sup> civil servants, public institution employees, urban enterprise workers, self-employed individuals, informally employed local residents, rural migrant workers and those who are not working. Civil servants, public institution employees and urban enterprise workers, generally enjoy comprehensive social protection through programs in which participation is _de jure_ mandatory, even if workers and employers in some sectors might avoid participation. For rural migrant enterprise employees, current policy directives aim to extend coverage, but with the exception of work injury insurance, they remain voluntary in practice. All programs targeting the self-employed are explicitly voluntary.

Pension coverage for working-age adults is shown in Figure 2 below using data from the 2005 and 2010 waves of the China Urban Labor Survey (CULS). Among local residents, coverage rates are nearly 75 percent among men and 68 percent for women in 2010, which are higher than reported coverage rates from the 2005 Population Sample shown in Table 2. A significant share of the difference between 2005 and 2010 likely reflects implementation of the 2008 Labor Contract Law, which increased sanctions for hiring workers without offering contracts and for failure to enroll employees in social insurance schemes. Nonetheless, as shown in Figure 3, the variation in coverage across ownership type remains evident. Much of this variation is driven by differences in compliance and risk of audit across ownership types. Participation in social insurance among local residents employed in government and Party agencies, SOEs and foreign invested firms is nearly 90 percent for men and 85 percent for women. In the private sector, where firms are smaller and face a lower likelihood of audit, only about 60 percent of local workers participate in the employer-based pension.

Given that there are significant differences in the types of programs available to subgroups of the working-age population, we first discuss available social insurance programs for employed workers by

> 14A discussion of the roll out of the new urban residents’ health insurance program can be found in Lin et al (2009). 15The 2008 Labor Contract Law created the mandate for provision of Social Insurance, and the 2010 Social Insurance Law provides the umbrella for the range of urban insurance schemes.

7
14 in the late 1990s. Since neither series overlaps sufficiently with the WVS data, we turn instead to the only expenditure variable that is available throughout the 1990s and 2000s and that has broad country coverage, general government final consumption expenditure from the WDI.

This variable differs from the IFS series in two ways. First, it includes general rather than central government spending and so includes government spending at subnational levels. However, coverage of subnational spending is sporadic and unlikely to be a significant issue. Second, and more importantly, final government consumption expenditure excludes the capital budget. Since previous research has identified capital spending as one source of funds for pre-electoral mobilization, this is problematic. However, Block (2002) finds for a sample of African countries that fiscal expansions around elections are particularly pronounced in government consumption expenditure and the correlation between this variable and the IFS data is 0.7. Assuming that cycles in government consumption and capital expenditure are related, this measure should be a good approximation of total government expenditure.

Moreover, any bias generated by using the general government measure is likely to be against the hypothesized relationship between budget cycles and party characteristics. Subnational spending introduces noise into central government spending, making it more difficult to detect cycles in central government spending. Similarly, to the extent that capital spending is more important for pre-electoral expenditures, it should once again be more difficult to detect cycles in spending using the general government consumption data.

Previous research has not addressed the noise that hyperinflation can introduce into analyses of political budget cycles. Hyperinflation generates very large within-fiscal-year differences in the real value of nominal government spending. As a consequence, small errors in assumptions regarding the timing of election spending can have large effects on the estimation. For example, if the election falls in the beginning of the fiscal year, even a large increase in government spending might go unnoticed, since nominal spending could be one-half of the nominal spending recorded in the last part of the fiscal year.
productivity. This is a fruitful area for future research.

# **References**

- Adamopoulos, T., Brandt, L., Leight, J., & Restuccia, D. (2017). _Misallocation, Selection and Productivity: A Quantitative Analysis with Panel Data from China_ . Working Paper 23039, National Bureau of Economic Research.

- Adamopoulos, T. & Restuccia, D. (2014). The size distribution of farms and international productivity differences. _American Economic Review_ , 104(6), 1667–97.

- Adamopoulos, T. & Restuccia, D. (2018). _Geography and Agricultural Productivity: Cross-Country Evidence from Micro Plot-Level Data_ . Working Paper 24532, National Bureau of Economic Research.

- Agness, D., Baseler, T., Chassang, S., Dupas, P., & Snowberg, E. (2020). Valuing the time of the self-employed.

- Ali, D. A., Deininger, K., & Goldstein, M. (2014). Environmental and gender impacts of land tenure regularization in africa: Pilot evidence from rwanda. _Journal of Development Economics_ , 110, 262–275.

- Anderson, M. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. _Journal of the American Statistical Association_ , 103(484), 1481–1495.

- Angrist, J. D. & Pischke, J.-S. (2008). _Mostly harmless econometrics: An empiricist’s companion_ . Princeton university press.

- Attwood, D. W. (2005). Big is ugly? how large-scale institutions prevent famines in western india. _World Development_ , 33(12), 2067–2083.

- Barrett, C. B. (1996). On price risk and the inverse farm size-productivity relationship. _Journal of Development Economics_ , 51(2), 193–215.

- Benjamin, D. (1992). Household composition, labor markets, and labor demand: testing for separation in agricultural household models. _Econometrica: Journal of the Econometric Society_ , (pp. 287–322).

46
Nonvide, 2019) in addition to PSM methods (Dillon, 2010; Palmer-jones, 2012). Authors with time series data have also used lagged variables to address endogeneity (Akber, 2020; Akber, Paltasingh, & Mishra, 2022) in a systems estimation approach including seemingly unrelated regression and 3SLS; and the single equation approach (OLS and 2SLS). To compare and assesses the interactions of impacts of (i) input subsidies; (ii) irrigation infrastructure; and (iii) food/cash transfers implies that our modeling must address three endogenous variables. We could not, however, find an applicable econometric approach that can address selection bias of three variables at once. Therefore, the study resolved to assess individual effects and paired effects.

Table 1: Instrumental variables used in the models.

|Endogenous variable|Instrumental variable used|
|---|---|
|Input subsidies|Member of Parliament visited the community in the past three<br>months.<br>Household head has always lived in village.<br>Deviation of particular-year rainfall from long term median.|
|Cash transfers/food aid|Deviation of particular-year rainfall from long term median.<br>Member of Parliament visited the community in the past three<br>months.|
|Irrigation infrastructure|Deviation of particular-year rainfall from long term median.<br>Whether the village chief receives payment for land use or sale<br>Member of Parliament visited the community in the past three<br>months.|

For models assessing the impact of production and social assistance programs on household DD, the Poisson model is appropriate because the outcome of interest is a non-negative count variable (Woodridge, 2010). Models are estimated using instrumental variable Poisson regression (IV Poisson) via the generalized method of moments (GMM) consistent with a prior study by Karamba (2013). This approach further allowed us to account for two endogenous variables in the estimation and assess joint effects of the interventions. Several instrumental variables are used depending on the endogenous variable under consideration as summarized in Table 1. For instance, receipt of a farm input subsidy is instrumented with three variables, namely: Member of Parliament visited the community in the past three months, household head has always lived in village and deviation of 10
# **1 Introduction**

The role of credit and demand factors in the 2008-2009 financial crisis is still not well understood. A negative credit shock to firms is generally thought of as a credit crunch— a reduction in the general availability of loans, or a sudden tightening of the conditions required to obtain a loan from the banks. Credit crunches squeeze firms’ working capital and cripple their production. On the other hand, adverse demand shocks to firms come from general declines in demand for firms’ products and services (i.e. a shift of the demand curve). While there is a consensus among researchers and policy makers that the 2008-2009 crisis was triggered by financial market disruptions in the United States, there is little agreement on whether the transmission of the crisis and the subsequent prolonged recession are due to credit factors or to a collapse of demand for the goods and services. Each type of factors has fundamentally different policy prescriptions. If credit factors are found to play the main role, the main prescription involves providing more and cheaper credit, directly injecting credit and liquidity to banks, or issuing loan guarantees. On the other hand, if demand factors are the main drivers, the focus should be on boosting investors’ and consumers’ confidence. Fiscal policies and the reduction of uncertainty regarding fiscal, monetary and regulatory policy are the conventional instruments in this case.

The existing literature almost exclusively focuses on the credit side to explain the transmission and propagation of the crisis. Recent theoretical contributions by Mendoza (2010), Devereux and Yetman (2010), Perri and Quadrini (2011) and Kalemli-Ozcan et al (2012) generally argue for a strong role of credit market frictions in the propagation and transmission of the crisis, following a long tradition starting from Kiyotaki and Moore (1997). An exception is Van Wincoop (2013), who develops a two-country model with leveraged financial institutions to show that the model cannot account for the impact of the crisis and the extent of the transmission.

Perhaps due to the strong influence of a well-established theoretical literature, the empirical literature also focuses on the credit side.<sup>2</sup> Tong and Wei (2011) use data on 3,823 listed firms in 24 emerging countries and find declines in stock prices to be, on average, more severe for firms intrinsically more dependent on external finance. Cowan and Raddatz (2013) use industry-level data for 45 countries to show that industries dependent on external finance decline significantly more during a sudden stop, especially in less financially developed countries. Paravisini et al (2011) use export data for Peruvian firms to show that credit shortages explain a 15 percent decline in Peruvian exports during the crisis. Similarly, Ahn (2013) also found bank liquidity shocks in Colombia had significant impacts on its imports. Not until

> 2 For early evidence see Bernanke and Blinder (1992), Khwaja and Mian (2008). Recent studies on the current crisis using aggregate data include Amiti and Weinstein (2009), Helbling et al (2011), and Chudik and Fratzscher (2011).

2
Table 1: Land Values and Groundwater Conditions

||Log(land|value/acre)|||
|---|---|---|---|---|
||(1)|(2)|Log(DTW)|GW quality|
|Land at head of watercourse|0.152***|0.152***|-0.0119***|0.0479***|
|(tertiary canal)|(0.0089)|(0.0089)|(0.0035)|(0.0077)|
|Outlet at head of channel|0.112***|—|-0.0265**|0.0607***|
|(secondary canal)|(0.0194)||(0.0108)|(0.0118)|
|Km to head of channel|—|-0.0137***<br>(0.0028)|—|—|
|_R_<sup>2</sup>|0.770|0.772|0.954|0.879|

_Notes:_ Robust standard errors in parentheses clustered on channel-head/tail (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). All specifications include 448 channel fixed effects and use 7,844 observations (in 896 clusters). Mean depth to water table (DTW) = 23.5 meters (median = 15.5 meters). Groundwater (GW) quality is coded as 3 = sweet; 2 = brackish but usable; 1 = unusable. Sample percentages in each category are, respectively, 74, 16, and 10.

section. In the final column, we see that groundwater quality, measured on a three-point qualitative scale of decreasing salinity (see notes to Table 1), follows the same pattern, higher at the head reaches of both tertiary (watercourses) and secondary channels. Crop-damaging salinity is another consequence of poor freshwater recharge, which leaves only deeper, more highly mineralized, groundwater available for pumping (Qureshi et al. 2010).

# **4.2 Canal water discharge**

Punjab Irrigation Department’s Program Monitoring and Implementation Unit (PMIU) has maintained daily records of authorized (designed) and _actual_ canal discharge since 2006. Figure 1 illustrates the location of PMIU discharge gauges at the head and tail of each – channel no gauges are set up at intervening outlets. Since tail discharge is measured at the last watercourse outlet of the channel, design discharge at the tail is never zero; all sanctioned outlets are entitled to off-take canal water. We construct a version of the “delivery performance ratio” or DPR (see, e.g., Waijjen et al. 1997) for the economically 21
Field reports from students and teachers in program schools describe how students actively competed for the scholarship when it was offered. One headmaster reported that the program “awakened our girls and was one step towards making the girls really enjoy school.”<sup>18</sup> One winning girl who was asked about her own performance versus those students who did not win remarked, “they tried to work hard for the scholarship but we defeated them.” It is plausible that this spirit of competition drove some girls to work harder, providing utility benefits beyond the direct monetary rewards.

Boys in Busia district program schools also have higher test scores than those in comparison schools, despite not being eligible for the scholarship themselves. The overall effect for Busia boys in both cohorts 1 and 2 was 0.16 (standard error 0.08, regression not shown), which is nearly significantly different than zero at 95% confidence. Cohort 1 gains in 2001 were even larger, at 0.20 standard deviations (standard error 0.10) in the restricted sample (Table 5, Panel C, regression 2). Boys and girls in western Kenya share the same teacher and classroom, so any benefits from added classroom effort on the part of teachers is likely to spill over to the boys. In the first year, it is also possible that some boys were confused as to whether they too were eligible for the scholarship. In the second year of the program, there are again positive, though not statistically significant, program impacts for boys – although we cannot reject that effects are the same in 2001 and 2002. Overall, there is no evidence the girls’ scholarship program discouraged or demoralized boys.

Field reports from the program schools also suggest that a spirit of competition with girls drove some boys to study harder. To the extent that this “gendered” competition was an important determinant of boys’ gains in program schools, it is an open question how large externality gains would be under alternative program that targeted boys rather than girls, or in which they competed against each other. Gneezy et al (2003) provide experimental laboratory data that females and males may react differently to competition, with females performing better when competing against other females than when competing against males, while males perform equally well regardless of the gender of the competition.

cohort 1; however, differences in program impacts across cohorts and years are not statistically significantly different for any of these four school subjects.

22
Policy Research Working Paper 5041

# **Abstract**

During the Vietnam War, more than 70 million liters of military herbicide were sprayed over the combat zone. This study uses self and proxy-reported data on cancer status obtained from a nationally representative health survey of the Vietnamese population (N=158,019), combined with measures of military herbicide exposure computed from detailed information on US and allied wartime military activities. No significant difference in the prevalence of reported cancer is detected between communes with some degree of exposure and those with none. When restricting the analysis to exposed communes and adopting a continuous measure of herbicide exposure, there is evidence of a dose-response relationship; among communes that were exposed, increasing exposure to past military spraying is associated with increasing prevalence of reported cancer in 2001– 2002. There is mixed evidence as to whether cohorts born before or after the end of the spraying campaigns are equally affected.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to understand post-conflict recovery. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at research@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
31 with causal studies (see Jedwab et al. (2020a) for a discussion). Furthermore, if returns are over-estimated because better able individuals study longer, the causal returns would mean a lower relative contribution of the education shock to the overall shock than what we obtain, reinforcing our result that experience significantly matters.<sup>54</sup>

**Youth Unemployment.** Returns to experience are higher among younger workers (Fig. 1). This could be important since employment was more severely disrupted by COVID-19 among younger workers (Section 5.2.1.). We repeat the simulation distinguishing between returns to experience in the 0-5 bin and the 10-25 bin.

**School Remediation.** We assume that the lost human capital cannot be made up for later.<sup>55</sup> Indeed, the literature indicates that disruptions to human capital accumulation have a significant long-term impact on earnings, focusing on schooling (see Azevedo et al. (2020) for a discussion). Notably, for the case of post-war Germany and Austria, Ichino and Winter-Ebmer (2004) show that this impact remains significant _even after 40 years_ . This is inconsistent with students or teachers being able to significantly make up for gaps in schooling. Consider that, to make up for missed schooling, either students would need to spend extra time to catch up, spend more time in school, or the production of educational services would have to become more efficient. During the crisis and recovery phases of the pandemic, such remedial education is unlikely.<sup>56</sup>

In fact, the shock could instead be larger than we estimate for several reasons. First, we assumed that students did not drop out of school, whereas there is ample evidence that suggests that there has been some drop-out (UNESCO, 2020a). Second, to the extent that knowledge is cumulative, having missed certain topics or not learned them properly hampers progress in future years. Thus, we think our study provides a useful benchmark that is not too far from reality even if there is recovery among some students. **School Dropouts.** Another factor we abstracted from that might increase the longrun impact of the COVID pandemic in developing countries is the possibility of 54Web Appx. Table A.7 shows that the estimated returns to education decrease but remain high if we add industry fixed effects and also control for the relationship to the household head, marital status, urban residence and self-employment. As explained above for the gender analysis, we cannot include too many possibly _endogenous_ controls. That is for example why we do not add occupation fixed effects.

55 Of course, any speculation that _x_ % of the loss can be made up for can be adjusted in our quantitative findings for each group accordingly: for such experimentation, our estimates provide a useful baseline. 56Fuchs-Sch ̈undeln et al. (2020) and Jang and Yum (2022) suggest that the ability of parents to substitute for formal schooling, as well as the elasticity of substitution, are key to mitigating schooling losses: we already factor this in as our estimates of losses are based on assessments of schooling loss over the period.
# **Long-Term Financial Incentives and Investment in Daughters: Evidence from Conditional Cash Transfers in North India**

Nistha Sinha and Joanne Yoong<sup>1</sup> World Bank RAND

> 1 We thank Rekha Varghese for her excellent research contribution, Beth King, Andy Morrison, Monica Das Gupta, Cristobal Ridao-Cano and Elizabeth Brown for helpful comments and suggestions. Funding from Hewlett Foundation is gratefully acknowledged