rafmacalaba/datause-encoder-data huggingface dataset:
subsidiary (Rajan, Servaes, and Zingales, 2000; de Haas and van Horen, 2013). When it comes to cultural proximity, the impact might be more ambiguous. On the one hand, cultural distance might operate like geographical distance and reduce the correlation between subsidiaries’ and parents’ default risk because more distant subsidiaries might be harder to monitor. On the other hand, cultural proximity might reduce the correlation between subsidiaries and parents if these subsidiaries are granted more independence because the parent is more comfortable decentralizing some control when it is more familiar with the culture and business environment in the host country.<sup>13</sup>

# **4. Results**

# _4.1. The correlation between the parent and subsidiary distance to default_

Figure 1 shows the average changes in Merton distance to default for all parent banks and, separately, for all subsidiaries over the period September 2008 to December 2009. It is clear from the figure that there is a very high correlation between changes in parent and subsidiaries distance to default. Because this figure does not control for other factors that can jointly influence these variables, we turn next to our empirical estimations that control for global factors and for changes in the distance to default of all firms operating in the corresponding parent and host countries.

Table 4 shows that foreign bank subsidiaries’ distance to default, calculated following Merton’s model, is significantly correlated with parent banks’ distance to default, even when we control for the average distance to default among all companies in the home and host countries,

> 13 Related to the idea that cultural proximity might result in better treatment for some subsidiaries, Giannetti and Yafeh (2012) show that cultural proximity affects financial contracts in a large dataset of international syndicated bank loans. For example, they find that lead banks offer larger loans at a lower interest rate to more culturally close borrowers.

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selected only 2010 and 2015 for the DiD analysis. The reason is that 2010 captures the respondents' quality of life just before the crisis, and 2015 is the latest data source. The other years are equally important; for instance, 2005 shows the difference in life satisfaction several years before the crisis, and 2017 to 2020 shows their projected life satisfaction, which seems to be converging.

Figure 1 compares the life satisfaction of in-camp and out-of-camp refugees, and the host population. It shows that the Syrian refugees generally tended to have higher life satisfaction than the Jordanian population before the crisis in 2005. However, as the Syrians became refugees in Jordan after 2010, their life satisfaction plummeted significantly until 2013. Both in-camp and out-of-camp refugees report improving life satisfaction starting in 2013, and they are very optimistic about the future. There are two ways to explain this trend. One is that their life satisfaction improved due to objective improvements in their circumstances, perhaps due to the change in the law in 2013 that gave priority to Syrians to obtain work permits. The second is the possibility of refugees adapting to their living conditions. This situation, especially adaptation of subjective life experiences, returns us to the need to evaluate QOL with several indicators.

Figure 2 shows that refugees living out of camps, especially those living in Amman, Jordan's capital city, reported less decline in their life satisfaction between 2010 and 2015. There is also a difference in the QOL refugees living in the Zaatari camp report compared to refugees living in the Azraq camp. Figure 3 shows that between 2010 and 2015, the life satisfaction of the host population tends to follow a similar trend irrespective of their place of residence, whether living close to camps (for example, those living in Zarqaa and Mafraq) or far from camps (for example, in Amman). Nevertheless, the host population living in Amman tends to be less optimistic about their future life satisfaction.<sup>5</sup> I measured "material living conditions" from the income and expenditure perspective. It captures the deprivations in income, assets, services, and housing, directly affecting refugees' QOL. For income deprivation, I measured household income per capita, the share of

> 5 It is beyond the scope of this paper to explain the reasons for the reduced projected life satisfaction of the host population in the capital. But I suspect that the influx of refugees may have something to do with it.

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The purpose of this paper is to identify public policy and program interventions that can increase rea,. rural wage rate. and hence reduce rural poverty.

Rural wages can only increase if the demand for rural labor grows faster than its supply. In other words, rural wages can be affected by changing both the demand for and supply of rural labor. An increase in the demand for rural labor, given its supply, can occur if there is an increased labor demand in either agricultural or nonagricultural activities. Agriculture has limited opportunities to absorb the growing labor force, however. Because of increased agricultural mechanization, the level of farm employment has stagnated or even declined in some parts of India (Bhalla, 1987; Bartsch, 1977). It is, therefore, difficult to raise agricultural real wage unless sufficienit productive employment is generated in the nonfarm sector.

Because India's large-scale industrialization policy creates few jobs, employment expansion in the urban sector is not enough to pull labor out of the rural sector (Hellor, 1988).2 Rural nonfarm growth is thus required to generate productive rural employment. The emergence and growth of rural nonfarm pursuits is, however, driven primarily by growth in rural income that leads to higher demand for rural nonfarm goods and services (Binswanger, 1983; Liedholm and Head, 1986). Farm income and wages are important elements of rural income and thus agricultural growth may have an important influence on the growth of rural nonfarm activities and hence demand for labor. According to NSS, the share of nonfarm to total workers 2/ Consequently, rural wages cannot be increased through a reduction in the rural labor force via labor migration.
Pande, Rohini. 2011. “Can Informed Voters Enforce Better Governance? Experiments in Low-Income Democracies.” _Annual Review of Economics_ 3: 215–37.

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Persson, T., and G. Tabellini. 2004. “Constitutional Rules and Fiscal Policy Outcomes.” _American Economic Review_ 94 (1): 25–45.

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Rajan, Raghuram G. 2009 _._ "Rent Preservation and the Persistence of Underdevelopment." _American Economic Journal: Macroeconomics,_ 1(1): 178-218 Rajan, Raghuram G. and Luigi Zingales, 2003. _Saving Capitalism from the Capitalists: Unleashing the power of financial markets to create wealth and spread opportunity_ , New York: Crown Business Ravallion, M., 2016. Are the world’s poorest being left behind?. _Journal of Economic Growth_ , _21_ (2), pp.139-164.

Robinson, James and Thierry Verdier (2003). “The Political Economy of Clientelism.” CEPR Working Paper No. 3205.

Rodden, J. and E. Wibbels, 2017. Decentralized Governance and Accountability: Academic Research and the Future of Donor Programming. Commissioned by USAID’s DRG Centre.

Rodrik, Dani, 1996. “Understanding economic policy reform.” Journal of Economic Literature, March: 9-41.

Rodrik, D. 2006. Goodbye Washington consensus, hello Washington confusion? A review of the World Bank's economic growth in the 1990s: learning from a decade of reform. _Journal of Economic literature_ , _44_ (4), 973-987.

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include French, German, Scandinavian, Socialist and the omitted category of English common law. The data source for the variable is Djankov et al (2007).

Fifth, starting with the seminal work of Max Weber, a number of studies have highlighted the importance of religion in shaping the quality of institutions. For example, Stulz and Williamson (2003) show that the low level of creditors’ protection present in Catholic countries is most likely due to the anti-usury culture pervasive in the Catholic tradition. Following this literature, we use dummy indicators identifying the main religious group in the country as either Muslim or Catholic, Protestant and the residual category of all other religions. The data source for these indicators is La Porta et al. (1999).

Consistent with the literature discussed above, we find a strong link between some of the control variables discussed above and reform. Nevertheless, the positive relationship between democracy and reform easily survives these controls. Figure 1 provides a graphical illustration of the democracy-reform linkage controlling for GDP per capita and regional fixed effects.

# **3. Main empirical results**

The main empirical results are provided in Table 4. The estimated coefficient values and their significance levels are obtained using a logit specification with Huber-White robust standard errors clustered on the country. Without any additional controls, the estimated coefficient of _Democracy_ equals 0.828, significant at the 1% level (column 1). The coefficient estimate implies that a move from below-average to above-average level of 10
4 respectively, from the World Bank’s database.<sup>1</sup> NRAs to producers and CTEs to consumers differ whenever there are domestic subsidies or taxes on production or consumption in addition to border measures. Thus the indexes capture in a single scalar number the aggregate trade- or welfare-reducing effects of all policies directly affecting consumer and producer prices of farm products from all measures in place. Non-product-specific distortions are not captured in the indices, which by construction aggregate only product-specific data. However, we attempt to gauge the importance of this limitation in the final section of the paper.

The present paper is aimed at encouraging not only the OECD to add these indexes to their current set of indicators calculated each year, but also developing country governments or policy think-tanks to generate them so as to be able to monitor each year the trade and welfare effects of their national policies. A new FAO/OECD project, funded by the Bill and Melinda Gates Foundation and getting under way in 2010, aims to estimate agricultural policy indicators for a large sample of poor African countries over the next few years. Since many of those countries do not have a sector or economy-wide model of their economy, the two indicators outlined in this paper could provide at least a partial equilibrium indication of the effect of national policies in reducing agricultural trade and national economic welfare. They could then be compared with those provided in the present paper for high-income countries.

The paper begins with a presentation of the methodology for computing partial-equilibrium trade and welfare reduction indexes. It then outlines the data in the World Bank’s database, which are used for computing the indices. Next, the index results are presented and discussed, following which is a section addressing several caveats. The paper concludes with lessons learned and areas for further research.

# **Methodology**

There is a growing literature that identifies ways to measure the trade- and welfarereducing effects of international trade policy in scalar index numbers. This literature serves a key purpose: it overcomes aggregation problems (across different

> 1 NRAs and CTEs are similar to PSEs and CSEs, except they are expressed as a percentage of the undistorted price whereas PSEs and CSEs are expressed as a percentage of the distorted price (and the CSE has the opposite sign to the CTE).
> <sup>6</sup>

themselves can police and punish deviations by own-group members against the other group. Lake and Rothchild (1996) also trace conflict to a breakdown in the collective organization of an ethnic group. They observe that ethnic conflict is most likely when political entrepreneurs, operating within ethnic groups, exploit fears of insecurity. Other research examines how leaders exploit ethnicity to trigger conflict (Fearon and Laitin 2000, Blimes 2006). Such behavior is less likely to emerge when ethnic groups can collectively constrain counterproductive behavior by group leaders. The analysis here can therefore explain why ethnicity and conflict are particularly linked in the African context: intra-ethnic organization, as manifested in the ability of political parties to make credible ethnic appeals, is weak, making inter-ethnic agreements difficult to enforce.<sup>2</sup> A large literature examines ethnic voting, particularly in Africa. In a recent contribution, Eifert, Miguel and Posner (forthcoming) show that respondents to Afrobarometer surveys were 1.8 percent more likely to identify themselves ethnically rather than in terms of another social categorization, such as occupation, for every month closer a _competitive_ election was to the timing of the survey in a country. Lindberg and Morrison (2008) question the importance of ethnicity, arguing that in their Ghanaian sample, most respondents explain their political preferences in terms of incumbent performance; few acknowledge ethnic motivations or vote-buying. Ferree, Gibson, Hoffman and Long (2009), also looking at Ghana, also find that performance evaluations are important, but in their analysis ethnicity is still a significant determinant of partisan preferences.

2 This evidence is also consistent with evidence in Keefer (2008), showing that insurgency is more likely when political parties are vehicles of individual leaders and party members are less able to act collectively to sanction leaders for not defending their interests.
Policy Research Working Paper 6619

# **Abstract**

Using two surveys from Bangladesh, this paper provides evidence on the effects of microfinance competition on village moneylender interest rates and households’ dependence on informal credit. The views among practitioners diverge sharply: proponents claim that competition of microfinance institutions reduces both the moneylender interest rate and households’ reliance on informal credit, while the critics argue the opposite. Taking advantage of recent econometric approaches that address selection on unobservables without imposing standard exclusion restrictions, this paper finds that microfinance competition does not reduce moneylender interest rates, thus partially repudiating the proponents. The effects are heterogeneous; there is no perceptible effect at low levels of coverage, but when microfinance coverage is high enough, the moneylender interest rate increases significantly. In contrast, households’ dependence on informal credit tends to go down after they become a member of a microfinance institution, which contradicts part of the critic’s argument. The evidence is consistent with a model where microfinance institutions draw away better borrowers from the moneylender, and fixed costs are important in informal lending.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fshilpi@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
A Duration Analysis of CONALEP Girls in both cohorts enter CONALEP with slightly lower scores than boys. The first year (1999) cohort entered with higher scores than the third year (1997) cohort. Thus, we could conclude that CONALEP student entry scores have actually risen more than suggested by the data. In the third-year cohort, entry scores positively correlated with rising socioeconomic indicators for both boys and girls. However, there seems to be little relationship between socioeconomic status and entry score in the 1999 cohort, except for higher-class girls.

In sum, CONALEP students tend to come from relatively low socioeconomic backgrounds and tend to score at the lower passing end of the higher secondary school entry test. About half have general basic secondary education, with another third coming from basic technical secondary schools. Somewhat less than half of the third year students indicate that the CONALEP option was their first choice of higher secondary school, and somewhat more than half of the first year cohort say it was their first choice.

A second questionnaire was given to firms that hired CONALEP graduates from regular courses or training courses. In general, the interviewed firms who hire students from CONALEP and use its training services think highly of the organization. Approximately 72% of firms (public lower, private higher) think that the academic level attained by CONALEP students is high or very high. About 55 to 60% of companies said that the technological level of a CONALEP education is high or very high, with large public companies giving the lowest ranking (46%).

Using matched pair methods, Lopez-Acevedo (2002) re-evaluates the labor market performance of CONALEP graduates. She shows that individuals in the control group find jobs faster than CONALEP graduates do, but a higher proportion of the CONALEP graduates work in the occupational category congruent with their field of specialization or training. CONALEP graduates earn between 20 to 28 percent higher wages than the control group. The results indicate that employers invest more in training CONALEP graduates than they invest in the control group.

# **4. Data: The CONALEP Graduate Tracer Surveys**

The first CONALEP graduate tracer survey was conducted in February 1994 (CONALEP, 1994) on the basis of a random sample of 1,500 former CONALEP students who graduated between June 1991 and June 1993. The surveyed graduates were selected to represent 8
that is legal to be employed - given that the minimum age is low enough (14 years in the case of Pakistan) such that the employers do not have to pay adult wages.<sup>22</sup> (Basu (2005) also suggests that the wages of children will decrease in the short run. Unfortunately, we do not have the wage data to verify this claim since very few working children in our data set report earning any wages whether in cash or kind.

# **5 - Conclusions and certain policy implications**

The objective of this paper is to try to identify whether the Employment of Children Act, implemented in April 1991, had the desirable impact of reducing children’s employment. Exploiting a quasi-experimental approach, we use regression discontinuity design generated by the legislation defining 14 year of age as the minimum age for work and use the difference-indifference estimation to isolate the effect of law from other factors affecting the employment of children. Our empirical analysis uses micro-level data from the Pakistan Integrated Household Survey 1991 and the data is divided in pre-intervention (January-April) and post-intervention (May-December) periods. Since the EoCA 1991 specifies the minimum age of work as 14 years, so a treatment group of less than 14 year old children is compared against various control groups.

The results, using a treatment group of children age 10-13, shows a significant decrease in work for boys in the sample. Since the socio-economic set-up of the country is such that girls are less likely to be involved in work outside the household (the law does not prohibit children working with the family on family enterprise), so the results suggest that the law affected only boys. The model estimated taking control group of 14 year old children and treatment group of

> 22 In other words, if the minimum work age was 17 years, substituting 17 for an 18 year old would mean paying the 18 year old adult wages, thus increasing the cost of hiring that person. In such case Basu’s punishment cost appears to be the model that might be followed by the employers.

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Table 4: Heterogeneity Analysis

||(1)<br>_Outcom_|(2)<br>_e: Survey c_|(3)<br>_ompleted_|
|---|---|---|---|
|_i._Phone survey|0.348***|0.420***|0.456***|
||(0.057)|(0.045)|(0.045)|
|_ii._Female|-0.030|||
||(0.055)|||
|_iii._Female x Phone Survey|0.153**|||
||(0.077)|||
|_iv._Shares phone||-0.009||
|||(0.065)||
|_v._Shares phone x Phone survey||0.032||
|||(0.091)||
|_vi._Has Tigo money|||0.130**|
||||(0.062)|
|_vii._Has Tigo money x Phone survey|||-0.101|
||||(0.085)|
|Total effect on women ([_i_] + [_iii_])|0.501***|||
|Total effect on people who share phone ([_i_] + [_v_])||0.451***||
|Total effect on people that have Tigo money ([_i_] + [_vii_])|||0.356***|
|Mean of Dep.Var (WhatsApp)|0.299|0.299|0.299|
|R-squared|0.217|0.212|0.219|
|Observations|597|597|597|
|Controls (Age, Sex)|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|

_Notes:_ This table shows the heterogeneous impacts of the phone-based survey – relative to WhatsApp-based survey – on completion rate by gender, phone exclusivity, and use of electronic transfer technology. Female is an indicator variable for whether the respondent is a woman, “Shares phone” is a dummy variable for whether the respondent shares a phone with a relative, and “Has Tigo Money” is an indicator variable for whether the respondent uses Tigo Money. “Phone survey” consists of a dummy variable equal to one if the survey was per call and zero if was assigned to the WhatsApp group. The dependent variable in columns (1)-(3) is a dummy equal to one if the survey was completed (See more details in Table A8 in the Appendix). The control variables include age in years and sex of the respondent. To account for potential time-invariant characteristics within municipalities, we estimate a model with municipality fixed effects. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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