rafmacalaba/datause-encoder-data huggingface dataset:
the control group. The average effect sizes for program impacts on self-employment are displayed in Panel D of Table 4. Average effect sizes range from 46 to 87 percent depending on the specification and self-employment indicator.

# <u>5.2 Impacts on employment status</u>

Table 4 (Panel B) makes clear that young university graduates are particularly vulnerable to the lack of salaried jobs. Only 28 percent of students in the control group were employed one year after graduation, contrasting with 48 percent being unemployed.<sup>20</sup> While the program had impacts on self-employment, we find no evidence that the program significantly affected overall employment as captured by the likelihood of being employed in the last 7 days. In fact, estimates are negative and point to a reduction in the probability that program beneficiaries hold salaried employment. Even though the effect is not significant, the decrease in wage employment is of the same magnitude as the increase in self-employment. Similar to findings in Fairlie et al. (2012) in the US, these results suggest that the program changed the composition of employment by inducing a substitution from wage employment to selfemployment for participants in the entrepreneurship track.

Importantly, the substitution from wage to self-employment is a partial equilibrium effect, and does not take into account potential general equilibrium effects. Indeed, the shift from wage employment into self-employment may free up job opportunities for non-participants, therefore potentially leading to higher employment overall in general equilibrium. The impact evaluation design would not allow identifying such general equilibrium effects.

Overall, while the program increased graduates’ self-employment in a context where the supply of jobs is limited, the results show that the entrepreneurship track did not promote graduates’ chances of finding a salaried job nor did it seem to have any impact on beneficiaries’ probability of being employed in any activity one year after graduation. On the one hand, this is explained by the fact that the entrepreneurship track is only effective in increasing self-employment for a limited (although significant) number of students. On the other hand, the evidence does not

> 20 These results are comparable with data from a tracer survey of university graduates from the class of 2004, which found that 46 percent of graduates were still unemployed 18 months after graduation (MFPE and World Bank, 2009).

18
internationally, and therefore accept a lower net profitability. Foreign banks have lower net profits in most developed countries, whereas they generally have higher net profits in developing countries.

Table 4 provides average accounting data for banks by different country groupings. Considering the breakdown by income, we see that foreign banks on average obtain lowest interest margins in high-income countries, and they achieve highest margins in lower income countries. At the same time, foreign banks achieve higher (lower) interest margins than domestic banks in low income and lower middle income (high middle income and high income) countries. Overhead expenses, taxes and net profitability of foreign banks in low-income countries similarly tend to be relatively high. Note that banks in low-income countries have higher overhead expenses than banks in high-income countries, despite lower wages in low-income countries. This probably reflects bank overstaffing and difficulties in evaluating loans in low-income countries. Interestingly, for all four income groups foreign banks have higher loan loss provisioning than domestic banks, despite the fact that foreign banks generally provide relatively little risky consumer credit. The reason may be that foreign banks are at an informational disadvantage in identifying good credit risks, or that they have more conservative reserving policies.

Turning to the breakdown by geographical region, we see that foreign banks achieve far better interest margins in Africa than the domestic banks. Generally, for domestic and foreign banks alike the achieved interest margins are highest in Latin America and in the transitional economies. In both cases, high overhead expenses seem to be the driving factor behind the high interest margins. Except in Africa, foreign banks have higher non-interest income to total asset ratios compared to domestic banks since they tend to engage in nonlending activities to a greater extent. Turning to taxes paid, the taxation of banking appears to be very high in the transitional economies, followed by Africa. Foreign banks pay lower taxes only in transitional and industrialized economies. Finally, only in Africa and in Latin America do foreign banks achieve higher net profitability than the domestic banks.

# 4. _Empirical results_

Barth, Nolle, and Rice (1997) using bank-level accounting data for 1993 have considered the impact of bank powers on their return to equity for a set of 19 countries. Demirgi.i<;-Kunt and Huizinga (1997) use a broader data set for banks in 80 countries for the period 1988-1993 to examine how a of variety bank variables (including ownership), and additional tax policy, legal and financial structure variables affect banks' net interest income and profitability. The results indicate that foreign ownership (as a bank characteristic) leads to higher net interest margins and profits in developing countries, while this result is reversed in developed countries. Extending this work, this section examines how foreign bank penetration measures affect the operation of domestic banks, specifically their margins, profits and overheads.

8
# **1. Introduction**

This paper uses cross-sectional evidence to explore how farmers adapt to exogenous environmental factors such as climate and soils. By comparing choices of farmers who face different conditions, the model uncovers how farmers adapt. We specifically examine how climate affects which species Latin American farmers choose to own. We test whether climate alters species choice. Understanding species switching (adaptation) is an important goal in itself to assist planning by policy makers and private individuals. However, understanding adaptation is also important if one is interested in quantifying the impacts of climate change. The impacts of climate change not only require understanding how each species will be affected but also how farmers will switch across species.

Climate impact studies have consistently predicted extensive impacts to the agricultural sector from climate change across the globe (Pearce et al. 1996; Tol 2002). The bulk of agriculture studies on the effect of climate change have focused on crops. However, a large fraction of agricultural output is from livestock. Yet there are very few economic analyses of climatic effects on livestock. Most of the livestock studies related to climate change have not explored the economic impacts (Watson et al. 1995; McCarthy et al 2001).

Two important exceptions are the study of the effects of climate change on American livestock (Adams et al. 1999) and the study of climate and African livestock (Seo and Mendelsohn 2006). Individual American livestock are sensitive to climate. For example, beef catle cannot tolerate high temperatures. However, by using cool locations, protected environments (sheds, barns, etc.) and supplemental feed (e.g. hay and corn), the American livestock sector is not sensitive to warming (Adams et al 1999). In Africa, by contrast, the bulk of livestock have no protective structures and they graze off the land. They currently live in the coolest locations available. African livestock, and especially beef cattle, are sensitive to climate (Seo and Mendelsohn 2006). Warmer temperatures cause African farmers 2
# **Table 9: Receiving SMS vs. Reporting Less Travel**

|||Less Tr|avel=1; Do|not=0||||Less Tr|avel=1; Do|not=0||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.259***|0.250***|0.237***|0.032***|0.032***|week|0.204***|0.222***|0.143***|0.068***|0.070***|
||(0.007)|(0.007)|(0.007)|(0.006)|(0.006)||(0.014)|(0.016)|(0.018)|(0.020)|(0.020)|
|Urban||<br>-0.033***|<br>-0.034***|||Urban||<br>-0.029***|<br>-0.030***|||
|||(0.010)|(0.010)|||||(0.011)|(0.010)|||
|Ln(income pc)||0.030***|0.030***||0.004**|Ln(income pc)||0.033***|0.033***||0.004**|
|||(0.002)|(0.002)||(0.002)|||(0.002)|(0.002)||(0.002)|
|Constant|0.711***|0.632***|0.505***|0.768***|0.755***|Constant|0.763***|0.670***|0.527***|0.773***|0.761***|
||(0.005)|(0.009)|(0.035)|(0.001)|(0.005)||(0.005)|(0.009)|(0.035)|(0.000)|(0.005)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.062|0.087|0.174|0.001|0.002|R-squared|0.003|0.033|0.125|0.001|0.001|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 15
WPS3487

# **Child Labor, School Attendance, and Indigenous Households:**

## **Evidence from Mexico**<sup>**++**</sup>

### **Rosangela Bando G.**

Universidad de las Américas, Puebla

### **Luis F. Lopez-Calva**

Universidad de las Américas, Puebla

**Harry Anthony Patrinos** The World Bank

**Abstract** :  This paper uses panel data for Mexico for 1997 to 1999 in order to test several assumptions regarding the impact of a conditional cash transfer program on child labor, emphasizing the differential impact on indigenous households.  Using data from the conditional cash transfer program, PROGRESA (OPORTUNIDADES), in Mexico, we investigate the interaction between child labor and indigenous households.  While indigenous children had a greater probability of working in 1997, this probability is reversed after treatment in the program.  Indigenous children also had lower school attainment compared with Spanish-speaking or bilingual children.  After the program, school attainment among indigenous children increased, reducing the gap.

#### **JEL classification code** : I21, I32, J13, J24

**Keywords** : Child labor, Mexico, Indigenous, Conditional cash transfers

World Bank Policy Research Working Paper 3487, January 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ++ The authors gratefully acknowledge support from the UNICEF/ILO/World Bank Understanding Children’s Work program and comments from Furio Rosati.
# **Table 15: Project impact on child development for 1 year old cohort**

||**Physical He**<br>**Well-b**<br>**Unadjusted**|**alth and**<br>**eing**<br>**Adjusted**|**Social Com**<br>**Unadjusted**|**petence**<br>**Adjusted**|**Emotional**<br>**Unadjusted**|**Maturity**<br>**Adjusted**|**Language and**<br>**Develop**<br>**Unadjusted**|**Cognitive**<br>**ment**<br>**Adjusted**|**Communic**<br>**General K**<br>**Unadjusted**|**ations and**<br>**nowledge**<br>**Adjusted**|
|---|---|---|---|---|---|---|---|---|---|---|
|Village is Batch 1����|0.120**|0.108*|0.097*|0.081|0.009|-0.014|0.183***|0.151***|0.065|0.058|
||(0.054)|(0.055)|(0.054)|(0.054)|(0.052)|(0.053)|(0.052)|(0.049)|(0.049)|(0.049)|
|Village is Batch 3����|0.220***|0.207***|0.054|0.020|-0.000|-0.035|0.115**|0.099**|0.104**|0.078|
||(0.049)|(0.051)|(0.052)|(0.052)|(0.048)|(0.050)|(0.053)|(0.048)|(0.051)|(0.051)|
|Constant|-0.840***|-1.937***|-0.180***|-1.469***|-0.466***|-1.612***|-0.631***|-2.069***|-0.589***|-1.545***|
||(0.080)|(0.310)|(0.067)|(0.254)|(0.066)|(0.273)|(0.055)|(0.235)|(0.050)|(0.260)|
|Number of observations|2,725|2,445|2,728|2,450|2,731|2,452|2,737|2,456|2,738|2,457|
|Adjusted R2|0.102|0.118|0.117|0.167|0.137|0.171|0.250|0.360|0.262|0.298|
|Number of clusters|310|309|310|309|310|309|310|309|310|309|
|p-value of test of��:<br> ��� ��||0.056||0.225||0.687||0.291||0.694|
|**Controls Included**|||||||||||
|Child Characteristics|N|Y|N|Y|N|Y|N|Y|N|Y|
|Household Characteristics|N|Y|N|Y|N|Y|N|Y|N|Y|
|Height and Weight|N|Y|N|Y|N|Y|N|Y|N|Y|
|Parenting<br>practices<br>at<br>baseline|<br>N|Y|N|Y|N|Y|N|Y|N|Y|
|District dummies|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|

_Note:_ Robust standard errors clustered at the village level. Child characteristics include: child’s age and gender. Household characteristics include household size, household wealth and mother’s education. Height and weight include stunting and wasting status at baseline. Parenting practices are self-reported by caregiver at baseline. District dummies are controlled for in both adjusted and unadjusted models.

49
hard requirements to meet since enumerators are typically educated and have basic knowledge of mathematics and statistics.

In any event, the quality of the enumerators may still affect the accuracy with which probabilities are elicited – some enumerators may do a better job explaining expectations questions than others. We check this using the Sri Lanka microenterprise dataset of de Mel et al. (2008). 19 enumerators were used by the survey firm (AC Nielsen Lanka) to interview 587 microenterprise owners spread over 24 geographic areas, with multiple enumerators used per geographic area. We regress the absolute value of the difference between the realization and subjective mean of profits against enumerator fixed effects and geographical dummies. The enumerator fixed effects are jointly insignificant (p-value=0.43) when the mean from the subjective distribution is used as expectation, but they are jointly significant (p-value=0.03) when the “what do you expect” question is used instead. The F-test of enumerator effects in the simple expectation question becomes insignificant once outliers are removed, so perhaps some of the enumerators are better than others at identifying and double-checking crazy answers. This is the only example we are aware of testing for enumerator effects in expectations questions, so it would be useful for future research to further examine this issue.

In terms of survey time, eliciting a point estimate may take from 2 to 5 minutes, while eliciting a full distribution can take up to 10 minutes if the support also needs to be elicited. But because there is a fixed cost of explaining the question first, subsequent elicitations can be obtained much quickly. Despite the fact that eliciting expectations can take some time, our experience has been that interviewees respond well to such questions. If visual aids are used, the questions provide a nice break from more tedious survey questions. Indeed, item non-response on the expectations questions in all of the datasets we have collected have been negligible.

It is true that most of the surveys which have elicited expectations in developing countries have been specialized surveys with relatively small sample sizes, leading some to wonder if these questions can be scaled up into large-scale nationally representative surveys. However, several recent surveys have demonstrated that it is feasible to include such questions in large surveys. For example, Delavande and Kohler (2008a) include a large number of expectations questions in a survey of over 3,000 adults in Malawi, and Attanasio and Kaufman (2008) include them in a survey of 23,000 young adults in Mexico.

- 20 -
63 Huffman, S.L. "Women's Activitles and Impacts on Child Nutrition" Food Policy Integrating Sply, Distribution and Consumption, Baltimore, Johns Hopkins University Press, 1987.

Johnston, B.F. and Meyer, A.J. Nutrition, Health and Population in Strategies for Rural Development Vol. 26, No. 1 Economic Development and Cultural Change, 1977.

Kallen, D.J., (ed) "Nutrition, Development and SocialBehavior". Washington D.C., DHEW Publication No. (NIH) 73-242.

Kennedy, E. T. and Alderman, H. Comparative Analysis of Nutritional Effectiveness of Food Subsidies and Other Food-Related Interventions. Washington D.C. International Foa Policy Research Institute. 1986.

Kielmann, A.A. and Associates., Child and Maternal Health Services In Rural India, Vol. 1. Baltimore: The John Hopkins University Press, 1983.

Knudsen, 0. and Scandizzo, P.L."Nutrition and FoodNeeds in Developing Countries". World Bank Staff Working Paper No.328 (Washington D.C. The World Bank, 1979).

Knudsen, O.K. (prepared by)., Economics of Supplemental Feeding of Malnourished Children: Leakage, Costs, and Benefits, World Bank Staff Working Paper No. 451 (Washington D.C.: The World Bank, 1981).

Lancaster K.J. "A New Approach to Consumer Theory".Journal of Political 8conomy, April 1966 Latham, L., Latham, H., and Basta, S.S., "The Nutritional andEconomic Implications of Ascaris Infection in Kenya". World Bank Staff Working Paper No. 271 (Washington D.C. The World Bank,1977).

Levinson, J.F., Morinda: An Economic Analysis of Malnutrition among Young Children in Rural India. (Cambrldge,Mass. Cornell/MIT International Nutrition Policy Series, 1974).

Martin, T.H., "Nutritional Consequences of Agricultural Development Projects: A Survey of the Evidence". Mimeo. (Washingon D.C., World Bank. July, 1983).

Martin, T.H. "A Literature Survey Exploring the Impact of Agricultural Development Projects on Nutrition", Unpublished paper written for World Bank. 1982.

Martorell, R. and Ho, T.J. "Infant Feeding practices and Morbidity and Mortality: A Comprehensive, critical Review of the Literature". (Washington D.C. The World Bank, 198?).

Martorell, R., Mason, J., Rasmussen, K., Ho, T.J. and Habicht, J-P. "Child Feeding Practices: Knowledge, Research Needs and Policy Implications." ???
|Ofcials for Kono and Western Area<br>**Western Area Urban**<br>**Coefcient**<br>**Std. Err.**<br>0.29<br>0.15<br>0.54_∗∗_<br>0.20<br>-0.03<br>0.07<br>-0.26_∗_<br>0.13<br>-1.18<br>0.90<br>-0.05<br>0.35<br>0.38_∗∗_<br>0.12<br>0.00<br>0.13<br>0.62_∗_<br>0.28<br>0.53<br>0.84<br>-0.37<br>0.88_∗∗∗_<br>0.24<br>-1.79<br>1.13<br>0.29<br>0.54<br>0.18<br>0.42<br>own.|
|---|
|Local Government<br>**Kono**<br>**Std. Err.**<br>1.59<br>0.28<br>0.11<br>0.22<br>0.79<br>0.28<br>0.29<br>0.17<br>0.29<br>0.56<br>0.38<br>1.04<br>0.98<br>1.41<br>0.35<br>6.07<br>0.53<br>0.43<br> the results are not sh|
|rustworthiness of<br>**Coefcient**<br>-1.37<br>0.44<br>-0.18<br>0.60_∗∗_<br>1.70_∗_<br>0.35<br>-0.43<br>-0.31<br>0.20<br>2.16_∗∗∗_<br>1.01_∗∗_<br>-3.33_∗∗_<br>-2.80_∗∗_<br>-2.36<br>1.65_∗∗∗_<br>0.28<br>0.28<br>0.19<br>d in the model but|
|T<br>t<br>e|
|Table 4: Model 1: Multilevel Logistic Regression on <br>Urban (2007 and 2008)<br>**Variable**<br>Year 2008<br>Listen to radio every day/past week<br>Population density of enumeration area<br>Ethnic majority in ward<br>Ethnic fractionalization<br>NGO projects in community<br>Community exposure to war violence<br>Belong to a religious group<br>Belong to a political party<br>Experience with registering deaths<br>Health clinic in community has working fridge<br>Clinic in community: district hospital<br>Clinic in community: maternal/child post or community pos<br>Clinic in community: private<br>Local councillors spending money well<br>Random Efects<br>Intercept (Kono=1,080; Western Area Urban=1,660)<br>Enumeration Areas (Kono=46; Western Area Urban=97)<br>Wards (Kono=26; Western Area Urban=44)<br>_∗p < ._05; _∗∗p < ._01; _∗∗∗p < ._001<br>Other socio-demographic variables are includ|

29
# **_Pakistan_**

# **_South Africa_**

|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Winsorized_<br>Data Set|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Winsorized_<br>Data Set|
|---|---|---|---|---|---|---|---|---|---|
|Number of observations||107,652|107,524|103,847|Number of observations||91,904|88,374|70,608|
|Price (USD)|Median|83,934.34|83,934.34|83,934.34|Price (USD)|Median|95,588.23|93,286.45|105,498.7|
|Price (USD)|Mean|156,756.10|154,420.40|140,740.9|Price (USD)|Mean|155,699.4|147,055|154,418|
|Square meter|Median|151.76|151.76|151.76|Square meter|Median|350|313|350|
|Square meter|Mean|955.86|220.93|210.80|Square meter|Mean|2,081.1|555.01|803.73|
|Price per square meter|Median|535.38|535.38|535.38|Price per square meter|Median|360.38|387.17|357.06|
|Price per square meter|Mean|634.36|633.16|627.51|Price per square meter|Mean|887.42|629.44|584.53|
|Price per square meter|IQR|344.14|344.14|339.65|Price per square meter|IQR|614.62|624.15|599.32|
|Price per square meter|SD|652.22|480.86|395.14|Price per square meter|SD|9,302.60|853.02|700.54|
|Number of bedrooms|Mean|3.86|3.86|3.87|Number of bedrooms|Mean|3.04|2.99|3.01|

Note: The _original_ data set contains the original set of all listings. The _truncated_ data set retains listings that contain sale prices, size data, and whether the property is an apartment or house and truncates the data based on sqm<9 or sqm >3,000 and Price < $US 5,000 or Price > $US 5,000,000. The _winsorized_ data set removes the first and 99th percentile of price and size; SD= Standard Deviation; IQR=Interquartile range 42
The researchers administered a baseline household survey in 2005 and another survey 15 to 18 months after the branch was introduced to approximately 65 households in each neighborhood (6,850 households total) to measure the program‟s impact on outcomes related to consumption, new business creation, business income, education, health and women‟s empowerment. It is worth noting that other microfinance institutions operated in the area and some of them opened up in both treatment and control areas. The evaluation strategy is an intentto-treat analysis that compares averages in treatment and comparison areas, averaging over both borrowers and non-borrowers. The study finds no effect of the program on average monthly expenditure per capita but that expenditure on durable goods increased in treatment areas and the number of new businesses increased by one-third in treatment areas. They find that treatment effects were heterogeneous, with households with a high propensity for starting a business cutting back on non-durable consumption in an apparent effort to save for the fixed costs of starting a business. The study finds that there were no discernible effects on education, health or women‟s decision-making.

# **4.1.5  Expanding Credit in Thailand: The Thai Million Baht Village Fund Program**

Kaboski and Townsend (2007) study the effectiveness of microcredit programs in the context of rural Thai villages. In particular, they develop and structurally estimate a dynamic model of credit-constrained households deciding on consumption, indivisible investment, and savings and study how these households were affected by the introduction of a large-scale government microfinance program, the Thai Million Baht Village Fund Program. This program, begun in 2001, transferred one million baht (about $25,000) to each of almost 80,000 villages in Thailand to start village banks that would lend to local households. The total amount of funding to each village was the same regardless of the size of the village, so village size provides a plausibly exogenous source of variation in the amount of credit increase per household. The funds were not directly targeted at women, but women were among the program beneficiaries. Kaboski and Townsend‟s (2007) analysis samples came from the Townsend Thai project, which gathered panel data on rural and semi-urban households and businesses from 64 villages in four Thai provinces from 1997 to 2007.

The behavioral model is based on the standard buffer stock model of savings behavior under income uncertainty (e.g., Aiyagiri (1994) and Deaton (1991)) with the additional feature that households have an investment option in each period. The household‟s problem is to maximize 34