rafmacalaba/datause-encoder-data huggingface dataset:
## Estimating the E¤ects of Credit Constraints on Productivity of Peruvian Agriculture8

As Horowitz (2001, theorem 2.2) shows, bootstrapping an asymptotically normally distributed estimator that can be represented by an in‡uence function yields a consistent variance-covariance matrix and consistent con...dence intervals.<sup>2</sup> In the application, we bootstrap the estimator.

# 3. Productivity in Peruvian Agriculture

We apply the approach proposed above to a dataset about Peruvian agriculture. This dataset contains information about whether the farmer was credit constrained, which crops were grown on her/his farm, the amount of labor used, etc. The data was data was collected in 1997 and 2003 and were previously analyzed by Guirkinger and Boucher (2008). We follow their speci...cation and use regression coe¢ cients that are constant over time. The only di¤erence between our speci...cation and Guirkinger and Boucher (2008) is that we apply the methodology of the previous sections and use a selection equation to account for the endogeneity. We report the results in Table (1). The ...rst column reports the ...rst stage Probit regression. The second and third columns report columns (D) and (E) in the original paper. The fourth and ...fth columns report our estimates corresponding to their original columns (D) and (E). The sixth and seventh columns are columns (F) and (G) from the original paper. The last two columns are the corresponding estimates using our method. The standard errors are calculated by bootstrapping the two-step estimation together. We also implement a Wald test to compare the di¤erences between the credit constrained farmers and the credit unconstrained farmers. We ...nd that adding a selection equation to the model shrinks the di¤erences between the coe¢ cients of the constrained and unconstrained farmers. This ...nding has important policy implications since the e¤ect of being credit constrained is not as large as previously thought. Our statistical test con...rms these ...ndings. In particular, we used a Wald test with 11 degrees of freedom to test the statistical di¤erence between the credit constrained farmers and the credit unconstrained farmers. Using the speci...cation of the original paper, we ...nd that the value of the Wald test is 56.62 (comparing columns D and E) and 61.17 (comparing columns F and G). Adding the selection equation reduces these numbers to 22.92 (comparing

> 2 Horowitz (2001, Theorem 2.2) averages gn(Xi):
largest city, accounting for nearly 20 percent of the urban population.<sup>6</sup> Another 8.9 million people (57 percent) live in small and medium towns with a population of 50,000 or less. The remaining 5.3 million people live in major towns with a population of 50,000 or greater. Within urban areas, poverty incidence is relatively high in small towns with a population of less than 20,000 (20 percent) (World Bank 2020). Living standards in terms of access to basic services are also lagging behind in those small towns, though they are still better than rural villages.

Structural transformation has been lackluster in Ethiopia, as well. The low productivity in Ethiopian economy is due in large part to its low-skilled labor force. Nearly half of the urban workers have not completed primary education as of 2010. Nevertheless, the education levels of workers have been gradually improving in urban Ethiopia. The share of workers who have not completed primary education declined from 46 percent in 2010 to 38 percent in 2016.

Despite the improvement in labor force skills, the worker mobility to more productive sectors has been limited. Figure 1 shows that the share of workers in each industry has been remarkably stable. As of 2016, 6 percent of urban workers were engaged in agricultural jobs, while 12 percent and 72 percent were engaged in the manufacturing and service sectors, respectively. Despite such moderate scale, the shift in labor from agriculture to services and construction explains up to a quarter of per capita growth between 2005 and 2013 due to a large productivity gap between the sectors (World Bank 2016b).

Although consumption gains among self-employment households have made a significant contribution to urban poverty reduction, households with non-wage workers in the private sector are still more likely to be poor. Ethiopia’s urban poverty rate dropped from 35.2 percent in 2005 to 14.6 percent in 2016 (World Bank 2020).

Although the share of household heads with self-employment jobs remained at 45 percent during the decade, the rise in their consumption supported their escape from poverty. Nearly half of the urban poverty reduction between 2005 and 2016 took place among households headed by selfemployed workers. Nevertheless, house- holds headed by wage workers in the private sector are less likely to be poor than those headed by self-employed workers. Even among the latter group of households, those that have a member working with a private wage job tend to be less in poverty.

> 6While the population of Addis Ababa is by far the largest, its primacy rate (20 percent) is not so high as other African countries (Henderson and Kriticos 2018).

6
# **Figure 29. Consumption of infrastructure services in Uganda is highly differentiated by budget, 2001-2009**

a. Mode of water supply, by income quintile <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>Piped water Stand posts<br>Wells/boreholes Surface water<br>% population<br><!-- End of picture text --> b. Prevalence of connection to power grid among population, by income quintile <!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>Power<br>% population<br><!-- End of picture text --> _Source_ : Banerjee and others 2009.

_Note_ : Q1—first budget quintile, Q2—second budget quintile, and so on.

**Figure 30. Electricity and water subsidies that reach the poor, 2006**

<!-- Start of picture text --> a. Electricity b. Water<br>Nigeria Senegal<br>Gabon CAR<br>Congo Gabon<br>Côte d'ivoire Congo<br>Nigeria Kaduna<br>Cape Verde<br>Togo<br>Togo DRC<br>Sao Tome Nigeria FCT<br>Senegal Cameroon<br>Cameroon Côte d'ivoire<br>Mozambique Niger<br>Ghana Chad<br>CAR Cape Verde<br>Burundi<br>Guinea<br>Burundi Malawi Blantyre<br>Ghana<br>Burkina Guinea<br>Chad Uganda<br>Malawi Malawi Lilongwe<br>Uganda Burkina<br>Rwanda Rwanda<br>0 0.5 1 0 0.2 0.4 0.6 0.8 1<br>Omega Omega<br><!-- End of picture text --> _Source:_ Banerjee and others, 2009.

_Note_ : Omega is a measure of distributional incidence, or the share of subsidies received by the poor as a percentage of their share in the population. The higher the value of omega, the better the distributional performance of the subsidy. Values of omega below 1 denote a regressive subsidy and values above 1 denote a progressive subsidy.

CAR = Central African Republic; DRC = Democractic Republic of Congo 47
# **Measuring Household Usage of Financial Services: Does it Matter How or Whom You Ask?**<sup></sup>

Robert Cull _Development Research Group, World Bank, Washington, D.C. 20433, USA_ Kinnon Scott _Development Research Group, World Bank, Washington, D.C. 20433, USA_

>  We thank the Knowledge for Change program for providing funding for this project and Mircea Trandafir for excellent research assistance. We thank Asli Demirguc-Kunt, Pete Lanjouw, Jonathan Morduch, and Colin Xu for insightful comments. We also owe a large debt of gratitude to the survey team at the Ghana Statistical Service who were excellent colleagues without whom we could not have done this work. Finally, we should acknowledge that this project grew out of discussions between World Bank and FinMark staff about harmonizing survey approaches, and thus we thank Anjali Kumar, Norman Bradburn, Lorraine Ronchi, Adam Parsons, Mark Napier, Darrell Begin, Bob Curran, Anne Marie Chidzero and Karen Ellis for setting out the issues for us. The views expressed in this paper do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.
Table 2: Comparison of key features across methods

||||ML method||
|---|---|---|---|---|
|Feature|EBP|Cubist|XGBoost|BRF|
|Prediction target|Census data without<br>assets|Census data without<br>assets|Census data without<br>assets|Census data without<br>assets|
|Functional form|Single linear function|Multiple linear functions<br>(one at each decision tree<br>node)|Decision Trees|Decision Trees|
|Assumed data<br>generation process|Specifed linear model<br>with nested error<br>structure|Average linear prediction<br>based on specifed<br>predictors at each decision<br>tree node|Sum of decision trees<br>based on specifed<br>predictors and residuals|Sum of decision trees<br>based on specifed<br>predictors and residuals|
|Objective (Loss)<br>function|Minimize mean squared<br>error of predictions|Minimize standard error<br>of dependent variable<br>when determining each<br>split. Minimize mean<br>squared error at each node|Minimize mean squared<br>error of predictions|Maximize penalized<br>heterogeneity in the<br>dependent variable when<br>determining each split.<br>The penalty favors<br>balanced splits.|
|Random efect<br>conditioned on<br>survey data|Yes|No|No|No|
|Predictors used|All selected by LASSO|Selected through growing<br>decision trees|Selected through growing<br>decision trees|Selected through growing<br>decision trees from<br>randomly selected subset<br>of candidate predictors at<br>each node|
|Use diferent<br>subsamples of the<br>data for growing<br>trees and making<br>predictions|N/A|No|No|Yes|
|Transparency/<br>Ease of use|More|Less|Less|Less|

Note: See Appendix C for more details.
The mixed logit results are reported in columns 5 and 6 of Table 4. An estimate of both the parameter and the standard deviation is reported for each variable except for bill, which standard deviation is set equal to 1 to identify this model. The relative size of the standard deviation parameters relative to the corresponding point estimates indicates the degree of preference heterogeneity. In the mixed logit, a large and significant standard deviation indicates that there is considerable taste heterogeneity among the respondents.

As shown in Table 4, the overall pattern is similar between conditional and mixed logit results although individual coefficient estimates and significance levels are different. The proposed monthly bill is negative and significant as expected. Both consumption volume and supply hours generate positive utility. Consumption volume has a large and significant standard deviation, which indicates preference heterogeneity among the households (though the conditional logit model assumes that this heterogeneity is not necessarily pronounced between poor and non-poor households). Similar to the conditional logit results, supply hours show a diminishing marginal utility for increases in this attribute.

In terms of water safety variables, the coefficients are ordered as we expect. Better quality and safer water yields the highest utility, while unsafe water generates the lowest utility. Except for the safest quality water ( _i.e._ , water that you could drink “straight from the tap”), households did not seem to have diverse preferences for other levels of safety. In comparing service alternatives, the large coefficient on the status quo dummy suggests that households are happy with their current status, preferring it to all three service alternatives offered. Amongst the proposed alternatives, households rank mini-grids over private taps, and private taps over public stand posts. The large and significant standard deviations in the mixed logit model suggest households exhibit considerable heterogeneity in their preference for both private taps and mini-grids. The poor tend to value private taps significantly less than other households.

# **5. Concluding Remarks**

In this paper we used conjoint analysis to research the preferences of the poor and non-poor regarding alternative WSS programs. The data from the Sri Lanka study demonstrates how conjoint analysis can provide information to aid in the design and evaluation of WSS programs. Findings from our research in Sri Lanka suggest that consumers are interested in attributes other than costs and that demand for services will be reflected in their provision; that poor and non-poor households place similar values on water 16
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# **TABLES**

|Table 1. Number of participants and assets in the PPM scheme............................................. 12|
|---|
|Table 2. Major Groups Covered by Contractual Benefits in Sweden, 2005........................... 15|
|Table 3 Individual Benefits as a Percent of Final Earnings .................................................... 16|
|Table 4. The Financial Portfolio of Households, year-end figures ......................................... 18|
|Table 5. Income of persons 65+ as a percent of income of persons 55-64. ............................ 22|
|Table 6. Composition of Old Age Pensions 2004................................................................... 23|
|Table 7. Assets and premium payments to occupational, individual voluntary and PPM plans,|
|2006.......................................................................................................................................... 26|
|Table 8. Distribution of Assets and Premium Payments for Life and Pension Insurance<br>among major Swedish Insurance Companies, 2006................................................................. 29<br>|
|Table 9. Overview of Key Financial Data for Swedish Life Insurance Companies<sup>1)</sup>, 2006.|
|Millions of kronor .................................................................................................................... 29|
|Table 10. PPM assets held by the seven largest private insurance companies and the Sevent|
|AP fund (default fund), September 28, 2007 ........................................................................... 30|
|Table 11. Overview of products by category of provision...................................................... 32|
|Table 12. Overview of DC benefit options within the occupational schemes ........................ 33|
|Table 13. Overview of insurance products in the Swedish market......................................... 34|
|Table 14. Assets of life insurance companies. Billions of kronor, year-end figures and<br>percent of total assets (within parentheses).............................................................................. 40|
|Table 15. Traditional Premium-based Insurance, with a Guarantee Ratio of Assets to Pension|
|Liabilities. End of year figures................................................................................................ 41<br>|
|Table 16. Occupational Defined Benefit<sup>**1**</sup>**Insirance Ratio of Assets to Pension Liabilities.**41|
|Table 17. The PPM Life Expectancy Assumption. Illustrated with LE from birth. .............. 48|
|Table 18. The FSA’s Supervisory Role................................................................................... 50|

|**FIGURES**|
|---|
|Figure 1._._The guarantee pension and its relation to the combined NDC-FDC pension.......... 13|
|Figure 2. Persons utilizing a tax deduction for pension saving............................................... 20|
|Figure 3. 10-year government bond rate ................................................................................. 46|
2. _The Parenting Stress Index-Short Form (PSI/SF) (Abidin, 1990),_ an adapted 43-item scale that asks caregivers to report on their perceptions of parenting the target child in the study. The PSI/SF consists of three 12-item subscales: _Parental Distress, Parent-Child Dysfunctional Interaction_ , and _Difficult Child_ . The PSI/SF has been used in South Africa (Allen et al., 2014) and Kenya (Oburu, 2005). Sample items include, “You expected to have closer and warmer feelings for [CHILD] than you do, and this bothers you,” “[CHILD] makes more demands on you than most children,” and “You often have the feeling you cannot handle things very well.” All items were translated and back translated, and changes were made per recommendations by the PSI/SF author. During administration, all items were read to the caregiver, who indicated the degree to which they agreed with the statement. The three subscales were summed to create a Total Stress score. Higher scores indicated more stress related to parenting this child. We added 7 items to also capture positive feelings related to caring for the child, which were interspersed into the PSI/SF, which were not used in calculating the Total Stress score.<sup>32</sup> The final index was obtained by standardizing the Total Stress score using the control group’s mean and standard deviation at each round (baseline and 18-month follow-up) and then multiplying it by -1 so that a higher score indicates less parental stress.

3. _Support for Learning and Positive Parenting_ (UNICEF, 2010) module was adapted from the UNICEF Multi-Indicator Cluster Surveys (MICS) and other sources (Hamadani et al., 2010; Kariger et al., 2012). Support for learning was determined by both the availability of materials (books, toys etc.) that promote development, as well as activities adults do with children to encourage learning. We expanded the activities to include caregiver-child interactions that were related to the parenting intervention, and which would encourage school readiness. These included helping the child learn numbers and letters; teaching the child the name and use of

> 32 The PSI/SF can be purchased through: <u>http://www4.parinc.com/Products/Product.aspx?ProductID=PSI4:SF.</u>

61
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# **2. Description of the intervention and its implementation**

# <u>2.1 Data setting</u>

The government of Senegal designed a strategy in 2002 to fight malnutrition that is scheduled to reach 50% of children under five years of age by the year 2011, the _Programme de Renforcement de la Nutrition_ – Nutrition Enhancement Program (PRN). The first phase of the Nutrition Enhancement Program targeted 20% of children under the age of five with growth promotion and the integrated service of child diseases at the community level. In the three regions under consideration (Fatick, Kaolack, and Kolda), child nutritional status is low and knowledge of best practices very limited: the percentage of children who are exclusively breastfed in the first four months of their lives varies between 1.1% in Kaolack and 2.6% in Kolda, even though the WHO recommends exclusive breastfeeding up to at least six months of age. Children in Senegal are also suffering from the lack of micronutrients that remains widespread despite interventions that have taken place in the past. 84% of children less than 5 years of age suffer from anemia, as do 61% of women. Vitamin A deficiency is a public health problem, with about 61% of children under the age of six years suffering from this deficiency.

Summary statistics for the sample population are presented in Table 1. The z-scores of weight-for-age is more than one standard deviation lower than the mean for the US reference group and indicates the poor nutritional standard of the children in the sample: for example, a two year old boy would on average weigh about 12.2 kg in the US; in Senegal, his average weight could be expected to be about 1.5 kg lower at about 10.8 kg (WHO Child Growth Standards).<sup>3</sup>

> 3 These standards can be accessed at http://www.who.int/childgrowth/en/.

3
are severe measurement problems. Researchers have compiled measures of income inequality for many countries over many years, but the problems with their accuracy and comparability are well-documented (Deininger and Squire, 1996; World Bank, 2006). Moreover, there are no systematic measures of the intergenerational persistence of cross-dynasty relative income differences or equality of opportunity. Constructing more precise measures of financial development, access to financial services, and inequality would make exceptionally valuable contributions.

In light of the gaps between theory and evidence, we first review theories of finance and inequality and then turn to empirical assessments. By discussing the theory in one section, it is easier to develop a coherent conceptual framework and discuss limitations with this framework. By organizing the empirical discussion around distinct econometric methodologies, we can better highlight directions for future research. Throughout, we stress the desirability of improving our understanding of how financial sector laws and regulations affect inequality.

Our examination has noteworthy limitations. First, we examine the connection between finance and inequality. We do not seek to update the _Handbook of Income Distribution_ ’s sweeping collection of surveys on income distribution. Rather, we examine how the emergence of financial contracts, markets, and institutions under specific laws, regulations, and policies affect inequality. Second, we do not examine the impact of inequality on finance as in Haber (2007), Haber and Perotti (2008), and Perotti and Von Thadden (2006). In reviewing the econometric work on finance and inequality, we naturally critique the exhaustive efforts of researchers to identify the causal effects of finance on inequality. Nonetheless, there are good reasons for believing that income distribution shapes public policies, including financial sector policies. Thus, understanding the channels through which the distribution of income shapes the operation of financial systems and the formation of financial sector policies are extraordinarily valuable lines of research. Third, we focus on theories and empirical evidence that abstract from the importation of financial services from other economies. Since financial globalization is substantively changing where firms and households access capital and financial services, future research on inequality will benefit from focusing on international finance.

5