rafmacalaba/datause-encoder-data huggingface dataset:
tics were also elicited, along with the main income sources of the household. Households then responded to a series of social capital questions. In addition, the household survey asked questions about both housing characteristics and small assets. We used these to generate wealth indices that help shed light on the socioeconomic effects of the program generally (Alix-Garcia, Sims, Orozco-Olvera, Costica, Medina, Romo-Monroy & Pagiola 2018) and the distributional consequences of changing targeting approaches in this paper<sup>8</sup> . We examined a variety of methodologies for aggregation, including simple summation of the presence or absence of characteristics, principal components, polychoric principal components (which takes into account categorical variables), and inverse proportion weighting (which applies higher weight to very scarce items). Our analyses use the village level averages of the simple asset indices, though the other more complicated aggregations of the household data yield similar results.

# 3.2 Deforestation risk

In order to conduct targeting exercises below, we create a measure of deforestation risk<sup>9</sup> . We would like this measure to be specific to the risk affecting the types of properties that apply for the program. To this end, we use the national level program data to examine deforestation behavior within the polygons that were submitted for consideration during the study period (2011-2014) but were rejected from the program. We analyze polygons with unique histories – that is, if an applicant submitted different pieces of overlapping land in different years, we separate the submitted parcel into multiple non-overlapping units in order to avoid double-counting of areas.

To ensure that the deforestation index is exogenous to program participation during the period that we analyze and simulates an ex-ante information set, we create the index using measured deforestation between 2005 and 2010, prior to application. The deforestation data come from Hansen et al. (2013). This dataset provides information on annual forest loss beginning in 2000

> 8Housing characteristics included brick or wooden walls, concrete, metal, fiber, cement, or tile roof, cement, wooden, mosaic or marble floor, number of rooms. The housing index also includes measures of access to public services, including existence of water and sewage connections, and amount of water and electricity availability. The assets index is composed of responses to questions on the presence or absence of the following items: television set, refrigerator, blender, microwave oven, laptop, car, truck, motorbike, bicycle, gas or electrical cooker, telephone landline, and mobile phone.

> 9The Mexican government has its own deforestation risk index (“Index of Economic Pressure”) which is used for targeting the PES program and setting payment levels (Table 1). We do not use this index due to the fact that it does not have complete coverage of the entire country, is not specific to the types of land that are of interest for the current study, and is not limited to the specific, pre-treatment years that we use to create our index.

14
## **Table A11. Incidence of fertilizer shortfall**

# **<u>Any fertilizer shortfall (dummy)</u>**

||(1)<br>Ethiopia|(2)<br>Malawi|(3)<br>Nigeria|
|---|---|---|---|
|Distance to nearest market|-0.00157***|0.00357**|-0.00107**|
||(0.000506)|(0.00166)|(0.000520)|
|Distance to nearest road|0.00575***|0.00204|-0.00554|
||(0.00199)|(0.00285)|(0.00418)|
|Distance to nearest population center|-0.000795|-0.00181|0.00218*|
||(0.00106)|(0.00210)|(0.00124)|
|Wealth quintile|-0.0415**|-0.0186|-0.0384**|
||(0.0177)|(0.0153)|(0.0163)|
|Constant|0.611***|0.693***|0.624***|
||(0.0758)|(0.0714)|(0.0635)|
|Observations|945|1,472|1,096|
|R-squared|0.039|0.015|0.024|

Note: Results from OLS regression of extensive margin fertilizer shortfall on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

32
## Probability density function for _ε_

<!-- Start of picture text --> −4 −3 −2 −1 0 1 2 −3 −2 −1 0 1 2<br>x x<br>1.0<br>0.8<br>0.8<br>0.6<br>0.6<br>0.4<br>0.4<br>dens.epshat(x) dens.epshat(x)<br>0.2 0.2<br>0.0 0.0<br><!-- End of picture text --> Figure 2: Normal-mixture density (Black) versus benchmark density (Red): (a) _ρ_ = 0 _._ 05 (left panel); (b) _ρ_ = 0 _._ 25 (right panel)

# **4.2 Implications for estimates of poverty**

Next we investigate whether having more precise estimates of _Fu_ and _Gε_ will also give us more precise estimates of poverty, and whether any gain in precision is economically meaningful. For ease of exposition we will focus on the percentage of households with incomes below the poverty line as the measure of poverty which we wish to estimate. Tables 1 and 2 provide estimates of the bias for different values of the shape parameter for _Gε_ (the shape parameter for _Fu_ is fixed at _pu_ = 0 _._ 5), and for different values of the (log) poverty line. The poverty rates associated with the different (log) poverty lines are roughly 15, 20, 30, and 45 percent. It should be noted that the bias in this case is estimated as the difference between the estimated and the true poverty rate averaged over the 500 target areas for one given replication of the “census”.

At least two observations stand out. First, estimates obtained under the (incorrect) assumption of normal errors can be severly biased, with a bias of 3 to 4 percent on a poverty rate that ranges between 20 and 30 percent depending on the shape parameter for _Gε_ . Our approach provides far superior estimates of poverty in these cases with a bias of less than a percent. Second, the benefit of accommodating non-normal errors as opposed to assuming normal errors changes with the value of the poverty line. At the far left tail of the (log) income distribution (i.e. for particularly low values of the poverty line), it will be hard to empirically separate the two distributions. The difference will be more pronounced where the distributions have more “mass”, i.e. for intermediate to higher values of the poverty line.

# **4.3 Implications for estimates of inequality**

Table 5 compares the bias for estimates of income inequality for different values of the shape parameter and different values of the area location effect. We focus on the Mean-Log-Deviation (MLD) as the measure of inequality which we wish to estimate. Note that by definition we have that the realizations of the area error _ua_ will drop out of the true measure of MLD for that area, 19
An alternative to the firm closure approach followed by Kong, Meng, and Zhang (2009) is to examine directly the demand for labor among firms. This approach was followed in the China National Firm Survey (CNFS 2009) conducted by the People’s Bank of China in October 2009.<sup>4</sup> The CNFS is a representative sample of over 2,000 manufacturing firms from eight provinces: four coastal provinces (Guangdong, Jiangsu, Shandong, and Zhejiang), one northeast province (Jilin), one central province (Hubei), one northwest province (Shaanxi), and one southwest province (Sichuan). The sampling frame is certainly skewed toward larger and well-established firms: it comprises all firms operating in August 2009 that ever had a credit relationship with any financial institution. Still, it provides interesting evidence of the impact of the financial crisis on the formal sector.

The survey collected data on six-month intervals beginning in 2007, allowing the researcher to examine changes in employment among firms that had survived the financial crisis through October 2009. Table 1 shows the percentage changes in employment over the previous six months. Between January and June 2008, and in the wake of macroeconomic adjustment and implementation of the Labor Contract Law, employment grew by 3 percent. Behind this number, however, was a negative shock to employment in the state and collective sector that was more than offset by continuing increases in employment in other ownership sectors. By December 2008, overall employment in these firms had dropped a half-percentage point **,** but again the average obscures the sharper hit that exporters experienced. Exporting firms shed 1.9 percent of their employees, which implies a reduction in the growth rate of employment of 4.9 percentage points relative to the pre-crisis trend of 3 percent. Ownership sectors—the sector open to foreign investment, of which a significant share goes toward exports to developed economies—shed nearly 5 percent of their employees.

In a comparison of changes in the employment of migrants to changes in the employment of local residents in these enterprises, as Table 2 illustrates, migrants were more likely to suffer adverse employment shocks over the period of the crisis, particularly those employed in export8
Policy Research Working Paper 5420

# **Abstract**

Environmental performance rating and disclosure has emerged as an alternative or complementary approach to conventional pollution regulation, especially in developing countries. However, little systematic research has been conducted on the effectiveness of this emerging policy instrument. This paper investigates the impact of a Chinese performance rating and disclosure program, Green Watch, which has been operating for 10 years. To assess the impact of Green Watch, the authors use panel data on pollution emissions from rated and unrated firms, before and after implementation of the program. Controlling for the characteristics of firms and locations, time trend, and initial level of environmental performance, the analysis finds that firms covered by Green Watch improve their environmental performance more than non-covered firms. Bad performers improve more than good performers, and moderately noncompliant firms improve more than firms that are significantly out of compliance. The reasons for these different responses seem to be that the strengths of incentives that the disclosure program provides to the polluters at different levels of compliance are different and the abatement costs of achieving desired levels of ratings are different for different firms.

This paper—a product of the Environment and Energy Team, Development Research Group—is part of a larger effort in the department to understand and improve environmental governance in developing countries.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hwang1@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
of South Africa—by Ardington 2008). Indeed, the 2008 UNICEF “State of the World’s Children” cautions that _Children can no longer rely on the support of the traditional extended family system, which provided care and support for the aged, orphans and any vulnerable and disadvantaged family member. This coping mechanism has been overstretched by poverty and by the sheer numbers of children to be cared for, given the fact that AIDS affects the most productive family members in the prime of their productive and reproductive lives. As a result, children have sometimes gone into homes that are already overstretched and where they are really not welcome (Essay by Elizabeth N. Mataka, United Nations Special Envoy of the Secretary-General for HIV/AIDS in Africa in UNICEF 2007, p42)._ At the same time, little is known about the systematic shifts that are taking place in caregiving patterns and particularly, to whom the burden of care is falling over time—and it is this gap that this paper seeks to address. Using Demographic and Heath Survey (DHS) data from 21 Sub-Saharan Africa countries, this study systematically documents differences in the distribution of living arrangements across countries and time. The extent to which orphan rates are associated with these trends is investigated, as is whether there is a tendency for orphaned children in countries with high and increasing orphan rates to live with more distant relatives— evidence that would support the claim that rising orphan rates are placing pressure on the extended family. The broad patterns in living arrangements for orphans and non-orphans and changes in caregiving patterns are explored; however, an investigation of the consequences of those changes is not the focus and is left to future work.

The remainder of the paper is structured as follows. Section II describes the data and the methods used to define orphans and household structure; Section III discusses trends in orphan rates—and develops a country typology of these trends; Section IV then discusses the changes in household living arrangements associated with those trends. Finally a concluding section summarizes and discusses the implications of these findings.

5
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel E: Formation/operation of non regulated microcredit institutions (0 4)</u>

|||<br>Version 1(Ful sampl|<br>Depvar: Financial S<br>es)|elf-Sufficiency<br>Version 2|<br> (DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Formation/operation of non-regulated microcredit institutions (0-4)|0.0243|0.0496**|0.048|0.0209|0.0477**|0.0393|
||[0.614]|[0.026]|[0.220]|[0.667]|[0.042]|[0.337]|
|NBFI dummy * Formation/operation of non-regulated microcredit institutions (0-4)|-0.033|-0.037|-0.0436|-0.031|-0.0401|-0.038|
||[0.435]|[0.202]|[0.172]|[0.476]|[0.187]|[0.257]|
|Combined group dummy * Formation/operation of non-regulated microcredit institutions (0-4)|-0.0708|-0.0999***|-0.1039***|-0.0671|-0.1000***|-0.0961**|
||[0.212]|[0.002]|[0.010]|[0.258]|[0.004]|[0.021]|
|NBFI dummy|0.0751|0.1092|0.1136|0.0631|0.1073|0.0889|
||[0.482]|[0.102]|[0.188]|[0.573]|[0.126]|[0.322]|
|Combined group dummy|0.1166|0.1980***|0.2061**|0.0771|0.1717**|0.1538|
||[0.396]|[0.006]|[0.035]|[0.618]|[0.034]|[0.154]|
|Observations|636|636|611|579|579|556|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.215|0.233|0.261|0.223|0.24|0.262|
|Adjusted R-squared|0.191|0.209|0.237|0.196|0.214|0.236|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.00873|0.0126|0.00441|-0.0101|0.0076|0.00129|
||[0.720]|[0.536]|[0.847]|[0.679]|[0.721]|[0.956]|
|Combined group dummy|-0.0465*|-0.0503**|-0.0559***|-0.0462|-0.0523**|-0.0568**|
||[0.075]|[0.033]|[0.009]|[0.134]|[0.040]|[0.025]|

45
# **4.3 Control Variables**

## **Financial Resources (Sectoral Grants)**

In Sierra Leone, the grant transfer system provides largely for tied sectoral grants where the vertical envelope is fixed for each year. This implies that the quality of decentralized service delivery is likely to be, in large part, a function of the amount of funding the council receives from the central government for performing its mandated functions. For instance, as early as 2006, one quarter of the national health budget was transferred to local councils (Zhou, 2009). Accordingly, we control for the log of the amount of sectoral transfers to district councils for 2006 and 2007. We include the following itemized transfers: education, birth and death registrations, and health. We use official data on fiscal transfers provided by the Local Government Financial Department, Ministry of Finance and Economic Development, Sierra Leone (Local Government Finance Department (LGFD), Ministry of Finance, 2011).

## **Socio-Demographic Variables**

We control for standard socio-demographic variables that can affect citizens’ evaluations of government including age, gender, education and religion. We include several reasonably good proxies for income, including whether any of member of the household owns the following items: a bicycle, a mobile phone, a generator, a radio or cassette player, and, a television, and a car, truck or motorcycle. We also include an indicator of whether respondents or a member of their household had malaria within the past year. We include an indicator of the type of material of the roof, walls and floors of respondents’ current residence. We also control for the size of household and marriage status, since these are demographic factors that household resources.

Living in a remote and marginalized area might affect citizens’ access to public services, as well as the quality and frequency of their interaction with local authorities, and, consequently, their beliefs toward these authorities. Thus, we also control for the remoteness of respondents’ residences. We derive this measure from the community-level NPS questionnaire. Community leaders were asked about the distance in miles between their locality and Freetown, as well between their locality and the district headquarters. Finally, we control for the population density of enumeration areas, as large population might pose additional pressures on Local Councils.<sup>8</sup>

## **Local Government Outreach Activities and Access to Information**

Unfortunately, the NPS did not ask respondents about the quality of their interaction with councillors. The NPS asked respondents whether they have talked with a local councillor or participated in a meeting organized by the council within the past year. We control for

> 8We also calculated the distance between the center of enumeration areas and Freetown. This variable was not significant and did not affect model estimates. We are also in the process of calculating the distance between the center of enumeration areas and district headquarters. We will include this variable in the model, which we will estimate for the next version of this paper.

12
immensely at this time. In light of the patterns of heterogeneity seen in the data, the most plausible explanation appears to be an epidemiological spillover, transmitted via older siblings and neighbors to infants in dewormed communities.

In relation to deworming specifically, this evidence lends further support to expanding initiatives worldwide that treat deworming en masse. Taken together with the recent work of Baird, et al., (2014) and Croke (2014), this study helps paint a complete picture of long-run benefits of deworming in developing countries. This expanding body of evidence has already led to real policy initiatives. In Kenya, for example, national deworming was undertaken in 2009, and began recurring annually in 2012. Infection levels in Kenya have dropped substantially since the original Miguel and Kremer study began in 1998 (Mwandawiro, et al., 2013). However, high-intensity infections remain prevalent around Sub-Saharan Africa and the world.

More broadly, the evidence that early childhood health shocks have ramifications for subsequent human capital in a variety of forms builds on, and gives empirical substance to the models of Grossman (1972), Cunha and Heckman (2008), and their successors. The present study, demonstrating the presence of early childhood deworming spillovers, in essence asks whether this inexpensive health intervention can act as an input to early cognitive skills. As in these and other models of human capital formation, the present findings do not rule out the value of interventions later in life. In fact, a successful early childhood intervention such as this one might well be complementary to the more frequently-studied interventions that become relevant later in the life cycle. As elements of policy, however, the cost-effectiveness of deworming in highly worm-infected settings, and potentially that of other early-life health and nutritional interventions, will be difficult to match.

21
22 Further, the product design captures damage from excessive rainfall independent of tank storage levels. Excessive rainfall invariably leads to crop damage, particularly in the crop flowering stage. It can also lead to flash-flooding and waterlogging. However, if localized flood data are available, there is scope to further refine the product by carrying out a multivariate correlation analysis between excess rainfall, flooding and tank storage levels. Such a study may show that the risks of excess rainfall affect irrigated and non-irrigated farmlands differently, since flash-floods are caused by inundation in tanks (from which non-irrigated lands would be immune). However, in the absence of localized flood data currently, no such conclusion can be drawn. Thus, the excess rainfall cover for the product has been structured independent of tank water levels.

# **9. Conclusion**

This paper captures the pilot experience of developing a weather index insurance product for irrigated agriculture in Sri Lanka, from a practitioner’s perspective. Our experience has shown that incorporating irrigation water levels contributes to an improved ‘hybrid’ index insurance product, thereby expanding the use of rainfall index insurance even to irrigated farmlands.

The results of the pilots in two districts show that the hybrid index product is better correlated to crop damage than the pure rainfall index for farmers with access to major irrigation. It is especially better at detecting the incidence and severity of actual losses.

It is important to acknowledge that there may be other types of indices (that may also improve correlation) which can be used in combination with tank storage levels, such as a yield index and a vegetative index in particular contexts, and their relevance in terms of costs and benefits can be compared.

While the pilot exercise was limited to a few seasons and regions, and the experience is too recent to draw general conclusions about the sustainable performance of the hybrid weather index insurance product, the experience so far in Sri Lanka suggests that index insurance can be successfully used in irrigation contexts with relevant improvements in product design, and the product is a value proposition for smallholder farmers in Sri Lanka.