rafmacalaba/datause-encoder-data huggingface dataset:
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH across subregions is seen in water and sanitation, with all regions reporting water access in the 60–70 percent range and sanitation access in the 30–45 percent range.

**Table 2.3 Infrastructure endowments by African subregion**

|||African sub<br>|region<br>||
|---|---|---|---|---|
||ECOWAS|EAC|SADC|Central|
|_Transport_|||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|38|8|92|41|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|301|93|3,636|416|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|144|105|214|132|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,279|1,286|6,164|1,790|
|_Information and communication technology_|||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|28|6|74|13|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|72|54|180|74|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.4|2.1|5.5|1.7|
|_Energy_|||||
|Electrical generating capacity (MW per 1 million<br>people, 2003)|31|24|175|44|
|Access to electricity<br>(% of households with access, 2004)|18|7|21|18|
|_Water and sanitation_|||||
|Water<br>(% of households with access, 2002)|63|64|71|58|
|Sanitation<br>(% of households with access, 2002)|35|45|43|28|

_Sources_ : As for table 2.1.

East Asia is the closest comparator for the SADC countries; South Asia for the other African subregions. Figure 2.1 brings together the regional and subregional analysis by plotting infrastructure stocks against income for all of the regions and subregions we have considered so far. In income terms, the SADC countries are most comparable to East Asia and the Pacific. Nevertheless, the SADC countries have less infrastructure than the countries of East Asia and the Pacific by all measures, particularly access to sanitation and electricity. The only exception is paved road density, where SADC has some edge over EAP. By contrast, all other African subregions are, in income terms, similar to each other and to South Asia. On the whole, their endowments do not diverge substantially from that of the whole of Sub-Saharan Africa, although the Central African countries lag behind in terms of water and sanitation.

8
# **3 Econometric Analysis**

In the following section, we examine the role of location for export participation using regression analysis. In particular, we study whether the determinants of exporting are different if firms are located in core versus non-core regions.<sup>8</sup>

## **3.1 Econometric Model**

We follow the theoretical model of exporting of Roberts and Tybout (1997). A firm _i_ ’s export propensity at time _t_ depends on a firm’s expected revenues _R_ and costs _c_ plus sunk entry costs of exporting, _S_ : where _ex_ denotes an export dummy at the firm-level. Sunk entry costs of exporting, _S_ , are 1 if a firm exported in period _t-1_ and 0, otherwise. In other words, a firm exports if expected profits _π_ > 0.

A firm’s expected profits _π_ are affected by firm-level characteristics, regional characteristics, and agglomeration economies which can generate or lower revenues _R_ and/or costs _c_ . Equation (1) translates into: Pr( _exit_  1)  Pr(  _it_   _firmit_  _regrt_  _aggrt_  _S_ (1  _exit_  1)  0) (2) where subscript _r_ designates regions, _firm_ firm-level determinants of exporting, _reg_ regional determinants of exporting, and _agg_ agglomeration economies.

Sunk entry costs of exporting _S_ might explain a large part of export activity. Since our data sample only covers the most recent Enterprise Surveys for each country, we cannot capture sunk entry costs of exports by adding lagged exports as an explanatory firm-level variable. We propose an alternative way to account for sunk entry costs of exporting. We make the following assumptions: (i) Exporters that haven’t exported in the previous period (new exporters) export a share of their output that is below threshold _ζ_ . Ruhl and Willis (2008) show for a sample of Colombian manufacturing firms between 1982 and 1986 that new firms export on average 3 to 4% of sales upon entry into the export market, while export intensity increases with every additional year exporters stay on the export market. Ma and Zhang (2008) show the distribution of export

> 8 Besides location, we also take into account firm size. Results are shown in the Appendix.

16
Note the difference between  _j_ and  _j_ .  _j_ is the simple difference in the net present value (NPV) of labor earnings for those with _j_ years of schooling compared to those with _j_ –1 years. Later, we will call this the “one year” gain to completing year _j_ .  _j_ is a probability weighted recursion of the gains to completing years of schooling beyond _j_ . We will call this the “forwardlooking” gain to completing year _j_ .

Note that either  _j_ or  _j_ is an estimate in cedis of the NPV of completing year _j_ of schooling. As such, we can use these estimates directly to compare to the costs of providing that year of schooling to obtain a benefit-cost ratio. We draw information for the government’s costs of providing schooling from the Ghana Ministry of Education.<sup>13</sup> We also include private costs incurred using the average amount reported for tuition and fees, expenditure on books, uniforms and sports clothes, transport to school, and parent-teacher association (PTA) dues in the 2016/17 GLSS for students in public schools, by level.

# _Data and specification_

The data for the regressions come from the 2016/17 GLSS. We use the labor earnings of workers ages 15 to 45 because older workers attended school in a prior regime that included many more years (15) of primary and secondary education than the current regime. We include only wageemployed workers as the 2016/17 GLSS does not ask about the labor earnings of self-employed workers.<sup>14</sup> The 2016/17 GLSS also does not ask about years of work experience in either one’s current job or one’s working life, so we assume that experience is equal to age less the number of years spent in school plus five, a common assumption in the literature. See appendix A for precise definitions for these variables as well as for years of schooling and mother’s and father’s level of schooling.

We assume that work occurs between the ages of 15 and 60, and use a discount rate of 8 percent. This is higher than most education studies (e.g., Evans and Yuan 2019), but consistent

> 13 We exclude “internally generated funds”—mostly tuition—from these data and include estimates of tuition and fees paid from the 2016/17 GLSS in the private costs estimate.

> 14 There is an extensive agricultural questionnaire in the 2016/17 GLSS that allows the estimation of farm profits, but at the household level.

17
**Table 7: Summary statistics of non‐compliance rate**

||**Before pr**|**ogram announ**<br>|**cement**<br>|||**After pro**|**gram announce**<br>|**ment**<br>|||**Change**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**mean**|**median**|**sd**|**min**|**max**|**mean**|**median**|**sd**|**min**|**max**|**mean**|
|All questions|0.098|0.087|0.061|0|0.379|0.079|0.068|0.054|0|0.360|‐0.019|
|Disclosure|0.132|0.136|0.085|0|0.455|0.118|0.091|0.088|0|0.455|‐0.014|
|Child labor|0.076|0|0.167|0|0.667|0.038|0|0.125|0|0.667|‐0.038|
|Compensation|0.099|0.083|0.099|0|0.667|0.078|0.042|0.091|0|0.583|‐0.021|
|Contracts and human resources|0.136|0.133|0.133|0|0.667|0.132|0.133|0.116|0|0.667|‐0.004|
|Discrimination|0.003|0|0.010|0|0.086|0.003|0|0.014|0|0.229|0|
|Forced labor|0.001|0|0.009|0|0.111|0.000|0|0.004|0|0.111|‐0.001|
|Freedom of association|0.070|0|0.068|0|0.333|0.034|0|0.050|0|0.556|‐0.036|
|Occupational safety and health|0.190|0.163|0.116|0|0.558|0.150|0.140|0.104|0|0.581|‐0.04|
|Working time|0.135|0.091|0.122|0|0.727|0.134|0.091|0.115|0|0.545|‐0.001|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

16
First, we run separated regressions by different levels of household wealth, which, it is generally agreed, influences the extent to which maternal mental health affects children (Aizer et al 2016, Lovejoy 2000; Shonkoff and Phillips 2000). For our analysis, we compared the vocabulary of children living with mothers with different mental health levels within the upper or lower half of the wealth distribution. Using data from the 2002 round, we estimated three indexes: Housing quality,<sup>13</sup> consumer durables,<sup>14</sup> and services indexes.<sup>15</sup> Then, we created a wealth index for each household of our sample that consisted of the average of the three first ones mentioned above. Using the wealth index distribution, we separated our sample by the median of the wealth index distribution. Results are presented in Table V. Our estimations indicate that 1 standard deviation of maternal mental health issues reduces vocabulary by 0.58 to 0.63 standard deviation of children living in less wealthy households (columns 4-6). This is around 0.08 standard deviation more than the impacts in the total sample. The effects of maternal mental health on vocabulary of children living in wealthier households is not statistically significant.

These results are an important contribution to the evidence of intergenerational transmission of poverty. Poor households are less able to protect themselves from external shocks, such as crop or livestock losses, which then increases stress levels for household heads. In poor families where there is a pregnant woman or with a child younger than 1 year old, our results indicate that the negative shock translates into a reduction of the child’s cognitive skills in the short term. Given that the development of these skills during early childhood is the foundation of future ones (Case and Paxson 2008; Cunha and Heckman 2007; Schady 2011; Powell and Diamond 2012), this negative effect can have long-lasting impacts in terms of human capital accumulation, which is in line with existing literature indicating that events before five years old can have large long-term impacts on adult outcomes (Almond and Currie 2010).

The existing literature has also found that maternal schooling levels may modulate the impact of depression (Shonkoff and Phillips 2000; Wachs 2005; Patel 2006). A recent paper by Aizer et al (2016) finds that mothers with low levels of human capital are characterized by higher stress levels and that the negative impact of their elevated stress levels on their children is greater.

> 13 It included 4 components: Scaled number of rooms per person and type of materials of walls, roof, and floor. The quality index is a number between 0 and 1.

> 14 For this index, we add each of the 12 potential assets the household might own and then divide by the total number of assets (12) so the index is a continuous value between 0 and 1. Productive assets are not included here. 15 For this index we look at whether or not the household has electricity, the source of drinking water, type of toilet facility and the most common type of fuel used for cooking. The services index is a number between 0 and 1.

15
1 I. I -ODUCTION The World Bank introduced Adjustment Lending as another form of lending in 1979 to assist member countries in their adjustment to the second oil shock. The main difference between this form of lending and traditional project lending is that Adjustment Lending is quick-disbursing, and it is made conditional on policy reforms. After growing rapidly in the first half of the 1980s, Adjustment Lending leveled off in 1986-88, when it averaged 24 percent of Bank Group lending commitments. In 1989 it reached 27 percent of total lending commitments. The two principal instruments of adjustment lending are: Structural Adjustment Loans (SALs) and Sectoral Adjustment Loans (SECALs). SALs support economy-wide reforms and institutional building, while SECALs have more of a sector focus.' One could ask why was adjustment lending introduced in 1979. By the late 1970s a large number of countries were having difficulties in financing their balance of payments deficits. At the same time, their growth performance was very weak. Common causes of these difficulties were many years of distortionary trade, regulatory and exchange rate policies, and an expansion of the state's role in the allocation process. Ultimately, these factors resulted in an inefficient use of reeources, discouraged exports and created an internationally uncompetitive structure of production. Countries facing this type of problems were spread throughout different geographical regions, including among otherse Ghana, Senegal, and Tanzania in Africa, Jamaica, Argentina, Bolivia and Turkey elsewhere. When the second oil shock hit in 1979, these economies were ill-prepared to absorb higher prices without Both types of loans generally disburse against general imports with a small list of excluded items.
Policy Research Working Paper 5907

# **Abstract**

International migration has increased rapidly in recent decades and this has been accompanied by a remarkable increase in transfers made by migrants to their home countries. This paper investigates the effect of the rural economic growth brought about by migration and remittances on Nepal’s Himalayan forests. The authors assemble a unique village-panel dataset combining remote sensing data on land use and forest cover change with data from the census and multiple rounds of living standards surveys to test various inter-relationships between population, economic growth and forests.

The results suggest that rural economic growth spurred by remittances has had an overall positive impact on forests. The paper also finds that remittances caused an increase in rural wages and an increase in income, but a decrease in land prices. Considered together, however, the relationship between forests and remittances is driven largely through the income channel, indicating that the demand for amenities provided by forests in the rural Nepali setting may have been more important than factor prices in influencing land use changes for the period of the study.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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 stiwari@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
0.32, p<0.01). To address other potential confounders, sunlight exposure is controlled for, by using a variable measuring commune average daily sunshine over the period 1975‐1999. Socio‐ economic status, measured using education levels and per capita expenditure, and an urban/rural binary variable are also added as control variables in the regression specifications.

In addition, it is plausible that areas more heavily sprayed in the past were areas of dense forest, or more generally agro‐climatic zones that require more herbicide (and pesticide) use to maintain high agricultural productivity. Thus, cancer outcomes might rather be driven by contamination due to agricultural herbicides and pesticides. Contemporaneous measures of herbicide and pesticide use, however, show a negative correlation (correlation = ‐0.1082, p<0.01) with past military herbicide application intensity (figure 2, upper left panel). Finally, herbicide spraying might also be correlated with other risk factors of cancer: smoking, alcohol, and occupational risk such as the use of coal and firewood for energy consumption. Figure 2 also displays the correlations between the patterns of herbicide exposure and commune‐level averages of household per capita expenditure of coal and firewood (upper right panel), cigarettes (lower left panel) and alcohol (lower right panel). If anything, the correlations appear to be negative.

## [FIGURE 2 ABOUT HERE]

# **5. Conclusion**

This study quantitatively assessed the link between herbicide exposure and the prevalence of cancer in the Vietnamese population 30 years after the end of the Vietnam War. Since systematically replicating methodologies that have been conducted among US veterans is beyond Vietnam’s current capacity, an alternative approach was adopted instead. Using self‐ and proxy‐reported measures of cancer prevalence, a positive association between past exposure to military herbicides and current prevalence of cancer is detected in the data. Admittedly, the adopted methodology is constrained by the reliability of self‐ and proxy‐ reported cancer status, the use of prevalence rather than incidence as outcome measures and the lack of further information on types of cancer. Until further studies are undertaken, there is however a risk of losing the evidence with the disappearance of most affected older cohorts.

16
# **1 Introduction**

We propose a method for estimating distribution functions that are associated with the nested errors in linear mixed models. The proposed estimator is accommodating Empirical Bayes prediction and makes minimal assumptions about the shape of the error distributions. Our objective is to accurately predict nonlinear functions of the dependent variable, where the shape of the error distribution functions potentially plays an important role. The application we have in mind is the small area estimation of poverty and inequality, although this denotes by no means the only application. This particular application has been made popular by the work of Elbers, Lanjouw and Lanjouw (2003; henceforward ELL). The approach they put forward has since been applied to obtain maps of poverty and inequality in over 60 countries worldwide. The highly disaggregated estimates of poverty and inequality have in turn inspired a range of other applications, for example: Demombynes and Ozler (2005) investigate whether inequality at the small area level has an impact on local crime rates, and find that it does using data from South Africa; Elbers et al. (2007) conduct an empirical experiment in order to estimate by how much one could potentially lower the costs of getting resources to the poor if one had access to a poverty map, and conclude that the gains can be substantial; Araujo et al. (2008) examine whether villages with higher levels of inequality are less likely to invest in public goods that would benefit the poor. Using data from Ecuador they find empirical support for this hypothesis which they attribute to elite capture. Recently, Fujii (2010) modified the approach to make it better suited for the small area estimation of child malnutrition outcomes with an application to Cambodia.

## **1.1 Problem statement**

Consider the following standard linear mixed model for log income of household _h_ residing in area _a_ : Let _yd_ denote a vector of log incomes for all households from domain _d_ (where “domain” typically refers to a small geographic area). We are interested in estimating _W_ ( _yd_ ), where _W_ is some (possibly nonlinear) function of _yd_ . It is assumed that we have data on _x_ for the entire population, while data on both _x_ and _y_ is available for a sample _S_ of households only. ELL propose to estimate _W_ by: where the expectation is taken over the unobserved errors _ua_ and _εah_ . Because _W_ is nonlinear, the shape of the error distributions will matter for the expected value of _W_ . Estimation of _β_ is obviously of primary importance when estimating _E_ [ _W_ ], but in this paper we will concentrate on the error distributions and assume – except for the application in section 6 – that _β_ is known. We argue that even if _β_ is estimated perfectly, getting the error distribution wrong still has the 2
This paper reviews the main components and objectives of the Hungarian pension reform, and makes a preliminary assessment of the first years of its implementation. The paper is structured as follows. The second section provides some background material, examining briefly the performance of the pension system before the reform, and showing long-run projections of the system in the absence of reform. The third section describes the overall reform package, examines the switching results, and provides a number of long-run actuarial simulations of the new multi-pillar system. The fourth section examines the structure and performance of the private pillars in the early stages of implementation. Finally, the fifth section provides some conclusions and identifies possible lessons for other countries.

# **2. The Situation of the Pension System Before the Reform**<sup>**2**</sup>

_The Performance of the System in the Post- War Period_ The Hungarian PAYG system matured rapidly in the post-war period, as reflected in the rapid increase in the system dependency ratio (the ratio of pensioners to workers), and the increase in the average replacement ratio (the ratio of the average pension to the average net wage). As shown in Figure 1, the system dependency ratio surpassed the old age dependency ratio already in the mid1 970s, due primarily to the low retirement age of women, who comprised an increasing proportion of the pensioner population, and whose life expectancy at retirement rose from 20 to 33 years in the last decades.

The average replacement ratio also increased steadily in the post-war period (Figure 2), as a result not only of longer average contribution periods, but also of more generous benefits and more permissive eligibility rules. The increase in the number of pensioners and in the average benefit levels led to a steady increase in pension expenditures-from less than 5 percent of GDP in 1970s to more than 10 percent of GDP in 1990, requiring increasing contribution rates to balance the system.

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