rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3a: Poverty Dynamics - Synthetic vs. Actual Panel Data for Alternative Poverty Lines, Chile 1996 and 2006 <!-- Start of picture text --> Poor in 1996 and Poor in 2006 Poor in 1996 and Non-poor in 2006<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Poverty Line (USD at 2005 PPP) Poverty Line (USD at 2005 PPP)<br>Non-poor in 1996 and Poor in 2006 Non-poor in 1996 and Non-poor in 2006<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Poverty Line (USD at 2005 PPP) Poverty Line (USD at 2005 PPP)<br>Lower Bound Actual Upper Bound<br>Poor, Poor<br>Poor, Non-poor<br>Non-poor, Poor<br>Non-poor, Non-oor<br><!-- End of picture text --> Data source: Chile CASEN 1996 and 2006.

Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Upper bound estimations are based on 50 repetitions.

38
# **2 A Model of Exports, Engineers and Blue-Collar Workers**

We combine elements from Verhoogen (2008) with elements from Acemoglu and Autor (2011) and Costinot and Vogel (2010) in a simple partial equilibrium model of exports and employment composition. From Verhoogen (2008), we adopt the theoretical framework underlying the idea that exporting requires quality upgrades that are inherently intensive in skilled labor. This framework can also be found in Kugler and Verhoogen (2012), Brambilla, Lederman and Porto (2012), Brambilla and Porto (2015), and Bastos, Silva and Verhoogen (2014). From Acemoglu and Autor (2011) and Costinot and Vogel (2010), we adopt an assignment model of skills to tasks. In this setting, quality for exports is produced with a collection of tasks that can be performed by workers with different skills. Exporting firms assign higher skilled workers such as engineers to fundamentally similar tasks to achieve higher quality. As a consequence, exporting firms hire a higher share of engineers over blue-collar workers than non-exporters.

We adopt a multinomial logit demand framework. The aggregate demand function of product _j_ with quality _θ_ and price _p_ is where _α_ captures quality valuation which, as in Verhoogen (2008), is higher for exporting firms.

Firms produce differentiated products in monopolistically-competitive markets. Firms can choose the level of vertical differentiation of their products _θ_ . This depends, ceteris paribus, on the quality valuation parameter _α_ . This gives firms the option to provide more vertical differentiation to export markets with higher _α_ . Exporting, in turn, incurs a fixed cost _Fx_ .

As in Verhoogen (2008), production of output of quality _θ_ requires activities to produce physical units and activities to produce quality itself. We assume that the production of quantity (physical units of output) is separated from the production of quality. Firms produce quantity and quality with tasks (Acemoglu and Autor, 2011). To simplify the exposition, we assume that there are three different types of tasks: maintenance services (cleaning, machine and building maintenance), production and design (production activities, accounting, packaging, marketing, engineering, logistics, input control, supervision), and managerial activities (firm direction). We refer to these tasks as task 1, 2, and 3, respectively. In turn, tasks are produced by either skilled or unskilled workers which earn wages _wH_ and _wL_ , with _wH > wL_ .

3
<!-- Start of picture text --> Output per worker, 2011 PPP US$ ('000)<br>100 = Interquartile range<br>60<br>z [2]<br>0 _-<br>Advanced economics EMDE LIC<br><!-- End of picture text --> <!-- Start of picture text --> Output per worker, 2011 PPP US$ ('000)<br>100 @ Interquartile range<br>8060 | > | Mean<br>40<br>200 <x<br>Advanced EMDE oil EMDE = _EMDE non-<br>economics exporters commodity oil comm.<br>importers Exporters<br><!-- End of picture text -->

<!-- Start of picture text --> Output per worker, 2011 PPP US$ ('000)<br>100 m Interquartile range<br>60<br>o gm | g<br>0 hai<br>AE EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

<!-- Start of picture text --> Percent<br>1.0<br>0.80.6<br>0.4<br>0.0 °sssssgsgsssssssgs<br>=<br>TK NOTON OR DBDOKT AMT YO<br>2011 PPP US$<br><!-- End of picture text -->
# Table V. External Public Debt and Debt Sernce Ratios.

||External P<br>(% o|ublic Debt<br>f GNP)|Debt**S**<br>(% of|**ervie**<br>GNP)|Debt<br>(% of Exports of|**Service**<br>Goods and Service)|
|---|---|---|---|---|---|---|
||1970|1986|1970|1986|1970|1986|
|Developing Economies|13.1|35.5|1.5|4.4|10.1|_20.0_|
|Oil Exporters|12.2|47.6|1.7|6.2|12.0|30.5|
|Exporters of Manufactures|<br>n.a.|22.7|n.a.|3.1|n.a.|14.0|
|Highly Indebted Countris|10.2|45.8|1.6|5.0|12.4|29.5|
|Sub-Saharan Africa|13.1|57.4|1.1|4.3|5.3|19.3|
|India|15.0|14.0|1.0|1.2|25.8|17.9|
|Including Private Debt|15.2|15.1|1.1|1.6|27.3|24.6|
|China|n.a.|6.3|n.s.|0.9|n.a.|7.8|

_Source:_ World Development Report 1988; World Bank, Washington, D.C., June 1988.
- **1S** -

Table 1: Dlrect, Indirect, and Total Nominal Protection Rates for Exported Products (percent)

|Country|Product|Direct|1975-79 --<br>Indirect|<br>Total|Direct|8-<br>1CaOe4<br>Indirect|Total|
|---|---|---|---|---|---|---|---|
|Argentina<br>Brazil<br>|Wheat<br>Soybeans<br>|.25<br>-8<br>|-16<br>-32<br>|-41<br>-40<br>|-13<br>-19<br>|-37<br>-14|-50<br>-33|
|Chile<br>|<br>Grapes<br>|1|22<br>|23<br>|0|-7<br>|-7<br>|
|Colombia<br>|<br>Coffee<br> <br>|-7|**-25**|-32|-5|-34|-39|
|Cote D'ivoire<br>|<br>**Cocoa**<br>|-31<br>|**-33**<br>|-64<br>|-21<br>|-26<br>|**-47**<br>|
|Dominican Re<br>|pubCoffee<br>|-15<br>|-18<br>|-33<br>|-32<br>|-19<br>|-51<br>|
|<br>Egypt<br>|<br>Cotton|-36|-18<br>|-54<br>|-22|-14|-36|
|<br>Ghana<br>|**Cocoa**<br>|26<br>|-66<br>|-40<br>|34<br>|**-89**<br>|**-55**<br>|
|Malaysia<br>|Rubber<br>|-25<br>|-4<br>|**-29**<br>|-18<br>|-10<br>|-28<br>|
|<br>Pakistan<br>|Cotton<br>|-12|-48|-60|-7|-35|-42|
|Philippines<br>|Copra<br>|-11<br>|-27<br>|-38<br>|-26<br>|-28<br>|-54<br>|
|<br>Portugal<br>|<br>Tornatoes<br>|17<br>|-5<br>|12<br>|17<br>|-13<br>|4<br>|
|<br>Sri Lanka<br>|Rubber|**-29**|-35|**-84**|-31|-31|-62|
|Thailand<br>|Rice<br>|-28<br>|-15<br>|-43<br>|-15<br>|-19<br>|-34<br>|
|Turkey|Tobacco|2|-40|-38|-28|-35|**-63**|
|<br>Zambia|Tobacco|1|-42|-41|7|-57|-50|
|Average||-11|-25|**-36**|-11|-29|-40|

Source: Krueger, Schiff, and Valdes, 1988, 'Agricultural Incentives in Developing Countries,' World Bank Economic Review 2(3):p.262.

Note: Korea and Morocco are not included because all main agricultural products are are imported.

The direct nominal protection rate is defined as the dfference between the total and the indirect nominal protection rates, or equivalently as the ratio of (1) the difference between the relative producer price and the relative border price, and (2) the relative adjusted border price measured at the equilibrium exchange rate and in the absence of all trade policies.

# Table 2 (from KSV) presents comparable data for representative kmport-competing

food crops in 16 countries. In contrast to the export crops, which were taxed at both the direct and Indirect levels on average, the knport-competing commodities are usually given significant direct protection, but then are Indirectly taxed at higher rates so that the total effect Is taxatlon of import-competing crops on average by about five percent. However, If two countries which have given exceptional protection to rice--Korea and Malaysia--are excluded, the average for total protectlon changes to -15 and -18 percent for the two periods. Some countrles tax kiport-competing commoditles at both levels,
early returnees. About 40% of the migrants who stay for less than one year were forced to return.

Being forced to leave the destination country as opposed to leaving voluntarily impacts the rate of return to the migration endeavor, and the likelihood to start entrepreneurship after return. As shown in Table 3, earnings to cost, excess earnings to cost, and excess savings to cost indicators are all significantly larger for migrants who returned voluntarily compared to those who were forced to return. Panel (b) of Figure 9 visualizes the left shift in the distribution of excess earnings over costs for forced returnees compared to voluntary returns. Whereas slightly more than one-fifth of the latter fail to break even, that share rises to one third for migrants who were forced to return. Since the ability to start entrepreneurship after return is tied to savings accumulated overseas, forced returns also negatively affect self-employment after return. As shown in Table 3, migrants who were forced to return are less likely to become entrepreneurs back in Bangladesh: 55 percent of forced returnees are self-employed back in Bangladesh, compared to 64 percent of migrants who voluntarily return.

Table 3: Return rate of migration and employment status after migration by forced return

||Forced return|Normal return|
|---|---|---|
|Earning-to-cost, _r_1|6.5|10.3|
|Excess earning-to-cost, _r_2|4.3|6.4|
|Excess saving-to-cost, _r_3|2.3|3.2|
|Waged workers|41.5%|32.5%|
|Self-employed|55.0%|63.7%|
|Employer|3.6%|3.9%|

_Note:_ Calculations based on the data summarized in Table 2, and measures described in the text.

# **4.4 Human Capital as an Alternative Mechanism**

A channel other than financial wealth accumulation that could foster self-employment after return is human capital accumulation abroad. The vast majority of temporary migrants from Bangladesh are employed in brawn-based, low-skilled occupations in the construction sector overseas – primarily in construction, with a minority in service activities such as retail or transport. While studies in other contexts suggest that some workers accumulate human capital overseas which benefits them in wage employment back home, this channel does not 21
Demetriades and Hussein (1996) and Luintel and Khan (1999) use the VEC specification and test for weak exogeneity of finance to GDP per capita by testing the null hypothesis that the corresponding loading factor in the GDP per capita regression in (21) is zero, while they follow Toda and Phillips’ (1993) suggestion and use the product of loading factor and the cointegrating parameter to test for long-run causality. While Demetriades and Hussein (1996) find evidence for bidirectional causality and reverse causation from income to finance across a sample of 16 developing countries with at least 27 annual observations, with results varying substantially from country to country, Luintel and Khan (1999) find consistent evidence for bidirectional causality across a sample of ten developing countries with at least 36 years of data.

In the case of a cointegrating relationship between finance and GDP per capita, however, a levels VAR as in (20) can be used to test for short-term Granger causality, with conventional F-test statistics applying (Toda and Phillips, 1993, 1994; Sims, Stock and Watson, 1990)<sup>22</sup> and the VEC representation in (21) to estimate the adjustment speed γ. Rousseau and Wachtel (1998) use both the VAR specification of (20) and the VEC specification of (21) to determine the direction of causality between economic and financial development for five industrialized countries for the period 1870 to 1929. Specifically, using the VEC specification of (21), they find a co-integrating relationship for all five countries, while Granger causality tests suggest that finance leads GDP per capita in all five countries.<sup>23</sup> In addition, Neusser and Kugler (1998) apply the Granger and Lin (1995) test to measure the strength of causality from finance to GDP per capita at frequency zero, i.e. in the long-term, which is a function of the correlation of the errors in a bivariate VEC model and the adjustment coefficient vector γ.

In order to gain degrees of freedom, as unit root and cointegration tests have low power in the case of short time-series, several studies have expanded the time-series approach to panel data (Neusser and Kugler, 1998; Christopoulos and Tsionas, 2004). Averaging individual Dickey-Fuller unit root tests yields the Im, Pesaran and Shin (2003) test, while combining p-values from individual ADF tests yields the Maddala and Wu

> 22 Specifically, Toda and Phillips (1993, 1994) and Sims, Stock and Watson (1990) show that in the case of cointegrated series the conventional Wald statistic converges to a χ<sup>2</sup> distribution.

> 23 Following this approach, Rousseau and Sylla (2005) use data for the U.S over the period 1850 to 1997, Bell and Rousseau (2001) use data for India and Xu (2000) uses data for 43 countries over the period 1960 to 1993; all find robust evidence for a leading role of finance.

20
# **1 Introduction**

The global spread of a coronavirus has led to renewed calls for greater investments in public health systems around the world. Policymakers are being advised to increase public spending on health to combat the disease as well as provide a fiscal stimulus to the economy (Wolf, 2020). A previous disease outbreak, of Ebola, had led researchers to conclude then that public health systems in poor countries need Community Health Workers (CHWs) as the frontline health workers to prevent, detect and manage disease outbreaks (Miller et al, 2018; Lo et al, 2017; Moon et al, 2015). This is the workforce that can ultimately implement any public health policy strategy advised by senior scientists and health professionals. Without the institution of such a workforce the world remains at risk of not being able to effectively manage and prevent disease outbreaks, especially those that originate in and spread from low capacity, poor countries.

This paper provides new ideas for policymakers to invest in frontline public health workers using economic theory of "principal-agent" relationships, in which one type of actor, the agent, takes actions on behalf of another, the principal. Insights from economic theory are supported by survey evidence from Bihar, the poorest state of India, a place which is representative of the challenges involved in building capacity of public health systems in poor environments. These ideas are: to combine steady wage structures, as opposed to high-powered incentives where salaries are conditioned on specific inputs or outputs of health workers, with management to strengthen peer-to-peer monitoring and professional norms; and political engagement of local leaders around public health. These ideas can immediately be put to work in policy responses to the COVID-19 crisis and strengthen public health systems to prevent future pandemics.

Policy-makers in poor countries, such as India, have typically relied on voluntary or quasivoluntary CHWs, rather than contracting CHWs to perform specified tasks under a wage structure. Any remuneration that is provided is typically incentivized on indicators of service delivery, such as in the case of the Accredited Social Health Activists (ASHAs) in India who receive payments when they bring pregnant women of their village to deliver babies in public 1
WPS3420

# **Have Consumers Benefited from the Reforms in the Electricity Distribution Sector in Latin America?**<sup>**1**</sup>

## Antonio Estache

World Bank and ECARES, Université Libre de Bruxelles

## Martin Rossi

Department of Economics, University of Oxford

### Abstract

This paper brings new empirical evidence on the impact of the choice of ownership and regulatory regime on firms’ productivity and prices paid by consumers. The evidence is collected from a sample of electricity distribution companies in Latin-America. For efficiency, we rely on estimations of a parametric labor and operation and maintenance (O&M) input requirement functions using alternative. We then analyze the correlation between prices and productivity changes. The main conclusions of the paper are: a) private firms perform better (approximately 30%) than public firms; b) the regulatory regimes matter; c) private firms operating under rate of return are at most as efficient as public firms; d) there is no clear pattern of differences in electricity prices according to the regulatory regime; and e) final prices fell in general but the drop did not match the productivity gains, implying that the operators and the state share some of the gains in the form of rents and higher tax revenue, respectively.

> 1 We are grateful to Simon Cowan, Martin Rodriguez-Pardina, Sergio Perelman and Lourdes Trujillo for useful discussions in the preparation of this paper. Any mistake is ours.

1
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While this is difficult to test, empirically, we provide some checks to assuage this concern. We use census data on migration rates at the municipality level and sequentially trim our sample by dropping observations in municipalities with high migration rates.<sup>15</sup> Specifically, we estimate the baseline specifications under the three sets of fixed effects in Table 2 while systematically dropping observations in municipalities where the migration rates exceed the following thresholds: 60%, 50%, 40%, 30%, 20%, 10%, 6% and 3% respectively. The results are shown in Figure 7. Across the various specifications, the results for the respective subject groupings remain positive and statistically significant, regardless of the migration threshold that is applied. Thus, we can rule out the potential of selective migration confounding our results.

# **5.3.4 Alternative Measure of Catchment Area**

So far, we have used a very conservative measure of treated locations using a 2.5km radius around the coffee mills. However, as shown in Macchiavello and Morjaria (2021), the size of the catchment area of coffee mills could extend to about 5km. Thus, if indeed the true benefits of coffee mills extend beyond 2.5km, then our baseline estimates may underestimate the true effect of the coffee mills on students’ outcomes.

In this section, we redefine the catchment area to include communities within a 5km radius of a coffee mill and replicate the analysis forgoing analysis. The corresponding results are shown in Tables A3 - A7 (Section B.2) in the appendix. Reassuringly, the results align with the baseline results, albeit the effect sizes are relatively low compared to the latter. Overall, both sets of results point to a positive impact of exposure to coffee mills in early life on educational performance in the medium term.

# **5.3.5 Spillovers and Geographic Distribution of Impacts**

Next, we address two potential threats to our assignment of treatment and control locations: I. There may be concerns that our estimated increase in student performance could be driven by a deterioration in student performance in control communities driven by say a negative spillover effect of the coffee mills on nearby communities. For instance, if the coffee mills induce a displacement of (low-income) non-coffee farming households from treatment to control communities, then the treatment effects could in principle be non-linearly distributed across space. II. Contrary to (I), there is also the possibility that given our "arbitrary" and fuzzy definition of catchment areas, some farmers (students) in nearby communities (just outside our treatment threshold) could in principle benefit from the coffee mills (i.e. treated) despite being assigned

> 15Municipality here is the second administrative region.

14
Policy Research Working Paper 7518

# **Abstract**

This paper seeks to understand what drove the trends in learning outcomes in Argentina between 2000 and 2012, using data from four rounds of the Program for International Student Assessment. A year-specific education production function is estimated and its results used to decompose the changes in learning outcomes into changes in inputs, parameters, and residuals via microsimulations. Estimates of the production function show the importance of socioeconomic status, gender, school autonomy, and teacher qualifications to determine learning outcomes. Despite an important increase in the level of resources invested in public education, learning outcomes in public schools decreased vis-à-vis private schools. According to the results presented here, the increase in the number of teachers in the system, pushing the pupil-teacher ratio in Argentina to 11, had no effect on learning outcomes. The microsimulation further confirms that changes in the system’s ability to transform inputs into outcomes accounted for most of the changes in test scores. Overall, the study shows the ineffectiveness of input-based education policies to improve learning outcomes in Argentina.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rdehoyos@ 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