rafmacalaba/datause-encoder-data huggingface dataset:
as follows. The flow originates from country _i_ if it is adjacent to the segment circle of country _i_ and it has the same color as the segment; the flow ends in country _i_ if it is adjacent to the segment circle of country _i_ and does not have the same color as the segment.<sup>19</sup> Similar colors are used to identify larger geographical areas: pink for East Africa, violet for North Africa, green for West Africa, yellow for Asia, red for the Americas, and blue for Europe.

The investigation of the origins of migrant flows shows that West African countries (green) are the origin for most migrants. Outside Africa, Asia, specifically Bangladesh (yellow), plays a major role. Analysis of the destination of migrant flows reveals that Europe (blue) is the preferred destination for migrants. The second-most-preferred destination in 2017 is Western Asia (yellow: Kuwait, Israel, Saudi Arabia, Turkey), but this almost disappears in 2018.

Comparing 2017 and 2018, changes in the direction of flows originating in a specific area can be identified. For East Africa, namely Ethiopia, Eritrea, and Somalia, we observed an increase in the destinations reached, as shown by the increase in the number and width of the pink flows.

Finally, the figures allow the composition of migrants’ nationality at destination to be analyzed by examining the colors of incoming flows. For all receiving countries, most migrants originated in West Africa (green), but we also observed that East Africa played an increasing role, with a significant increase in the number of pink flows reaching Italy, the Netherlands, and Sweden. Less notable, but still relevant, is the increase in the number of yellow flows, representing the Asian countries, reaching France and Germany.

# _5.2.5 International migration flows through Libyan provinces_

Using data about origin and destination countries registered at the FMPs, we track migration flows connecting migrants’ origin countries to Libyan provinces and Libyan provinces to migrants’ preferred destinations.

Figure 11 presents the network of origin countries and Libyan provinces for 2017 and 2018. Origin country _i_ is connected to Libyan province _j_ if, for at least one round in a given year, the majority of migrants observed at an FMP in province _j_ departed from country _i_ . These graphs show two important changes that occurred between 2017 and 2018; the number of origin countries decreased,<sup>20</sup> and there is a notable decrease in the density of the graph, meaning that migrants from a given origin country are

> 19 For ease of interpretation, we removed connections signaling migrants returning to their home country or intending to remain in Libya.

> 20 The top origin countries disappearing from the graph are Benin, Cameroon, Central African Republic, Democratic Republic of Congo, Guinea, Guinea-Bissau, Kenya, Malawi, Morocco, Togo, and Tunisia. At the same time, we observe the appearance of Pakistan.

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# WPS3147

## Human Capital and Earnings Inequality in Brazil, 1988–1998: Quintile Regression Evidence

### G. Reza Arabsheibani,<sup>†</sup> Francisco Galrão Carneiro,<sup>*</sup> and Andrew Henley<sup>†</sup>

#### **Abstract**

This paper undertakes an empirical examination of rates of return to human capital for men in Brazil through the period of macroeconomic stabilization and trade liberalization, using data from the PNAD household surveys. Simultaneous quintile equations are estimated to gain a picture of the impact of human capital on earnings across the hourly earnings distribution. We conclude that there is evidence for growing inequality in rates of return to education in Brazil. However we find evidence that education is no longer used as a screening device in the labor market, but rather rewarded for its innate association with higher productivity. Although increases in rates of return to education have been more pronounced at the top of the earnings distribution, this has not led to increased inequality. This is because levels of education and other labor market-rewarded endowments have increased and offset the rate of return effect.

Keywords: Earnings, Human Capital, Inequality, Quintile Regression JEL Classification: J31, I20, C14

- School of Management and Business, University of Wales Aberystwyth, UK.

- The World Bank – Poverty Reduction and Economic Management (PREM).

World Bank Policy Research Working Paper 3147, October 2003 _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._
Tablc 4: Effcctivc Ratcs of Protcction and Producer Subsidy Equivalcnts for Major Crops. 1991-92 Tend Valucs

|Product|E<br>|| PSE(%)|
|---|---|---|
|Wheat, Gujranwala|-46|-15|
|Bastati, Gujranwala|_-59_|-33|
|I_, Larkana|-39|-1O|
|Cotton, Mulian|-63|-40|
|Sugarcane, Faisalabad|70|92|

Sourcc: Longmuirc and Dcbord. Tabic 16. Thcsc mcasures **were** constructed using the free tmdc cxchange rate While PSEs and ERPs are not directly comparable, their respecLive orders of magnitude can be used to indicate relative subsidy effects. As Pakistan has slowly liberalized its agriculture sector, levels of disprotection for export crops have fallen significantly in recent years. However sugarcane remains highly protected.

However, two significant omissions from the PSE calculations are the capital costs of the irrigation system, or defaults on loans. Both represent uncounted subsidies, and so the stated figures would tend to overstate the adverse incentive effects. However. some argue that the capital costs of the irrigation system were recovered before the mid 1970s when revenue from fairmers exceeded current costs, and that consequently there is no need to allocate the capital costs of the system.

Talcing all transfers into account significantly reduces the extent of price distortions. So much so, say Longriire and Debord, that net disprotection of agriculture was close to zero in 1991-92. This, however, ignores serious distortions and intersectoral policy bias (see the next subsection) rnot captured in subsidy calculations. While an aggregate estimate may show small discrimination against agriculture, overall efficiency in resource allocation is impaired because of significant disprotection for some crops and strong protection for others. All estimates clearly show that there is significant disprotecfion for wheat and cotton Cinducing a transfer of resources away from those crops), whereas sugarcane is highly protected (attracting resources towards the crop).

Moreover, even as price distortions have fallen, the efficacy of the price and subsidy regime remains in question. Simply using subsidies as compensation for reduced prices omits the crucial question of whether the subsidies are going to the intended recipients. Considerable evidence suggests that subsidies are not helping farmers, especially small farmers. While canal water may command a price one-fifth of its social price, who benefits from this subsidy? While water is supposed to be equally distributed, in practice, bribes and rent-seeking determine the distribution of water rights (World Bank, 1994). Public procurement has similar abuses. Anecdotal evidence from the field suggests that procurement agents exploit farmers by absorbing most of the difference between the market price and support price when support price regime is in effect. Thus intermediaries and not farmers absorb the rents in the systemn.

7
Table 1 also provides the average number of nightly observations each quarter that is used to construct the nighttime light data. Nighttime lights of AEs, most of which are located in the northern part of the Earth, are affected by late sunsets in summer months and therefore have on average a lower number of effective observations than EMs. The average AE has about 26 nights of observations each quarter, compared with 29 in an average EM. LIDCs, many of which are situated in tropical areas, also have relatively low numbers of observations due to frequent cloud cover. Even at the 90th percentile, the number of nightly observations is just 39, and hence less than half of a quarter. The variation in the number of nightly observations across countries and over time can be used to quantify the measurement errors in nighttime lights, which allows us to identify the elasticity between nightlight growth and GDP growth.

A country’s annual nighttime lights averaged across the monthly data are very strongly correlated with those from alternative annual VIIRS data.<sup>15</sup> However, the correlation of the annual growth rates averaged across the monthly data with the direct annual growth rates is weaker, suggesting that elasticity estimates from annual and monthly data should not be used interchangeably.<sup>16</sup>

# **4.2 Cross-Sectional and Temporal Relationships**

The recording of nighttime lights is affected by extraterrestrial and atmospheric conditions. As a result, temporal fluctuations in the data can be large, making it problematic to use time-series changes in nighttime lights as a proxy for changes in economic activity directly. However, because the noise is often common to all countries at a point in time, the relationship between nighttime lights and economic activity is strong and relatively stable when the analysis controls for time-specific factors.

Table 2 examines the roles of country- and time-specific factors in the relationship between nighttime light growth and GDP growth. Without any fixed effects, the correlation coefficient of 0.32 is statistically significant only at the 10 percent level, as shown in column (1). With country fixed effects, as shown in column (2), the average temporal correlation between nighttime light growth and GDP growth is not statistically different from zero, suggesting that time-series changes in nighttime lights are a poor proxy for changes in economic activity if time-specific factors are ignored. Columns (3) and (4) show that when the analysis controls for time specific factors, the relationship between nighttime light growth and GDP growth is strong and statistically

> 15Separate annual VIIRS data (VNL-V2) provided by Earth Observation Group have undergone an additional filtering and outlier removal processes.

> 16 Appendix C provides more details.

10
negative shock while in utero that lowers child cognitive ability by 0.235 standard deviations would lead to declines, relative to baseline levels, of 32 percent for ever being enrolled, 24 percent for timely grade progression, and a 113 percent increase in starting school late.<sup>21</sup> Turning to child labor activities in Table 5, for the same sample of 2,862 children ages 5 to 15 years old, higher ability children engage in less child labor than their lower ability siblings. Negative rain shocks experienced while in utero lowers cognitive ability by 0.235 standard deviations and leads to increases for these children of 6.96 percentage points in the likelihood of tending for siblings or sick members and 6.56 percentage points in the likelihood of working on the family farm. Relative to baseline levels, these shocks represent increases of 26.8 and 21.9 percent, respectively compared to a child‟s higher ability siblings. Even though a child is not more likely to perform chores around the household such as cooking, fetching water, sweeping or doing the dishes, a child with 0.235 standard deviations lower cognitive ability due to negative rain shocks in utero will spend 0.65 more hours on said tasks than his higher ability siblings, which is 21.2 percent of the baseline level. Similar calculations show that a negative rain shock in utero leads to declines in ability and these further lead to increases of 0.24 hours per day tending for siblings or sick household members, 1.19 hours per day on farm labor, and 2.71 total hours of child labor per day. Relative to baseline mean levels, these additional hours represent increases of 39.3, 126.6, and 48.9, respectively. Experiencing a 1 standard deviation rainfall shock at age 0 or age 1 corresponds respectively to 0.146 or 0.272 standard deviations lower

> 21 To further explore the relationship between the specific timing of rainfall shocks and education, in Appendix Table 1, we present the household fixed effects IV results using each instrument (rain shock experienced while in utero, age 0, or age 1) separately. Results for each instrument are consistent with those in Table 4, indicating that higher ability siblings within a household, compared to their lower ability siblings, are more likely to receive positive schooling investments. The magnitude of the impact of ability on enrollment is largest for those children who experience negative rainfall shocks while in utero and diminishes for those experiencing shocks at later ages.

21
15 right-hand side of (12) and (13) represents the long-run equilibrium relationship, given by equation (I 1) above.

From the results of Johansen (1992) and Boswijk (1995), it follows that OLS in (12) alone provides a super-consistent estimator of the long-run coefficient vector j3 and the speed of adjustment X. However, other properties of the OLS estimator of the long-run parameters, such as its efficiency and asymptotic distribution, as well as the properties of the estimator of the short-run parameters in (12), depend critically on whether the conditioning variables w are (weakly) exogenous, an issue that will be taken up later.

Leaving this question aside for the time being, the starting point for the conditional analysis is an unrestricted autoregressive-distributed lag specification'<sup>8</sup> for _kpr,_ including the current and two lagged values of the conditioning variables, plus the linear time trend. The OLS estimation results appear in Table 6. While the equation is clearly overparameterized, and individual coefficients are in some cases estimated imprecisely, the long-run solution is well-determined. It is of the form: _kpr = constant + 0.045 time - 0.839 kg + 0. 659 z + 0.162 crpr - 0.213 Pk_ (14) Thus, the estimates indicate that public infrastructure capital has a positive long-run effect on the private capital stock, while non-infrastructure capital has the opposite impact. Further, the estimated magnitude of these effects is numerically quite large (recall that, with all the variables in log form, the coefficients in (14) can be interpreted as elasticities). In addition, real credit to the private sector also affects the private capital stock positively, while the relative price of capital has the expected negative effect.

The bottom half of Table 6 reports a battery of diagnostic tests that reveal no signs of misspecification. According to the test statistics, the residuals show no traces of autocorrelation or autoregressive conditional heteroskedasticity (ARCH), and Ramsey's RESET test detects no functional form misspecification. At most, the Jarque-Bera statistic provides some mild suggestion of non-normality of the residuals.

The unrestricted equation in Table 6 can be re-arranged and simplified to arrive at an error-correction specification describing both the dynamics of the capital stock and its long-run determinants, along the lines of equation (13) above. This leads to the following model (standard errors in brackets): 18 This is what Hendry (1995) labels the "GUM" (General Unrestricted Model) specification.
# **1 Introduction**

In Latin America and the Caribbean, the average number of extreme weather events<sup>1</sup> has increased by more than 60% in the past two decades compared to the 1980s and 1990s, while in Paraguay it has more than doubled (OECD, 2022). These increases in frequency and intensity of extreme weather events are expected to continue as a consequence of climate change (IPCC, 2021). Due to its consequences on economic activities, ecosystems, and populations, which are significant and, in many cases, irreversible, climate change is considered one of the most systemic threats to our societies. Empirical evidence indicates that low- and middle-income countries (LMCs) are disproportionally affected by the consequences of climate change. In the most recent Global Climate Risk Index –an index that analyzes to what extent countries and regions have been affected by impacts of weather-related events– eight of the ten countries most affected by the quantified impacts of weather shocks in 2019 were LMCs (Eckstein, Künzel, & Schäfer, 2021). In the Global Climate Risk Index, Paraguay is ranked 20<sup>_th_</sup> worldwide (Eckstein et al., 2021).

Climate change is expected to worsen existing inequalities and poverty levels across the globe (Olsson et al., 2014). A recent cross-country study finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty (measured at the USD $1.90 poverty line), as well as a 0.8 percent increase in the Gini index (Dang, Cong Nguyen, & Trinh, 2023). Furthermore, existing socio-economic inequalities can lead to uneven impacts of weather shocks in LMCs, in particular, if vulnerable communities depend on natural resources for income generation, or when the poor are largely concentrated in environmentally risky areas (Reckien et al., 2017).

In Paraguay, climate-related shocks may have contributed to stalled poverty levels in the last decade. While Paraguay was able to reduce poverty in half between 2003 and 2022, from 51.4% to 24.7%, the pace of poverty reduction has stalled since 2014. At the same time, Paraguay experienced an increase in the frequency of floods and droughts, as well as in temperatures. In 2019 alone, the country not only recorded the highest average temperature, with an annual temperature of 24<sup>_◦_</sup> C, but also experienced severe droughts, floods, and even heat waves (GRIDGenever European Comission, 2023). Furthermore, historical data denote that years in which the country experienced droughts and floods were associated with sharp reductions in Gross Domestic Product (GDP) per capita and with increases in poverty levels (Gayoso de Ervin & Rubiano, 2023), evidencing the possible link between climate change and socioeconomic indica-

> 1Defined as natural disasters (landslides, storms, droughts and floods) resulting in 100,000 or more people affected, or 1,000 or more deaths, or at least 2% of GDP in estimated economic damages (OECD, 2022).

1
USAID MotherCare project. Country programs are assumed to vary among a set of typical approaches. Evaluating these approaches allows for more intense scrutiny of the most widely used alternatives.

Given the limitations of current data and knowledge, a number of field studies are warranted. Data need to be collected from a variety of projects on inputs, prices, services delivered, and, if possible, health outcomes. These studies will help to define the continuum of possible Safe Motherhood programs represented by existing work. As data become more available, it will be possible to develop more reliable cost-effectiveness metrics to apply to both existing and proposed programs.

# VII. CONCLUSIONS

Workshop participants concluded that we have little information on the actual costs of implementing Safe Motherhood programs, and on how these costs vary with program structure and country settings. It is also clear that we are far from consensus on the precise goal, content, and structure of "Safe Motherhood Programs." This is due, to a large extent, to the paucity of information on the relative effectiveness of individual health interventions; different levels of the health care system; and non-health sector interventions. Program impact on mortality is very difficult to measure, and debate continues on the appropriateness of various intermediate or process indicators.

Apart from the logic of ensuring that operational resource allocation decisions within Safe Motherhood are as sound as possible, it is important to ensure that resources are allocated to Safe Motherhood at all by illustrating that reductions in mortality (or improvements in other key indicators, if mortality reduction cannot be measured directly) can be brought about with a relatively small investment. Safe Motherhood competes with several other better established sectoral interests, Child Survival for example, which use cost-effectiveness figures regularly as advocacy and fundraising tools.

The participants noted that applied research will be vital to developing a better understanding of Safe Motherhood costs. It will also be essential to determine the cost and impact of a range of care packages in different settings, which will enable us to gain a fuller understanding of the economic aspects of Safe Motherhood programs. We need to identify program elements and effectiveness measures; norms and standards, to accompany coverage indicators to give some measure of quality, and thus impact; and, ultimately, comparable indicators of outcome. In addition, it will also be essential to develop a workable, common framework of categories and indicator for inputs, activities, and services, in addition to outcome. The lack of such indicators constitutes one of the greatest barriers to the confident use of costing in Safe Motherhood and to the comparability of results.

There was considerable debate on the relative importance of rigorous research. Some 18
Job vacancy rates linked to skill requirements can also be used as a labor market signal.

While measuring the level of job vacancies, the trend is also important as in the case for labor force actN'ity and the movement of relative wages and employment. The shift in demand and supply for skills is observed through trends in these measures. Thus, when job vacancy rates fall, unemployment rates rise, or employment growth slows or declines for workers with specific levels of schorling or training, this can be interpreted as a market signal of a downturn in the need for the skills associated with the workers' level of schooling or training. This is a signal to prospective students and trainees and to managers of education and training institutions concerning future economic benefits for public and private investments in these skills. Labor market signalling, as such, is focused on the dynamic nature of skills demand and supply, distinguishing it from the technique of manpower requirements forecasting.

Other market-based signals can be developed by planners monitoring the placement of graduates and the demand for particular courses or programs of study. Schools and training centers that follow graduates into the labor market for a year or two with tracer studies gathering information about their earnings, hours of work, and spells of unemployment can link this information with the skills acquired to produce a market-based measure of the economic benefts attached to the skills involved. Thus, surveys of graduates from a general secondary school or a vocational technical institute can generate labor market signals involving graduate placement rates and earnings as a guide to prospective students and trainees, but also to managers of other schools and institutes who face curriculum a,ad staffing decisions.

Still oth.er market signals can be developed by examining enrollment data for institutions, programs of study, and even individual courses. High applicant to admission ratios are a market signal of demand for specific schools and training centers or for particular programs of study or courses. By comparison, low applicant to admission ratios and half-filled training centers or classes are a signal of low demand to be used by school and training center managers in curriculum and staffing decisions. The regular monitoring bv manpower planners of these signals, followed by timely dissemination of the findings to parents and students and to school and training center counsellors and managers, are 10