rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III. Determinants of Internal Migration**<sup>**10**</sup>

The literature has long recognized that migrants tend to move in response to differences in economic opportunities. For instance, the human capital model of Sjaastad (1962) hypothesizes that the migration decision is influenced by the present value of the difference in income streams between alternative locations. According to this model, the higher rate of migration among the young can be understood as a logical outcome of investment decision-making since the young face a longer life investment horizon. Extending the wage-differential approach, Todaro (1969) formulates a rural-urban migration model that incorporates the probability of obtaining a modern sector job as an additional determinant of migration. Furthermore, the model of Harris and Todaro (1970) demonstrates that, under certain circumstances, urban job creation may actually increase local unemployment by inducing more migrants to move to urban areas.

There exists a large body of literature which investigates the determinants of migration empirically. One popular approach is to use the macro gravity model of migration, inspired by Newton’s law of gravity.<sup>11</sup> Several studies have applied the macro gravity model (or a variation thereof) to Vietnam (Dang _et al._ , 1997; Nguyen-Hoang and McPeak, 2010; Phan and Coxhead, 2010). Studying the 1989 census data, Dang _et al._ (1997) find that while government intervention played a key role in moving people from sending provinces/cities to targeted resettlement areas, people still moved into the urban areas which gained population, on balance, from migration. Their study also finds that, even during the initial phase of the _doi moi_ policy, women tended to go to areas with higher relative levels of industrial development.

Using the 1989 and 1999 _Censuses of Population and Housing_ data, Phan and Coxhead (2010) estimate a standard macro migration equation to examine the determinants of inter-provincial

> 10 This section is not intended to be exhaustive. See,
Policy Research Working Paper 8609

# **Abstract**

One reason that poor people may not capture the full benefit from participation in international markets is that the goods they produce tend to be subject to relatively high trade barriers. This paper analyzes market access barriers faced by households in different income deciles by matching household survey data from India based on the industrial classification of their economic activity. Tariffs in international markets are higher, and nontariff measures more numerous, on goods produced by poor workers than on goods produced by rich workers. Tariffs faced by exporters are higher on goods produced in rural and more remote areas than on those in urban centers, on goods produced by informal enterprises than by formal ones, and on goods produced by women than by men. Furthermore, the global reduction in tariffs from 1996 to 2012 failed to ameliorate these differences. How did we get there? Efforts to protect poor workers across countries resulted in a coordination problem. Indeed, tariff protection in China and the United States is higher on goods produced by poor workers than on goods produced by rich workers. Therefore, if poor workers are employed in similar sectors, then each country’s attempts to protect its poor workers by imposing higher tariffs and more nontariff measures on such goods will reduce the access of all poor workers to international markets, and thus limit the gains from trade.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/research. The authors may be contacted at gnayyar@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.
sity of Venezuelans in the corresponding population, and adopt a difference-in-difference estimation strategy that compares the evolution of our labor market indicators in regions that received immigrant inflows to regions that did not around the time of arrival of Venezuelan migrants. Our analysis of the migrants’ location choices also makes use of data on their point of entry into the country and measures of aggregate income at the regional level.

Our paper contributes to the recent literature on the effects of forced migration on the host labor markets, which has focused on the exodus of Syrians primarily to Turkey (e.g. Ceritoglu et al. (2017), Tumen (2016), DelCarpio and Wagner (2015) and Loayza et al. (2018)) and Venezuelans to Colombia (Caruso et al. (2019) and Penaloza-Pacheco (2019)), which we review in detail below. Our contribution is twofold. First, we provide the first analysis of the labor-market effects of Venezuelan migration to Ecuador, one of the main receivers of Venezuelans over the last few years. Secondly, we make use of new data based on cell phone contracts and usage that allow us to measure with a high degree of accuracy the density of Venezuelan migrants across Ecuador’s geography, and merge these data with the household labor force survey and administrative records on migrants’ points of entry.<sup>3</sup> The main findings of the study are the following. First, based on our data, 470,095 Venezuelans were living in Ecuador in the first quarter of 2019, which amounts to almost 3% of Ecuador’s population. We also observe that four _cantons_ (out of a total of 221) host 52% of Venezuelan migrants.<sup>4</sup> Secondly, Venezuelan migrants are highly geographically mobile, mainly choosing to locate in higher income regions, with the point of entry into Ecuador having very little explanatory power. Specifically, we estimate that _cantons_ with 10% higher income receive 5% more Venezuelan migrants
Figure 22. Financial Impact of Introducing a Funding Pillar. Years 2018-2080 <!-- Start of picture text --> 2%<br>0%<br>-2%<br>-4%<br>-6%<br>-8%<br>-10%<br>-12%<br>Current Introduction of a Funding Pillar<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078<br><!-- End of picture text --> Source: author elaborations based on administrative data, IESS So far, simulations have been made without reforms aiming at facing the pressure on financial sustainability of the contributory pension system posed by population aging. However, an additional challenge facing the elder income protection system is coverage expansion. Given that the contributory system, providing coverage to approximately 26% of 65-aged older people, is financed by general income, there arises a horizontal inequity issue: those who are not covered finance, by paying taxes, the protected population that, as above stated, belong also to the top income quintiles. This fact justifies public intervention by means of granting a social pension, which in part has already been taking place, to the excluded population. Thus, a transfer, contributory and non-contributory scheme is achieved to allow an almost universal coverage.

In order to estimate the financial impact of a scheme like this, the income and expenditure paths of the pension system are simulated, taking into account two key assumptions: i) universal pension is granted, made of contributory transfers for those who are eligible and social pensions for the remaining excluded 70-aged older people; and ii) the amount of the non-contributory benefit is USD100.

32
liberalization of markets and deregulation. However, as it has been difficult to find robust evidence from cross-country data that the policy prescriptions of the Washington consensus – hence, aid based on those - generate growth, subsequent empirical work by researchers from the World Bank

> 36 See Martin Ravallion, “Research in Practice at the World Bank,” DECRG, September 2007.

> 37 The most well known was probably the 1986 volume by Robert Cassen (who had been the main author of the 1981 World Development report), _Does Aid Work?_ Oxford: Clarendon Press

24
# **Endnotes**

- l Henson, Spencer, et al, 2000. Impact of Sanitary and Phytosanitary Measures on Developing Countries. University of Reading, p 10.

## 2 United National Development Programmes.

3Food and Agriculture Organization.

## 4 World Health Organization.

5JECFA estimated that implementing a 10 ppb total aflatoxin standard, population potency is 39 cancer deaths per year per billion people, with uncertainty range between 7 and 164 people. In comparison, a 20 ppb standard will yield potency rates equal to 41 cancers per year per billion people with uncertainty range between 8 and 173 cancer deaths. This implies that the 20 ppb standard will lead to 2 additional cancer deaths per billion people compared to the 10 ppb standard. The estimates assumed a population with 1 percent carriers of hepatitis (i.e. European population) and used potency values equal to .3 cancers per year per 100,000 people among carriers of Hepatitis B and .01 cancers per year per 100,000 population among noncarriers.

# **6** This number is under estimation based on the United Nations trade statistics.

7 It should be noted, however, that it had standards on other micotoxins that may be much stricter than the aflatoxins on cereals. These micotoxins include searalenone and ochratoxin (FAO, 1995). Standards on these chemicals may remain after the EU standards harmonization. If so, the estimated decrease may be smaller in magnitude.

8 This number is under estimation based on the United Nations Trade Statistics.
transformation Entry* Exit*<br>Log-changes<br><!-- End of picture text -->

Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Fluctuations in labor productivity in the mining sector are largely due to idiosyncratic factors within individual firms operating in the sector. The between-firm component—corresponding to changes in the correlation between firms’ market share and productivity—was the primary driver of changes in productivity over the 2014-2021 period (see Figure 26). This trend reflects how the mining sector has a high level of industry concentration and individual mines are subject to significant operational variability from year to year (for instance, variation in ore grade or unexpected disruptions). For example, the sharp decrease in productivity seen in 2016 was due to safety incidents that occurred in some of the largest copper mines in Zambia, which led to temporary closures. On the other hand, the sharp increase in productivity in 2017 was due to the rapid expansion of a newly opened mine that employed the latest technology (i.e., a high-productivity firm), resulting in improved efficiency in resource allocation.<sup>11</sup>

# 5. Firm dynamics and formal job flows

## 5.1. Methodology for jobs analyses

This paper analyzes trends in the stock, flow, and wages of formal jobs in Zambia by leveraging the PAYE database. Paying into PAYE taxes serves as a proxy for formal employment. For the purposes of this paper, formal employment (i.e., jobs covered by PAYE taxes) can occur at both formal firms (i.e., firms paying into CIT) and informal firms. Similarly, formal firms may have both formal and informal employees. We define one formal full-time equivalent worker (FTE) as 12 monthly returns to account for seasonal labor. Wages are annualized and deflated by the Consumer Price Index (CPI) to align real wages to spending power.

>
technology is not available commercially. There are some experimental or pilot projects either operational or planned to use electric kilns for brick-firing, such as the Kortemark plant in Belgium (Wienerberger, 2023), Uttendorf plant in Austria (NEFI, 2023), a full understanding of technical and economic viability of an electric kiln might take years. (Geres et al., 2021) project that economically viable GHG-neutral brick production would be available in developed economies like Germany by 2050. Another pathway for carbon-neutral brick production is the use of green hydrogen. However, the technical feasibility and economic viability of this technology are yet to be tested.

Considering the availability of clean electricity and the series of environmental and fiscal problems caused by imported coal, Nepal should explore the opportunities to use electric kilns for brick production. Since the technology is not yet commonplace, pilot programs on such technology should be undertaken. During the rapid survey (Timilsina and Malla, 2023), brick producers showed a strong interest in electric kilns. Some producers are willing to host pilots if they can get financial and technical support from the government or development partners.

# 6.4 Alternate (not-fired) bricks

Policies to promote alternative bricks would be a solution to reduce CO2 emissions and other local pollutants from the brick industry in Nepal. At present, there are two alternative bricks in Nepal: unfired or uncooked bricks and cement blocks/bricks. The former is produced with a mix of clay, sand, stone dust, and some chemicals (for binding), whereas the latter is produced from cement. Since these bricks are not as strong as traditional bricks, they face marketing challenges. Many producers started to produce alternative bricks, mostly medium and small entrepreneurs, however, they face market barriers. Consumers are skeptical about the durability of such bricks. They argue that traditional bricks can survive thousands of years whereas alternative bricks may not survive for the lifetime of their buildings. While small producers are continuing production of alternative bricks, they are also uncertain about their future
lead to the direct loss of 49 observations.’ In order to achieve a minimum time-series dimension, as well as to reserve sufficient observations to implement our instrumental-variable estimators described below, we limit our sample coverage to those countries with at least 5 consecutive annual observations. After all these adjustments, the sample for our "core" specification consists of 1,148 observations. Since four observations per country must be set aside for the construction of instruments, the "core" regression sample consists of 872 observations for 69 countries — 20 industrial and 49 developing. As explained below, we also estimate regressions for the national saving rate and for private saving rates derived from a narrower definition of the public sector. For these regressions, the available sample comprises about 1,800 annual observations for 98 countries in the case of national saving rates and between 750-900 observations for 69countries in the case of private saving rates, depending on the precise definition of the private and public sectors.* This sample coverage exceeds that of Edwards (1996), who considers 32 countries, and Masson, Bayoumi and Samiei (1995), whose sample includes 61 countries.

Finally, note that these panel data sets are heavily unbalanced, with the number of time-series observations varying considerably across countries. The top panel of Table 1
climate-related uncertainty is often studied using indirect, non-weather measures like the Climate Change News Index (Engle et al. 2020) and in climate policy uncertainty literature (Gulen & Ion 2016), recent studies have increasingly focused on direct measures of weather variability, as highlighted by Kahn et al. (2021), Kotz et al. (2021), Donadelli et al. (2022), Sheng et al. (2022). Our approach builds on these recent efforts by emphasizing weather variability as a key factor influencing economic activity. In doing so, we also draw on the established tradition of using gridded output data, particularly Nordhaus (2006) and later studies employing grid-level or sub-national geographic data, such as Kalkuhl & Wenz (2020), Wenz et al. (2023), Gortan et al. (2024).

Our main contribution is the construction of grid-level economic data based on firm-level activities. Unlike regional GDP data, firm-level data shows how the private sector, driven by profit motives, responds to climate change and the risks it poses. This firm-level data enables us not only to capture the overall relationship between weather variability and economic activity but also to test important hypotheses, such as the moderating effects of investment irreversibility in this relationship—something that is difficult to achieve without detailed firm-level data. By doing so, we bridge the climate change impact literature, particularly studies using grid-level output data (Nordhaus 2006, Kalkuhl & Wenz 2020), with research on firms’ behavior under uncertainty (Myers 1977).

The paper is structured as follows: Section 2 reviews the real options theory. Section 3 discusses the main data sources, cleaning process, and key descriptive statistics. Section 4 outlines the empirical methodology and presents our findings. Section 5 concludes the paper.

3
framework, implemented originally through the Community Innovation Surveys (CIS) in the EU, has dominated innovation measurement in most countries, to the extent that even countries that do not conduct CIS have adopted the same questions at least in relation to the measurement of innovation outcomes.

One challenge in measuring innovation outcomes is the subjective nature of many of the questions used in the surveys. The Oslo manual defines innovation as “the implementation of a new or significantly improved product (good or service) or process, a new marketing method, a new organizational method in business practices, workplace organization or external relations”. Most surveys use this definition to identify innovations, by directly asking firm managers and owners whether they have implemented any “new” products, processes, marketing methods, or organizational practices or “significant” improvements in existing ones in the last three years. In this context, the answer to this question is problematic since 5
equator. Each pixel contains the annual mean of the corresponding pollutant. For nitrogen dioxide, the data span between 1997 and 2011, while for PM 2.5, the period is 1998 to 2015. Because different models may differ in how they estimate the PM 2.5 surface concentration levels, we complement NASA data with PM 2.5 raster data (with the same resolution) estimated by van Donkelaar et al. (2016). Their work uses geographically weighted regression to calibrate ground-based PM 2.5 concentration levels using satellite data and ground level data were available.

We overlay WBES masked firm locations with pollution data rasters and extract NO2 and PM 2.5 levels for each location. WBES data include a cross section of firms surveyed between 2006 9
number of active agents per month is below 370. The total number of transactions across all active agents reached over 31,000 transactions in November 2017, corresponding to an average of 76 per active agent.

Although the agent rollout specifically targeted treatment areas, the implementation was not exact. To verify agent location, we used GPS coordinates that are available for 375 agents. About 45 percent of GPS locations are inside a treatment cluster and another 39 percent are within 1km of a treatment area, with a total of 94 percent falling within 2km of a treatment area. Two GPS locations are inside a control area, about 2 percent are within 1km of a control area and 13.6 percent fall within 2km of a control area.

According to data from the Bill and Melinda Gates Foundation, the average distance to the closest agent for the cluster in our sample was 10km in 2015, suggesting that the agent rollout brought agents much closer for treatment clusters that received an Airtel agent during the rollout. Appendix figure A2 shows the Gulu district as an example, illustrating the location of treatment and control clusters, along with pre-existing agents and Airtel agents that were rolled out as part of this study.

Agents were not distributed evenly across EA clusters. Only 35 percent of treatment clusters received at least one agent. This number is higher among treatment clusters with a high priority rating from Airtel (46 percent of clusters) and lower in treatment clusters with a low priority rating (24 percent). Appendix figure A3 shows the clusters in our study by low and high priority rating.

# **4. Data**

# **4.1 Survey Data**

We use data collected in two survey rounds. Based on the household listing exercise conducted in September and October 2015 (see section 3.1), we randomly selected 9
years, the rise has been steeper among ninis aged 15 to 18 years, especially in the case of women. The increasing share of young women—particularly below 18 years of age—starting a new household, suggests that a potential driver of the high rate of female ninis is marriage before age 18, compounded by teenage pregnancy.

**Figure 12: Percentage of ninis that decide to start their own household in LAC by gender and age, 1992-2010**

Panel A: 15-18 years Panel B: 19-24 years <!-- Start of picture text --> 52.7 52.9<br>27.7 48.5<br>25.3 46.2 47.1<br>22.3 41.9 41.3<br>19.0 19.9 18.4 38.0 38.6 38.4<br>17.1<br>14.0 15.1<br>12.9<br>1.0 1.2 1.7 2.2 2.5 9.7 9.1 10.6 11.4 11.4<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Source: authors’ own computation with data from CEDLAS and The World Bank (2015)<br>Figure 13: Percentage of ninis that decide to start their own household with children in LAC by gender and age,<br>1992-2010<br>Panel A: 15-18 years Panel B: 19-24 years<br>18.4<br>17.5 43.3 43.6<br>15.8 39.2 40.3 40.9<br>14.2<br>13.1 31.7 32.6 31.9 33.8 33.3<br>11.6 12.
observed in the phone survey dataset, such potential bias is addressed by controlling for the individual effect _ρi_ also captures. Also, using only the phone survey data set, we do not face issues of attrition bias. Estimates are all weighted by the sampling weights provided with the phone survey to infer results at the national population level.

# **4 Results**

In this section, we discuss the results of the estimation of the effect of COVID-19’s lockdown measures on school attendance after the school system’s re-opening. For convenience, in the rest of the paper, we refer to this effect as the COVID-19 effect.

## **_(i) Baseline Results_**

Table 2 displays our baseline estimations’ results. Panel A displays the results for all 5-18 old. We that COVID-19’s lockdown measures re- respondents aged years find duce school attendance by 6.94 percentage points after the school system’s reopening, and the result is highly statistically significant (Panel A, column 1). Moreover, when we interact the nation-wide lockdown measures with state-specific measures, we find that the severity of the lockdown measures which accompanied schools’ closure has no statistically significant effect on children’s school attendance probability after reopening (Panel A, column 2). These results suggest that the severity of lockdown measures did not make a difference in children’s school attendance probabilities. One possible reason for this result may be a low compliance with these measures, particularly non-binding measures. For example, Bargain and Aminjonov (2021) in a study of several developing countries that work-related the lockdown was find mobility during first period higher among the poor than the non-poor. Additionally, we find no statistically significant differences in the effects of COVID-19’s lockdown measures between rural and urban areas (Panel A, column 3). This finding suggests that the socioeconomic crisis due to COVID19’s lockdown measures did not disproportionately affect children based on their area of residence.

When we break down the above results