rafmacalaba/datause-encoder-data huggingface dataset:
Why do families choose to split, with some members working in cities and others left behind? One might be tempted to dismiss the question altogether and conclude that, as rational and voluntary decisions made by the migrants and their families, living apart must be beneficial to them. While family splitting is indeed a choice, it is made under constraints imposed by institutions and government policies. No single institution in China is purposefully designed to split families, but the _hukou_ system and the land tenure system have the unintended consequence of doing so by increasing the costs of urban living for migrant families and the opportunity costs for entire families to leave villages.

Taking these two institutions as given, scholars in economics, sociology, and public health have started focusing on the impact of migration on left-behind family members. A simple framework suggests that when a family is split by out-migration, left-behind members may be influenced in four ways: increased family income through migrant remittances, benefits from information transmission about technology from urban family members, decreased family time together, and behavioral changes when no longer living together and interacting daily (Chen 2013). The income and information effects may be positive for family and individual welfare, while the time effect is generally non-positive. The impact of behavioral changes is context specific. Within this framework, how individuals fare in split families is an empirical question that cannot be determined theoretically.

Below we examine empirical studies of the welfare of left-behind children, women, and older adults. Many of these studies do not account for migration selection, and so the accumulated evidence often reflect correlations. Nevertheless, these studies raise topics for future study by identifying key variables and hypotheses to be tested under more designs. In addition, the consistent findings across multiple studies and contexts, for example on the negative impacts of parental migration on child school enrollment and mental health status, on the negative impacts of spousal migration on the mental health of left-behind wives, suggest that the observed associations are not merely spurious but warrant further investigation. These correlational studies also highlight areas of concern for policymakers and can inform the design of support programs for left-behind populations, even as more definitive causal evidence is being developed.

**Left-Behind Children.** According to the data from the Child Questionnaire of CFPS (2016), rural children under 16 spend less time, 1.6 to 2.6 months on average, with their parents than urban children, except for infants and toddlers under 2 (Table 2). Media attention on split families has focused on “the left-behind children,” with anecdotal reports of abuse, mental health issues, and behavioral problems due to insufficient care and protection (e.g. Zhang 2018). These stories raise concerns about the well-being of rural children and challenge a view of migration solely as an income-enhancing option for rural families.

21
# 2: Internal and External Migration

Migration, both internal and external, is an important feature of the Turkish labor market. For understanding the development of unemployment it is necessary to come to grips with the development of migration.

i)Internal Migration. The main source of information is the population censuses. For 1970 a breakdown of the population by province of birth is available (Table 2.1). Of the total population 16.3 percent were born in provinces other than the province of residence. For males and females the figures were 18.5 and 13.9 percent, respectively, males apparently being more mobile than females. For the three big cities the figure for total population was 45.1 percent with 48.4 and 41.5 percent for males and females, respectively. For 1975 a breakdown of total population (with males and females separately) by change of place of residence (Table 2.2) revealed that 76.2 percent did not change place of residence during the five-year period 1970-75 with 23.8 percent apparently changing place of residence. A breakdown of the latter is, unfortunately, not complete. In any case, the 1970 and 1975 censuses thus indicate a high degree of mobility with migration from rural to urban areas predominating. For 1980 we have a cross-tabulation of population, existing 32 -

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Estimating the Health Effects of Air Pollutants A Method with an Application to Jakarta by Bart Ostro, PhJD.*

*Dr. Ostro _is_ Chief, Air Pollution Epidemiology Unit. Office of Environmental Health Hazard Assessment California Environmental Protection Agency. This study was undertaken in his capacity as an independent consultant and has not been reviewed or endorsed by the California Environmental Protection Agency. The author expresses his appreciation of Gunnar Eskeland for his oversight and technical assistance in this project

This research was undertaken as part of _PRDPE'B_ Research Project 676-48 in association with Bank sector work (Studies 11871 IND and 12083 MID) and with additional fimding from LATEN.
Table A4: Food Insecurity Experience Scale (FIES) Questions in Nigeria

|41|
|---|

||Pre-COVID-19 Surveys||COVID-19 Surveys||
|---|---|---|---|---|
|FIES|Question|Recall|Question|Recall|
|FS1|You or any other adult in your household worried<br>about not having enough food to eat because of<br>lack of money or other resources?|30 day|You or any other adult in your household were<br>worried about not having enough food to eat be-<br>cause of lack of money or other resources?|30 day|
|FS2|You, or any other adult in your household, were<br>unable to eat healthy and nutritious/preferred|30 day|You, or any other adult in your household, were<br>unable to eat healthy and nutritious/preferred|30 day|
||foods because of a lack of money or other re-<br>sources?||foods because of a lack of money or other re-<br>sources?||
|FS3|You, or any other adult in your household, ate only<br>a few kinds of foods because of a lack of money or<br>other resources?|30 day|You, or any other adult in your household, ate only<br>a few kinds of foods because of a lack of money or<br>other resources?|30 day|
|FS4|You, or any other adult in your household, had to<br>skip a meal because there was not enough money<br>or other resources to get food?|30 day|You, or any other adult in your household, had to<br>skip a meal because there was not enough money<br>or other resources to get food?|30 day|
|FS5|You, or any other adult in your household, re-<br>stricted consumption in order for children to eat?|30 day|You, or any other adult in your household, ate less<br>than you thought you should because of a lack of<br>money or other resources?|30 day|
|FS6|You, or any other adult in your household, bor-<br>rowed food, or relied on help from a friend or rel-<br>ative?|30 day|Your household ran out of food because of a lack<br>of money or other resources?|30 day|
|FS7|You, or any other adult in your household, re-<br>stricted consumption in order for children to eat?|30 day|You, or any other adult in your household, were<br>hungry but did not eat because there was not<br>enough money or other resources for food?|30 day|
|FS8|You, or any other adult in your household, went<br>without eating for a whole day because of a lack<br>of money or other resources?|30 day|You, or any other adult in your household, went<br>without eating for a whole day because of a lack<br>of money or other resources?|30 day|
**Figure 2: Effects of Worker Mobility on Productivity and Wages**

<!-- Start of picture text --> 2.5 100 8 Note: Danish data shows positive firm productivity<br>2 1.64 80 25 effects of hiring workers from more productive firms. The<br>60<br>1.5 40 effect is 0.35 percent per year for an average firm one<br>1 20 66 year after hiring. The accumulated gain is 1.64 percent in<br>0.5 0.35 0 4 years. Of the total output gain, two-thirds goes to the<br>Share of the new firm, 25 percent to incumbent workers, and 8 percent<br>0 productivity gain to the workers who bring the spillover.<br>Productivity gains Source: Stoyanov and Zubanov, 2012<br>25 Note: Norwegian data from Linked Employer-Employee<br>shows that the wage premium for movers from MNEs to<br>20<br>non-MNEs is almost 5 percent, compared to workers who<br>15 stay in non-MNEs. The productivity gain premium is as<br>10 20 much as 20 percent in the new employer as compared to<br>hiring workers without MNE-experience.<br>5<br>5 Source: Balsvik, 2011.<br>0<br>Productivity gains Wage premium<br>1400<br>1400 Note: Data from Brazil shows a 10-percentage point<br>increase in the share of former multinational workers’<br>900 wages by $23 per year, or roughly $1,400 per year for the<br>average domestic firm in Brazil (for firms with<br>400 approximately sixty workers).<br>23<br>-100 Source: Poole, 2013.<br>Increase per worker Average firm increase<br>in % Share of the<br>productivity gain ...<br>Share of the effect<br>Share of the effect in %<br>Dollars<br>Average Increase in US<br><!-- End of picture text --> These studies demonstrate that future employers are able to benefit from hiring previously trained workers, indicating the presence of general skills in the training (useful across multiple firms). In addition, they demonstrate the positive externalities that exist due to the provision of OJT. The future employer benefits by not paying the full cost of this advantage. Thus, the benefit to the firm that provides training is less than the benefit to society. This includes the future firm and workers in the future firm who enjoy spillover advantages of having a trained person in the workforce. In an unregulated market, with positive externalities, less is produced than the socially optimal level.

How general are the general skills imparted through OJT? Firms do not seem to provide the type of (very important) general skills, such as behavioral, psychological or socio-emotional skills, perhaps because these are better provided during the formal education years of the worker, or because firms may not have the methodology to train in these skills (Gonzales-Velosa, Rosas, and Flores 2016). In fact, some studies indicate the presence of industry-specific general skills in OJT. In other studies, the productivity effect of hiring workers from more productive firms _within the same sector_ was found to be twice as high as for workers from other sectors. This could imply that the knowledge brought by new workers is, in large part, industry-specific, but also general enough 17
not fall as much as the tariff if markets are not very competitive, a common occurrence in small African states.

**Trade diversion** as opposed to trade creation occurs when imports are diverted away from more efficient producers in non-preferred countries and toward less efficient producers in the free trade area. In the case of EU-ECOWAS, trade diversion would occur if as a result of the implementation of EPA (which implies free trade) more efficient suppliers from Non-EU trading partners into ECOWAS are displaced by inefficient producers from the EU.

The estimate of the **fiscal revenue effect** of EPA using a partial equilibrium model is simple. The potential change in tariff revenue is equal to the sum of a reduction in tariff revenue collected on imports from the EU, which are facing a lower tariff (zero tariff rate under the EPA), and the loss of tariff revenue due to the substitution of EU imports (for which lower tariff are applied) for non EU imports and domestically— produced goods.

Since a large share of ECOWAS imports originates in the EU and trade revenue constitutes a significant proportion of total revenue, there are concerns that the elimination of customs duties on most EU products in the context of the EPA could lead to a significant decline in government revenue (UNECA, 2004) and add an additional challenge to fiscal management. For nearly all ECOWAS countries, the EU is the single largest trading block. The share of the EU in total imports for each country of the region varies from about 29 percent for Niger to about 74 percent for Cape Verde. Duties on imports from the EU represent 10 to 15 percent of government revenue in Burkina Faso, Ivory Coast, Guinea Bissau, Nigeria and Senegal; 15 to 20 percent in Benin, Ghana, Guinea and Mali; 25 to 30 percent in Cape Verde, the Gambia, Niger and Sierra Leone, and more than 30 percent in Togo.

A study by CAPE<sup>15</sup> (2002), commissioned by the WAEMU Commission, finds that, in the case of a complete trade liberalization vis-à-vis the EU, the members of UEMOA could encounter a decline in fiscal revenue ranging from US$3.2 million (1.6 percentof GDP) in Guinea Bissau to US$ 140.6 million (0.5 percent of GDP) in Ivory Coast.

Busse, Borrmann and Grossmann (2004) also examined the (trade and) fiscal revenue effects of EPA on ECOWAS countries, using a partial equilibrium model. They find that, apart from the impact on trade flows, the tariff elimination will lead to a decline in import duties and, hence, total government revenue. Their decline in fiscal revenue ranges from 0.71 percent of GDP in Burkina Faso to 4.1 percent of GDP in Cape Verde for the mean scenario<sup>16</sup> . The projected decline in fiscal revenue is 1.2 percent of GDP for Nigeria, 1.8 percent of GDP for Ghana and 1.9 percent of GDP for Senegal.

Using a CGE model for Nigeria, Adenikinju and Alaba (2005) find that a zero tariff on EU imports (as envisaged under the EPA) will lead to a decline in government revenue by 1

> 15 Cellule d’Analyse de Politique Economique, a research institute based in Benin.

> 16 Using a price elasticity of 0.7 and elasticity of substitution of 2.

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**_5_**

1'he power of the presidency was embodied in a number of constitutional and legal provisions.<sup>4</sup> **'I'he** president could issue decrees with the force of legislation and had line-item veto power over legislationi (overrule required a two-thirds majority of both houses), and could suspend elected officials for cause (Wurfel 1988, 76, 77, and 90). In times of national emergency Congress could authorize the president to rule by decree for a limited period. The president could also declare martial law without the consent of the Congress. While Congress could not increase budget items proposed by the president, the president could treat the budget approved by the Congress as partially fungible. Thus mnembers of Congress needing to keep their 1,tronage machines will oiled were at the mercy of the president.

_* Weak jidiciary._ The judiciary's weakness was manifest mainly in the control of its resources by Congress and the president.<sup>5</sup> The president, with the approval of Congress, appointed judges at all levels, while Congress set judicial budgets and salaries. Although the Supreme Court gained respect for its relative independence, political appointees at the lower courts were often entangled in patronage politics (Wurfel 1988, 88-90). This system allowed the elite to control the judicial process while the non-elite suffered the effects of the inordinate delays and costs of pursuing justice. The system also gave the president a relatively free hand in issuing executive orders and taking arbitrary action without being challenged.<sup>6</sup> The Supreme Court was further restrained by a number of constitutional provisions, particularly the requirement of a two-thirds majority vote for declaring a law or treatv unconstitutional.

_a Two-parrt system with weak, indistinguishable parties._ Competition for control of the presidency was intense, since the presidency brought with it the power to channel enormous resource<. Elite groups outside the ruling coalition were always looking for ways to replace those as the highly centralized nature of the Philippine government and the president's considerable control over local government do not support this view.

41ndices developed by Shugart and Carey (1992) to gauge the relative legislative and nonlegislative powers of the popularly elected presidents around the world place the Philippines in the group of countries withi the Imost powerful presidencies (figure 8.1, 156).

5The executive Jlso exerted some control over cases being brought before the courts by having state prosecutors screen lawsuits before they are seen by a judge (Bacungan and Tadiar 1988, 178).

'Several clearly excessive cases, however, were brought before the Supreme Court and struck down, among them President Quirino's attempt to ram the budget through by executive order in 1949 and President Marcos's creation of thirty municipalities by decree in 1968 (Wurfel 1988, 77).
# **Table 3. Technological parameters and innovation**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Cost of entry|_fe_= 1||
|Fixed cost of production|_f_= 0.2|Exit rate of 6%|
|Exogenous exit rate|_δ_= 0.02|Exit rate of large firms of 0.55%|
|Step size of productivity|Δ_z_= 0.2|Standard deviation of employment<br>growth of large firms|
|Scale parameter of|_hz_= 0.001|R&D ⁄ GDP = 2.7%<br><br><br><br>|
|innovation cost|||
|Curvature parameter of<br>innovation cost<br>_Note_: GDP = gross domestic product|_bz_= 13.54<br>R&D = research and|φ500≤l≤1000<br>ma<br>/φ1000≤l≤5000<br>ma<br>= 38.1%<br>development.|

_Note_ : GDP = gross domestic product; R&D = research and development.

I chose Δ _z_ so that the standard deviation of the growth rate of employment of large firms in the model would be 25 percent (annualized), following Atkeson and Bursteinn (2010), which is based on data in line with estimates from Davis et al. (2007).

I chose the exogenous exit rate _δ_ so that the model’s annual employment-weighted exit rate of large firms would be 0.55 percent, which is consistent with the exit rate for large firms in the United States, according to the Statistics of U.S Businesses database.<sup>18</sup> Note that in the model, large firms do not exit because of lack of profitability; they exit because of this exogenous shock. Hence, _δ_ directly determines the annual exit rate of these firms. Next, I parameterize the distribution _G_ of productivity draws so that all firms enter with the same level of productivity, _z_ = 0. In the full model, firms will also enter with _τ_ = 0.

Using data from the Business Dynamics Statistics, Atkeson and Bursteinn (2010) find that the size of the median firm in the U.S. employment-based firm size distribution is 500 employees. In the model, firm size in terms of number of employees is a normalization. I set the level of employment _L_ so that entering firms’ employment will be 6 percent of the median employment in the economy, as reported by Luttmer (2007). Finally, I normalize _fe_ = 1 and set _f_ = 0.2, which implies an exit rate of 6 percent.<sup>19</sup>

> 18 These statistics can be found at <u>www.sba.gov.</u>

> 19 Notice that the exit rate is determined by the fixed cost of producing _f_ , the entry cost _fe_ that determines the minimum productivity required to stay in the market, and the stochastic process. In a model with rent-seeking, the stochastic process for the firm-specific revenue tax or subsidy will also play a key role in determining survival. In that case, firms with low productivity could stay in the market if they had subsidies large enough.

22
When there is a choice we use consumption in preference to income, on the grounds that consumption is likely to be the better measure of current welfare on both theoretical and practical grounds.<sup>23</sup> Of the 675 surveys, 417 allow us to estimate the distribution of consumption; this is true of all the surveys used in the Middle East and North Africa, South Asia and Sub-Saharan Africa, though income surveys are more common in Latin America.<sup>24</sup> Given that savings and credit can be used to smooth consumption from income shocks, one expects higher inequality for incomes than consumptions, for the same place and data.

The measures of consumption (or income, when consumption is unavailable) in our survey data set are reasonably comprehensive, including both cash spending and imputed values for consumption from own production. But we acknowledge that even the best consumption data need not adequately reflect certain “non-market” dimensions of welfare, such as access to certain public services, or intra-household inequalities. For these reasons, our poverty measures need to be supplemented by other data, such as on infant and child mortality, to obtain a more complete picture of how living standards are evolving.

We use standard poverty measures for which the aggregate measure is the (populationweighted) sum of individual measures. In this paper we report three such poverty measures.<sup>25</sup> The first measure is the headcount index given by the percentage of the population living in households with consumption or income per person below the poverty line. We also give estimates of the number of poor, as obtained by applying the estimated headcount index to the population of each region under the assumption that the countries without surveys are a random sub-sample of the region. Our third measure is the poverty gap index, which is the mean distance below the poverty line as a proportion of the line where the mean is taken over the whole population, counting the non-poor as having zero poverty gaps.

> 23 Consumption requires fewer imputations and assumptions, is likely to be reported more accurately and is arguably a better measure of current economic welfare than income. For further discussion see Ravallion (1994, 2003) and Deaton and Zaidi (2002). It has also been argued that consumption is a better welfare indicator in developed countries; see Slesnick (1998).

> 24 For a few cases we do not have consumption distributions but we still have survey-based estimates of mean consumption. Then we replace the income mean by the consumption mean leaving the Lorenz curve the same (i.e., all incomes are scaled up by the ratio of the consumption mean to the income mean).  There is, however, no obvious basis for adjusting the Lorenz curve. 25

_PovcalNet_ provides a wider range of measures, drawn from the literature on poverty measurement. See http://econ.worldbank.org/povcalnet.

15
**Table 10. Interactions: Barriers to female entrepreneurship**

|Dependent variable||Female entrepr<br>|eneur<br>||
|---|---|---|---|---|
||Barrier: time required<br>for females|to start a business<br>(in days)|Barrier: finan<br>account, female|cial institution<br>(% for age 15+)|
||(1)|(2)|(3)|(4)|
|F_cj|-0.027|-0.011|0.083|-0.029|
||(0.086)|(0.065)|(0.189)|(0.142)|
|F_cj * Barriers|-0.003**|-0.002*|-0.004|-0.001|
||(0.001)|(0.001)|(0.003)|(0.003)|
|F_c|0.240<br>(0.155)||0.584***<br>(0.183)||
|F_c * Barriers|0.012***<br>(0.004)||0.000<br>(0.004)||
|Barriers indicator|-0.001**<br>(0.000)||0.002***<br>(0.000)||
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|45,596|46,150|43,219|43,579|
|AdjustedR<sup>2</sup>|0.056|0.084|0.063|0.085|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

32