rafmacalaba/datause-encoder-data huggingface dataset:
4 levels of educational provision may make it difficult for individuals to fulfil optimal plans. Individuals may be unable to implement their best strategies.

The preceding discussion summarizes the basis of individua!s' private calculations. The social return does not necessaril) coincide with the private return and, from the perspective of the state it is the social return which matters. The social rate of return of increasing educational expenditures is based on pre-tax salaries and costs borne by society as a whole (foregone output opportunities, resource costs of education, etc.). It is usually found for advar led economies that the private return exceeds the social return (Ziderman, 1973, Morris and Ziderman, 1971). These calculations, which are m'de by a comparison of the incomes of the educated with those of the uneducated, omit any externality effects which might be significant within the context of developing and developed economies. They do not reflect the fact that primary education may lead to more efficient use of expenditure on public health, or that education at more advanced levels may increase the rate at which developing countries can take technical advances on board.

A separate problem arises as far as the calculations of private rates of return are concerned. These represent the average return, but, for any individual, there is a substantial margin of uncertainty over the benefits of education. In consequence the average return will have to include a premium over the return on 'safe' assets before risk-averse individuals can be persuaded to invest in education (Levhari and Weiss, 1974).

The greatest direct return derived from education in developing economies for which the evidence can be described as unambiguous is the improvement to labor productivity. These lead individuals to attain higher incomes which is reflected in studies that have focused on rates of return.<sup>2</sup> Psacharopoulos (1985) surveys a large number of such studies (see table 1) and finds high rates of return to education in developing countries. For example, the average social return to education in sub-Saharan Africa ranges from 26% for primary education to 13% for higher education; for Asia the figures are 27% and 13%, respectively, for Latin America and the Caribbean 26% and 16%, respectively, with the private return tending to be higher still.

_World Development Report, 1991_ (p. 57) quotes the results of recent studies suggesting that an incremental year of education raises wages by between 5% and 25%. These figures are complimented (p. 43) by estimates for the effect of extension of education on the level aIhe majority of research on the link between education and productivity is conducted by looking at returns based on income streams. This is very much within the framework of the human capital approach. Within the context of the U.S. Horowitz and Sherman (1980) have undertaken a study of the direct effects of human capital on productivity. This study was conducted on the U.S. Navy.
can be checked in a preliminary fashion through Figure 3, which shows a scatter plot with the long run improvement (between 1970 and 2010) in child mortality rates on the Y-axis and the 2010 level of GDP per capita (measured in 2011 PPP dollars) on the X-axis.

**Figure 1: Change in child mortality rates relative to initial levels**

<!-- Start of picture text --> SAU OMN<br>ARE IRNDZA<br>QATBHR LBY<br>KWT<br>IRQ<br>NGA<br>GNQ<br>GAB<br>COG<br>AGO<br>0 100 200 300 400<br>Initial child mortality<br>Figure 2: Change in gross secondary school enrollment rates relative to initial levels<br>OMN<br>NGADZA<br>AGO LBY<br>GABARESAU<br>IRN QAT<br>COG<br>IRQ<br>GNQ BHR<br>KWT<br>0 50 100 150<br>Initial secondary enrollment, gross<br>100<br>80<br>60<br>40<br>20<br>Percentage change in child mortality: 1970-2010<br>0<br>6<br>4<br>2<br>0<br>Percentage change in secondary enrollment, gross: 1970-2010<br><!-- End of picture text -->

**Figure 2: Change in gross secondary school enrollment rates relative to initial levels**

3
negative coefficient on these variables would then indicate that FCS in China or India is growing at the expense of FCS in other regions. As will become clear later, the standard empirical versions of the KCM in the literature have ignored the potential role of natural resources as drivers of international investment. Hence we also test the robustness of the augmented KCM model to the inclusion of empirical proxies that capture the relative abundance of natural resources in host countries.

Another challenge for the existing literature is the commonplace assumption that the effect of China is homogeneous across sectors. In fact, the available data from the U.S. foreign capital abroad indicates that the evolution of manufacturing-sector FCS across countries has been different than the trends of aggregate FCS (Cravino, Lederman, and Olarreaga 2006). The manufacturing sector seems particularly sensitive to relocation of multinationals into China and India as they search for lower labor costs. Thus, we take an additional step and estimate the effects of China and India on manufacturing-sector FCS in other countries, based on data on outward FCS from the U.S. We use a similar specification to the one with the aggregate data, which allows for different coefficients across host regions within the manufacturing sector.

Yet another concern about the existing literature is the exclusive application of OLS or other linear estimators to empirical models of the determinants of FDI or FCS. In the context of the gravity model of international trade, it is now known that such linear estimators can yield inconsistent coefficients due to the correlation between the expected value of bilateral trade flows among country pairs and the variance of their regression errors. This systematic heteroskedasticity produces log-linear estimates that are driven by the disproportionate influence of observations with high expected bilateral trade flows.

5
indicating that they need to be able to communicate reliably amongst their various offices, and that they are sensitive to the cost/price uncertainty that extensive irregular payments can imply.

While these results are suggestive, we stress that our relatively small samples render our estimates imprecise, and we do not therefore use Table 8 to draw any strong policy conclusions at this stage.

# **_4.4 Counterfactual Simulations_**

In order to give our analysis more concrete policy content, it is useful to construct basic monetary estimates of the trade gains that could be associated with improved trade facilitation in Southeast Asia. We follow the approach in Wilson et al. (2005), in which the estimated coefficients from the gravity model are used as the basis for counterfactual simulations which can then be analyzed comparatively. We emphasize that this approach is only designed to give a broad idea of the relative impacts of different policy reforms, and is subject to numerous technical caveats (see below).

> Our analysis includes five counterfactual scenarios. In _Scenario 1_ , the quality of maritime port infrastructure as measured by our Global Competitiveness Report data is improved so that no country scores below the current regional average (4.6 out of 7). _Scenario 2_ performs the same exercise for airport infrastructure (regional average = 5.1/7), while _Scenarios 3_ and _4_ consider improvements in the control of irregular payments and competition among internet service providers (regional averages of 4.6/7 and 4.9/7 respectively). As a point of comparison for the other counterfactuals, _Scenario 5_ considers a cut in applied tariffs to the current regional average (8.6%). Our reason for choosing the current regional average as our benchmark in each case is that it represents an ambitious but feasible program of reform, given current regional capacities.

- 20 -
savings rates being substantially below those of other households (all urban) and their poverty head counts being highest (NBS, 2012). These targeted payments are introduced in the model as transfers from the government to households relative to government income. The base subsidy rate, four scenarios, their subsidy rates and any accompanying transfers are listed in Table 3.

**Table 3: Base Case, Four Simulation Scenarios, Subsidy Rates (percent), and Household transfers (US$ million)**

|Scenario|Description|Subsidy on p<br>(fuel) consu|etroleum product<br>mption (percent)<br>|Government<br>transfers to|
|---|---|---|---|---|
|||Imported|Domestic<br>production|poor<br>households|
|Base|Base|33.0|--|--|
|Scenario 1|Full removal of subsidy on<br>imports|0.0|--|--|
|Scenario 2|50 percent removal of subsidy on<br>imports|16.5|--|--|
|Scenario 3|50 percent removal of subsidy on||Transfer of||
||imports and paying subsidy on<br>domestic|16.5|subsidy to local<br>production|<br>--|
|Scenario 4|50 percent removal of subsidy on<br>imports and providing transfers to<br>households|16.5|--|Transfer of<br>subsidy to poor<br>households|

Source: Base subsidy rate from PPPRA (2012). Scenarios based on authors assumptions.

# **4 Results**

This section starts with a discussion of the effects on the prices of petroleum products in Nigeria, followed by the impacts on output and consumption. Finally, macroeconomic and welfare impacts are discussed.

## **4.1 Prices**

Figure 1 shows the percentage changes in the prices of petroleum products due to the four scenarios. Full-removal (Scenario 1) causes the highest change in prices as it removes the subsidies on imported petroleum products without applying any compensation mechanism. After the complete removal of the subsidy in January 2012, the fuel price increased from 65 Naira per liter to 140 Naira per liter (The Economist, 2012). Complete removal of the subsidy did not continue 12
Swindale and Bilinsky 2006, FAO 2011). Dietary diversity is one method of measuring data quality. The paper also uses the components of the household dietary diversity score to measure the effect of increased production on the share of total calories by food group.

Using recent panel data from Nigeria that includes observations from both planting and harvest seasons within an agricultural season, our econometric strategy uses degree day and rainfall deviations from historical means as well as agricultural input prices and quasi-fixed agricultural capital to instrument for production variables (agricultural revenue or crop production diversity) which are simultaneously determined with consumption. Degree days, a cumulative measure of optimal temperatures for plant growth, have been found to be correlated with reduced yields and agricultural income (Hatfield et al. 2008). We identify the effect of revenue variation on dietary diversity and diet composition in an empirical application of the nonseparable household model by using exogenous variation in rainfall and degree days that affect plant growth and agricultural revenue, but do not necessarily change market level prices at harvest which affect consumption patterns. This mechanism through which the exclusion restriction assumption could be violated can be tested in our data.

A small literature has investigated the effects of agricultural production on nutrition primarily via reduced form identification strategies. Muller (2009) found that production of food crops such as beans and certain tubers as well as a category composed of heterogeneous food of high quality had positive impacts on nutritional statuses, while the production of traditional beers and nonfood crops was found to have negative effects for nutrition. The authors also found production of other fruits and vegetables was associated with better health status.

3
electricity and fuel, _exi_ and _efi_ , respectively, given by: with _ηx_ as the carbon content of the fuel mix used to generate electricity _xi_ , and _ηf_ as the carbon content of the fuel _fi_ used as direct input for producing _yi_ , both taken as given.<sup>10</sup> We assume that emissions _exi_ and _efi_ are taxed at a fixed rate _λ_ . Furthermore, we denote the cost of capital, labor, electricity, and fuel as _r, w, zx,_ and _zf_ respectively.

**Intermediate Good Producer’s problem.** Each intermediate good _i_ is produced by a monopolist which chooses the optimal combination of inputs _{ki, li, xi, fi}_ to maximize profits:

> <u>1</u> _−_<sup><u>1</u></sup> The first three terms in (5) are the same as in Hsieh and Klenow [2009]. The output price _pi_ = _y σ yi σ p_ is such that to maximize the representative producer’s profits in (2),<sup>11</sup> whereas the wedges _τyi_ and _τki_ are interpreted as distortions in the output and capital markets, which alter the optimal input choices of producer _i_ . Output distortions _τyi_ reflect government size restrictions, output subsidies or transportation costs, whereas capital distortions _τki_ are likely the result of credit constraints or subsidized access to credit.

The fourth and fifth terms represent the additional energy input costs. The wedges _τxi_ and _τfi_ are existing distortions in the electricity and fuel markets. In practice, firms facing higher (lower) wedges _τxi_ or _τfi_ have a higher (lower) cost of energy for each unit of production, resulting in under-consumption (overconsumption) of fossil fuels. Those wedges may capture information asymmetries regarding energy efficient technologies, bounded rationality, and/or managerial myopia or incentives to adopt energy efficiency measures (DeCanio [1993], Howarth, Haddad, and Paton [2000]). Importantly, the wedges _{τyi, τki, τxi, τfi}_ can be generally correlated within firms. For example, recent research in Iovino, Martin, and Sauvagnat [2021] has documented a positive relationship between subsidized access to debt financing and higher emissions intensity, whereas a work by Shapiro [2021] has shown that emissions intensive industries are more protected by import trade policies. In the case of the DR, the energy market suffers from significant energy losses due to poor infrastructure and electricity theft, which effectively increase operating costs. A compounding

> 10Note that _ηx_ and _ηf_ are kept constant across intermediate goods’ production functions _i_ = 1 _, . . . m_ . In the calibration section, we let _ηx_ and _ηf_ vary across industries and regions.

> 11That is, each producer _i_ acts monopolistically, anticipating the effect of its production choices on the intermediate good price _pi_ . This specification is taken from Hsieh and Klenow [2009].

5
disincentive effects of income transfers to differ in labor markets with different levels of risk and segmentation into high vs. low wage, and modern vs. agricultural, sectors. In urban Albania, unemployment risk is high and a small high-wage (formal) sector coexists with a large low-wage (informal) sector, where job tenure is shorter and the wage distribution is more unequal. Hence, urban transfer recipients, who are likely to initially enter the labor market with a low-paying job, may lower their labor supply in response to an unconditional income transfer.

On the other hand, in rural Albania, where (i) the rate of unemployment (labor force participation) has been traditionally lower (higher), (ii) the main activity, i.e. farming, is risky, and (iii) insurance/credit markets are either missing or incomplete, transfer income may serve as insurance, and the negative labor supply response may be absent. Additionally, since women record significantly lower labor force participation rates, and exhibit higher levels of unemployment and discouragement, the disincentive effects of income support, if any, may be stronger among females. Lastly, if social transfers prove inadequate to lift poor households out of poverty, and welfare recipients lack other forms of social insurance and access to credit to smooth consumption over time, negative labor response to program participation may translate into reductions in household welfare. These possibilities will be carefully considered as the links between transfer programs and household welfare and labor market outcomes are explored.

# **4. Data**

The data used for the cross sectional, household-level analysis come from ALSMS02 and ALSMS05. The surveys are nationally representative and their sampling frames are stratified into four regions – namely coastal, central, mountain and Tirana (the capital city). ALSMS02 and ALSMS05 collected information on 3,599 and 3,640 households, respectively. A typical household questionnaire with general household demographics, education levels, asset 13
# Summary and Conclusions

This study offers an in‐depth exploration of female migration in order to provide new insights for Lesotho’s economic development. Our guiding questions are (i) What are the drivers of female migration, from the perspective of households and migrants?; and (ii) How can policy better adapt to the needs of female migrants and their families to respond to development challenges? Our findings can be summarized under three themes: the role of migration in Lesotho, the dynamics of women’s employment, and the need for policies and programs to support female migrant workers.

## _Role of Migration in Lesotho_

The prevalence of migration in Lesotho is well‐known. Its ubiquity as a livelihood strategy in the rural communities visited by the research team made it difficult to even find non‐sending households to interview. Unlike other migration‐dependent countries, Lesotho has relied on migration for over a century, which may explain why, despite declining opportunities in South Africa, 80 percent of Basotho households continue to live in rural areas rather than move to the city for work. As the old model of relying on the remittances of a single male family member becomes less viable, households now send multiple migrants, including women, to live long‐term in the city or abroad. As a result, rural households increasingly function as “anchors” where adult children return in between stints of work, and leave their children with grandparents when they do have work. One implication of the shift to female migration, in light of the low earnings of female migrants, is that households can no longer use remittances to build assets. This was echoed throughout the focus groups with rural households. Remittances from women are used only for basic needs, whereas with male mining workers, the amounts of remittances were larger and hence were more able to support asset accumulation of receiving households.<sup>47</sup> This finding is consistent with that of an earlier World Bank report<sup>48</sup> that migration only contributed to poverty reduction in the case of male migration to South Africa. Finally, lack of access to secondary education is a key contributor to female migration, as young women report migrating after dropping out of school, and older women migrate in order to support their children’s educational expenses.

> 47 Crush et al. (2010) estimated that in 2002, miners earned an average of M2,900 per month, while garment workers received only M650 per month. The situation was even worse in 2006, when miners were receiving a substantially higher wage of M4,500 a month, while the garment workers’ salaries had not changed. Fieldwork indicated increases in salary since then: textile workers earned an average monthly salary of M1230 while domestic workers earned an average of M600 per month.

> 48 World Bank (2010).

51
# CONTENTS

|Preface ......................................<br>v|
|---|
|Acknowledgements ................................<br>vii|
|Introduction .....................................<br>ix|
|Projection Results ............<br>....................<br>ix|
|Data and Methods ...........<br>.....................<br>x<br>|
|Total population and age-sex composition .....<br>.........<br>x|
|<br>Mortality level and trend .........................<br>x|
|<br>Fertility level and trend .........................<br>xiii|
|<br>Migration level and trend .......................<br>xviii|
|<br>Summary of Results ..............................<br>u<br>xi|
|<br>World and continents .......<br>....................<br>xxi|
|Geographic regions and income groups .....<br>.........<br>xx|
|<br>WorldBank regions .........<br>..................<br>**xxi**|
|<br>Some country results ........<br>.................<br>xvi|
|<br>References ...................................<br>ix|
|Appendix: Sources of Population Data .....<br>............<br>xl|
|<br>Tables .. ......................................<br>Iii|
|Detailed Population Projections ......................<br>1|
|World, Geographic Regions, and Income Groups ....<br>.... 1|
|World ..<br>......................................<br>2|
|Less developed countries<br>.4|
|<br>M dld ti<br>6|
|ore eveope counres<br>.|
|Geographic Regions|
|Africa<br>.8|
|East Africa .10|
|West Africa .12|
|North Africa .14|
|America .16<br>|
|Latin America and the Caribbean .18|
|Northern America .20<br>|
|Asia .22|
|<br>East and Southeast Asia .24|
|South Asia .26|
|Southwest Asia .28|
|<br>Europe and U.S.S.R .30|
|<br>Oceania .32|
|Income Groups|
|<br>Low-income economies .........................<br>34|
|Lower-middle-income economies ...................<br>36|
|Upper-middle-income economics .<br>.<br>38|
|High-income economics .........................<br>40|
|<br>Nonreporting nonmember eccnomies .............<br>42|
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