rafmacalaba/datause-encoder-data huggingface dataset:
## [INSERT TABLE 1 AROUND HERE]

The remainder of this paper is organized as follows. Section 2 describes our data collection, while our econometric strategy and the accompanying results are presented in Section 3. In the following section, we introduce our nuanced Brain Drain indicators before we present our global assessment of human capital mobility in Section 5. Finally, we conclude.

# **2 Data Compilation**

The first contribution of the paper is in compiling a more complete global data set of bilateral migrant stocks, disaggregated by education level and gender for the years 1990 and 2000, by including as many developing destination countries in our sample for which data are available. Our data collection builds upon the previous database of DLM, which documents migrant stocks disaggregated by education levels to 30 OECD destination countries. Our methodology in this section is a direct extension of this earlier work. We add four new OECD members (for both 1990 and 2000), 66 non-OECD destinations in 2000 and 27 non-OECD countries in 1990 for which comparable data could be found. The data are disaggregated by gender and two separate education levels. We distinguish males and females, _g_ = ( _m, f_ ), and two skill types _s_ = ( _h, l_ ) with _s_ = _h_ for individuals with post-secondary or college education (referred to as the highly skilled), and _s_ = _l_ for less educated individuals (referred to as low-skilled). In each decade, we thus have migrant stocks of high-skilled males, low-skilled males, high-skilled females and low-skilled females for each bilateral corridor.

8
15% in 1981-5 to 23% in 2001-5. Across LAC sub-regions, road quality is lowest in the Andean countries and the Southern Cone, and highest in the Caribbean.

# **2.2.2 Survey measures of infrastructure quality**

We next summarize the information available from the surveys of international experts conducted for the World Economic Forum’s _World Competitiveness Report_ . Rather than quality alone, they tend to capture perceptions on both the quality of infrastructure services and their availability —which is likely to be closely related to the volume of infrastructure stocks. The coverage of these data is more limited than that of the official statistics reviewed so far. The time series dimension is short, so for the most part we show data for 2000 and 2006 only. Country coverage of Latin America, as well as other middle-income countries, is somewhat limited, and hence the regional medians shown below have to be taken with some caution.

Figure 1.1 summarizes perceptions regarding the _overall_ quality of infrastructure across world regions, with higher bars denoting higher quality, within a range from 1 to 7. It is clear from the figure that Latin America lags behind East Asia and the group of middle-income countries – as well as industrial countries. Moreover, the gap has, if anything, gotten worse since 2000.

Figure 1.2 reports perceptions of the reliability of power. In this case, we report data for 2002 and 2006. The subjective index shows a significant negative correlation with that on the percentage of power losses described earlier. Perceived quality is highest in industrial countries. Among developing regions, only East Asia shows a definite trend towards improving quality. In contrast, perceived quality seems to be on the decline among middle-income countries. Nevertheless, quality perceptions are still lowest in Latin America. This is in agreement with results found earlier when using power losses as quality proxy: in both cases Latin America does worst among the country groups shown.

Figure 1.3 depicts the perceived reliability of telephones. The regional rankings for 2000 show a counter-intuitive pattern, with industrial countries lagging behind all 9
While here the number of common factors _r_ is assumed to be known, for the estimation it is determined using information criteria, as will be discussed below.

Due to the presence of the factors and their loadings, the number of parameters in the model increases with the sample size. Focusing on _η_ as the parameter of interest, and concentrating out the factors and their loadings applying principal component analysis, the concentrated log-likelihood is <u>1</u> _n K K_ where _G_ ( _η_ ) = _nT_ � _i_ = _r_ +1<sup>_μi_(</sup><sup>_S−_</sup> � _k_ =1<sup>_Zkδk_)(</sup><sup>_S−_</sup> � _k_ =1<sup>_Zkδk_)</sup><sup>_′_.TheQMLestimatoris</sup> derived from the optimization problem in equation (5). The estimate of the factor loadings Ψ is computed from the eigenvectors associated with the first _r_ largest eigenvalues of ( _S −_<sup>�</sup><sup>_K_</sup> _k_ =1<sup>_Zkδk_)(</sup><sup>_S−_�</sup><sup>_K_</sup> _k_ =1<sup>_Zkδk_)</sup><sup>_′_.</sup> The estimate of _FT_ is obtained analogously by switching _T_ and _n_ .

The QML estimator of the regression coefficients is consistent and asymptotically normal. However, it may be asymptotically biased owing to an incidental parameters problem that arises from the presence of predetermined regressors (the lagged dependent variable) as well as the interaction between the spatial effects and the factor loadings. To tackle this issue, Shi and Lee (2017) develop a bias correction that yields an asymptotically normal, properly-centered estimator.<sup>5</sup> The estimations reported below for the specifications including common factors employ the bias-corrected estimator; Appendix B provides a brief description of the correction.

# **3 Data**

We use a large cross-country data set drawn from the United Nations National Accounts database. To circumvent potential outliers and data errors, we exclude (i) very small economies with total population less than 500,000, owing to their often extreme volatility; (ii) countries featuring any observations with annual real GDP growth in excess of 40%; and (iii) countries with standard deviation of real GDP growth exceeding 10%. This yields a balanced panel of 117 countries over the period 1970-2016.<sup>6</sup> The sample countries account for more than 90% of world GDP in 2016.

> 5Vega and Elhorst (2016) also employ QML estimation in a setting featuring both spatial effects and common factors, specifying the latter as cross-sectional averages as in Pesaran and Tosetti (2011), and assuming that each cross-sectional unit is too small to affect the averages, so that they can be assumed exogenous – an assumption unlikely to hold in our setting as the sample includes some very large economies.

> 6We employ GDP growth data from the United Nations National Accounts database because it reaches up to 2016. In contrast, PWT data only reaches up to 2014. Over the common time sample, the correlation between the GDP growth rates derived from both sources exceeds 0.99.

7
Reform programs 2 and 3 generate better outcomes in terms of growth and unskilled employment, but the largest effects (unsurprisingly) are achieved with Program 4. The growth rate of real GDP increases by 2.7 percentage points per annum (or, equivalently, to 6.3 percent, given a base value in the model of 3.6 percent), the unskilled unemployment rate falls by about 5.3 percentage points, and the skilled unemployment rate by 0.5 percentage point. Accounting for the relative weight of each type of worker in the labor force, the aggregate unemployment rate falls by about 4.5 percentage points, that is, given an initial unemployment rate of 9.4 percent, a drop to 4.9 percent. The reason why the unemployment for the skilled does not fall much is the same as before: the increase in demand is matched by an increase in supply, because more unskilled workers choose to become skilled.

These results suggest that far‐reaching structural reforms could boost Morocco’s potential growth by up to 2.7 percentage points over the long term. Are these growth rates high enough for the country to avoid falling into a middle‐income trap and achieve high‐ income status within the next two decades? To address this question, consider first Reform Program 1.

According to World Bank data, in 2017 GDP per capita in constant 2010 US dollars was 3,292 in Morocco and 42,568 in France. Suppose that France grows on average at the rate of 1.1 percent in the next decades. Suppose also that the full implementation of the reform program takes 4 years, and that during that period, Morocco continues to grow at the average rate observed during the period 2010‐2017, that is, 3.6 percent (Figure 2.3). Thus, if the package is implemented in 2019, France’s income per capita in 2022 will be 42,568*(1+0.011)^5 = $44,961 and Morocco’s 3,292*(1+0.036)^5 = $3,929. The initial ratio is thus 3,929/44,961 = 8.7 percent. After that, Morocco’s growth rate shifts to 3.6 + 1.1 = 4.7 percent. Thus, to calculate the number of years (after the 4 years of implementation between 2019 and 2022) needed for Morocco to catch up with France requires solving for N from the equation

# 3,929*(1+0.047)^N = 44,961*(1+0.011)^N,

That is, taking logs on both sides,

# N = [log(44,961) ‐ log(3,929)] / [log(1+0.047) ‐ log(1+0.011)]  69.7,

which implies that it would take Morocco almost 70 years to achieve France’s per capita income. Applying a similar calculation with Reform Program 2, with an annual growth rate now of 3.6 + 2.7 = 6.3 percent yields N = 48.6, or almost 49 years.

Of course, achieving high‐income status does not mean matching exactly France’s income. Suppose that the target is one‐third of that income, which corresponds approximately, to the current entry point for high‐income status. With Program 4, the formula gives now N = [log(0.33*44,961) ‐ log(3,929)] / [log(1+0.063) ‐ log(1+0.011)]  26.5.

53
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

**Table B2b. Dakar piped water**

Percent

|Division|Access rate<br>Take-up rate given<br>access<br>Coverage rate|Unserved population|Pure demand-side gap|Supply-side gap|Pure supply-side gap|Mixed demand and<br>supply-side gap|Proportion of deficit<br>attributable to demand-<br>side factors only|Proportion of deficit<br>attributable to supply-<br>side factors only|Proportion of deficit<br>attributable to both<br>supply and demand-<br>side factors|
|---|---|---|---|---|---|---|---|---|---|
|Nolivet/Barak|100<br>100<br>100|0|0|0|0|0|—|—|<br>—|
|Arafat|100<br>93<br>93|7|7|0|0|0|100|0|<br>0|
|Hann|100<br>91<br>91|9|9|0|0|0|100|0|<br>0|
|Dalifort|100<br>59<br>59|41|41|0|0|0|100|0|<br>0|
|Santiaba|100<br>99<br>99|1|1|0|0|0|100|0|<br>0|
|Taïf|100<br>93<br>93|7|7|0|0|0|100|0|<br>0|
|Guinaw|100<br>93<br>93|7|7|0|0|0|100|0|<br>0|
|Médina|100<br>95<br>95|5|5|0|0|0|100|0|<br>0|
|Wakhinane|100<br>64<br>64|36|36|0|0|0|100|0|<br>0|
|Diokoul|100<br>97<br>97|3|3|0|0|0|100|0|<br>0|
|Simple average|100<br>88<br>88|12|12|0|0|0|100|0|<br>0|
|Weighted average|99<br>89<br>89|10|10|0|0|0|97|0|<br>0|
|**Table B2c. Johannesb**<br>Percent<br>Division|**urg piped water**<br>Access rate<br>Take-up rate given<br>access<br>Coverage rate|Unserved population|Pure demand-side gap|Supply-side gap|Pure supply-side gap|Mixed demand and<br>supply-side gap|Proportion of deficit<br>attributable to demand-<br>side factors only|Proportion of deficit<br>attributable to supply-<br>side factors only|Proportion of deficit<br>attributable to both<br>supply and demand-<br>side factors|
|<br>Alexandra|100<br>93<br>93|7|7|0|0|0|100|0|0|
|Central Region|100<br>94<br>94|6|6|0|0|0|100|0|0|
|Diepkloof / Meadowlands|81<br>65<br>53|47|28|19|12|7|60|26|14|
|Diepsloot|75<br>84<br>63|37|12|25|21|4|32|57|11|
|Doornkop / Soweto|91<br>74<br>67|33|24|9|7|2|73|20|7|
|Ennerdale / Orange Farm|90<br>74<br>67|33|23|10|7|3|70|22|8|
|Jhb Sth|75<br>92<br>69|31|6|25|23|2|19|74|6|
|oannesurg ou|<br> <br>|||||||||
|Midrand / Ivory Park|100<br>94<br>94|6|6|0|0|0|100|0|0|
|<br>Northcliff|100<br>68<br>68|32|32|0|0|0|100|0|0|
|Roodepoort|73<br>88<br>64|36|9|27|24|3|25|66|9|
|Sandton / Rosebank|100<br>100<br>100|0|0|0|0|0|—|—|—|
|Simple average|90<br>84<br>76|24|14|10|9|2|68|27|6|
|Weighted average|91<br>82<br>75|25|16|9|7|2|72|19|6|

57
|Country/<br>Year|77|77|35|42<br>56|56|32<br>24<br>of the Gini<br>mple 2:|
|---|---|---|---|---|---|---|
|Years<br>ations|10|10|7|9<br>10|10|6<br>8<br>efficients<br>s; (**) Sa|
|Countries<br>Observ|56|56|22|34<br>42|42|22<br>20<br>Only the co<br>/year point|
|Individual|56451|39161|39641|55971<br>38685|27945|36667<br>29963<br>ed effects.<br>, 77 country|
|<br><br>Dropped<br>Years|1|1|2|1<br>3|3|2<br>1<br>and year fix<br>bservations|
|Dropped<br>Countries|2|2|0|3<br>1|1|0<br>4<br>, country<br>95,612 o|
|vif<br><br>Gini WIDER|79.4|95.1|15.8|122.0<br>137.4|124.6|13.2<br>na<br>onal clusters<br>) Sample 1:|
|z-stat<br>|-1.35|(3.32)**|(3.44)**|-1.73<br>(2.21)*|(2.85)**|-0.42<br>(4.99)**<br>errors, regi<br>nt at 1%. (*|
|coeff.|-0.019|-0.086|-0.057|-0.023<br>-0.016|-0.074|-0.006<br>-0.091<br>standard<br>** significa|
|Dropped<br>Years|1|0|2|1<br>3|3|1<br>1<br>ogit, robust<br>ant at 5%;|
|Dropped<br>Countries|2|3|0|3<br>1|1|1<br>4<br>h ordered l<br>n. * signific|
|vif<br>Gini WVS|18.2|20.1|9.4|13.0<br>13.8|15.3|15.1<br>na<br>Table 1 wit<br>satisfactio|
|z-stat|(4.27)**|(4.24)**|(5.59)**|(2.46)*<br>-0.93|(7.24)**|-0.22<br>(4.99)**<br>same as in<br>ep. Var.=Life<br>ear points.|
|coeff.|-0.023|-0.031|-0.035|-0.016<br>-0.01|-0.039|-0.003<br>-0.021<br>ble is the<br>iduals. D<br>country/y|
||Sample 1*<br>Poor individuals|Non poor individuals|Western countries|Non Western countries<br>Sample 2**<br>Poor individuals|Non poor individuals|Western countries<br>Non Western countries<br>The model used in this ta<br>are reported. Units=Indiv<br>66,630 observations, 56|

24
empower women economically, add to household incomes making households more resilient to economic shocks, and further national development goals. This approach may yield greater traction with policy makers insofar as it integrates the potential benefits to enhanced female productivity within a more generally accepted set of national development goals.

**Figure 1: Closing the Gender Gap in Education, But Not in Labor Force Participation (World Simple Average Female-To-Male Ratio in Percent)**

<!-- Start of picture text --> 95 69.0<br>68.5<br>90<br>68.0<br>85<br>67.5<br>80<br>67.0<br>75<br>66.5<br>70 66.0<br>1980 1985 1990 2000 2005 2010 2012 2013<br>Female-to-male labor force participation rates (RHS, world average)<br>Female-to-male ratio of average years of schooling (LHS, world average for ages 25+)<br>% %<br><!-- End of picture text --> Source: World Bank World Development Indicators for labor force participation rates and Barro and Lee (2013), UNESCO Institute for Statistics (2013b) and HDRO estimates based on data on educational attainment from UNESCO Institute for Statistics (2013b) and on methodology from Barro and Lee (2013).

Our approach aligns with the definition of inclusive growth through productive employment in the paper by Ianchovichina and Lundstrom-Gable (2012) since our goal is to reduce barriers to productive female employment. However, we operationalize the approach by focusing on gender distortions that manifested in gender gaps and either bind growth or undermine national growth strategies by creating inefficiencies in the use of economic resources. We show that the welfare gains from a reform that targets jointly the binding constraint to economic growth and those limiting female labor force participation surpasses the gains from a pro-growth reform addressing only the binding constraint to growth or those from a pro-gender reform aimed at closing gender gaps. We recognize however that policies aimed at generating gender-enhanced growth may have an ambiguous effect on well-being (particularly women’s wellbeing). Nancy Folbre and other feminist economists have documented the importance of unpaid, nonmarket services provided by women; we agree that these services are essential for a household’s well-being and for children’s growth into productive adults and that they may contribute to different degrees to the life satisfaction of women.

4
coefficient, may not be suitable to test this proposition. In that case, only transfers of income from the “poor” to the very rich would be predicted to reduce the probability of _x_ - good projects.

Finally, in many social funds, including Ecuador’s FISE, communities were permitted to apply for more than one project. In our framework, this corresponds to adding a third project type: π 3 : ∀ _i_ ∈ _j_ receives one unit of _x_ **_and_** _j_ receives an increment of public good of ∆ _g j_ .

With these three projects, and given the pre-Social Fund consumption profile in (4), it is clear that π 3 _Pi_ π 1 _Pi_ π 2 for i: _yi_ < ~ _y_ , and π 3 _I i_ π 2 _Pi_ π 1 for i: _yi_ ≥ ~ _y_ , where P (I) denotes the strict preference (indifference) of individual i (as in the proof of Proposition 1). It is easy to check that Rothstein’s order restriction condition is still satisfied in all pairwise comparisons across all elements in Π = {π 1, π 2 , π 3 } _._ The key consideration for our results is the indifference of non-poor voters ( _yi_ ≥ ~ _y_ ) between _π2 and π3._ If the pivotal voter were non-poor, it would be enough to introduce any infinitesimal additional cost – such as a greater supervision cost – to him or her of choosing _π3_ over _π2_ , to induce him to choose _π2_ . In that case, both a greater incidence of poverty and progressive transfers across<sup>~</sup> _y_ would be associated with observing _π3_ : multiple projects that include _x_ -good (latrine) projects. Lower poverty and greater inequality (due to regressive transfers across<sup>~</sup> _y_ ) would lead us to observe public good projects _π2_ , but no latrine projects.

We are now in a position to take the predictions of this simple model to the data, which refers to Ecuador’s Social Fund in the period from 1993 to 1996. The next section describes our data sets, and the empirical results are discussed in Section 5.

# **4. The Data Sets.**

# _Project-Level Data_

The Ecuador FISE project included the introduction of a computer-based management and information system (MIS) that was intended to assist with monitoring of the project cycle and the overall performance of the project. The MIS provides 16
sions, the survey separately asked the household head and the head’s spouse about their satisfaction. Each respondent could choose “very satisfied,” “fairly satisfied,” “not very satisfied,” or “not at all satisfied,” with each welfare dimensions. Out of the 13 dimensions reported, we focus on the five dimensions that correspond to the consumption and employment-based metrics that significantly declined following the capture of the citysatisfaction with food consumption, income, work, life, and health.<sup>14</sup> Additionally, there was another subjective question on welfare in a separate food security module that was very similar to the question on satisfaction with food consumption. Specifically, the most senior woman in the household was asked whether there was a time in the past week that the household “did not have enough food or money to have food.”<sup>15</sup> If the respondent answered affirmatively, then the respondent was asked if the household engaged in each of five common food coping strategies in the past week, and the number of days in which those coping strategies were employed.<sup>16</sup> Table 1 presents summary statistics of the share of households that reported each level of satisfaction separately for the head and spouse. The vast majority of respondents had low levels of satisfaction- this was especially true for satisfaction with food, life, and health. These satisfaction levels likely reflect the decline in living standards over the decade prior to the survey. Poverty significantly increased in the Republic of Yemen since 2005, in part driven by the political uncertainty of 2012 and the sharp increase in food prices beginning in 2011 (e.g., WFP 2012; Dang and Ianchovichina 2016; World Bank 2017a).

Somewhat surprisingly, subjective welfare was similar in the capital city to the rest of the Republic ofYemen despite the fact that the capital city had significantly lower poverty

> 14Although the focus is on the five dimensions described in the text, the appendix further reports the change in all remaining dimensions captured by the survey. Other dimensions include satisfaction with one’s education level, dwelling, friends, family, freedom in controlling your life, neighborhood/city/community, ability to help others, and security. All results are qualitatively identical for changes in these other dimensions aside from security.

> 15In Sana’a, 13.4 percent of households answered affirmatively to not having enough food in the past week; and in the rest of the Republic of Yemen, 7.8 percent of households answered affirmatively to not having enough food in the past week.

> 16The coping strategies included relying on less preferred and less expensive food, borrowing food or relying on help, limiting portion sizes of meals, giving priority to small children to eat, and to reduce the number of meals eaten in a day.

11
# **Strengthening the Management ot Tralning Systems**

The manpower planner is a source of information for strengthening the management of training systems. The information furnished on the balance of skills demand and supply can be used by managers in curriculum planning and staffing to single out skills where demand is growing and where it may be declining. Manpower planners are also a source of information on market demands for new skills as planners track the introduction of new technologies and industries. By studying training costs as part of evaluating economic returns to alternative skills investments, planners can produce valuable information on cost norms for managers of training programs. The information developed can help program managers better match skills supply and demand and improve the quality of skills training.

# **Improving Labor Market Efficiency**

Manpower planners can identify labor market bottlenecks affecting skills development by tracking measures of demand and supply for specific skills and their determinants. The analysis of these bottlenecks and their determinants by planners provides policy makers with an early warning of market imperfections or failures requiring public interventions to correct. Markets may not always get the training incentives right. Government policy interventions in an economy to achieve diverse social and economic objectives can distort market prices and alter training incentives from competitive levels. Market failures arising from economic externalities, for example, can also impede efficient skills development. Thus, the manpower planner is a potential monitor of labor market performance as well as market signals for skills training.

# **Planning Public Investments In Training**

Finally, even in a market economy, the manpower planner will continue to plan for public investments in education and training. The scope of planning for this purpose, however, can be reduced by the rationalization of the public sector's role in the delivery and financing of skills training. This rationalization calls for public interventions in skills development where labor markets fail to anticipate and 3