rafmacalaba/datause-encoder-data huggingface dataset:
(Unlike in equation (2), firms' choices of activity bundles are not deterministic here because we do not have access to the entire information that they base their decision upon.) As with equations (la) and (lb), we adopt an error components specification for the disturbances, **_-b_** _=#I +Vb ,_ and we address the associated initial conditions problem using Wooldridge (2000)'s technique. That is, we express each element of the vectorp _b_ as a linear function of the vector of initial states, **_bjo,_** plus noise.

In principle there are some efficiency gains to be reaped by estimating the system of equations (2') jointly with those in the system (la), (Ib) and (7) above. However, so long as the residual plant effects in equation (2') are not correlated with ,u in equations (1 a) and (1 b), this is not necessary for consistency. We therefore opt keep the model manageable and estimate the activity determinant equations separately.

# **III. Empirical Results**

To implement our estimator, we shall exploit annual industrial survey data from three semi-industrialized countries: Colombia, Mexico and Morocco. (See Roberts and Tybout, 1996, for detailed descriptions of the data.) Ideally we would like to study _all_ of the activities identified in table 1, but unfortunately, each data set provides information on only a subset. The Colombian data reveal whether firms are exporting and whether they are importing intermediate goods; the Moroccan data identify exporters and firms with foreign owners (FDI), and the Mexican data identify exporters, importers of intermediate goods, and importers of capital goods. Thus, although no single country 19
The results show: (i) that the returns to schooling are more concentrated around their respective means than previously thought; (ii) the basic Mincerian model used is more stable than one may have expected; (iii) the returns to schooling are higher for women than for men; (iv) returns to schooling and to experience are strongly and positively associated; (v) both returns show a decreasing pattern over time; and (vi) the returns to tertiary education are the highest and to secondary education the lowest.

When we combine our new estimates with the Psacharopoulos and Patrinos (2004) review, we mostly confirm previous findings and stylized facts. The combination also allows us to create a time-trend dating back to the 1960s, confirming that returns to schooling decline over time.

This comparable data set on returns to schooling and to potential experience should be helpful empirical work in a variety of fields. Our comparable estimates provide a reasonable proxy for the value of human capital for a broad group of economies. This new data set is useful for studying the links across economies between schooling attainment and the returns to schooling. Moreover, it can be used to examine economic growth, competitiveness, inequality, democracy, institutions and political freedom.

Given the high returns to tertiary education, an immediate concern for policy makers is to consider the large implications. While our estimates are for _private_ returns to schooling, the high returns to tertiary education will fuel demand for post-secondary education. Governments will need to consider the appropriate policy for financing tertiary education.

19
**Table 2: Summary Statistics of Child Characteristics**

|Child Characteristics|All|Coresident|Non-coresident|P-values|
|---|---|---|---|---|
|Age|41.95|39.12|42.54|<0.001|
|Oldest son (%)|15.90|22.37|14.53|<0.001|
|Youngest son (%)|27.30|61.29|20.12|<0.001|
|Daughter (%)|46.04|12.04|53.22|<0.001|
|Married (%)|91.38|78.49|94.10|<0.001|
|# of children (age<16)|0.34|0.47|0.31|0.001|
|_Education (%)_|||||
|Illiterate|16.91|12.26|17.89|0.001|
|Primary|34.91|33.12|35.29|0.369|
|Middle School|28.65|38.06|26.66|<0.001|
|High School|13.65|13.55|13.67|0.945|
|College and above|5.89|3.01|6.49|<0.001|
|Coresident(%)|17.44|100.00|0.00||
|Observations|2667|465|2202||

Note: 1) Sample are adult children no younger than 25 with at least one parent who is older than 60. 2) P-values are from t-test of the coresident and non-coresident groups.

**Table 3: Transfer Patterns**

||All|Zhejiang|Gansu|Urban|Rural|
|---|---|---|---|---|---|
|Incidence(%)<br>||Chil|dren topare|nts||
|Financial transfer|38.07|47.56|28.19|47.18|31.61|
|Regular|8.47|13.70|3.01|11.66|6.21|
|Irregular|30.09|34.74|25.25|36.34|25.66|
|In-kind transfer|42.23|40.52|44.02|46.27|39.37|
|Total|59.51|64.15|54.67|68.03|53.46|
|Incidence(%)<br>||Pare|nts to childr|en||
|Financial transfer|1.93|2.00|1.85|3.55|0.78|
|Regular|0.42|0.44|0.39|1.00|0.00|
|Irregular|1.55|1.63|1.47|2.64|0.78|
|In-kind transfer|1.70|1.11|2.32|1.82|1.62|
|Total|3.33|2.89|3.78|5.01|2.13|
|Amount(RMB/year)|||Net transfer|||
|Financial transfer|548.46|864.78|218.70|894.03|303.18|
|Regular|190.34|360.96|12.47|339.25|84.65|
|Irregular|358.12|503.81|206.23|554.78|218.53|
|In-kind transfer|192.32|275.09|106.04|297.92|117.37|
|Total|740.78|1139.87|324.75|1191.96|420.55|

Note: Sample are non-coresident adult children 25 and older with at least one parent older than 60.

26
**Figure 6. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to Combined Target Survey Sample (TA 1+TA 2+TA 3) for Different Sample Sizes, using 2020/21 Poverty Line, Normal Linear Regression Model, Tanzania**

<!-- Start of picture text --> Model 1 Model 2<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>(211) (422) (633) (844) (1055) (1266) (1477) (1688) (1899) (2110) (211) (422) (633) (844) (1055) (1266) (1477) (1688) (1899) (2110)<br>Model 8 Model 9<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>(211) (422) (633) (844) (1055) (1266) (1477) (1688) (1899) (2110) (211) (422) (633) (844) (1055) (1266) (1477) (1688) (1899) (2110)<br>% of target survey % of target survey<br>10% (464) 20% (930) 30% (1393) 100% (4644)<br>35 35<br>30<br>30<br>25<br>25<br>20<br>20<br>15<br>15<br>10<br>35<br>35<br>30<br>30<br>25<br>25<br>20 20<br>15 15<br><!-- End of picture text -->

**Note:** The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed poverty rates for TA (TA1+TA2+TA3) use the estimated parameters based on the NPS 2020/21. 1000 simulations are implemented. All estimates are obtained with the population weights. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line from NPS 2020/21 deflated to 2022 prices. The sample size of TA (TA1+TA2+TA3) is selected as a percentage of the target survey sample varying from (randomly selected) 10% to 100% of the TA sample (with the sample size in parentheses). The sample size of NPS 2020/21 is selected as a percentage of the base survey sample varying from (randomly selected) 10%, 20%, 30% to 100% of NPS 2020/21 sample (with the sample size in parentheses). The total sample size of TA (TA1+TA2+TA3) is 2,110 households, and the sample size of NPS 2020/21 is 4,644 households.

43
33

**Table 3. Decomposition of Overall Rural Income Inequality in Egypt and Jordan**

|Income source|Proportion of<br>households<br>receiving<br>income source<br>(PK)|Share in total<br>income<br>**(SK)**|Gini<br>coefficient for<br>income source(a)<br>(GK)|Gini<br>correlation with<br>total income<br> <br>rankings<br>(RK)|Contribution of<br>income source<br>to overall<br>income<br>inequality<br>(SKGKRK)|Relative<br>concentration<br>coefficient of<br>income source<br>(<br>_R.G,_|Percentage<br>contribution<br>to overall<br>income<br>inequality|
|---|---|---|---|---|---|---|---|
|(a) Rural Egypt||||||||
|Nonfarm<br>|0.607<br>|0.422<br>|0.634<br>|0.590|0.158|0.703|29.7|
|Agricultural<br>|0.669<br>|0.246<br>|1.155|0.750|0.214|1.628|40.2|
|Transfer<br>|0.509<br>|0.154<br>|0.848<br>|0.488|0.064|0.778|12.0|
|Livestock<br>|0.695<br>|0.094<br>|0.935|0.376|0.034|0.661|6.4|
|Rental|0.317|0.083|0.924|0.805|0.062|1.398|11.7|
|Total|-|1.000|-|-|0.532|-|100.0|
|(b) Rural Jordan||||||||
|Nonfarm<br>|0.685<br>|0.506<br>|0.567|0.760|0.218|1.046|53.5|
|Agricultural<br>|0.176<br>|0.085|0.919|0.439|0.034|0.979|8.3|
|Transfer<br>|0.913|0.273|0.726|0.560|0.111|0.987|27.2|
|Livestock<br>|-<br>|-|-|-|-|-|-|
|Rental<br>|0.848|0.136|0.535|0.625|0.045|0.812|11.0|
|Total|-|1.000|-|-|0.408|-|100.0|

Notes: All estimates are based on annual per capita household income.

(a) Source ginis **(GK)** are high because they include households with zero and negative incomes from different income sources. Source ginis can exceed unity if many of y, are negative.
- 8 -

In the case of Boreholes too, Table 2.2 shows that large size firms can afford to have private water supply facilities in all three countries, but more of small Nigerian firms seem to suffer more from the lack of alternative sources of water supply than those in Indonesia. The extent of private supply of water was much lower in Thailand which implies a better public water supply system in Thailand than in Indonesia and Nigeria.

Tables 2.4 show that small firms are those that cannot afford capital investment for radio equipment and for motorcycles for couriers. This is also true for investment in vehicles for shipment of goods and for workers' commuting in Indonesia although the picture is not so clear in the other two countries (Table 2.3).

The heavy incidence of infrastructural failures among small firms has an implication for the growth of industries and the generation of employment. According to the 'incubator hypothesis' that was tested in the earlier Bank research on industrial location in Bogota (Lee, 1989) and in Seoul (Lee, 1985), it was observed that small new firms spend their early years near the city center or in an old industrial area with easy access to good utilities and other essential services. They do so because it is prohibitively expensive for small firms to operate in outlying areas where infrastructure services are poor. As they grow and become more independent, they tend to move out of the central area for more space. The findings from our survey imply that large cities with poor infrastructure cannot offer the incubator function for small new firrns. Since small firms cannot afford their own generators and boreholes and other facilities, the burdens of inadequate public infrastructure services are especially severe for the small firms which start and grow in those cities. This has a serious negative implication for the birth and growth of small firms and for the generation of employment and income, hence, on
|Flour-free|Kg|5.000|0.700|5.700|12.3|5.6|0.8|
|---|---|---|---|---|---|---|---|
|Flour-nat.|Kg|2.000|1.430|3.430|41.7|2.3|1.6|
|Electricity||||||16.0|6.4|
|0-100|kWh|0.9010|0.6600|1.5610|42.3|0.8|0.5|
|101-150|kWh|0.9689|0.5900|1.5589|37.8|0.8|0.5|
|151-200|kWh|0.9689|0.5900|1.5589|37.8|2.9|1.8|
|201-300|kWh|1.0541|0.5100|1.5641|32.6|4.6|2.2|
|301-500|kWh|1.2474|0.3167|1.5641|20.2|4.4|1.1|
|501 and<br>more|kWh|1.4407|0.1200|1.5607|7.7|2.4|0.2|

Source: Official Bulletins No. 6222, 16 January 2014 and No. 6288, 4 September 2014 and CDC.

Figure 1 below shows how important subsidized products are for households (left-hand panels representing expenditure on subsidized products as a share of total expenditure) and how important are subsidies in per-capita terms (right-hand panels representing subsidies per capita). On the x-axis we have the household expenditure distribution in percentiles, the poorest percentiles are on the left and the richer percentiles are on the right of the figures. Each line in the graphs represents one product and we have divided products into three panels; food products (flour and sugar – top panel), petroleum products (LPG, gasoline and diesel – middle panel) and electricity (bottom panel).

Starting from the food products (top panels), we can see that sugar and flour are both more important for the poor than for the rich (both curves are downward sloped in the left-hand panel). The poorest percentiles consume between 2% and 3% of total expenditure on these products while the richest percentiles consume a tiny share of total expenditure. However, if we look at subsidies per capita (rightpanel), flour subsidies per capita are larger for the middle class than for the poor or the rich while sugar subsidies are pro-rich with higher subsidies per capita going to richer households.

The pictures are different for petroleum products (middle-panels). Here we can see that LPG is a very important item for the very poor and declining in importance very quickly for richer households while gasoline and diesel are not very relevant for the poor but they become increasingly relevant for the rich (left-hand panel). In terms of subsidies per capita (right-hand panel) the only important product is LPG and this product is really pro-rich, meaning that richer households receive higher subsidies. Hence, LPG is the most important subsidized product for the poor but subsidies per capita are larger for the rich despite the fact that the poor have larger households. This is one example of inequitable distribution of subsidies.

The picture changes again if we look at electricity (bottom panels). The share of expenditure on electricity subsidies is declining with poorer households consuming larger shares than richer households (left-hand panel). Instead, in terms of subsidies per capita, electricity is pro-rich. Similarly to LPG, electricity is another product that strikes as particularly inequitable given the importance of this product for the poor and the pro-richness of subsidies per capita.

10
dimensions: education, health, and financial security.

# _5.2.1 Ethiopia_

In Ethiopia (Table 7), deprivations in education are high, with around eight in ten poor children not attending school, regardless of displacement status (Table 7). Gender gaps in school attendance are significant only for the refugee community, where girls are more deprived than boys (3-percentage points among the wider population, and 5-percentage points among the poor). Meanwhile there are no significant gender differences in years of schooling, by displacement or poverty status. Most refugee in Ethiopia children attend NGO schools, although some attend public schools, and they also have higher enrollment rates than the national average of their country of origin (Pape et al. 2018).

_Table 7: Proportion of deprived individuals across displacement status by gender, Ethiopia_

||Refugees<br>|<br>||Host<br>|s<br>||
|---|---|---|---|---|---|---|
||Male|Female|Difference|Male|Female|Difference|
|_All households_|||||||
|Years of schooling|0.66|0.68|-0.02|0.37|0.33|0.04|
|School attendance|0.16|0.19|-0.03<sup>**</sup>|0.11|0.11|0.00|
|Unemployment|0.20|0.17|0.03|0.02|0.02|0.00|
|Legal identification|0.38|0.38|0.00|0.54|0.64|-0.11<sup>***</sup>|
|Early marriage|0.19|0.72|-0.53<sup>***</sup>|0.18|0.60|-0.42<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|0.83|0.86|-0.03|0.78|0.77|0.01|
|School attendance|0.23|0.28|-0.05<sup>**</sup>|0.36|0.40|-0.04|
|Unemployment|0.40|0.28|0.12<sup>***</sup>|0.04|0.04|0.01|
|Legal identification|0.56|0.56|0.01|0.76|0.81|-0.05<sup>*</sup>|
|Early marriage|0.36|0.84|-0.48<sup>***</sup>|0.41|0.62|-0.21<sup>**</sup>|

Source: Authors’ calculations using the Ethiopia SPS (2017). Significance at 1% ***, 5% **, and 10% * levels.

The only significant gender gap in legal identification is in the wider population (an 11percentage points gap), where women are more deprived than men in the host community. Meanwhile, the only significant gender gap in unemployment is in the poor population (a 12-percentage points gap), where men are more deprived than women in the refugee community, although this may reflect women’s overall inactive labor status. Early marriage rates are significantly higher among women across the board.

18
# _4.2 Sample Descriptive Statistics_

Panel A of Table 1 shows that the portfolio holdings of Asian and Latin American firms show different patterns. Funds invest in 31 percent of non-ADR firms in Asia as compared to only 19 percent in Latin America. Of all the Asian firms with an ADR program, 65 percent are held by at least one fund. For Latin America the corresponding number is 73 percent. Panel B of Table 1 reports the same pattern when the market value of firms is used instead of number of firms. It shows that of the Latin American firms covered by Worldscope, those that have an ADR represent 66 percent of that region’s market capitalization. However, Asian firms with ADRs represent only 43 percent of that region’s market capitalization.

Several conclusions can be drawn from this analysis. For the universe of firms covered by Worldscope: 1) A larger proportion of Latin American firms issue ADRs than Asian firms; 2) Funds are more likely to hold positions in firms that have ADRs; and 3) Funds are more likely to invest in non-ADR firms from Asia than non-ADR firms from Latin America (this may reflect the fact that Latin American firms are almost two times more likely to have ADRs than are Asian firms).

# **5. Empirical Results**

This section describes the methodology employed to test the hypotheses discussed in section 3 and reports our major findings.

# _5.1 Differences between Latin America and Asia_

Most emerging markets have some ADRs listed in the U.S. The fraction of ADR listings is particularly high for Latin America as seen in Table 2. For example, 20 percent of all Mexican firms (covered by Worldscope) have a listed ADR. The corresponding numbers are 14.29 percent, 7.55 percent and 10.92 percent for Argentina, Brazil and Chile, 19
expected to be heavier as compared to a snowstorm in terms of recovery time required. The national highway is the major corridor in this area, though a detour route is still available. The proposed bypass tunnel would run through the landslide-prone area and allow for this road section to remain free from the risk of landslides.

# Indicator Data

Table 3 shows the necessary data for the calculation of resilience indicators for each dimension in Case 3. As with the above cases, the traffic volume in the ordinary state is taken to be the design traffic volume. The travel time is calculated by a route search system like Google Maps. Since alternate routes to National Highway No. 9 exist, a disruption to the road section causes only a 4-minute increase in travel time between Toyooka and Tottori. Using a route search system, areas accessible to the major hospital can be determined and, hence, the population with access can be calculated by drawing on demographic statistics. Because the disrupted section is just a single point, none of the population is isolated, even in the disrupted state.

The expected time for recovery depends on the type of hazard event. A landslide event requires 2 days for recovery, on average.

Table 3. Case 3 Resilience Indicator Data

|Indicators of<br>LoF|Relevant Data|Data source|Content|
|---|---|---|---|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>volume|6.11 pcu/min<br>(8800 pcu/day)|
|Ei|Traffic volume per min under thedisruptedstate (x�)|Simulation|3.06 pcu/min|
|conomc<br>Loss|Travel time between representative OD (Asago – Tottori)<br>withoutthe project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|109 mins|
||Travel time between representative OD (Asago – Tottori)with<br>the project under theordinarystate (t<sup>����</sup>)|Route search<br>system|109 mins|

30
when we use a one standard deviation decline in domestic real GDP growth – i.e., 2.3 percentage points – there is a significant, albeit smaller, 18 percent increase in import protection.<sup>16</sup> Finally, Figure 2 illustrates the importance of economic growth in foreign trading partner _i_ . A one standard deviation reduction to a trading partner’s real GDP growth – i.e., 3.5 percentage points - is associated with a 60 percent increase in import protection against that partner based on specification (1) and a 70 percent increase based on specification (2).

The remaining columns of Table 2 examine the robustness of our results to alternative specifications of the underlying model.

Column (3) of Table 2 reports a specification with alternative fixed effects. Instead of importer _j_ and exporter _i_ pair-wise fixed effects, we introduce _separate_ importer _j_ and exporter _i_ fixed effects. The qualitative pattern to the results is unchanged; the only distinction is a reduction to the estimated size differential (away from 1) of the IRRs on foreign real GDP growth and the statistical significance of the IRRs for foreign real GDP growth and the time trend being reduced to the ten percent level.

In column (4), we modify the baseline model by substituting the second lag of each of the macroeconomic variables for the first lag. The qualitative pattern to the results is unchanged; however, estimated magnitudes of the impact of the domestic unemployment rate change and the foreign trading partner’s real GDP growth are slightly smaller. We return to this issue of the timing of shocks in more detail in our economy-specific estimates discussed below.

Next we redefine the dependent variable in column (5) so that it includes only antidumping (AD) import protection. The estimated IRR on the impact of an appreciating domestic currency is no longer statistically different from 1. Furthermore, while the qualitative nature of the IRRs is similar to column (1), there is again a slight reduction to the size differential (away from 1) of the estimated IRRs for the domestic unemployment rate change and the foreign trading partner’s real GDP growth. This result stands in contrast to research covering earlier periods of data whereby an exclusive focus on TTBs through AD policy alone may have been sufficient to capture the relationship between macroeconomic shocks and import protection (e.g., Knetter and Prusa, 2003). One important implication of our results is that a sole focus on antidumping underestimates the relationship between macroeconomic shocks and time-varying import protection for these economies.

In specification (6), we introduce a new covariate to the baseline model – the _interaction_ of the domestic unemployment rate change _jt-1_ with real GDP growth of trading partner _it-1._ We

> 16 This is consistent with earlier results for the United States alone based on annual data (e.g., Irwin, 2005).

13