rafmacalaba/datause-encoder-data huggingface dataset:
28 child’s province has an effect on his or her survival prospects independently of its impact on government health spending, conditional on the wealth of the child’s household.<sup>20</sup>

# VII. **ESTIMATES OF PROJECT BENEFITS**

This section uses the approach outlined above to estimate the benefits of the two Bank health projects in Vietnam. We first report some results of regressions aimed at shedding light on the degree of fungiblity between the health sector and other sectors, and within the health sector between different provinces. We also present some naïve diffs-indiffs estimates of the impact of the Bank projects on infant mortality. These results help motivate the analysis that follows.

# **Fungibility and naïve diffs-in-diffs results**

The regressions in columns (1) and (2) of Table 1 shed light on the issue of intersectoral fungibility. These equations, like those used in other studies of fungibility (cf. e.g. Devarajan _et al._ 1999), regress (provincial) government health expenditures (gross of development assistance (ODA) to the health sector) on GDP per capita, and net provincial revenues or expenditure (for _all_ sectors, net of ODA).<sup>21</sup> The data are crosssection and for the year 2000. Regressions are reported for both provincial total revenues and provincial total expenditures. Arguably the latter is a better measure of the resources

> 20 We explored empirically the possibility that these two variables might belong in the outcome equation, keeping one as the identifying instrument and including the other in the outcome equation. In the general model, convergence was achieved with the revenue share variable included in the outcome equation but not when GDP per capita was included. When restrictions were imposed on the models (see below), convergence was typically achieved for GDP per capita and was always achieved for the revenue share variable. The coefficient on GDP per capita was not significant in the outcome equation, nor was the coefficient on the revenue share variable. We also explored other possible _Z_ -variables, such as population density, communes per 1000 population, etc., but none had a significant effect in the selection equation.

> 21 In computing net (total) revenues and expenditures, only health sector ODA was subtracted, due to lack of data on ODA to other sectors.
||||(0.04177)||||(0.04177)||||(0.04178)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Remittances & Others|||-0.00897||||-0.00848||||-0.00915||
||||(0.09640)||||(0.09639)||||(0.09639)||
|Property income and investments|||0.20760**||||0.20791**||||0.20749**||
||||(0.08436)||||(0.08426)||||(0.08435)||
|Transfers and other benefits|||0.11600***||||0.11506***||||0.11623***||
||||(0.04167)||||(0.04166)||||(0.04167)||
|Own > median size of land (2.5 acres)|||0.19984***||||0.19983***||||0.19968***||
||||(0.03191)||||(0.03188)||||(0.03192)||
|Observations|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|
|Kleibergen-Paap rk Wald F|17.39|17.39|13.58|12.70|38.09|38.10|31.02|29.36|12.63|12.63|9.736|9.148|
|Root MSE|0.594|0.594|0.534|0.362|0.593|0.593|0.532|0.360|0.594|0.594|0.536|0.363|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|education, occupation, other income sources,<br>|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|land ownership)|||||||||||||
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

30
Conclusion Understanding the impact of Covid-19 on households in different parts of the welfare distribution was an important factor when considering appropriate policy responses in response to the pandemic and will provide lessons for similar events in the future. We combined the High-Frequency Phone Surveys collected by national governments since the beginning of the pandemic with older surveys with information on household consumption to shed light on this question. We find that the initial impact of Covid-19 has been fairly widespread throughout the consumption distribution. In the initial aftermath of the crisis, poorer households were equally likely to report income declines but less likely to report the inability to work as usual. While government assistance slightly favored poorer households initially, this progressive targeting disappeared quickly and the share of households receiving government assistance was similar for the bottom 40 and top 60 percent for the year after the crisis. In the summer and fall of 2020, there was a large increase for households in the bottom 40 in farming activities and non-farm household enterprises. This coincided with economic recovery, as seen by large reductions in food insecurity and distressed asset sales among the bottom 40 during this time, as well as a reduction in the prevalence of income declines. However, the engagement of children in educational activities remains a concern. By the end of 2020, only about one third of households with children reported that children aged 6 to 18 were engaged in educational activities, a decline of about 15 percentage points.

To sum up, the results suggest that the initial impact of the shock was severe throughout the welfare distribution, and that there was a marked recovery for poor households associated with an increase in farming and non-farm household enterprises. However, poor households remained more likely than nonpoor households to report income declines and not working as usual in the medium term. The impact on educational engagement was severe and felt throughout the distribution. The results highlight the importance of policies to ensure productivity in the agricultural sector, as well as non-agricultural household enterprises, in response to the crisis.

The analysis in this note could be extended in several directions. We do not closely examine variation in the impact of the global pandemic across the 14 countries in our analysis sample, or differences between urban and rural areas, which could provide further insights. We also do not examine the differences across various socio-economic characteristics such as age, gender, and labor market characteristics of individuals, although this is challenging in phone surveys because of the non-representative nature of the sample. As more face-to-face surveys are implemented and analyzed, a clearer picture will emerge of the distributional impacts of this prolonged pandemic in Sub-Saharan Africa.

12
**Table 10. Wage rigidity vs different types of union contracts. Separate regressions for each country.**

||**Aus**|**tria**|**Sp**|**ain**|**Fr**|**ance**|**I**|**taly**|**Slo**|**venia**|
|---|---|---|---|---|---|---|---|---|---|---|
||DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|
|Only outside agreement|0.528|0.470**|1.359**|0.794|1.142|0.847|0.882|0.822|0.446|0.850|
||(0.168)|(0.049)|(0.036)|(0.704)|(0.526)|(0.402)|(0.741)|(0.740)|(0.140)|(0.521)|
|Observations|392|392|1815|1815|1533|1533|782|782|639|639|
||**Est**|**onia**|**Hun**|**gary**|**Lit**|**huania**|**Po**|**land**|||
||DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|||
|Only outside agreement|||||||1.174|4.811*|||
||||||||(0.884)|(0.063)|||
|Only firm-level agreement|1.008|1.652|0.949|1.478|0.509|2.048|1.056|1.193|||
||(0.991)|(0.739)|(0.888)|(0.192)|(0.164)|(0.126)|(0.880)|(0.636)|||
|Observations|307|307|1496|1496|321|321|784|784|||
||**Gre**|**ece**|**Czech**|**Republic**|**Ire**|**land**|**Por**|**tugal**|||
||DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|DNWR|DRWR|||
|Only outside agreement|0.727|0.866|5.848**|6.774**|0.866|1.272|1.234|1.319|||
||(0.456)|(0.696)|(0.026)|(0.049)|(0.670)|(0.535)|(0.253)|(0.302)|||
|Only firm-level agreement|0.672|0.285|1.004|1.567|1.371|3.250*|2.275*|5.324***|||
||(0.658)|(0.266)|(0.988)|(0.357)|(0.637)|(0.059)|(0.059)|(0.001)|||
|Both agreements|||0.718|2.893*|0.469|3.615***|0.610|0.918|||
||||(0.478)|(0.071)|(0.151)|(0.002)|(0.208)|(0.872)|||
|Observations|315|315|364|364|752|752|1188|1188|||

Notes: The table presents the estimated odds ratios for nominal wage rigidity vs flexible wage (DNWR) and real wage rigidity vs flexible wage (DRWR). Worker skill groups, % permanent workers, % labour cost, and sector, size and country fixed effects are added in all specifications. P-values in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The reference group includes the firms having both types of contracts for Austria, France and Italy; the firms having only firm-level contracts for Spain and Slovenia; and the firms with no union contracts for the rest of the countries.

29
- Persico, N., Postlewaite, A., and Silverman, D. (2004). The Eect of Adolescent Experience on Labor Market Outcomes: The Case of Height. Journal of Political Economy, 112(5):10191053.

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- Ra, S. and Singh, B. (2005). Measuring the economic costs of conict. Working Paper Series 2, Asian Development Bank,.

- Rutstein, S. O. and Johnson, K. (2004). The dhs wealth index. DHS Comparative Reports 6, Calverton, Maryland: ORC Macro.

- Satyanarayana, K., Prasanna Krishna, T., and S Narasinga Rao, B. (1986). Eect of early childhood undernutrition and child labor on growth and adult nutritional status of rural indian boys around hyderabad. Human nutrition. Clinical nutrition, 40:1319.

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31
The last regressor, ln _n_<sup>_c_</sup> _dt_<sup>,isacountofthefirm-productpairsfromcoun-</sup> try _c_ active on destination _d_ . If _c_ is Senegal and one Senegalese firm sells two HS6 products in the E.U. and another one sells three, _n_<sup>_c_</sup> _dt_<sup>=5forall</sup> five observations with _c_ = Senegal and _d_ = E.U. in year _t_ . It is a proxy for the size of the bilateral trade relationship.This variable is never significant except in Column (2).

Column (2) of table 3 is very similar to Column (1) except that all the explanatory variables are lagged by one period. Results are essentially unchanged, except for _n_<sup>_c_</sup> _dt_<sup>whosecoefficientbecomesnegativeandsignificant.</sup> What that means is that more firm-product combinations from a given origin to a given destination are associated with a lower probability of survival past the first year. Without making too much of this result, one can interpret it as follows. Given that we include origin-destination fixed effects, _n_<sup>_c_</sup> _dt_<sup>picks</sup> up only the time-variant component of bilateral shocks, like booms in the destination market. The negative coefficient suggests that a growth expansion (a boom) in _t −_ 1 triggers crowding in followed by retrenchment.<sup>12</sup> Column (3) introduces an additional regressor. The literature on multiproduct firms suggests that firms have core and marginal products, and that they have a stronger competitive advantage in the former (see for instance Eckel and Neary 2010 for a theoretical model and Iacovone, Rauch and Winters 2010 for an empirical test of this hypothesis). For each multiproduct firm _f_ and product _p_ , we proxy how close is that product from the firm’s ‘core’ by ln _zfp_ , the log of its share in the total firm’s export sales. Results suggest that it correlates positively with first-year survival probability even after controling for dollar initial value; that is, the probability of survival for ‘core’ products is substantially higher than for others. For instance, a product representing 80% of the firm’s export sales (all destinations together) would have a first-year survival probability on a given destination higher by

> 12Confirming this interpretation, when we exclude the destination fixed effects, the coefficient on this variable becomes positive, suggesting that _permanently_ more attractive markets are associated with longer survival, which is consistent with our interpretation. This “crowding-in” result is also consistent with a finding by Bussolo, Iacovone, and Molina (2010) who found, using firm-level data from the Dominican Republic, that the reduction of tariffs following the signature of CAFTA led to some over-crowding of Dominican exports, followed by retrenchment.

21
ranked trainers in Table A.8. This us with additional reassurance Appendix provides about the validity of this empirical exercise. Table 7 examines the effect of trainer characteristics on the first-stage outcomes. Because the majority of individuals who attended a matching meeting started training, it is not surprising that we find only limited effects on any of these first-stage outcomes.

We examine the transmission of skills in Table 8. We measure craft skills using a short test designed in collaboration with industry experts. Innovation is measured using a series of self-reports such as the number of new designs created. Job skills are measured based on the skill content of a job following the World Bank STEP surveys. A higher value of this index reflects a shift away from jobs that are intensive in physical tasks. This index is only relevant for individuals who are working. Managerial skills are measured by the number of improved management practices adopted. These are relevant only for individuals who run their own business.

Apprentices assigned to trainers with the best math skills did not obtain better skills (Column 1). Nor did training with the most profitable trainers affect apprentice skills (Column 2). Training with the most experienced trainers (Column 3) improved test scores by about 0.25 _σ_ (p-value _<_ 0.1) compared with trainees assigned to less experienced providers. These trainers increased the skill content of jobs by a similar magnitude (p-value _<_ 0.1). Neither of these effects is robust to multiple testing adjustments, however. Apprentices paired with trainers with the highest wage bill (Column 4) scored almost 0.2 _σ_ higher on the skills test than those assigned to trainers with smaller wage bills, although this effect was just outside the standard thresholds for statistical significance. Training with these providers also increased innovation (or creativity) by 0.18 _σ_ (p-value _<_ 0.05).

Finally, we examine the effect of trainer characteristics on labor market outcomes for apprentices in Table 9. For brevity, we focus only on wage and self-employment outcomes as well as on aggregate outcomes such as total earnings. Apprentices assigned to trainers with the best math scores did not have better labor market outcomes (Column 1). Those assigned to trainers with the highest profits (Column 2) were 16 percentage points more likely to be working (p-value _<_ 0.01). This was driven primarily by the almost 10 percentage point increase in self-employment (p-value _<_ 0.1). This match increased total monthly earnings by 63 GHS (p-value _<_ 0.05). This was driven mostly by increased wage earnings, but the estimate is imprecise. We find that there were limited (or imprecisely estimated) labor supply effects when apprentices trained with the most experienced trainers (Column 3). But apprentices paired with these trainers had 65 GHS more in total monthly earnings (p-value _<_ 0.01) than their peers assigned to less 20
# **3.2. Defining the study sample**

According to the Ministry of the Economy, the program targeted industries with four characteristics ( _Secretaría de Economía_ 2009). First, they produce durable goods, allowing consumers to postpone purchasing decisions and thus potentially leading to large drops in demand during economic downturns. Second, employees possess a high level of firm or industry specific knowledge, making job loss particularly costly for firms and employees. Third, much of their production is exported either directly or indirectly, exposing them to downturns in other countries. And fourth, they have displayed high levels of growth in the past few years and may thus be able to resume their high growth after the crisis.

In practice, all 29 industries in IMSS classification groups 36 (Manufacture, assembly and/or repair of machinery, equipment and related parts; excluding electronics), 37 (Manufacture and/or assembly of electronic machinery, equipment, appliances, accessories and related parts) and 38 (Constructions, reconstruction and assembly of transportation equipment and parts) were declared eligible for the program.<sup>5</sup> The empirical challenge for measuring the impact of the program on employment then is to find a set of ineligible industries that are comparable to the eligible industries in the following sense: in the months and years following the program, average employment levels across these industries should follow the same trend as average employment levels across eligible industries would have displayed in absence of the program. Whether or not it is possible to find a credible set of control industries depends on how exact the targeting of the program was. If IMSS industry groups 36, 37, and 38 indeed include all industries with the four characteristics mentioned above, it may not be possible to find a set of industries with similar employment trends. However, some other industry groups may also include subindustries with similar characteristics.

To define a comparable set of eligible and ineligible industries, I use the following procedure. I first keep only IMSS industries that produce durable goods (71 industries in total). For classification of goods as durable or not, I relied on the US Economic Classification Policy Committee aggregation of sectors into groupings called “Supersectors.”<sup>6</sup> Within the set of durable goods industries, I then use propensity score matching (Dehejia and Wahba 2002) based on the following variables: average employment levels in each semester of 2004, 2005, and 2006, average employment levels in each quarter of 2007 and the first two quarters of 2008, and average employment levels each two‐month period for July/August, September/October, and November/December of 2008.

> 5 Some firms producing nondurable goods, such as rubber and plastic, were also declared eligible for the program in the April 10, 2009 revision of the operating rules, under the condition that 90% of their production is used as an input in other eligible industries.

> 6 As posted on the Bureau of Labor Statistics website: http://www.bls.gov/ces/cessuper.htm

6
# Figure 14: MNA: Infrastructure, health, and education indicators

# A. Infrastructure investment needs

# B. Quality of infrastructure

<!-- Start of picture text --> Percent of GDP Index, 1-7, 7 = best<br>8 7 Advanced economies EMDEs SAR<br>7 6<br>6 5<br>5<br>4<br>4<br>3<br>3<br>2 2<br>1 1<br>0 0<br>Transport Electricity Water and Flood Irrigation<br>sanitation protection<br>C. Universal health coverage D. Health spending below EMDE median<br>Index, 0-100, 100 = best Share of MNA economies<br>100 2019 2010 80<br>80 70<br>60 60<br>40 50<br>40<br>20<br>30<br>0<br>20<br>10<br>0<br>E. Selected human capital indicators F. Infrastructure investment needs<br>Index, 0-1, 1 = best Number of Percent of GDP<br>Human Capital Index years Energy Telecommunication<br>1.0 12 Transport: Ports Transport: Rail<br>EMDE average 11 6 Transport: Road Water<br>0.8 Learning-adjusted years of schooling (RHS)10 5<br>4<br>0.6 9<br>3<br>8<br>2<br>0.4 7<br>1<br>6<br>0.2 0<br>5 Morocco Jordan Egypt, Tunisia Saudi<br>0.0 4 Arab Arabia<br>GCC Other oil Oil importers Rep.<br>exporters<br>SSA SAR MNA LAC EAP ECA High-income<br>countries<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

# D. Health spending below EMDE median

Sources: Rozenberg and Fay (2019); Global Infrastructure Outlook; World Economic Forum Global Competitiveness Index; World Health Organization; World Bank.

Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa; GCC = Gulf Cooperation Council.

A. Investment needs in a preferred scenario as defined in Rozenberg and Fay (2019).

B. Unweighted averages of survey data from the World Economic Forum Global Competitiveness Index. Data was collected using the question: “How would you assess general infrastructure (e.g., transport, telephony, energy) in your country? (1 = extremely underdeveloped—among the worst in the world; 7 = extensive and efficient—among the best in the world).” Oil importers include Egypt, Jordan, Lebanon, Morocco, and Tunisia. Non-GCC oil exporters include Algeria, the Islamic Republic of Iran, Libya, and the Republic of Yemen. GCC countries include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

C. Unweighted average. Based on Universal health coverage services coverage index.