rafmacalaba/datause-encoder-data huggingface dataset:
whom we have data on the education and earnings of male siblings, and use "within" estimators which include the total education of all wage-earning male siblings as a control (see Card 1999 for a general discussion, and Heckman and Hotz 1986, and Lam and Schoeni 1993 for applications in LDC settings). An additional advantage of these regressions is that including partial controls for ability should reduce the biases associated with non-random selection into the wage-earning sector if this selection is itself partly deterrnined by ability."' I also consider two semi-parametric regression models. In the first, experience and squared experience are still assumed to have an additive effect on log wages, but education is captured by fifteen schooling dummies:

# (2) logWi=a+lSi +X;+xi **j+Ei,**

This imposes fewer restrictions than a spline function (see also Hungerford and Solon 1987; Lam and Schoeni 1993; Strauss and Thomas 1996). Here, too, I estimate the "bare-bones" semi-parametric regression, as well as regressions which include parental An alternative would have been to correct explicitly for selection with the correction proposed by Heckman (1976). In separate results (not reported, but available from the author upon request), I compare OLS estimates with estimates from a Heckman selection model, in which education of the spouse is used as an identifying variable for a sub-sample of married men in the APIS. The selection-corrected estimates are very similar to the simple OLS estimates: In the full sample of married men, for example, the coefficient on own schooling is .122 in the OLS regression, and .128 in the selection-corrected regression. These estimates assume that spousal education helps determine participation in the wage-earning sector, through its effect on household income, but does not have an independent effect on own productivity and wages. This seems plausible, but assortative mating between husbands and wives in terms of unobserved ability may cast some doubts on this identification strategy (for a discussion, see Behrman, Birdsall, and Deolalikar 1995; Behrman, Rosenzweig, and Taubman 1994; and Lam and Schoeni 1993). Another oftused candidate for identification, the amount of non-wage household income, including public transfers, pensions, interest and dividends, is not a significant predictor of participation in the labor market conditional on age and education in the APIS sample. Note, finally, that as is well-known, the Heckmancorrected estimates assume homoskedasticity and joint normality in the residuals from the swithching and wage regressions.

12
online appendix as the ratio of COVID-19 infections in the current month to COVID-19 infections in the previous month) to fall by 48%. We then take two approaches to scaling this estimate. First, to evaluate the overall effect of mask manufacturing in Rwanda, we multiply our estimates by the average mask manufacturing exposure in our primary analysis sample (0.187), and interpret this as the effect of setting mask manufacturing exposure equal to zero nationally. Alternatively, we divide our estimate by the April impact on share of mask purchases from manufacturers (1.095), which we interpret as the effect of households exclusively sourcing masks from manufacturers. Applying the former, domestic mask manufacturing reduced the monthly growth rate of COVID-19 infections nationally by at least April through June. Applying the latter, shifting from no manufactured masks to complete adoption of manufactured masks causes the monthly growth rate of COVID-19 infections to fall by 45%.

We then compare this estimate to results from Chernozhukov et al. (2021) and Abaluck et al. (2021). Chernozhukov et al. (2021) find that mandating employees wear masks holding fixed behavior causes a 10% decrease in weekly case growth, which scales to a 35% decrease in monthly case growth; this is statistically indistinguishable from our estimate of the impacts of adoption of manufactured masks. Abaluck et al. (2021) find that promotion of masks led to a 29pp increase in mask wearing in public, which over 8 weeks was associated with an 11.2% reduction in symptomatic infections when surgical masks were provided. Scaling this estimate by the inverse of their estimated impacts on mask adoption yields an 19% decrease in monthly infection growth. These estimates suggest larger impacts of masks on the spread of COVID-19 in the United States and in urban and peri-urban Rwanda than in rural Bangladesh; alternatively, the source of variation in mask policy in Bangladesh is at a much narrower geography (village) than in Rwanda and in the United States, suggesting across-village spillovers may reduce estimated impacts of masks on the spread of A16
- 14 -

two variables included as explanatory variables in _X_ ( **Table 4** ). It was found that they have little explanatory power in the equations. Thus, they are not considered to belong to the explanatory equations.

The estimation results are similar to the above: People’s health care service demand declines with the distance between villages and health facilities as well as poverty prevalence. The availability of medical supplies also affects people’s demand. To improve medicine availability, health facilities need to be connected to the road network and the district capital, which has an important role in the medical supply distribution system.

**<u>Table 4. Three-stage least squares estimation results with two instrumental variables included</u>**

||<br>Without re|<br>gional dumm|<br>ies|<br>With regio|<br>nal dummie|<br>s|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||
|_N_/_POP_equation:|||||||
|KM_FKT|-1.010|(0.364)|***|-0.880|(0.360)|**|
|RAI|-0.671|(0.157)|***|-0.891|(0.154)|***|
|POV|-0.675|(0.132)|***|-0.702|(0.241)|***|
|AVG_M|2.015|(1.134)|*|1.719|(1.107)||
|SLOP|-1.390|(1.238)||-1.021|(1.198)||
|ELEV|0.0005|(0.0037)||0.018|(0.014)||
|Constant|-85.697|(104.125)||-66.532|(125.669)||
|_M_equation:|||||||
|KM_RD|-0.221|(0.064)|***|-0.169|(0.062)|***|
|TIME_DIST|-1.739|(0.700)|**|-1.693|(0.671)|**|
|POV|-0.043|(0.024)|*|-0.130|(0.028)|***|
|SLOP|0.234|(0.276)||0.089|(0.261)||
|ELEV|-0.0006|(0.0008)||-0.011|(0.002)|***|
|Constant|96.057|(2.215)|***|112.046|(3.030)|***|
|Obs.|892|||892|||
|R-squared: N/POP|-0.172|||-0.071|||
|M|0.027|||0.157|||
|Overidentification statistic:|4.286|||5.993|||

Note: Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
|**Table 6. Effect of Business En**|**vironment Obstacles on**<br>Depend|**Employment Growth by**<br>ent variable: employment|**Sector**<br> growth|
|---|---|---|---|
||**Manufacturing**|**Sales**|**Services**|
|**Variable**|**(1)**|**(2)**|**(3)**|
|Laborsize|-0.026***<br>|-0.014***<br>|-0.021***<br>|
||(0.001)|(0.002)|(0.002)|
|Age|-0.024***|-0.025***|-0.018***|
||(0.002)|(0.002)|(0.003)|
|Multi|0.022***|0.014***|0.014***|
||(0.003)|(0.004)|(0.005)|
|Exporter|0.026***|0.017**|0.010|
||(0.003)|(0.007)|(0.007)|
|Foreign|0.011***|0.009*|0.012**|
||(0.003)|(0.005)|(0.005)|
|Govt|-0.009|-0.036**|-0.020*|
||(0.007)|(0.015)|(0.011)|
|Access to finance|-0.004***|-0.002*|-0.004***|
||(0.001)|(0.001)|(0.002)|
|Informal sector competition|-0.004***|-0.002*|-0.004***|
||(0.001)|(0.001)|(0.001)|
|Inadequate education|0.008***|0.007***|0.003**|
||(0.001)|(0.001)|(0.002)|
|Electricity|0.002***|0.001|-0.002|
||(0.001)|(0.001)|(0.001)|
|Customs and trade|0.004***|0.006***|0.007***|
||(0.001)|(0.001)|(0.002)|
|Access to land|0.003***|0.003***|0.001|
||(0.001)|(0.001)|(0.002)|
|Political instability|-0.002|-0.001|-0.002|
||(0.001)|(0.001)|(0.002)|
|Courts|-0.001|-0.001|-0.002|
||(0.001)|(0.002)|(0.002)|
|Crime|0.001|-0.001|0.001|
||(0.001)|(0.001)|(0.002)|
|Tax rates|-0.002**|0.001|-0.001|
||(0.001)|(0.002)|(0.002)|
|Tax administration|0.001|-0.002|-0.001|
||(0.001)|(0.002)|(0.002)|
|Licensing and permits|0.000<br>|0.000<br>|0.002<br>|
||(0.001)|(0.002)|(0.002)|
|Corruption|-0.001|-0.001|-0.001|
||(0.001)|(0.001)|(0.002)|
|Transport|0.000|0.001|-0.001|
||(0.001)|(0.001)|(0.002)|
|Labor regulations|-0.002*|-0.003|0.004**|
||(0.001)|(0.002)|(0.002)|
|Constant|0.191***|0.151***|0.167***|
||(0.005)|(0.007)|(0.009)|
|Adjusted_R_-squared|0.146|0.118|0.114|
|Number of observations|15,322|6,014|5,237|
|Number of countries|95|95|95|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Model: _EG_ = _b_ 0 + _b_ 1All 15 Obstacles + _b_ 2Firm Characteristics + Region + Country Fixed Effects + _e_ . * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

26
20 Even though the basis risk for the Venezuelan crudes was found to be relatively high, it does not mean that there is no scope for hedging. Even with a basis risk of 30%, the use of futures contracts will reduce the volatility associated with oil prices by 70% (100-30%) over short-run periods (one to three months). For longer-term hedges, the reduction in volatility will be somewhat less, but no less than 62% (Claessens and Varangis, 1991).

While basis iisk was not formally analyzed for the refined products due to the lack of data, an indication of the basis risk can be obtained from seeing how closely cash prices of refined product from Venezuela and international market prices for these products (as expressed in the New York market) move together. Regression analysis for monthly data for 1990 shows that changes in the New York spot fuel oil price and the **WTI** crude price explain about 78% and 67%, respectively, of the changes in MARAVEN's fuel oil prices. For the same time period, changes in the New York spot diesel price explain about 84% of the changes in MARAVEN's diesel oil prices. Also, 82% of the changes in MARAVEN's gasoline prices are explained by New York gasoline prices.

Doing the same regression analysis for gasoline, but using weekly data instead of monthly data, we find that the correlation between gasoline spot price changes in New York and in MARAVEN changes significantly. Using weekly data for 1989 and 1990, the changes in the New York gasoline spot price explain less than 10% of MARAVEN's gasoline spot price changes. If, instead, the New York gasoline spot price change lagged one week is used, about 50% of MARAVEN's gasoline spot price changes can be explained. With regards to fuel oil,
(2001), Anderson et al. (2007), Battiston et al., (2007), Weisbuch and Battiston (2008), Coluzzi et al. (2010), and Henriet et al (2012) investigate this issue and propose modeling approaches to account for these effects. In this paper, we approach this problem in an implicit manner, i.e. without explicitly representing supply chains and heterogeneous impacts. In practice, we take into account these heterogeneities at the sector level, in the assessment of production bottlenecks and substitution capacity. As will be shown, assumptions on heterogeneity and substitution capacity have a large influence on model results.

The third difficulty is related to the role of inventories, to production dynamics, and more generally to the representation of supply shortages (Okuyama, 2004; Okuyama et al., 2004). Some inputs are absolutely necessary for production, and a short interruption can cause significant perturbation in production. Examples are electricity, water, fresh goods, and all other goods that are required for production and cannot be stocked. Other inputs are also necessary for production, but they can be stocked, and a short interruption in supply does not create large difficulties. An example is steel and tires for automakers, which are indispensable but stockable. Finally, other inputs are not absolutely necessary for production, and reasonably long interruptions can be managed. This is the case for instance of many business services, education and professional training and — to a certain extent — for maintenance.

Inventories matter because they influence bottlenecks in the production system. They introduce an additional — and critical — flexibility into the system, as mentioned in the case of the Honda production in the New York Times (2011b). Investigating inventories is also interesting because modern production organization tends to reduce the use of inventories with productionon-demand and just-in-time delivery. Added to other trends (e.g. outsourcing, reduction in the number of suppliers), these changes may make each production unit more dependent on the ability of its suppliers to produce in due time the required amount of intermediate goods. As a result, 7
10 percentage points than a product representing 20% of the firm’s export sales.

In column (4) of table 3, we control for a potential omitted variable that could bias our results if country _c_ had a comparative advantage in product _p_ , explaining both that it had more exporters of that product (in destination _d_ or elsewhere) _and_ that product _p_ had a better survival outlook. As a control for this, we use the initial (sample-start) value of Balassa’s revealed comparative advantage (RCA) index defined, for product _p_ , as where _vpc_ stands for country _c_ ’s exports of product _p_ and _xpw_ for world exports of that good. Balassa’s index measures the ratio of the share of product _p_ in country _c_ ’s export basket relative to it share in the world’s export basket. The higher it is, the more that country is revealed to have a comparative advantage in that product. We compute it at HS6 from mean exports for 1999, 2000 and 2001. Results are robust to the inclusion of this control.

Finally, Column (5) provides a key test of whether our synergy effect is spurrious by replacing it with a ‘placebo’. Namely, we replace ln _n_<sup>_c_</sup> _pdt_<sup>,the</sup> number of firms exporting the same product to the same destination from the same country, by ln _n_<sup>_−_</sup> _pdt_<sup>_c_,thenumberoffirmsexportingthesameproductto</sup> the same destination from _other_ countries. For instance, consider an export spell of boys’ swimwear (HS611239) to Germany by a Senegalese firm. On the right-hand side of the equation, instead of the number of other Senegalese firms exporting HS611239 to Germany, we will now have the number of firms exporting HS611239 to Germany from other countries in our sample (Tanzania, Malawi and Mali). This variable may be positive or zero. It may also be missing, as our national samples have some non-overlapping years, so the sample size is substantially lower. It should also be kept in mind that the placebo we are using is neither random nor “matched”, being dictated by data availability. It is thus not a rigorous counterfactual. Be that as it may, whereas the synergy effect comes out very strongly in all specifications, whether pooled across countries (as reported in Table 3) or run separately by country, the placebo effect is never significant. This test contributes to increase our confidence that are “synergy effect” is not identi22
<!-- Start of picture text --> Figure 2. Impact of AGOA and GSP across years<br>Panel A. AGOA apparel Panel B. GSP LDC<br>Estimates of AGOA apparel coefficients by year Estimates of GSP LDC coefficients by year<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year Year<br>Panel C. AGOA non-apparel Panel D. AGOA non-LDC<br>Estimates of AGOA non-apparel coefficients by year Estiamtes of AGOA for non-LDC coefficients by year<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Year<br>Year<br>.3<br>.2<br>.1<br>0<br>-.1<br>-.2<br>.4<br>.3<br>.2<br>.1<br>0<br>-.1<br>.1<br>.05<br>0<br>-.05<br>-.1<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Notes: figures show coefficients and 95 percent confidence intervals based on robust standard errors, clustered by HS 6-digit product obtained by estimating Eq. (1) allowing for separate coefficients in each year after 1997 for GSP LDC (panel B) or 2001 for AGOA (panels A, C, and D).

42
effects on EAP’s potential output growth—the growth rate of output that can be sustained at full employment and capacity utilization. The sustained weakening of investment growth in the 2010s, together with declining total factor productivity growth, has already contributed to a slowdown in labor productivity growth in EAP and, as a result, slowed EAP’s convergence with per capita incomes in the advanced economies (Dieppe 2020).

# 5.3 Regional investment needs

Infrastructure. Income and demographic shifts, urbanization, and climate change are the main forces driving investment needs in the region. Rapid urbanization, large-scale migration, and population aging place heavy strains on urban infrastructure. In many East Asian countries, about one-third of the population lives in substandard housing. Meeting the growing demands of these trends while mitigating and adapting to climate change requires a balance to be struck between economic growth and environmental protection. Estimates of the costs of the needed investment vary widely (ADB 2017; ESCAP 2022; Hansen 2022; OECD 2019a), but it is clear that EAP countries need to invest more than 5 percent of their GDP over the next decade to meet the infrastructure needs of their growing economies (ADB 2017).

The largest costs would be involved in the upgrading of power and transport infrastructure, investment in telecommunications, and real estate development. There are significant disparities across the region, including within countries, in the density and quality of transport networks, electricity provision, housing, water, and sanitation. The within-country gaps are largest in China, primarily because of its size; Indonesia; and the lower-income ASEAN economies (figure 5). But there are substantial needs for upgrading and maintenance of infrastructure in other EAP economies, including Malaysia, the Philippines, and Thailand.

Despite some remarkable successes, providing adequate transport networks, power and water supplies, and other utilities remains a challenge across much of the region. Extensive construction activities are underway, with transport, especially rail, accounting for the largest share, with aims including better integration of the region's transport networks and support for urbanization.

China’s highway network more than doubled in size between 2010 and 2021, and the share of high-speed railways was boosted from 33 to 50 percent of total railway kilometers. Yet, transport density in China still falls far short of that in advanced economies. Infrastructure needs vary considerably across Chinese regions and range from establishing new highspeed railways to installing basic municipal infrastructure and pollution-reducing (or - reversing) technologies.

Lack of adequate infrastructure is the main cause of Indonesia’s reduced but still high logistics costs (around 15 percent of companies’ total expenditure), including high transport costs. Middle-income ASEAN countries in general, such as Malaysia and Thailand, are still investing heavily in rail and other public transport systems. In
adults has its drawbacks, not only for rural families, but also for urban ones. For there are substantial differences in the levels of young adults’ human capital across the two sectors, so that the arrival of migrants reduces the average level of human capital in urban areas and slows down the rate of growth of λ2,t. In any event, e<sup>p</sup> 2,t<sup>andes</sup> 2,t<sup>both</sup> reach unity in period 3, so that thereafter, it is only the contribution of the family’s adults to the children’s capacity to build human capital that affects the evolution of λ2,t. As ξt peaks in period 2, the influence of the term (λ1,tξt + φλ1,t−1ξt−1) / (1 + ξt) weakens in later periods, and the growth rate of λ2,t pick ups strongly: the average growth rate of λ2,t over the whole horizon is about 23% per period, well below its asymptotic rate. Thus, while the level of urban young adults’ human capital in the cities is always higher than their rural cousins’, the ratio thereof falls from 2.0 in period 1 to 1.44 in period 6.

A salient feature of the urban sector is the use of physical capital in production. Premature adult mortality has an adverse impact on the savings of young adults, and yet st drops by 20% between periods 1 and 2, before starting to accelerate, attaining almost two and a half times its initial level in period 6. Other factors are also at work, of course; so that a comparison with the counter-factual D = 0 is needed to settle matters (see Section 4.2 below). The consumption of a young adult increases six-fold.

A brief comment on the main national aggregates is also required. From Table 2 it is seen that the total population of those aged 5 and older increases by a factor of 4.3 over the time horizon from 2000 to 2100. If this seems rather staggering, the reader is reminded that the Ethiopian population doubled over the past 25 years alone. Given the country’s very youthful population, only a much faster demographic transition or a sharp reduction in completed fertility due to the HIV/AIDS epidemic will impose a much less numerous population in the steady-state.

The other index of central importance is GDP per head. Let Y1,t ≡ y1,tF1,t and gradually acquire the organizational characteristics and judicial status of towns.

23
of job protection in developing countries, particularly among females (Bonnet, Vanek and Chen, 2019; de Paz Nieves, Gaddis and Muller, 2021). Jobs that are amenable to working from home, which have been found to moderate the impact of the pandemic on employment (Dingel and Neiman, 2020), while also reducing its gender effects (Alon et al., 2021), are relatively scarce across LMIC countries. As such, the impact of the COVID-19 crisis within these countries may differ substantially from the impacts that have been observed across developed countries.

Indeed, there are indications that the declines in employment seen in developing countries are steeper than those observed in developed countries; descriptive evidence from the HFPS studies undertaken in developing countries suggests that about 30 to 40 percent of workers stopped working during the second quarter of 2020<sup>10</sup> (Khamis et al., 2021; Bundervoet, Davalos and Garcia, 2021; Schotte et al., 2021). Such a magnitude is similar to that reported in other non-HFPS studies in developing countries as well (Jain et al., 2020, in South Africa; Biscaye, Egger and Pape, 2021, in Kenya and Nigeria). Consistent with the findings in developed countries, some of these studies report that females are significantly more likely to lose their jobs and to report larger declines in earnings (Hoehn-Velasco et al., 2021; Kugler et al., 2021; Deshpande, 2020; Jain et al., 2020). In South Africa, the findings of Jain et al. (2020) suggest that women experienced a 49 percent reduction in active employment over the February–April 2020 period, which is 15 percentage points larger than the reduction experienced by men in the same period.

The negative employment effects among females are also confirmed by similar results from simulation exercises using pre-pandemic data. For example, Lavado et al. (2022) find that, during the onset of the pandemic, female workers in the Philippines experienced a 4.5-percentage-point

> 10 Whereas in the United States and Canada previous studies have found that employment had dropped by only 21 percent and 15 percent, respectively, in April 2020. One should cautiously interpret the magnitude of the differences, as they could arise due to the different respondent types and sampling methodologies between the HFPS and nonHFPS studies. HFPS studies, such as Khamis et al. (2021) and Kugler et al. (2021), are typically only representative at the household level.

10