rafmacalaba/datause-encoder-data huggingface dataset:
online appendix Table A10: Mask manufacturing exposure decreases mask prices across multiple approaches to constructing prices

|||_Depende_|_nt variable:_||
|---|---|---|---|---|
||Ef mask Q fro<br>Real m|m manufacturers<br>ask Q|Mask purchases <br>Mask|from manufacturers<br>purchases|
||(1)|(2)|(3)|(4)|
||-0.016|0.155|-0.030|0.098|
|Mask manufacturing exposure_d ×_Nov_t_|(0.123)|(0.202)|(0.156)|(0.249)|
||[0.897]|[0.444]|[0.850]|[0.696]|
||0.035|0.135|0.026|0.116|
|Mask manufacturing exposure_d ×_Dec_t_|(0.114)|(0.214)|(0.148)|(0.258)|
||[0.760]|[0.531]|[0.861]|[0.653]|
||0.057|0.224|0.053|0.202|
|Mask manufacturing exposure_d ×_Jan_t_|(0.115)|(0.211)|(0.148)|(0.251)|
||[0.620]|[0.290]|[0.723]|[0.424]|
||0.046|0.130|0.027|0.115|
|Mask manufacturing exposure_d ×_Feb_t_|(0.089)|(0.181)|(0.114)|(0.210)|
||[0.608]|[0.475]|[0.812]|[0.584]|
|Mask manufacturing exposure_d ×_Mar_t_|-|-|-|-|
||1.095|0.973|0.999|0.896|
|Mask manufacturing exposure_d ×_Apr_t_|(0.252)|(0.413)|(0.281)|(0.465)|
||[0.000]|[0.021]|[0.001]|[0.057]|
||0.533|0.464|0.504|0.446|
|Mask manufacturing exposure_d ×_May_t_|(0.179)|(0.303)|(0.204)|(0.324)|
||[0.004]|[0.130]|[0.015]|[0.173]|
||0.196|0.513|0.254|0.518|
|Mask manufacturing exposure_d ×_Jun_t_|(0.194)|(0.293)|(0.223)|(0.314)|
||[0.316]|[0.083]|[0.258]|[0.103]|
||0.438|0.398|0.514|0.448|
|Mask manufacturing exposure_d ×_Jul_t_|(0.136)|(0.262)|(0.148)|(0.275)|
||[0.002]|[0.132]|[0.001]|[0.107]|
||-0.138|0.031|-0.111|-0.017|
|Mask manufacturing exposure_d ×_Aug_t_|(0.197)|(0.311)|(0.221)|(0.341)|
||[0.485]|[0.920]|[0.617]|[0.960]|
|Estimation method|OLS|OLS|OLS|OLS|
|Firm-by-product FE|||||
|Destination sub-District FE|X|X|X|X|
|Month FE|X|X|X|X|
|Residualized shocks||X||X|
|Controls _×_ Month FE||X|||
|# observations|506|506|506|506|
|# clusters (sub-Districts)|86|86|86|86|

# **G Number of paracetamol receipts as a proxy for COVID-19 infections**

In Section 3.1.2, we used number of purchases of paracetamol, a commonly recommended fever medicine, as a proxy for COVID-19 infections, absent data on subA33
government organizations outside the regulated financial system. Another important caveat is that the definition of housing finance will differ across countries. Some countries will only include collateralized housing finance where a mortgage lien has been established, whereas others may have a looser definition covering non-secured consumer loans used for housing purposes. Likewise there is often a fine line, especially in emerging markets, between what constitutes a secured business loan or a commercial property loan and a housing loan. For the purposes of this study, we abstract from these definitional variations across countries.

Figure 1 shows the enormous cross-country variation in mortgage depth. Here, we present average data for the period 2006 to 2010. Mortgage debt to GDP ranges from less than 1 percent in Rwanda to 109% in Denmark. Appendix Table A2 lists the average Mortgage Depth across 99 countries over the period 2006 to 2010.

The second housing finance measure— **Housing Loan Penetration** , the percentage of adult population with an outstanding loan to purchase a home—was obtained from the Global Financial Inclusion (FINDEX) Database. Unlike the Mortgage Depth indicator, the penetration indicator refers to any provider of housing loans, including regulated financial institutions, microfinance institutions, and informal sources. The FINDEX database was constructed with survey data collected from a randomly selected sample of 150,000 individuals in 148 countries. The survey, conducted by Gallup Inc. in 2011, constitutes the first comprehensive attempt to measure financial inclusion at a global scale and provides a substantial array of indicators on countries’ saving, borrowing, payments, and risk management practices (See Demirguc-Kunt and Klapper, 2012 for more information). Presently, FINDEX data are only available for a cross-section of 148 countries in 2011, for which reason the data can only be used for cross-sectional analysis. This analysis can be expanded to account for over-time changes in financial inclusion measures as the data collection effort is further pursued in future years.

6
Filmer, D. (2002). Fever and its treatment among the more and less poor in Sub-Saharan Africa. Policy Research Working Paper 2798. World Bank, Washington, D.C.

- Koella, J. and R. Antia (2003). “Epidemiological models for the spread of anti-malarial resistance.” _<u>Malar J</u>_ **2** (1): 3.

- Koella, J. C. (1991). “On the use of mathematical models of malaria transmission.” Acta <u>Tropica</u> **49** (1): 1-25.

- Laxminarayan, R. (2004). “Act now or later? Economics of malaria resistance.” _Am J Trop Med Hyg_ **71** (2 Suppl): 187-95.

- McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. _Frontiers in Econometrics_ . P. Zarembka. New York: Academic Press, 105-142.

- Nosten, F., M. v. Vugt, et al. (2000). “Effects of artesunate-mefloquine combination on incidence of _Plasmodium falciparum_ malaria and mefloquine resistance in western Thailand: a prospective study.” _Lancet_ **356** : 297-302.

- Pringle, G. and S. Avery-Jones (1966). “Observations on the early course of untreated falciparum malaria in semi-immune African children following a short period of protection.” _Bull World Health Organ_ **34** (2): 269-72.

- Watkins, W. M., C. H. Sibley, et al. (2005). “The search for effective and sustainable treatments for Plasmodium falciparum malaria in Africa: a model of the selection of resistance by antifolate drugs and their combinations.” _Am J Trop Med Hyg_ **72** (2): 163-73.

- Whittington, D., M. L. Cropper, et al. (2000). The Value of Preventing Malaria in Tembien, Ethiopia. Policy Research Working Paper 2273. World Bank, Washington, D.C.

15
We present below a list of generic household types that can be tailored to specific country contexts. At a minimum, these types should specify the sex and age ranges of household members, the relationships among the household members (for example, married or in consensual union, with and without dependents), and the employment or earning status of the principal adults in the household. Together, that information is important in establishing what the care and social reproduction needs of various households may be.

Constructing the household typology and undertaking the fiscal incidence analysis should follow a few steps. The first step is to compile information from censuses, household surveys, labor force surveys, and other sources, such as time use if available,<sup>21</sup> to get a big-picture understanding of the nature of gender inequalities in employment and social reproduction. The second step is to use this data to construct economic and demographic categories. A generic set of groupings that can be adopted in most countries is as follows:

# <u>Economic categories</u>

1. Single male/female member households, who are employed or not employed. In many countries, these are older retired adults.

2. Households with married couples or consensual unions.<sup>22</sup> This category should be divided into at least four subcategories: where both principal adults are employed, where only the principal male is employed, where only the principal female is employed, and where no adult is employed.

3. Households with working-age adults and dependents, disaggregated by which members are employed. Examples of this type of household may be an adult child living with an elderly parent or younger-generation children living with older-generation children (from the same parents).

## <u>Demographic categories</u>

Using the economic categories above, households should then be further classified by their care needs:<sup>23</sup>

1. without dependents and with dependents

2. total dependents 3. dependents disaggregated by age: for example, children 0 to 3 years old, children ages 4 to 12, adults over 75, and/or other age compositions relevant in a particular country context.

> 21. Additional contextual information is available in sociological, legal, and anthropological studies and in databases summarizing the institutional and legal structures that inform gender-based equity. See for example the Social Institutions and Gender Index, Afrobarometer, the World Values Survey, and the Women, Business and the Law databases.

> 22. In those countries where same sex marriages or consensual unions are recognized and such data exist, the types could also be male/male and female/female. Consensual unions also include polygamous structures.

> 23. In the first stage of work, country pilots classified households into separate economic and care categories. However, for fiscal incidence analysis, it is important to use a single combined classification.

14
_2. THE BELT AND ROAD INITIATIVE_ khstan, real wages will grow 5 times more in the locations around Almaty than in the northern locations at the border with the Russian Federation.

2. Complementary investments in trade facilitation accentuate economic gains around hubs while investments in domestic transport networks help in spatially spreading the benefits.

3. Barriers to domestic labor mobility exacerbate spatial inequalities whilst dampening overall welfare.

4. Domestic policies and investments that support spatial mobility of labor and internal connectivity can mediate potential spatial efficiency equity tradeoffs within countries.

The rest of the paper is organized as follows. In Section 2, we provide details on the Belt and Road Initiative investments and the countries of interest. In Section 3, we describe our modelling strategy to develop economic geography counterfactuals of the BRI in Central Asia. We discuss results on BRI winners and losers, and discuss risks from spatial inequalities in Section 4. Section 5 concludes.

# **2 The Belt and Road Initiative**

The Belt and Road Initiative roughly follows and expands the old Silk Road on the land side and complements it with a maritime part to build a series of economic corridors across Asia, Europe and Eastern Africa. The range of initiatives is very wide, including policy coordination, infrastructure, trade and investment, financial and people-to-people exchanges. In this paper, we focus on the consequences of transport infrastructures linked with the Belt and Road Initiative. Given that there is no official list of projects and countries in the Belt and Road Initiative, we rely on the list created by the World Bank (Figure 1, de Soyres et al. (2018)).

## **2.1 What is the Belt and Road Initiative?**

**Main corridors** The BRI is organized around six economic corridors: (1) the China-MongoliaRussia Economic Corridor; (2) the New Eurasian Land Bridge; (3) the China-Central AsiaWest Asia Economic Corridor; (4) the China-Indochina Peninsula Economic Corridor; (5) the China-Pakistan Economic Corridor; and (6) the Bangladesh-China-India-Myanmar Economic Corridor. This paper focuses on Central Asia and the "Belt corridors" but also considers how investments outside Central Asia will improve the connectivity of these landlocked countries to international gateways. The focus on Central Asia is motivated by the fact that large reductions in transport costs can have large internal impacts for economies that are not integrated through trade linkages<sup>3</sup> . Three main corridors are of particular interest for Central Asia: the

> 3Figure Annex.2 shows changes in shipping times per country for the entire world

4
(NUPTK), which is the national teacher database (Ministry of Education and Culture 2012a). Costs were calculated using the ingredients method (Levin and McEwan 2001), which included personnel, facilities, equipment and materials, fees charged to families, and other operational costs of playgroups and kindergartens. In order to estimate annual costs per child, we assumed that the average center size is 31 children per kindergarten and 21 children per playgroup (Ministry of Education and Culture 2012b), and that the average student to teacher ratio was 15:1 in kindergarten and 11:1 in playgroups (as per the Indonesian minimum service standards). Based on these assumptions, we estimated total annual costs to be approximately 150.97 USD per child in playgroups and 256.25 USD per child in kindergartens.

We divided the effectiveness of each early education pathway by its cost to calculate indicative effectiveness-cost ratios. This meant that the more effective the early education pathway, the larger the effectiveness-cost ratio. The results are presented in Figure 2, which includes 95 percent confidence intervals to take into account errors in the estimation of the effectiveness of various ECED pathways.

Our estimates are of two types – those that are statistically distinguishable from zero and hence cost effective and those that are not. Those pathways whose cost effectiveness is not distinguishable from zero have the following in common: they involve exposure to some playgroup or some kindergarten but not to both. The pathways that are positive and statistically distinguishable from zero have in common the fact that they are some combination of 1-2 years of playgroup with 1-2 years of kindergarten. These estimates range from 0.032 to 0.064 but cannot be distinguished from each other.

Thus, our results show that enrolling in a combination of playgroup and kindergarten at developmentally appropriate ages is cost-effective.<sup>7</sup> Focusing on providing access to both playgroups and kindergartens to young children at the appropriate ages can optimize public investments in early childhood education.

# **9. Discussion**

As developing countries increase investments in early childhood education, one of the challenges faced by policy makers is deciding what to fund given the wide range of programs that

> 7 Note that this does not take into account that kindergartens meet for more hours per week.

20
**Figure 1.1. Size of economy and volume of savings in focus countries, excluding South Africa and Nigeria**

<!-- Start of picture text --> US$ mns<br>34,000<br>29,000<br>24,000<br>Size of economy (2006 GDP)<br>Gross domestic savings<br>19,000<br>14,000<br>9,000<br>4,000<br>-1,000<br>Sudan Kenya Cameroon Côte d'Ivoire Ethiopia Tanzania Ghana Zambia Uganda Senegal Mozambique DRC Namibia Chad Burkina Faso Madagascar Benin Niger Malawi Rwanda Lesotho Cape Verde<br><!-- End of picture text --> Sources: World Bank, GEM and WDI databases.

Absolute savings only cover infrastructure investment needs, estimated at 10 percent of GDP, in 12 of the 24 countries (table 1.1).<sup>4</sup> Of course, gross domestic savings represents only a theoretical upper threshold as an indicator of the maximum available domestic investment available for meeting estimated infrastructure needs. Nevertheless, it is clear that half of the countries are severely constrained in their ability to put domestic savings to use toward infrastructure development, given that these 12 countries have a shortfall between these two indicators, in some cases significant. In the case of Ethiopia, which has the largest shortfall, the gap between gross domestic savings and infrastructure investment needs was more than $2.1 billion in 2006. The five other economies that have a shortfall are all very small and/or postconflict countries (Cape Verde, Democratic Republic of the Congo, Lesotho, Malawi, and Rwanda).

> 4 According to the most recent estimates of the World Bank’s African Sustainable Development Department (Africa Region).

3
<!-- Start of picture text --> Congo<br>a<br>wo Penk, ge ee oda<br>° ‘<br>ha ~e<br>ot a7) é<br>wae<br>+ ee ee + ee<br>o<br>~<br>i) 10 20 30 40 Quantile50 60 70 80 90 100<br>—-*-—- Total Difference -. +#- -- Composition<br>—*— Structural<br><!-- End of picture text --> <!-- Start of picture text --> Ghana<br>a<br>*<br>0 - ~e—*<br>7 +H _@<br>.<br>= ee ge T<br>ee * . cia a ee ee |<br>o<br>~<br>i) 10 20 30 40 Quantile50 60 70 80 90 100<br>—-*-—- Total Difference -. +#- -- Composition<br>—*— Structural<br><!-- End of picture text -->
the manager has studied abroad is worth about 5% and dispersed ownership about 30%. The latter suggests that, while it is the case that the fact that the US has more firms with diffuse ownership than the _z >_ 0 category as a whole, it also gets a bigger kick out of it in terms of managerial quality.

# **6.3 US vs. Worse Managed Countries,** _z <_ 0

Table 9 shows that the overall results are more dramatic for the gap between the badly managed countries vs. the frontier. Consistent with previous findings, the raw, MM and RIF measures all show that the gap is largest among the worse firms: for the RIF 0.729, 0.594, and 0.432 at the 10<sup>_th_</sup> , 50<sup>_th_</sup> and 90<sup>_th_</sup> respectively. This is the reverse of the finding for the better managed country ( _z >_ 0) sample. The MM only shows statistically significant explanatory power at the 10<sup>_th_</sup> quantile (23% of the gap). However, in this case the RIF explanatory power of the covariates is importantly higher and very significant at every quantile.

Looking at the significant individual covariates, we find the same negative contribution of managers studying abroad found previously and, as Table 1 suggests, for the same reasonthe US studies abroad very little. Whether the manager had a degree was not significant at the 10<sup>_th_</sup> and marginally so at the 50<sup>_th_</sup> and 90<sup>_th_</sup> , explaining about 2% the gap. Surprisingly, the share of managers with a degree never enters significantly. The share of employees with degrees is highly significant across the distribution but explaining only 2% for the lower two quantiles, although up to 11% at the 90<sup>_th_</sup> . Being a multinational is now very significant across the distribution with explanatory power of 2% at the 10<sup>_th_</sup> , 6% at the 50<sup>_th_</sup> and 8% at the top.

Ownership again enters with massive importance and significance and across more different types of structures than observed above. Diffuse ownership contributes 4.5%, 5.9% and 15% the 10<sup>_th_</sup> , 50<sup>_th_</sup> and 90<sup>_th_</sup> respectively, being family owned about 1.5% at the 10<sup>_th_</sup> and 50<sup>_th_</sup> , and Founder owned 3.5 to 6% moving up quantiles. Ownership differences appear to explain almost 10% at the 10<sup>_th_</sup> , 11% at the median and 21% at the 90<sup>_th_</sup> . Though the marginal effect of diffuse management is highest at the lower quantiles, the difference in 20
# A Data Appendix

## A.1 Sample Selection Criteria

The analysis is based on a sample drawn from the 5/100 public-use micro data ...les of the US Censuses of Population of 1980 and 1990.<sup>35</sup> The sample is restricted to male US immigrants who are between 25 and 64 years old, worked and earned at least $1000 wage or salary income in the year preceding the census and were not enrolled in school at the time of the census. Furthermore, immigrants in the sample arrived to the US after 1959 and completed their education in their home country. The latter restriction is motivated by the fact that both censuses provides only information on the highest educational degree obtained. It is hence impossible to identify years of experience in the home country for immigrants who acquired US schooling.

The censuses provides information on the year of immigration only within brackets of varying width. Following Bratsberg and Ragan (2002), Bratsberg and Terrell (2002) and Chiswick and Miller (2002) an immigrant is included in the sample if (6+ years of education) is lower than his age at the lower bound of the year of immigration bracket. This restriction ensures that no immigrant who acquired US schooling enters the sample. These sample selection criteria leave a total sample of 173137 observations, of which 58695 belong to the 1980 census and 114442 to the 1990 census. Descriptive statistics of the full sample are presented in Table 1.

The sample size reduces to 171445 observations when matching the census data with the Penn World Tables Mark 5.6 as some countries do not report information on openness.

## A.2 Variable Description

### A.2.1 Census Variables

The dependent variable of the wage regression is the natural logarithm of the annual wage or salary income in 1979 or 1989. Years of schooling in the 1980 census are based on the ”Highest Year of Schooling Attended”. If the respondent did not complete the highest grade attended, one year is subtracted. The rule used to convert educational attainment to years of schooling in the 1990 census is the same as in Bratsberg and Terrell (2002).

> 35 The data are available online at http://www.ipums.org. For more information on the data, see Ruggles and Sobek (1997).

28
with an average coefficient of 0.83pp, indicating that as more women pursued higher education during this decade, their participation in the labor force also increased.

**Figure 8: Effect of the female enrollment rate in tertiary education on the female labor force participation rate**

<!-- Start of picture text --> 3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Female enrollment rate tertiary education<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

In the following years, while female participation continued to increase, women's enrollment in higher education also grew but at a slower pace; thus, the increase in female participation could be attributed to increases in secondary education rather than in higher education, given that there was still room for improvements in the enrollment rate in secondary education. The relationship between higher education and female labor participation could be associated with the low enrollment rates for both men and women, despite the increase in women's enrollment. In fact, in 1985, Colombia's higher education coverage rate (10%) was still much lower than that of other countries, such as Argentina (38%), Chile (14%), Spain (29%), and the United States (64%). Furthermore, the careers that women studied, which according to Vélez and Winter (1992), were oriented towards educational fields that led to low-paying careers, may have harmed their labor force participation. Also, cultural and social norms regarding gender roles and expectations may have affected the relationship between female enrollment in higher education and female labor force participation.

Since the mid-1990s, the country started experiencing changes in social norms and economic conditions and increased access to education and employment opportunities for women. From 1995 to 2010, the female enrollment rate in higher education increased from 13.4% to 23.9%, mainly due to the measures established in the 1991 Constitution and Law 30 of 1992, which recognized education as a right and increased the contributions from the national government budget for higher education.

15