rafmacalaba/datause-encoder-data huggingface dataset:
Figure 5: Internal rate of return by education level: 1992-2002 <!-- Start of picture text --> 20<br>15 Prim/none<br>Sec/prim<br>10<br>Higher/sec<br>5 Univ/sec<br>0<br>1992 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text -->

# <u>Quantile Regressions</u>

The ordinary least squares (OLS) regression relies on the mean of the conditional distribution of the dependent variable. When it is suspected that various exogenous variables influence parameters of the conditional distribution of the dependent variable other than the mean, quantile regressions are particularly useful, because they allow the full characterization of the conditional distribution of the dependent variable, rather than the conditional mean only. In short, the quantile regressions method allows an investigator to differentiate the contribution of regressors along the distribution of the dependent variable. In particular, the estimation of returns to education entails much more than the fact that, on average, one more year of education results in a certain percent increase in earnings.

The quantile regression model (Buchinsky 1994) can be outlined as: where _Xi_ is a vector of exogenous variables; βθ is the vector of parameters; (Quantile)θ(ln _wi_ |X _i_ ) is

the θth conditional quantile of ln _w_ given _X_ , with 0<θ<1. The θth quantile is derived by solving the problem (using linear programming): 13
respondents (who did answer) did not consent. Neither of these rates are differential across treatment arms.<sup>7</sup> Residents were selected from a variety of localities within Lahore and Faisalabad - high density, low density, poor, rich. The sample is therefore representative of urban Punjab, but with two qualifications. First, all individuals in our sample own or rent a taxable property. This restriction is imposed by Khan et al. (2020), which we rely on to build our sample. Our sample therefore excludes individuals who live in informal settlements and individuals who live in private housing societies, the two extremes of the income distribution. Second, the individuals in our sample are almost entirely (96%) male. Men are overrepresented because they were more likely to participate in the Khan et al. (2020) surveys and, conditional on participation, more likely to share their phone numbers.

Appendix Table A1 presents additional descriptive statistics. Individuals have an average age of 37.3 years and 72% have completed secondary education. We did not measure income because of respondents’ reluctance to provide this information over the phone. However, there is considerable heterogeneity in respondents’ ability to smooth income while following state directives on minimizing the spread of Covid-19: 57.9% of respondents report compliance with state directives would result in at least a 50% loss in weekly income, while 19.1% of respondents report a loss of at most 20% of their weekly income. There is also heterogeneity in compliance with state directives: on average, the average response to past compliance with the directive to avoid congregational prayers is just 0.51 (on a scale from – 0 to 1 meaning the respondent declared not to have followed the directive at all or to have completely followed the directive, respectively). At 13.4%, there is also a sizeable share of the respondents who believe in some version of a conspiracy theory centered around the allegation that the Covid-19 pandemic has non-natural origins.

Finally, our sample reflects political divisions in Punjab and Pakistan more generally. 44% of respondents report voting for the ruling Pakistan Tehreek-e-Insaaf (PTI) in recent elections, while 29.5% of respondents report voting for the opposition Pakistan Muslim League - N (PML-N). Political preferences are also reflected in media consumption: 36.5% of the former group watch ARY News, which tends to favor the ruling party, while 51.8% of the latter group watch GEO News, which tends to oppose the ruling party.

# **2.2 Treatments**

All respondents received an overview of the Punjab government’s guidelines on minimizing the spread of Covid-19. These guidelines changed over time. In the first half of the ex-

> 7Results available upon request.

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wave of the HFPS ( _Internet_ ). Formally, (1) Where _i_ labels the individual and _j_ her occupation. Notice that by adjusting for home internet access the index of _WFH_ at the occupation level _j_ becomes an index defined at the individual level. Figure 5 shows the distribution of the WFH index at the individual level for each country before the pandemic. The distributions differ across countries because of the differences in their occupational structures. For instance, the occupational structure in Ecuador or Guatemala is much less compatible with remote work than that of Argentina or Chile.

# **4 Job Losses, Work from Home and Childcare**

Our main interest is to assess the relationship between job losses and the potential for working from home of the pre-pandemic occupation and whether this relationship differs by gender. Figures 6 and 7 show suggestive evidence on this regard. Figure 6 shows the correlation across countries between the share of workers who lost their jobs and the WFH index. In the first panel—wave 1—a clear negative association between WFH and job losses is observed for both women and men.<sup>10</sup> In other words, the initial impact of the crisis implied more job losses for countries where the occupational structure was less compatible with remote work, both for male and female workers. While this pattern persists for women in the following waves of the survey, for men the relationship between job losses and WFH loses strength and virtually disappears in the third wave. Figure 7 shows that a similar pattern arises from an individual-level analysis, i.e., the correlation between the probability of job loss and the individual WFH index is stronger for women, suggesting that the possibility of working from home was more decisive in preventing job losses for women than for men.

At least two mechanisms can account for the differential importance of WFH between men and women. One is related to gender differences in childcare responsibilities, which fall mostly on women as we mentioned above. Given the allocation of these responsibilities and the school closures during the pandemic, the only way for women with young children to continue working was to do it from home. Therefore the possibility of working from home would be more relevant to avoid job losses for women with young children than for men or women without children.

Another mechanism arises from the marked occupational segregation by gender that characterizes Latin American labor markets. Then, if the possibility of doing remote work was key to keep a job during the lockdown, the fact that men and women work in different occupations that differ in the potential of WFH could also explain gender differences in

> 10Moreover, the ability to work from home is larger for women than for men in all countries which is in line with evidence presented for other countries (Gottlieb et al. 2021, Saltiel 2020).

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predictive power. As mentioned above, when estimating the model we use a set of weights that make sentinel survey reflect a specific group of women in the DHS in terms of age/education distribution. We also take into account the clustering effect which results from the fact that there are groups of observations which come from the same sentinel site, and thus there are some unobservable characteristics common for all women from a specific sentinel site. The empirical results will be discussed later in the paper. We next describe how we obtain district-level predictions (point estimates).

# **2c. Predictions of HIV prevalence**

The estimation of regression equation (1) using the sentinel data (merged with the district/sub-district means) produces the vectors of parameter estimates α and β **,** as well as the vector of residuals. Note that since a separate specification is estimated for each region, these vectors will be region-specific.

The predicted values of HIV prevalence are computed in the following way. We first obtain the empirical distributions of α and β from the model by making 100 draws using bootstrap simulations. We denote these vectors as α **n** and β **n** , where n denotes the number of rows in the vector (with the number of columns being equal to the number of variables in the model). Next, to obtain the vector of simulated errors we save the vector of residuals after the model estimation, and then make 100 random draws with replacement from this vector using bootstrap simulations. We denote this drawn vector of residuals as **en** (with the number of columns being equal to one). The vectors α **n,** β **n** , and **en** are then added to form a matrix, which we can name **B** .<sup>20</sup> Each row of values found in this matrix is applied to the values of respective variables in the DHS 2000 data to make a prediction of individual’s probability of being infected for each observation in the DHS. This can be expressed as:

> 20 Note that since a separate specification is estimated for each region, the matrix **B** will be region-specific as well.

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to face: such detailed research is very expensive, it requires forewarning of the policy change to be studied and it entails the active participation in evaluation of both of the national governments involved.

Looking forward, we hope to be able to assess the developmental impact of the RSE. We hope to conduct three future rounds of surveys over the next two years including many of the same households as covered here. These will allow us to assess the impact for a particular household of participating in the RSE, relative to a similar household that did not participate and relative to their previous circumstances, and also to start to answer questions about the broader societal benefits of the scheme. In the longer run we very much hope that someone will be able to revisit these households and communities after many years to gauge the longer run effects of access to labor markets abroad as a tool for the development of small, poor, isolated nations.

# **REFERENCES**

Corcoran, Masrk (2008) “Vanuatu/New Zealand – Working for the Man”, ABC Australia Foreign Correspondent broadcast, April 15, 2008, <u>http://www.abc.net.au/foreign/content/2008/s2218384.htm</u> [accessed April 30, 2008].

Global Commission on International Migration (GCIM) (2005) _Migration in an Interconnected World: New Directions for Action_ . www.gcim.org Maclellan, Nic (2008) “Pick of the Crop: New Zealand Opens up to Seasonal Workers”, _Pacific Magazine_ February 28, 2008.

McDonald, Hamish (2008) “Rich and Poor share the harvest”, _Sydney Morning Herald,_ April 28, 2008 <u>http://www.smh.com.au/news/world/rich-and-poor-share-the-</u> = <u>harvest/2008/04/27/1209234655583.html?page fullpage#contentSwap3</u> [accessed April 30, 2008]

- 31 -
important to note that modeling the average size of the cross-border investments conditional on positive FDI does not result in a sample selection issue. Given that we are interested in how financial development influences average project size when FDI occurs, the observations for which FDI is positive form an appropriate subsample of the population of interest (Wooldridge, 2010).<sup>51</sup>

# **4.7 Stylized facts**

The five largest sources of greenfield FDI are OECD countries while the five largest recipient countries are the BRIC countries and the United States.<sup>52</sup> Figure 2 shows the unconditional relationship between greenfield FDI and financial development, on the source and destination sides, at the different margins of FDI.<sup>53</sup> Firms located in financially developed countries tend to engage much more in FDI than those located in less financially developed countries (upper panel) while financially developed destination countries tend to receive more FDI than less financially developed countries (lower panel). These positive relationships between FDI and financial development, which seem slightly weaker on the destination side, are apparent when looking at the overall value of FDI, the number of FDI or the average size of FDI projects.

The patterns displayed in Figure 2 suggest that financial development plays an important role in promoting bilateral FDI. However, they may also simply be driven by factors not related to financial development. We investigate in depth the causal impact of financial development on FDI in the next section, where we present

## our econometric results.

access to firm-level data, such as Manova (2013), have used the two-step approach developed by Helpman et al. (2008) to identify the effects of financial development on average firm-level exports. Santos Silva and Tenreyro (2009) have raised strong doubts about the robustness of this methodology, especially when heteroskedasticity is present.

> 51In unreported regressions, we also estimated two-parts models, where the probability of observing a positive FDI flow is first modeled and then determinants of the volume of FDI conditional on positive FDI are examined. We found that the relative effects of SFD and DFD are positive and statistically significant in both parts of the model. These results remain unchanged when we control for the possibility of correlation between the error terms of the two parts as well as for firm heterogeneity using the modification of Helpman et al. (2008)’s approach suggested by Egger et al. (2011).

52The BRIC countries are Brazil, Russia, India and China. 53Similar patterns are found if we control for differences in income and institutional quality.

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30

# **Table 5.  Per Capita Household Expenditure Estimates (Selection Corrected) for Households Receiving International Remittances (from African or other countries)**

|Variable|OLS|Lee-Selection<br>Corrected|BFG-Selection<br>Corrected|
|---|---|---|---|
|**Human Capital**<br>Number of members over age 15 with primary<br>education|1438026<br>(0.62)|<br>1182183<br>(0.67)|<br>865090.1<br>(0.47)|
|Number of members over age 15 with junior<br>secondary education|-1095597<br>(-1.03)|<br>-516971.6<br>(-0.54)|-535500.3<br>(-0.45)|
|Number of members over age 15 with senior<br>secondary education|-1998658<br>(-1.19)|<br>674688.6<br>(0.45)|<br>1679739<br>(1.00)|
|Number of members over age 15 with university<br>education|1.07e+07<br>(2.51)*|<br>1.27e+07<br>(2.86)**|1.34e+07<br>(2.99)**|
|**Household Characteristics**<br>Age of household head|-112609.1<br>(-2.23)*|<br>-66073.53<br>(-1.34)|-45665.6<br>(-0.84)|
|Household size|-992329.5<br>(-3.26)*|<br>-1128953<br>(-2.50)*|-1230470<br>(-2.26)*|
|Number of males over age 15|1797542<br>(1.97)|777170.7<br>(0.68)|323967.3<br>(0.24)|
|Number of children under age 5|-463751.2<br>(-0.37)|<br>-1516404<br>(-0.94)|-2864882<br>(-1.29)|
|**Migration Networks in ethno-religious group,**<br>**1998/99**<br>Fraction of people in top 3 religions|2355370<br>(2.20)*|<br>532962<br>(0.16)|1033035<br>(0.29)|
|Fraction of households receiving internal<br>remittances (from Ghana) in ethno-religious group|-3.06e+07<br>(-1.08)|-3.79e+07<br>(-.82)|-1.07e+07<br>(-0.18)|
|Fraction of households receiving international<br>remittances (from African or other countries) in<br>ethno-religious group|9.81e+07<br>(1.63)|<br>7.47e+07<br>(.56)|6.18e+07<br>(0.41)|
|Lambda (Selection control)||-1.85e+07<br>(-2.35)*|182577.5<br>(0.07)|
|Lambda 2 (Selection control)|||1.42e+07<br>(2.30)*|
|Constant|-2.09e+08<br>(-1.62)|<br>-2.38e+08<br>(-0.52)|-1.42e+08<br>(-0.29)|
|Adjusted R<sup>2</sup>|26.44|||
|Hausman χ<sup>2</sup>(18)||5.22|6.09|
|Joint test for Lambda and Lambda 2 χ<sup>2</sup>(2)|||5.34|
|N|210|210|210|

<!-- Start of picture text --> 210 210<br><!-- End of picture text --> <!-- Start of picture text --> 210<br><!-- End of picture text -->
# **Measuring the Statistical Capacity of Nations**

_Grant J. Cameron, Hai-Anh H. Dang, Mustafa Dinc, James Foster, and Michael M. Lokshin_<sup>*</sup> Key words: statistical capacity, statistical indicators, statistical index, national statistical system, data, measurement JEL: C8, H00, I00, O1

> * Cameron <u>(gcameron1@worldbank.org) is manager with the Advisory Services and Economic Statistics Unit,</u> Development Data Group, World Bank; Dang (hdang@worldbank.org; corresponding author) is economist with the Survey Unit, Development Data Group, World Bank; Dinc (mdinc@worldbank.org) is senior economist with the Advisory Services and Economic Statistics Unit, Development Data Group, World Bank; Foster (fosterje@gwu.edu; corresponding author) is the Oliver T Carr Professor of International Affairs and Professor of Economics, George Washington University; Lokshin <u>(mlokshin@worldbank.org) is manager of the Survey Unit, Development Data</u> Group, World Bank. We would like to thank Juderica Dias for helpful support with the data, and Junhe Yang for capable research assistance. We are grateful to the UK Department of International Development for funding assistance through its Knowledge for Change (KCP) program and Strategic Research Program (SRP).
Table 8: Descriptive statistics of BOS and private firms.

||Private frms|BOS|Public|Mixed|Participated|
|---|---|---|---|---|---|
|Hourly wage|8.75|27.00|17.71|27.09|40.54|
||(10.83)|(31.84)|(27.71)|(28.11)|(36.44)|
|Monthly wage|1219.51|4200.26|2572.84|4126.92|6609.86|
||(1016.48)|(5488.14)|(3984.26)|(4397.15)|(7300.15)|
|Employment|12.54|931.70|692.10|891.84|1319.84|
||(193.06)|(6347.82)|(6528.31)|(5835.50)|(6629.42)|
|Age|9.84|23.99|20.83|28.85|22.93|
||(8.88)|(19.27)|(17.79)|(21.78)|(17.02)|
|Fraction of innovative frms|3.04%|43.42%|22.24%|46.03%|70.68%|
||17.19|49.56|41.59|49.85|45.53|
|Innovation intensity|0.61%|5.66%|3.10%|5.14%|9.93%|
||(5.85)|(12.29)|(10.30)|(10.45)|(15.36)|

_Note:_ This table presents descriptive statistics for firm characteristics, using RAIS data from 2016 to 2020. Means and standard errors, in parentheses, are calculated for the subset of private firms and BOS, and for each BOS type: public, mixed capital, and private firms with state participation. Innovative firms are defined as firms with at least one worker employed in technical or scientific occupations (PoTec), while innovation intensity is measured as the share of PoTec workers relative to firm employment. Standard errors in parentheses.

and private firms were altered in 2020, across all BOS types and ownership shares. Once more, robustness checks show that our results are qualitatively unchanged by restricting the sample to competitive industries (columns (4), (5), and (6)), and excluding micro firms from the analysis (Table A9).

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did pioneering measurements, setting poverty lines and showing how people lived relative to those lines. Their work attracted much attention. The English public was shocked to learn that one million Londoners lived below Booth‟s poverty line of 21 shillings per week for a family; this was a frugal line—by my calculations it was equivalent to 1.5 pounds of good wheat per person per day.<sup>19</sup> Booth and his writings are thought to have been instrumental in Britain‟s introduction of a public pension in 1908. And debates began about where there is more poverty. For example, 15 years after Booth‟s books appeared, Alfred Marshall was argumentatively pointing out that there was even more poverty in Germany than Booth‟s figures suggested was the case in England; this was in response to his perception that “one of the few things which every German knows for certain about England is that there are a million people in London living in extreme poverty on the verge of hunger” (Marshall, 1907, p.12).

The poverty studies by Booth and Rowntree were influential. Robert Hunter followed their lead in estimating the extent of poverty in the US in 1904.<sup>20</sup> Booth‟s approach influenced the development of quantitative sociology in both Britain and the US.<sup>21</sup> Peter Townsend‟s (1979) empirical study of poverty in England some 80 years later clearly owed much to Booth and Rowntree. So too did the Chicago School of sociology that began studying urban poverty in the US during the 1930s.

The new sequence from evidence and debate to policy action on poverty was to be replicated often in the 20th century. In 1990 many people were also shocked to learn that there were about one billion people in the world living on less than $1 per day, at purchasing power parity (World Bank, 1990).<sup>22</sup> Such data, and (probably more importantly) credible poverty data for specific countries, have been the empirical foundation of domestic and international efforts to fight poverty since the 1980s.

> 19 Marshall (1907) estimates that 21 shillings was equivalent to three-quarters of a bushel of good wheat. At 13.5% moisture by weight a bushel of wheat weights 60lb according to the Wikipedia entry on “bushel.” I assume a household of 4.5 people, which is the lower bound of the range 4.5-5 given by Booth (1993, Chapter 4) for the average size of working men‟s families at the time.

> 20 See Bronislaw Geremek, (1994, Chapter 7).

21

> On Booth‟s influence see the Wikipedia entry on Charles Booth and the Archive maintained by the London School of Economics. On Hunter‟s work see

> 22 This calculation was based on household surveys for just 22 developing countries (Ravallion, et al., 1991). The $1 a day line was a typical poverty line for low-income countries. Since then there has been a massive expansion in survey data collection and availability, and refinements to the methodology; the latest estimates can be found in Chen and Ravallion, 2010).

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- Finally, we also control for a set of K explanatory variables Xk (k = 1; :::; K) which are not necessarily linked to international migration but potentially have an impact on fertility decision. We include the log of GDP per capita, the urbanization rate, regional dummies as well variables capturing the type and intensity of religious practice in source countries.

The benchmark empirical model can thus be written as: where a0 is a constant and "t is a iid error term.

Our main coe¢ cient of interest are a1 which determines the signi...cance and magnitude of the di¤usion of fertility norms. The expected theoretical sign of a1 is positive. We also interact the (log of) fertility rate at destination with the size of the diaspora to assess the robustness of the impact to the intensity of migration. In this set up, the emigration rate is also associated to the incentive channel (a4). We assume that the incentive e¤ect follows a concave pattern and is measured by ln (p0) : The expected theoretical sign of a4 is negative, as higher migration prospects increase the incentive to invest in education and reduce fertility at home.

# 4.2 Data

Our regression involve cross section data because the migration stocks used to build fertility at destination <u>n</u><sup>~~d~~</sup> are only available for 2000: Data on fertility rates (nt) are taken from the World Development Indicators. The fertility rate is the average number of children that women have during their lives, from age 15 to age 50. To compute data on average emigration rates (p0) and geographic shares of the emigrant population by destination (�d;t), we use the data set developed by Parsons, Skeldon, Walmsley and Winters (2007). They provide four versions of an international bilateral migration stock database for 208 countries and territories of origin and destination for the year 2000. We use the fourth comprehensive version which uses a variety of techniques to estimate the missing data. The ...nal matrix, comprising only the foreign-born reconcile all of the available information in order to provide the researcher with a single and complete matrix of international bilateral migrant stocks.

One striking picture coming out of the data is the importance of South-South migration (to non OECD countries). Not surprisingly, North-South migration is negligible as all OECD countries send most of their migrants to other OECD countries. To illustrate the importance South-South migration, it turns out that 47 percent of developing countries have their main destination in a non-OECD countries. Out of those countries, 81 percent send their migrants mostly to a neighboring country.

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