rafmacalaba/data-use-mentions-extended huggingface dataset:
** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1960 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

67
(2014) also provide a compelling framework linking market competition to productivity growth through creative destruction, while Conway et al (2006) and Alesina et al. (2005) trace the positive impact of pro-competition product market regulation on productivity, and Barone and Cingano (2011) find empirical evidence that pro-competition reforms in input sectors benefit productivity of the overall economy. Furthermore, the introduction of anti-cartel policy is also related to higher labor productivity growth. Symeonidis (2008) records a 20 to 30 p.p. lower labor productivity growth in industries with cartels than in industries without cartels. Some government interventions unduly inhibit firm’s ability or incentives to compete. The OECD Product Market Regulation (PMR) indicator measures the degree to which policies or regulatory measures promote or inhibit competition in areas of the product market where competition is viable. PMR data for Brazil in 2013 suggests that policies and regulations in key sectors are less conducive to competition than in comparator countries.

While other BRICS countries have promoted reforms over the last decade, the overall restrictiveness of product market regulation in Brazil remained unchanged between 2008 and 2013. The general degree of restrictiveness of product market regulations in Brazil has not changed since between 2008 and 2013 and compares less favorably to other economies which on average have moved towards a more pro-competitive regulatory framework (Figure 24). In fact, while others

> 37 _Competition policy_ encompasses a set of policies and laws that ensure competition in the marketplace is not restricted in such a way as to reduce economic welfare (Motta, 2004).

> 38 For more details on what competition policy encompasses, see Kitzmueller and Licetti (2012).

21
1 Human capital accumulation can occur through learning in school and learning at work (Lagakos et al., 2018b,a; World Bank, 2018; Rossi, 2020; Jedwab et al., 2021c). Macroeconomic events such as downturns affect the accumulation of human capital at work, with potentially persistent consequences. However, _downturns due to pandemics are different_ . Pandemic mitigation measures include not just workplace closures but also school closures. As a result, they _disrupt human capital accumulation not just at work but also at school_ , with potentially severe long-term consequences beyond those of a typical downturn. More generally, national school closures are rare.

We explore the potential long-term economic scars from pandemic-induced disruptions to human capital accumulation at school and at work through the lens of the COVID-19 pandemic, as both workplaces and schools were shut down around the world. Significant declines in employment occurred as a result of the initial lockdowns, and continued due to behavioral changes. Given that future pandemics are likely to be similarly disruptive, the COVID-19 experience is useful for calibrating the aggregate impact of different pandemic scenarios. In addition, given that the returns to education and experience are known to vary around the world, and given that the COVID-19 shocks to schooling and employment may have affected men and women differently, such an exploration would ideally distinguish between countries at different levels of development, and also study the impact on male and female workers independently.

We quantify the aggregate impact of pandemics through a development accounting framework that tracks the accumulation of human capital in the population over time. We calibrate the framework using returns to experience and returns to education by college status and gender that are consistently estimated using 1,084 household surveys across 145 countries (1990-2016). Finally, we use the framework to compute the short and long term losses from disruptions to human capital accumulation due to pandemic shocks for countries at different income levels, and for men and women separately.

We initially treat countries as though they were _symmetrically_ affected. We find that the long-term impact of
53.1|25.6|0.78|94.6|
|2018|24.5|61.6|120.6|60.8|91.4|50.8|53.5|24.1|0.84|95.0|
|Avg.|21.6|52.3|113.6|59.0|90.3|45.2|54.5|26.1|0.85|94.3|

1. Elderly poverty rate: percentage of elderly Pakistanis living in households below the national poverty line over the percentage of households with non-elderly household head under the national poverty line.

2. Co-residence: percentage of elderly individuals living in type 1 and type 2 households.

3. Res. wealth/net worth: residential wealth as percentage of total wealth of households (average).

4. Net worth (or Non-resid. Wealth) > 6 (or 60) mo.: percentage of households that have net worth (or non-residential wealth) greater than 6 (or 60) months of the household’s consumption needs.

# **5. Decomposition methodology**

Our goal is now to measure how average household net worth evolves along the life cycle, but using a single cross-section of data (i.e. from one specific year) severely understates household net worth accumulation. To see this, Figure 1 plots household net worth, measured in multiples of monthly household consumption, against the age of the household head for the 2001, 2010 and 2018 PSLM survey rounds. Age profiles thus constructed appear to increase by around 1 month of consumption per year. However, this fails to consider that each cohort is wealthier than the next: the 2018 cross-sectional profile is shifted upwards by around 20 months of consumption relative to the 2001 profile.

10
# **Appendix Figure 5. Predicted Investment for Connected and Unconnected firms**

This figure shows the predicted investment (for capital expenditures, acquisitions, research & development, and cash & short-term investments) of connected and unconnected domestic listed firms in the propensity score matched (PSM) sample. The figure plots, for each year, the predicted investment value for the average firm obtained by estimating Equation (1). The shaded areas capture the implementation of the Stock Connect program and the incorporation of domestic listed stocks into the MSCI Emerging Markets Index. For more information on these estimations, see Table 9.

Capex over 2012 Assets Acquisitions over 2012 Assets <!-- Start of picture text --> Stock<br>Connect<br><!-- End of picture text --> <!-- Start of picture text --> MSCI<br>Incorporation<br><!-- End of picture text --> <!-- Start of picture text --> Stock<br>Connect<br><!-- End of picture text --> <!-- Start of picture text --> MSCI<br>Incorporation<br><!-- End of picture text --> Research and Development over 2012 Assets Cash and Short-term Investments over 2012 Assets Unconnected
# **2.3 Country level estimates**

Table 3 presents breakdown of extended labor force by proximity to formal employment for each of 30 European countries as of 2008-2009 (data for Austria and Italy refer to 2007 and 2006, respectively), along with the LFS-based unemployment rate for the respective period of field work, and the estimate of the non-employed population which was recently informally employed. Figure 1, derived from Table 3, features current total informal employment and its two components, workers without contracts and informal self-employed, measured as proportions of extended labor force; on top of this, recent informal employment of currently non-employed population is shown in the same units. Adding the ̳recent‘ component significantly increases the estimated level of informality for a number of countries (see Figure 1 for details), but leaves the ranking basically intact. In the following discussion we refer to the current levels of informal employment, unless stated otherwise.

<!-- Start of picture text --> Share of Extended Labor Force Employed Informally<br>2008-2009<br>50<br>40<br>30<br>20<br>10<br>0<br>CYGR IL IT PTES PLUA SI BGCZSKRUROEEHULV LT IE UKATCHNLDEBEFR NODK FI SE<br>South East West North<br>Non-employed who recently were informal employees<br>Non-employed who recently were informal self-employed<br>Informal employees<br>Informal self-employed<br>Labor force extended to include discouraged workers<br>Austrian data refer to 2007; Italian data refer to 2006<br>Due to data limitations, shares of non-employed who were recently informally employed<br>are not perfectly comparable across countries. Average 'recent' period varies as follows:<br>10-15 months in the South and in the West, 11-18 months in the East, 10-11 months in the North<br>Source: Calculation with ESS data<br><!-- End of picture text -->

## *
∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

is why we consider it to be a robustness check and always focus our interpretation on the first set of results (unless indicated otherwise). The coefficients of executive constraints and fractionalization are significant in all estimations, apart from column (1) in panel (b) where the appropriate fixed effects are omitted and the coefficient on fractionalization turns marginally insignificant. An improvement in executive constraints by one standard deviation (2.28 points) is associated with a reduction in the duration of the decline phase of about 20
Naturally, the rapid and widespread adoption of digital platforms in the tourism industry leads us to ask (i) what their impact on the demand for tourism services is and (ii) how countries can tap into the opportunities that the new technologies present, as well as cope with some of the accompanying challenges. While there is a growing literature looking at the “material economy” implications of the digital economy, there are relatively few studies focusing specifically on international trade and specifically on trade in services and tourism. Freund and Weinhold (2004) provide an early look at how the Internet shaped international merchandise trade and find that, on average, the Internet led to a 1 percentage point increase in export growth from 1997 to 1999. More recently, Lendle et al (2016) look at eBay international trade transactions and conclude that the digital platform reduces the impact of distance on bilateral trade flow by 65 percent, on average. The authors posit that the reduction in search costs is the main reason behind their finding, with seller‐rating information having an additional impact. With regards to services trade, Freund and Weinhold (2002) provide the earliest look at the topic, focusing on how the Internet influenced US bilateral services trade with 31 different countries across 14 industries. They find that as Internet penetration in a partner country increases by 10 percent, service exports growth increases by 1.1 percentage points and import growth by 1.1 percentage points. Eichengreen et al (2016) focus on the foreign exchange market and find that greater connectivity via undersea cables dampens the impact of spatial frictions, by up to 80 percent, between local markets and major financial centers and increases offshore trading by 21 percent.

Related to the present paper, Hoonsawat (2016) explores the question of the extent to which the Internet has promoted tourism flows and, as in the present paper, applies a gravity equation model to bilateral tourism flows. The author motivates his analysis by modeling how the Internet helps mitigate a traveler’s lack of
attempt to target subsidy resources to specific customer groups. Although interrupted in 2001, following the economic crisis, they were reinstated and substantially increased during 2002.

4.25 Moreover, in May 2002, the government introduced a new supply-side subsidy for metropolitan bus services. This is controversial given the substantial excess capacity that exists in this sector, as a result of a sustained decline in the demand for these services during the last decade. These new subsidies are financed via a surcharge of 18.5% on the net price of diesel nationwide. The total cost is estimated at US$91.6 million, and translates into an implicit subsidy per passenger-trip of US$0.038 for the bus service, US$0.097 for the train service, and US$0.051 for the metro service.

4.26 Based on the pattern of commuting journeys for heads of household in the Metropolitan Area obtained from the OPSM survey, it is estimated that each commuter receives an implicit average weekly subsidy of US$0.64. Crossing commuting patterns against income, it becomes evident that the absolute value of the subsidy is highest for the middle-income quintiles, and for those using the suburban rail network. The concentration coefficients show a regressive distribution, and range from +0.36 for metro rail, +0.16 for the buses, and +0.09 for suburban rail (Figure 12 and Table 6).

**Figure 11: Distributional incidence of metropolitan public transport subsidy**

<!-- Start of picture text --> 35%<br>Monthly<br>30% Average<br>Subsidy<br>25%<br>20% Beneficiaries<br>Percentage<br>15%<br>10%<br>Total Subsidy<br>5% Percentage<br>0%<br>1 2 3 4 5<br>Income Quintile<br>Source: OPSM, May 2002<br><!-
(e.g., Nigeria). This level of detail, however, is absent in most other economies’ household surveys and only simple questions regarding ownership status are recorded. We define tenure security as the perceived tenure security as we deem the sheer ownership status of a housing unit too narrow of an estimate for tenure security. In order to measure perceived tenure security, we draw on a recent global perception-based survey of tenure security conducted by Prindex (cf. Section 3.2.3).

On access to electricity (6), we follow the definition of the International Energy Agency (IEA), custodian agency for SDG 7.2 on renewable energy. The IEA defines access to electricity as an HH having initial access to sufficient electricity to power a basic bundle of energy services, at a minimum, several lightbulbs, phone charging, a radio and potentially a fan or television.<sup>5</sup> This level of detail, however, is not available in most HCES. Hence, we apply a simpler measure. We classify HH as adequate if they are connected to an electricity grid, have a renewable stand-alone system, or have a mini-grid connection of enough capacity to deliver the minimum bundle of energy services mentioned above. In contrast, firewood, kerosene, torches, or gas lamps are considered inadequate sources of electricity.

Finally, we follow IEA’s definition on access to clean cooking (7). We consider gas and electricity to be adequate cooking fuels. In contrast, solid fuels like biomass, wood, coal, or kerosene often used in traditional stoves for cooking, are considered inadequate.

These seven criteria are the most-often cited criteria of adequacy with clear links to HH welfare, and socio-economic and health outcomes within economic literature. Due to data limitations in HH surveys and to ensure cross-country comparison of the AHI, we abstain from including other variables that are often referred to as a prerequisite for adequate housing such as location, affordability, or cultural adequacy.

Regarding
# **Figure 2. Share of Hours per Activity, by Socioeconomic Status and Gender**

<!-- Start of picture text --> Non-UP<br>36% 22% 8% 7% 26%<br>Women<br>UP Women 17% 6% 19% 2% 57%<br>UP Men 9% 7% 31% 7% 46%<br>Own Livestock Rearing<br>Own Agriculture or Other Business<br>Paid Labor: Agriculture<br>Formal/Salaried Employment<br>Paid Labor: Other<br><!-- End of picture text --> _Notes._ The figure compares the share of time spent on different income-earning activities by gender (primary UP women vs. primary UP men) and across socioeconomic status (primary UP women vs. primary non-UP women). Results are derived using non-UP and UP control group survey data at follow-up in 2018. Other paid labor includes maid services, non-agricultural activities outside of the household, and other paid work.

For women, the highest earnings per hour are in formal/salaried work at USD 2.39 PPP per hour (USD 0.65 nominal), followed by own livestock rearing (USD 1.14 PPP; USD 0.31 nominal), agricultural paid labor (USD 0.98 PPP; USD 0.26 nominal), and other labor (USD 0.78 PPP; USD 0.21 nominal) (Figure 3).<sup>11</sup> Since UP women spend the largest share of time working in the lowest paid activity (other paid jobs), this implies a much lower return on their time spent in income-generating activities than non-UP women. In addition, formal employment is accessible to only few women due to low education levels.

Finally, Figure 3 also reveals important differential returns by gender across activities within UP households. Primary men earn more per hour in all activities outside the household:
covered by the Enterprise Surveys.<sup>2</sup> Back-up generation is particularly common in low-income countries, as they are more prone to experiencing unreliable electricity supply through the central grid Figure 8.

Figure 7: Generator ownership and usage relative to firm size (based on World Bank Enterprise Surveys) <!-- Start of picture text --> 35% 40%<br>30% 35%<br>30%<br>25%<br>25%<br>20%<br>20%<br>15%<br>15%<br>10%<br>10%<br>5% 5%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Number of employees (percentiles) Number of employees (percentiles)<br>Figure 8: Self-generated electricity relative to wealth and power network reliability (based on World Bank<br>Enterprise Surveys)<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10,000 20,000 30,000 40,000 0 500 1,000 1,500 2,000 2,500<br>GDP per capita Outage hours per year<br>generator<br>total electricity use<br>Share of firms owning a<br>Self-generation as share of<br>electricity (%) electricity (%)<br>Share of self-generated Share of self-generated<br><!-- End of picture text --> Various studies have shown that such self-generation is significantly more expensive than conventional grid supply (Adenikinju, 2003; Foster & Steinbuks, 2009). For instance, for 25 African countries, Foster & Steinbuks (2009) show that self-generation is on average three times as expensive
). Review: Taking stock of Africa’s second-generation agricultural input subsidy programs. _Food Policy_ , _75_ (October 2017), 1–14. https://doi.org/10.1016/j.foodpol.2018.01.003

- Jones, A. D., Shrinivas, A., & Bezner-Kerr, R. (2014). Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. _Food Policy_ , _46_ , 1–12. https://doi.org/10.1016/j.foodpol.2014.02.001

- Kankwamba, H., Kadzamira, M., & Pauw, K. (2018). How diversified is cropping in Malawi? Patterns, determinants and policy implications. _Food Security_ , _10_ (2), 323–338. https://doi.org/10.1007/s12571-018-0771-x

- Karamba, R. W. (2013). _Input Subsidies and their effect on cropland allocation, agricultural productivity, and child nutrition: evidence from Malawi._ American University.

- Logistics Unit. (2017). _Final Report on Implementation of Farm Input Subsidy Programme 2016-17_ . Lilongwe, Malawi.

Mason, N. M., & Ricker-Gilbert, J. (2013). Disrupting Demand for Commercial Seed: Input 26
<u>Table 2. Within-country correlates of FLFP in 2018. Robustness checks. OLS estimates</u>

||Wome|n 25-55|Women with<br>|<br>Latino-<br>|Urban areas<br>|
|---|---|---|---|---|---|
||||kid 4-6|barometro 2018|only|
||(1)|(2)|(3)|(4)|(5)|
|_Individual & Household characteristics_||||||
|Age|0.034<br>|0.034<br>|0.0362<br>|0.0408<br>|0.0495<br>|
||[0.000811]***|[0.000809]***|[0.00141]***|[0.00232]***|[0.00104]***|
|Age squared|-0.000433|-0.000434|-0.000476|-0.000534|-0.000654|
||[1.20e-05]***|[1.21e-05]***|[2.10e-05]***|[3.68e-05]***|[1.38e-05]***|
|Primary level|0.0227|0.0226|0.0165|0.0207|0.0250|
||[0.00187]***|[0.00190]***|[0.00206]***|[0.00299]***|[0.00352]***|
|Secondary level|0.115|0.115|0.101|0.113|0.105|
||[0.00703]*
of income “initially” will catch up with individuals with high income. In our context, the “initial” period corresponds to the time of entry into the work force after the completion of formal education. Since labor income may vary initially for equivalent individuals, catchingup in this context measures the extent to which individuals with initially low incomes catch up to those with initially high incomes.<sup>12</sup> In the terminology of the growth literature, it measures convergence.<sup>13</sup>

# II.2. Mobility

As noted in the introduction, our empirical measure of income mobility between 0 and t, which we denote by mt, is the Hart index, defined as the complement of the correlation in (log) incomes at 0 and t (see Shorrocks (1993)): where we have used the notation σlnyi0 = ~~�~~ var[lnyi0] and σlnyit = ~~�~~ var[lnyit]. Using our income specification from the previous section, we find the following expression for the co-variance:

> 12In this theoretical section, our discussion relates to initial income differences and subsequent mobility between ex-ante identical individuals. In our discussion of empirical methodology and in our empirical application to Mexican data, we will study mobility between observationally equivalent individuals. That is to say, we examine income differences and mobility in residual income after conditioning for the standard determinants of income such as education and experience.

> 13To see this, suppose ρ < 1. In this case, we have convergence towards the “steady state”: E[lnyit|ωi0] → μ. Let ∆0 = lnyi0 − d<sup> ̄</sup> be the initial distance from the steady state and ∆t = lnyit − d<sup> ̄</sup> be the distance in period t. We can then define the time, T , it takes to get halfway towards the steady state, which is simply the solution to ∆