rafmacalaba/data-use-mentions-extended huggingface dataset:
difficult for individuals (workers, firms) to internalize those gains. There is an externality that is not accounted for in the individuals’ decisions concerning employment, technology adoption, and so on. Conceptually, it is not hard to understand what the issues are. What is less clear is how to incorporate these features into the TAC models in order to use them for this purpose. As a crude and simple example, suppose we decide to model green growth policies as a cost shock so that the cost of the firm is now higher (perhaps in selected sectors of the economy) because firms need to improve their capital equipment, retool it to make it environmentally more efficient, etc. As I just argued, it wouldn’t be too hard to incorporate these features into some of the TAC models and to simulate how the economy will respond to this specific example of a hypothetical green growth policy. But if we model this as a negative cost shock, the implications will be lower profitability, lower wages, lower employment, and, overall, lower welfare. But it would be incorrect to conclude that green growth policies lead to lower welfare, because there are social benefits that are being unaccounted for. We could amend the utility function of the individual to include a term related to the “environment” and link the size of the cost shock to this piece of the utility function. This idea is indeed implementable. The conceptual issues are clear, but working out all the full details empirically is not straightforward.

The last point I want to raise is data constraints in developing countries. To the extent that the green growth initiative focuses on developing countries, it is important to discuss whether any of the TAC models can be estimated (or at least calibrated) using data from developing countries. The modern approach exploits microdata as much as possible. Fortunately, micro-data have become increasingly available across the developing world and it is now easy to find firm surveys or household surveys for many countries. However, there are limitations
<!-- Start of picture text --> Mechanization in rural areas,<br>by headship type<br>@FHHHnomale O@FHHHwithmale G@MAHH<br>5035<br>45%<br>mB 35%<br>=<br>= 30%<br>w 25%<br>=<br>ey 2056<br>=<br>g@ 15%<br>= 10%<br>z<br>=<br>aS ll a<br>O35 T 7 7 7 - - : ; al . ln,<br>Pg SS of 2 fb LS} a a<br>rsFPé aePF LsPF xFFRas 25SF =LEESF GSP caSF eeES<br>FF PKK we = ad «eS<br>Source: RIGA data, most recent year available.<br>Note: FHHH with mele definedas FHHH with at least one working-age (15-59) male.<br><!-- End of picture text -->
# _3.3 Welfare Effects_

After estimating the price effects of trade controls, the next logical step is assessing the welfare implications of the price impact (if any) on farmers and urban consumers. We employ the classical static welfare analysis framework that uses measures of compensating variation or equivalent variation to estimate changes in household welfare due to price changes based on Deaton (1989): C � nn i (11) 0 ii0 ıı where CO is the compensating variation for a proportional change in price of good = �(ss −qq ) EE ( �); 0 is the initial YY ii=1 <u>Qi pi i0 i0</u> income; the budget share of consumption good, i.e. = 0 ; and = 0 iiis the value of output EEıı YY SS PP as a share of income. Note that ii is the household demand for good ii , ii is the output supply produced qq qq YY ss YY for by the household. The parameter i ) is often referred to as the net benefit ratio/NBR. It is also ii ii ii known as the net consumption ratio/NCR, which is the ratio of the net sales of commodity QQ ii SS to the household ii0 ii (ss −qq income (Minot and Dewina 2013). ii A caveat to be noted is that the above measure of welfare captures a static view of the welfare implications of price increases. Past research has found that the NBR measure can provide a reasonable approximation of the welfare effects in the short run. However, it fails to account for dynamic adjustments that a household might undertake in response to price increases over the long run. Such adjustments include changes in demand through substitution, income effects through changes in the labor market, or supply responses to the maize price changes, such as switching to other crops or investing less on agriculture inputs.

# **4. Data and Descriptive Statistics**

We secured and organized monthly price data from more than 150 markets across
# **E-Commerce Participation and Household Income Growth in Taobao Villages**

Xubei Luo and Chiyu Niu<sup>∗</sup> JEL codes: L8, O15, P25, L16 Key words: E-commerce, income growth, rural economy, industrial clustering

> ∗ The authors thank Shaohua Chen, Samuel Freije-Rodriguez, John Giles, Bert Hofman, Deepak Mishra, Ambar Narayan, Hoon Sahib Soh, Xuejiao Xu, and Salman Zaidi for valuable comments and discussion; Zhengwei Jiang, Zhenzhong Sheng, Ruidong Zhang from Alibaba Group for suggestions and for sharing the Alibaba online data for the research; Ramiro Flores Cruz, Hua Ding, Tao Kong, Qiang Li, Juan Munoz, Jinjie Wang, Yunbo Zhou, the entire team of the Institute of Social Science Survey of Peking University, China, and all survey interviewers for collaboration in the Taobao Village Survey; and all participants in the Taobao Village Survey for sharing their information and experience. The views expressed here are those of the authors and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **3 Empirical implementation**

The core of our empirical analysis is the estimation of (10) using a cross-country time-series dataset. However, the equation involves the quasi-innovations to the present value of income growth, which are not observed. We proceed on two stages. First, we construct the innovations based on an estimated time-series model of income growth. In the second stage, we use these innovations to estimate (10).<sup>16</sup> In this section we first discuss the econometric issues surrounding the estimation of the parameters of the risk-sharing model, and then we turn to those related to the construction of the quasi-innovations required for such task. Finally, we briefly summarize our data sources.

## **3.1 Implementing the risk-sharing model**

Estimation of (10) in a cross-country setting poses a challenging problem because each country’s consumption path depends in nonlinear fashion on the risk-sharing parameters of all countries. To make this explicit in what follows, it is useful to rewrite (10) as where ui,t is potentially heteroskedastic and possibly correlated across countries and over time.

To express the empirical model in more compact form, we introduce the following notation. Let ∆ **ci** = (∆ci,1, ..., ∆ci,T )<sup>_′_</sup> and **ε** i = (εi,1, ..., εi,T )<sup>_′_</sup> denote the (column) vectors of consumption growth and quasi-innovations to the present discounted value of income growth of country i, where T denotes the number of time series observations per country, and define the T _×_ N matrix **Z** = ( **ε** 1, ..., **ε** N). In addition, let **λ** = (λ1, ...,
segregation in firm profits as well.

This paper investigates the horizontal dimension of sectoral segregation by studying global data on female and male enterprise owners who operate in industries that are typically dominated by the same and opposite sex. We use a dataset spanning 97 countries that was administered to enterprise owners, managers and employees that use the Pages feature on Facebook for their business. A Facebook Page is a public profile specifically created for businesses and other organizations. Unlike personal profiles, pages do not gain "friends," but "fans," which are people who choose to "like" a page. The data were collected as part of the December 2018 wave of the _Future of Business_ survey (FoB) which is a collaborative effort between Facebook, the World Bank and the Organisation of Economic Co-operation and Development (OECD). The most recent round of these data was specifically designed to look at potential correlates of sectoral choice and hence allows us the first opportunity to look at sectoral segregation among firms across the world.

In the sections that follow, we label female enterprise owners who enter and operate in male-dominated sectors as “female crossovers” and those who remain in more traditional female sectors the “female noncrossovers.” We begin by testing the hypothesis that female crossovers make higher returns than female non-crossovers. We find that some of the earnings gap can indeed be explained by sector choice. On average, women who operate in male-dominated sectors make 66 percent higher profits than women who

> 2 The terminology “occupational segregation” is perhaps more widely understood to encapsulate gender segregation in the workplace, that describes the pattern that <mark>women and men still tend to work in different sectors and jobs</mark> . However, since the focus of this paper is on firms operating in different sectors and not on occupations, per se, we refer to the term as “sectoral segregation” to avoid any ambiguity.

2
To compute our measure of non-resource revenue, we use data on “General Government Revenue, Percent of GDP” from WEO and GRD from 1980–2016. For each country, we choose the dataset with the longest time series available. Nonresource revenue is computed as the difference between total revenue and resource revenue (columns (9) and (10) of Appendix Table 5). The annual change in nonresource revenue as a share of MAGDP, , is computed as above (for yy yy and ). iitt iitt ΔNNCCRRC ΔGG yy The country-specific commodity price index and the half-life are estimated iitt ΔCCRRC using the same methodology applied in Section III.B for SSA. Appendix Table 4 provides the OLS estimates of the half-life of the commodity price index for the 56 commodity exporters included in the sample. The dummy variable is calculated based on the ER index constructed in Ilzetzki et al. (2019), using the “Hard Peg” classification of Eichengreen and Razo-iitt MMPPGG Garcia (2011). Ilzetzki et al. (2019) provide a time series covering 1940–2016 for 191 countries of an index that ranges from 1 (currency union, the least flexible) to 13 (freely floating, the most flexible) (see Appendix Table 6 for a more detailed description of the ER index). A country _i_ is classified as having a fixed ER regime in period _t_ = 1, F = 0) if the ER index is equal to or less than 3 = (pre-announced narrow band), and as floating ER otherwise iitt iitt (MMPPGG FFAATT 0, F = 1).<sup>50</sup> Under this metric, about one-third of the observations in the iitt (MMPPGG sample are associated with a fixed ER, and 24 out of 56 countries have at least one iitt FFAATT year in this regime (see Appendix Table 6 for details). For robustness, in Section V.I.B we report the results using a different classification where a country is classified as having
# **1. Introduction**

Improving access to quality education is at the core of all development strategies. It has long been recognized that education is associated with tremendous effects on economic development (Nelson & Phelps, 1966; Lee & Barro, 2001; Hanushek, 2007; Hanushek & Woessmann, 2008; Pelinescu, 2015; Angrist, et al. 2019). Given the lack of necessary resources for proper investment in education, the Millennium Development Goals (MDGs), as well as the recent Sustainable Development Goals (SDG 4), called international partners for more assistance to developing countries to achieve universal basic education as well as quality education for all. As a result, the volume of aid to education has experienced a strong improvement since the 2000s, increasing from $4.8 billion to $12.7 billion in 2017.<sup>1</sup> Developing countries were also urged to allocate more resources to the education sector.

Government expenditure on education as a share of the GDP registered a significant increase in low‐ and middle‐income countries from an average of below 3.6 % in the 2000s to above 4 % in 2015 (Figure 1). The volume of government expenditure on education also experienced a strong improvement, tripling from around $400 billion in the 2000s to nearly $1,200 billion in 2015. The school‐age population (population of pre‐primary, primary, secondary and higher education) grew from about 2.02 billion to 2.14 billion over the same period. Therefore, government expenditure per school‐age individual experienced a strong improvement over the past 15 years, increasing from an average of US$457 to US$1,110 in low‐ and middle‐income countries. Combined with the donors’ contribution to education financing, this represents significant progress for education financing.

**Figure 1: Government expenditure on education in Low‐ and Middle‐Income countries**

<!-- Start of picture text
Nearest-neighbor matching estimates using individual, school and locality variables for the propensity score and exact matching on gender and state.<sup>10</sup>

We begin by presenting descriptive statistics for our samples.

8 We also carried out matching estimations using 2 and 3 neighbors respectively. Results were very similar, and for brevity we provide only those based on 1 neighbor.

> 9 The majority of rural Progresa recipients live in communities with medium to very high levels of marginality. We thus focus our analysis on primary schools located in these areas. Nationwide, about 70% of all Progresa sixth grade students attend schools in these areas, consistent with about 70% of the PROSPERA population living in rural areas. If the program increased the likelihood that sixth grade students traveled to schools outside to non-poor areas, this might impart a selection bias to restricting the sample in this way. We have also replicated the results here using the set of nationwide sixth grade students which necessarily includes urban students but avoids selecting the sample based on potential school selection and we obtain similar although slightly smaller results.

10 For the matching estimators for all students, we use a random sample of 20% of all schools because of computation issues for the matching.

14
lower-middle income economies. The selection of economies was based on two parameters: size of the economy, using Gross Domestic Product (GDP) as a basis, as well as participation in world services trade on a balance-of-payment (BoP) basis.<sup>7</sup> The full list of economies currently covered is reproduced in Annex 2 to this paper (2016 table). At the time of writing, data collection is being finalized for signatories of the Central European Free Trade Agreement (CEFTA), Jordan and Niger. Additional data on a significant number of economies in Africa are in the process of being collected or updated. These data will be incorporated in the STPD in due course.

# _Sectoral coverage_

The sectoral coverage is identical to that in the World Bank survey of 2008-2011. Apart from the continued importance of these sectors and sub-sectors for any economy – a factor that justified the World Bank's approach at the time – sticking to the same sectoral coverage in 2016 has allowed us to have a 'time' dimension in our data, and hence analyze policy developments in the last decade. However, three subsectors were added to ensure greater comparability with the sectors that overlapped with those in the OECD Services Trade Restrictiveness Regulatory (OECD STRI) database.<sup>8</sup> The database therefore covers 5 major service sectors, further disaggregated into a total of 23 subsectors, namely: financial services (commercial banking, life insurance, non-life insurance, reinsurance), telecommunications services (fixed-line, mobile, Internet), distribution services (retail, wholesale), transportation (air freight domestic, air freight international, air passenger domestic, air passenger international, rail freight, road freight, maritime freight transport, maritime auxiliary services, maritime intermediation and other), professional services (accounting, auditing, legal services on foreign law, legal services on domestic law, legal advice and representation).<sup>9</sup>

> 5 Consumers must be understood in a broad manner
still small (approximately 600,000 units), although it is expected to grow as rent control barriers are removed. The private rental sector is particularly significant in Warsaw (27.8 percent of households) and other larger urban areas (21.6 percent).

- The survey data indicate existence of only a marginal rental sector for Romania of 2.4 percent of the housing stock. This share is supported by the findings of the recent 2002 census about tenure structure among occupied units (although the large number of unoccupied units confuses the picture<sup>24</sup> ). However, high levels of tax evasion and the apparently widespread stigmatization of tenants arising from one of the most radical privatization programs suggest underreporting incentives for households living in rental units. A Bucharest city government survey of 2003 arrived at an estimate of the private rental share of 5 percent, more than 2.5 times the level recorded by the survey data (although in absolute terms the difference is only 2–3 percent). The public rental sector share, in contrast, has plausibly fallen to less than 2 percent of the housing stock.

- Similarly, the survey data are also likely to underestimate the size of the Russian private rental sector (at 3.6 percent). Puzanov (2004) estimates the sector size at about 5 percent; Kalinina and Klepikova (2002) describe private rental as the fastest growing real estate sector in Russia. The widespread tax informality and illegality of most rental arrangements in Russia is probably the largest factor contributing to under-reporting. The tenure status of the large reported public rental stock in Russia is undefined. Current legislation vests tenants with the free “callable” right to privatize, so public tenants can be interpreted as “pending” owners who have not yet exercised their privatization option. However, the new housing legislation recently adopted (the Housing Code) has stipulated a time limitation on these decisions. They would also curb the current practice of tenants in newly built municipal rental apartments
that is observed in community market price surveys (Gibson, 2007). The community market price surveys reflect current seasonal conditions because they are carried out only once in each cluster but the unit values should not because they are meant to refer to purchases made in a “usual” (that is, an a-seasonal) month.

13 Just over 52 percent of individuals in the respondent households maintained a personal diary, with the remaining members (typically children) allocated to a specific adult personal diary.
In what follows we explore the impact of financial and infrastructure services policy reforms on growth using time-series data for a panel of 24 transition economies covering the 1990-2004 period. Annex Tables 2, 3, and 4 describe the data and the sample, and provide descriptive statistics and pair-wise correlation coefficients. We exclude three countries for which the coverage of macroeconomic data over time is poor (Turkmenistan, Serbia/Montenegro, Bosnia/Herzegovina). We start with simple OLS country fixed effects regressions<sup>9</sup> (Table 5). The dependant variable is the growth rate of per-capita GDP. In most of the literature, the main factor driving growth is assumed to be investment (e.g., Levine and Renelt, 1992). Transition economies experienced large swings in investment in the first half of the 1990s, with the collapse of central planning and the initial lack of market institutions leading to sharp reductions in investment (Roland, 2000; Falcetti et al, 2002). Subsequently, a gradual buildup of a domestically and externally financed private capital stock occurred. This well-known U-shaped pattern of output and investment collapse and recovery suggests that the _change_ in the investment ratio be used as an alternative to the investment-GDP ratio as a measure of investment.<sup>10</sup> The reduced form models (1) and (2) test the alternative hypotheses of a linear vs. nonlinear relationship between investment and growth. Investment/GDP is statistically insignificant (model 1), while the change in the ratio is significant. Thus, the initial collapse and the subsequent recovery in GDP growth was associated with changes in the rate of change in investment at a fast pace: first falling and subsequently rapidly growing investment ratios. We therefore use the change in investment in the regressions. In model (3) we add variables to account for crises and inflation. Inflation, a measure of macroeconomic stability, is expected to have a negative impact on growth. Crisis is a dummy variable that equals one
2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Panel C: Percentage of youth who are ninis in LAC by Panel D: Number of ninis in LAC by<br>gender, (19-24 years) gender, (19-24 years)<br>41.1 41.2 12.1 12.3 12.3<br>37.7 11.1<br>35.6 10.6<br>33.5<br>8.5 8.8 9.1 9.0 8.8<br>27.3 27.0 26.5<br>24.3 23.4<br>12.6 12.2 14.7 12.9 13.1 3.0 3.2 3.5<br>2.2 2.3<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>25.0 6.0<br>5.0<br>20.0<br>4.0<br>15.0 Ninis (Millions) 3.0<br>Ninis (% relevant population) 2.0<br>10.0<br>40.0 12.0<br>10.0<br>30.0 8.0<br>6.0<br>20.0 Ninis (Millions)<br>4.0<br>Ninis (% relevant population)<br>10.0 2.0<br><!-- End of picture text --> Panel D: Number of ninis in LAC by gender, (19-24 years) Source: authors’ own computation with data from CEDLAS and The World Bank (2015) 17
pandemic. Presumably there are many unobservable, time-invariant variables (that is, invariant over the time frame we are examining), that explain digital payments. By including the level of digital payments just prior to the pandemic, we control for these time-invariant unobservable determinants of digital payments in 2021.

Equation 1 relates the share of the adult population that made or received a digital payment in country _i_ in 2021 ( _DigPay_ i2021) to the factors described above. We include the trend in digital payment usage, measured as the change in the share of the adult population that made or received a digital payment in 2017 versus 2014, to capture the baseline trend in DFS adoption in a country prior to the pandemic.<sup>13</sup> _Xi2019_ represents controls for GDP per capita, inflation, and country population. We expect GDP per capita to be positively linked to digital payment usage (and other DFS variables); we do not have strong priors about the links between DFS variables and inflation and population.

The explanatory variables of most interest in equation 1 relate to banking sector development, infrastructure and connectivity, human capabilities as reflected in years of education, mobile phone usage and regulation of the digital economy. Regarding banking, some observers have expressed concern that lightly regulated digital lending could not only pose harm to less sophisticated borrowers, but also threaten financial stability if enough users lose trust in digital

> 13 In using the 2017 data as the best available estimate of digital payment usage just prior to the pandemic, we implicitly assume that either digital payment usage evolved very slowly in 2018-2019, the years just prior to the outbreak of COVID-19, or that the coefficient on the 2014-17 digital payments trend variable that is also included in the model adequately captures the changes in 2018-19.

12