rafmacalaba/data-use-mentions-extended huggingface dataset:
reports of sibling survival status for siblings who survived until age 15 (2.8 million of these are for Sub-Saharan Africa). Of these, 2.9 million are reports on women (including respondents) and 2.1 are reports on men (corresponding figures for Sub-Saharan Africa are 1.6 million and 1.2 million).<sup>11</sup>

# **2.2) Determining socioeconomic characteristics**

The DHS include a large number of variables that could be used to characterize respondents’ socioeconomic status. However for the task at hand, we face two challenges. First, the data we have describe the socioeconomic characteristics of the respondent, and not necessarily their sibling whose mortality we are analyzing. Second, the data collected pertain to the time of the survey, and not necessarily to the timeframe for which we are estimating mortality.

We address the second of these potential problems by focusing on relatively stable indicators of socioeconomic status. We address the first by using information about the respondents to proxy the socioeconomic status of their siblings. We limit our analysis to two variables: urban/rural residence and educational attainment. It is impossible using the DHS to conclusively determine the extent to which these variables are correlated across siblings. Two sets of facts give us confidence that the approach is legitimate. First, some DHS datasets include a variable that indicates whether a respondent lived in a rural or urban area as a child. It is very likely that siblings would share the same residential location in childhood. We find that the results we get are extremely similar whether we use the current or childhood residential location (a discussion we return to below). Second, Conley and Glauber (2005) indicate that in the United States the correlation in the educational outcomes of siblings is 0.576. Dahan and Gaviria (2001) report that this correlation is substantially higher in a sample of 16 countries in Latin America, suggesting that using the educational status of a sibling as a proxy might
the labor market.

Future research on the questions discussed in this study could take several directions. We list a few below.

Benefits from reforms involving state-owned enterprises. First, many EMDEs host large state-owned enterprises or poorly regulated private monopolies. Reforms to these could trigger increases in productivity as capital and labor are reallocated towards more productive uses. A better understanding of the impact on potential growth for EMDEs (beyond individual case studies) as well as the identification of conducive preconditions and complementary reforms would be helpful.

Benefits from improvements of governance and business climates. Second, many EMDEs have weak governance and business climates. A fuller quantitative assessment of the effects of improvements in various dimensions of governance and business climates on potential growth, including through firm productivity and household decisions on labor force participation and informal employment, would be helpful.

Better understanding of longer-term impact of reforms. Third, the exercise conducted for this study rested on as wide a cross-country sample of data as possible, in order to be representative of the heterogeneity of EMDEs. Data constraints prohibited analysis of developments before 1990. However, for a smaller set of countries, earlier data should be obtainable which could allow analysis of the longer-term effects of the profound structural policy changes that occurred in the 1970s and 1980s. Analysis of a longer time period may also allow for a better assessment of the possible cleansing effects of adverse shocks at the macroeconomic level.<sup>20</sup> Additional analysis on climate-related infrastructure investment. Fourth, the climate change scenario explored in this study is based on regional estimates of infrastructure investment needs because of data limitations for a large number of individual EMDEs. Given the wide heterogeneity in climate challenges, these regional estimates should ideally be supplemented or replaced by country-specific estimates that can provide more precision. For some countries, country-specific infrastructure investment goals are available, including, for EU countries, in the national Recovery and Resilience plans funded by NextGeneration EU investments. For other regions, however, such countryspecific data are for now unavailable.

> 20
g., guarantees, loans, etc.) on contract success; and (c) evaluating the impact of multilateral support on project performance outcomes (e.g., coverage expansion, improvements to service quality, efficiency and productivity gains, tariff reductions, etc.).

The latter two proposed analyses require gathering of additional project data. Once additional data is available to capture variations in project status over time, it will be possible to study the role of project and institutional factors on paths of contract performance (e.g., paths to distress and recovery from distress). An important question amenable to study with more historical data is what role multilaterals play in facilitating recovery from distress and reversion to operational status versus cancellation.

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and significance levels are similar under Tobit and OLS, which is also confirmed by our estimation results. Nevertheless, for the predicted values to lie within zero and one as well, Tobit specification is preferred. Table 2 provides the results from estimating equation (1) by both OLS and Tobit using 46 observations. As expected, a larger number of accounts and a larger number of branches per adult are associated with a greater percentage of banked households. The model is then used to predict the percentage of banked households for the rest of the world for which household survey data do not exist. The correlation between the actual and predicted values is 87 percent. See Figure 1 for the fit of the estimated model.

Given the data constraints for the explanatory variables, it is possible to construct out-of-sample predictions for 55 countries. Combined with the actual data available for 46 countries, this enables us to pin down the percentage of banked households for a total of 101 countries. Kendall _et al._ (2010) develop a different methodology and predict the number of accounts for each type of regulated financial institution in the _Financial Access_ database. They then convert the number of accounts into the number of banked adults. With this methodology, they are able to predict the 4
sup> Nearly two-thirds of BRI-recipient EMs face elevated debt vulnerabilities, requiring high scrutiny (Figure 8 and Figure 9).<sup>21</sup> <!-- Start of picture text --> Figure 5. BRI Investment*<br>160% 140<br>In billions of U.S. dollars<br>140% 120<br>In percent of GDP<br>120%<br> 100<br>100%<br> 80<br>80%<br> 60<br>60%<br> 40<br>40%<br>20% 20<br>0% -<br>LAO TLS KHM GEO MNG ARM DJI ALB UZB KGZ PAK MYS TKM BIH NPL BGD MDV KEN MMR RUS TJK TZA JOR VNM BLR SRB LKA IDN MDA KAZ EGY UKR ARE IRN THA IRQ HRV AFG PHL ROU TUR IND AZE POL SYR<br><!-- End of picture text --> Source: WIND database and WDR.

- *Note: Total identified BRI investment between 2013 to 2018.

19 A list of countries is included in Annex I.

20 As of end September 2018.

21 Consistent with the definition adopted by the IMF for EM economies, high scrutiny countries are defined as those countries with (i) the public debt-to-GDP ratio equal or above 50 percent; or (ii) GFNs equal or above 10 percent of GDP; or (iii) have or are seeking exceptional access to Fund resources (IMF, 2013).

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(such as Saudi Arabia or Singapore) and recent OECD members (such as Israel, Mexico, and Slovenia). By keeping these countries in a first step, we are able to capture more variability in our results and eventually to remove them in a second step for robustness checks.

Applying these three criteria leaves us with a sample of 116 developing countries (the Syrian Arab Republic is removed due to the war) over the period 1960-2016. To maximize the number of countries displayed in our descriptive statistics, we focus mainly on the sub-period 2000-2016.<sup>22</sup>

> 20 More details on the variables in Table A.5. We consider the general government primary expenditures, instead of central government, total expenditure and net lending (GCENL series) used by Frankel et al. (2013) as the last one is not available since WEO archives 2009. Accounting for the general government coverage enables us to have comparable spending figures between unitary states and federal states (such as the Democratic Republic of Congo and Nigeria). Accounting for primary spending enables us to account for current performance of administrations, irrespective to the debt service burden.

> 21 To capture the population size, we use the “Population, total” series from the WDI data set (updated February 1, 2017), and removed all observations with a blank in the series. We keep Taiwan, China, which in no longer covered in WDI data set, as the population size is over the threshold of one million inhabitants for the whole period.

> 22 For example, we do not have data on general government primary expenditures available before 2000 for Nigeria. To avoid confusion, we do not attempt to apply the criteria #2, on the sub-period 2000-2016; as recalled by Alesina et al.

> (2008), in general, the larger the cutoff for inclusion is –relative to the time horizon we have-, the stronger the results are.

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the spread of the crisis. We also find that more developed countries as well as countries that were more severely affected by the crisis have experienced sharper declines in new business registrations during the crisis. Finally, we find that countries with more developed financial markets have experienced larger contractions in new firm creation, most likely because of the credit crunch that has characterized this crisis. This finding is consistent with Rajan and Zingales (1998) sector-level work on the relationship between financial dependence and growth.

Existing cross-country measures of entrepreneurial activity are limited, but what does exist demonstrates that much can be learned about the relationship between entrepreneurship and economic growth through the development of longitudinal data sets (Wennekers et al., 2005). Cross-country data have been used to demonstrate a U-shaped relationship between new firm creation and levels of economic development (Stel, Carree, and Thurik, 2005). Furthermore, from an evolutionary economics perspective, research suggests that disparities in economic growth between advanced and less developed countries can narrow owing precisely to the growth of entrepreneurial activity (Galor and Stelios, 2006). On a country-level, Haltwinger, et al (2009) use U.S. Census data to demonstrate that young firms, not small firms as is commonly believed, are the principal force behind both gross and net job creation. We are not aware of any other longitudinal, cross-country studies that examine the impact of the 2008-2009 financial crisis on entrepreneurial activity.

The paper proceeds as follows: Section 2 discusses the survey methodology; Section 3 summarizes the data; Section 4 discusses our empirical results; and Section 5 concludes.

# **2. Survey Methodology**

# **_2.1 Defining Entrepreneurship_**

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_Indicator 1.4.2:_ “Proportion of total adult population with secure tenure rights to land, with (a) legally recognized documentation; and (b) who perceive their rights to land as secure, by sex and by type of tenure” _Indicator 5.a.1:_ “(a) Proportion of total agricultural population with ownership or secure rights over agricultural land by sex; and (b) share of women among owners or rights bearers of agricultural land, by type of tenure” Under indicator 5.a.1, an individual in the agricultural population<sup>7</sup> is considered to have secure land rights if they have either the right to sell, the right to bequeath, or their name on a legally recognized document. Accordingly, the sub-indicators for 5.a.1 are computed as follows, in line with the official metadata,<sup>8</sup> where the numerator includes the number of individuals with at least one of the rights mentioned above:

**5.a.1** ( **a** ) =<sup># people in ag. population with ownership or tenure rights over agricultural land</sup> ×100, by sex Total agricultural population

**5.a.1** ( **b** ) =<sup># women in the a</sup> Total in the ag. population with ownership or tenure rights over agricultural lan<sup><u>g. population with ownership or tenure rights over agricultural land</u></sup> d ×100, by tenure type

We also construct indicator 1.4.2 according to the indicator metadata, which is largely informed by the “Voluntary Guidelines for the Responsible Governance of Tenure of Land, Forests and Fisheries in the Context of National Food Security”.<sup>9</sup> The United Nations World Committee on World Food Security endorsed these
we label “Any BDS”. The results are not different if we disaggregate the analysis.

# **<mark>3.2. Data Description</mark>**

The analysis presented in this study is based on two sources of data: (i) an in-person baseline survey that was conducted in January and February 2020, prior to any COVID-19 related restrictions; and (ii) phone surveys that were conducted in 3 rounds between June and November 2020 to retrospectively collect data from February to October on the impact of COVID-19 on business and individual outcomes.

The in-person baseline survey comprised 9,380 respondents from 15 counties throughout Kenya, whereas the phone surveys comprised 1,054 respondents from 5 counties where the study interventions had started to roll out.

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difference in educational persistence becomes negligible for the most educated parents.

# **Sources of Gender in Urban China**

To understand the role played by parental financial investment in children’s education, we utilize the data from the first round of the CFPS survey (2010) to estimate the parameters of the investment equation (i.e., equation (10) above) in Table T3.CU and test the equality of the coefficients across gender. The evidence in Table T3.CU is clear that parental financial investment in schooling does not depend on the gender of a child; we cannot reject the null hypothesis that _θ_ 0<sup>_d_=</sup><sup>_θ_</sup> 0<sup>_s_and</sup><sup>_θ_</sup> 1<sup>_d_=</sup><sup>_θ_</sup> 1<sup>_s._Theseconclusionsareconsistent</sup> with the other existing evidence (see, for example, Tsui and Rich (2002)).

The estimates of the parameters of the investment function and the estimates of the mobility equation discussed above can be summarized in the following binary relations for sons vs. daughters: We first look at the implications of the evidence on the functional form of the intergenerational schooling persistence regressions. The evidence of convexity in the case of sons (i.e., _ψ_<sup>ˆ</sup> 2<sup>_s>_0) implies that we can reject the null hypothesis of</sup><sup>_δ_</sup> 5<sup>_s_= 0 in favor of</sup><sup>_δ_</sup> 5<sup>_s>_0</sup><sup>_._This</sup> is interesting evidence for the Becker et al. (2015, 2018) hypothesis that higher educated fathers are more efficient in financial investment in education.<sup>32
and offer no additionality. For example, refer to <u>https://voxukraine.org/en/the-new-agricultural-support-system-in-ukraine-who-really-benefits.</u> 9 The IACS is used to manage, monitor and control all area or animal-based common agricultural policy (CAP) interventions (such as direct payments interventions and area and animal-based rural development interventions). It consists of several digital interconnected databases,

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the coping strategies also does not involve the instrumental variable approach. The analysis should receive a correlational interpretation, not a causal one (naturally, the change of occupation may be a result of the change in welfare). Table 8<sup>21</sup> shows that the few households who rely on commercial farming seem to benefit more.

> 19 Estimates in this section are estimated without instrumentalization due to the weakness of the corresponding instrumental variables when interaction terms are used and hence, two endogenous variables need to be dealt with. The corresponding Kleibergen-Paap rk LM statistics are indeed below one. Caution needs to be made not to interpret causally these results.

> 20 Further investigation reveals that indeed rural households emerge as beneficiaries compared with urban households (Appendix 4, Tables A27 & A28). However, the sample sizes when restricting the analysis to urban households is too limited to draw inference.

> 21 Details in Appendix 4; Table A29.

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# **1. Introduction**

Active labor market policies (ALMPs) have been used globally to help ease a wide range of labor market problems, including youth unemployment and persistent joblessness among displaced adults. Several active labor market policies are available to policymakers to boost employment. Among them, private employment subsidies have been used to cover part of the labor costs borne by employers, particularly for low-wage workers. In developing economies where unregistered – or informal – employment remains common, employment subsidies can also serve an additional purpose. In addition to increasing total employment, they can also help increase the proportion of workers who are employed formally – as opposed to being unregistered – by partially covering the labor costs of formal employment. By doing this employment subsidies reduce the relative attractiveness of hiring workers “off the books” for firms compared to formally employing workers. This is a potential benefit of importance, as reducing informality in the labor market is a common policy priority in many developing countries.

In this paper, we examine the effects of a geographically targeted subsidy that covered part of the employers’ social security cost on registered employment in Turkey. While small firms with fewer than 10 employees were not eligible for the subsidy before 2016, they became eligible in 2016. In addition, only small firms in certain regions of Turkey became eligible, which constitutes a quasi-experiment that we exploit. Our paper uses a very rich firm-level administrative dataset to study the effect of a social security cost subsidy on registered employment. We exploit geographical variation in eligibility for the subsidy and use several difference-in-difference specifications to estimate the causal effects of the subsidy on registered employment.

We find that the subsidy significantly increased registered employment in small firms and that the effects are sustained over time. The magnitude of the estimated employment effects is on the high end of the literature. The subsidy, which covered about 25% of social security costs and about 6% of total labor costs, increased registered employment in small firms that received the subsidy by 5% to 8%
of merchants with the latest technology to mitigate the effect of a second shock that impacts the whole economy.

Using card-sales transactions data, score matching and difference-in-differences methods, I propose in the next section to study the resilience of merchant with contactless payment to three shocks that happened in 2020 in France and then to estimate the substitution between payment methods. I intend to compare merchants who do contactless transactions to those who do only contact payment, with similar observable characteristics.

# **2.3 Raw Data and Sample Design**

Thanks to a partnership with Cartes Bancaires CB, I have access to a unique data set on card-sales transactions for all merchants in France from 2019:01 to 2020:12. Created 1984 by the French banks, Cartes Bancaires CB is one of the leading schemes in France with 73 million CB cards and 2 million CB-affiliated merchants. It collects the CB card transactions for all CB merchants including 9
of bankruptcy without access to finance.

In this paper, we examine how access to organizational resources and the pandemic control policy affects the underlying mechanisms behind firm performance, and the explanatory power of the mechanisms for firm performance during the pandemic. We ask, what resources, such as size, ownership, or CEO’s tenure, help firms survive and grow? What strategies did a firm choose that affect its survival and growth around the world? Is it the choice of stabilizing demand? Is it securing input supply? Or is it the effort to reduce uncertainties in finance? We also examine how the impact of the pandemic differs as the pandemic moves along, as captured by two different postpandemic surveys for the same set of firms. We also pay attention to how economic development level and COVID-19-related policies affect firm performance.

<mark>The rest of the paper is organized as follows. We develop our hypotheses in Section 2. Section 3 introduces the data and variables in the study. Section 4 presents our empirical results. We discuss our findings and conclude in Section 5.</mark>

# 2. Hypotheses

## 2.1. Strategic Choices and Performance during COVID-19

The existential threat of the pandemic has resulted in unprecedented uncertainties for firms. The strategies that firms would adopt to confront such challenges become matters of life and death. The drop in income and the rise in unemployment during the pandemic drastically reduce demand. Firms then have to utilize whatever resources are at their disposal to stabilize the demand for their products. The pandemic also drastically disrupts the supply of inputs. Various policies have been adopted to restrict domestic and international mobility, and financial access has also become more difficult. The factors would surely disrupt both imports and domestic supplies, and firms have to use their resources to restore and stabilize their input supplies. The pandemic also severely hampers the working of critical market-supporting institutions such as banks and other financial intermediaries, which play critical roles in maintaining economic prosperity (Levine 1997), especially at the time
# **2. Modeling the Labor Market Distortions in China**

_Empirical Evidence on Rural-Urban Wage Differences:_ As noted in the introduction, the presence of the _hukou_ system has given rise to a huge floating labor force in China. If workers cannot move permanently to the city with their family, then they must migrate temporarily if they wish to take advantage of the very significant wage differential that exists at present. For example, Yaohui Zhao (1999a) documents an average annual wage gap between rural and urban work of 2,387.6 Yuan for unskilled rural workers of comparable background and ability in Sichuan Province in 1995. She also finds that there is considerable evidence that these temporary migrants would prefer to stay at home – in the rural areas – and engage in non-farm work, if that were available (Zhao, 1999b). In her econometric analysis, she finds that only about 30% the total rural-urban wage gap can be explained by the direct costs associated with migration (transportation, housing and the cost of obtaining the necessary certificates). The majority of the wage gap is due to social costs associated with migration – including: the disutility of being away from family, poor quality of housing, limited social services for migrants, the danger of being robbed enroute to and from the work location, and the general uncertainty associated with being a non-registered worker in an urban area. While these transactions costs are unobservable, they clearly represent a very significant burden on the migrants and their families.

If there were no barriers to the movement of labor between rural and urban areas, we would expect real wages to be equalized for an individual worker with given characteristics. Shi, Sicular and Zhao (2002) explore the question of rural-urban inequality in greater detail for nine different provinces using the China Health and Nutrition Survey (CHNS). They begin by breaking the income differential into earnings and non-earnings components. Earnings are then broken into labor and non-labor earnings. The former includes both wage earnings and earnings 4