rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, a potential problem with interpreting the correlation coefficients is the timing of measuring vote buying versus measuring service delivery. The service delivery data specifically measures municipal actions over the past few years, during the term in office of the current incumbent. In the vote buying question posed to households, no particular date of elections was specified. The survey was undertaken three months before impending municipal elections, and three years after the last elections.<sup>28</sup> The question could therefore pick-up vote buying practices for the upcoming elections, although 3 months might be too long a lead time to already observe vote buying contracts. The follow-up questions asked specifically about actual voting behavior, which suggests that respondents would understand the vote buying question as relating to experience over past elections. Even if different respondents have different timing in mind when answering the vote buying question—some reflecting experience in past elections, and some their expectation from the upcoming elections—this measure of vote buying can still capture how vote buying is persistently more likely, across elections, in some municipalities than in others.

Nevertheless, one concern is that of reverse causation—vote buying in impending elections may be more likely in municipalities with a poor past record of health service delivery. The incumbent may be using vote buying to win political support, in lieu of the provision of broad public services. Challengers may be using vote buying because it is cheaper or more effective when incumbent performance has been weak. However, this interpretation runs against the evidence of no correlation with vote buying of other types of service delivery, including highly sensational forms of natural disaster assistance (reported in Khemani, 2013).

In the argument made here—that the survey measure of vote buying captures variation across municipalities in the proclivity towards using particularistic benefits as the basis of political support—the timing of vote buying is not important to interpreting the correlation as the equilibrium policy outcome of clientelism. A sequence of events where vote buying strategies are heightened following weak incumbent performance in delivering pro-poor
That is to say, poverty estimates derived from the sample are within our selected accuracy statistical range for all three targeted variables estimated at the district level.

**Figure 1 Annual district level accuracy statistics**

<!-- Start of picture text --> RSE Per Capita Nominal Consumption RSE Household Nominal Consumption SE Poverty Headcount Rates<br>7.5% - 10% 7.5% - 10% 7.5% - 10%<br>5.0% - 7.4% 5.0% - 7.4% 5.0% - 7.4%<br>2.5% - 4.9% 2.5% - 4.9% 2.5% - 4.9%<br>0% - 2.4% 0% - 2.4% 0% - 2.4%<br><!-- End of picture text --> Table 12 shows division-level RSE estimates of mean nominal per capita and total household expenditure, and SE estimates of poverty headcount ratios for urban and rural areas. We have also aggregated the lower district-level statistics.<sup>23</sup> Our analysis suggests that the HIES 2016/17 sampling design will meet all accuracy statistics, thus providing reliable and accurate measures of urban and rural poverty and welfare.<sup>24</sup> Lastly, Table 13 shows a summary of quarterly national RSE estimates of mean nominal per capita and total household expenditure. The table also shows SE estimates of poverty headcount ratios. All accuracy statistics are far lower than targeted thresholds. Moreover, the

> 23 The underlying assumption for aggregation of district-level statistics to stratum level is that the sample is drawn independently from each of the sub-strata.

> 24 In Section 5, we also discuss an alternative, direct way to construct division-level accuracy statistics for urban and rural areas; that is, without aggregating from the district-level statistics.

28
men have the right to beat their wives when they refuse to have sex.**

<!-- Start of picture text --> 81%<br>57%<br>Men<br>43%<br>Women<br>33%<br>28%<br>10%<br>5%<br>Egypt (rural) Egypt (urban) Ghana West bank / Gaza Nicaragua (rural) Singapore<br>strip<br><!-- End of picture text -->

**Source:** Various population-based surveys, cited in Heise, Ellsberg, and Gottemoeller, 1999.

11
, 1981.

- (c) excludes 152,682 employees in head offices and auxiliary activities.

- (d) excludes 69,448 employees in head offices and auxiliary units.

- (e) excludes 1,128,400 employees in administrative offices and auxiliaries.

- Source: Persons engaged: Brazil from IBGE, Censo Industrial (1981a) (see also table 2.1), Mexico from SPP, Resumen General (1979a) (see also table 2.4), USA from US Dept. of Commerce, Annual Survey of Manufactures 1975-76 (1979) (see also table 2.7); Population: Brazil from IBGE, Censo Demografico (1983), Mexico from Bank of Mexico, Indicadores Economicos (1986), USA from OECD, Labour Force Statistic (1987).
# I. INTRODUCTION

Family are ubiquitous, particularly in emerging economies (e.g. La Porta, Lopez-de-Silanes and Shleifer 1999). While the evidence is mixed on whether family ownership is beneficial to firm performance, a large and growing literature suggests that family-managed firms have substantially worse managerial quality and performance than those with professional managers. Understanding the forces that drive the decision of firms to be family-managed is therefore potentially important in explaining productivity differences across countries.

This paper provides new evidence on the way in which weak contracting environments affect the choice of firms to be family-managed, and assesses this channel’s importance in explaining cross-country productivity differences. First, we construct a new dataset on firm management and ownership structures that includes over 119,000 firms from 134 regions across 11 European countries. Second, to guide our analysis we develop an equilibrium model where heterogeneous firms sort across family and professional management modes when the latter are subject to contracting frictions. Third, we test a number of unique predictions from the model using regional variation in a proxy for contracting institutions–trust–within countries. In contrast to the prior literature which has relied on cross-country comparisons, this allows us to to difference out country-level unobservables. Our results support the idea that by increasing the cost of hiring professional managers, weak contracting environments lower productivity by constraining the large firms who hire them and causing more firms to use less productive family managers.

Theories of family can be broadly into two categories. On the one hand, family ownership can provide a “competitive advantage” allowing firms to achieve superior economic outcomes than non-family counterparts. On the other hand, the “private benefits of control” view emphasizes that family firms might maximize value for the family rather than investors overall (Bertrand and Schoar 2006, Villalonga and Amit 2010). The empirical evidence established so far, though, is mixed. The positive effect of family ownership on firm value has been documented by Anderson and Reeb (2003), Villalonga and Amit (2006) and Fahlenbrach (2009
the period from age 15 to 49 – is observed only for some cohorts, those born between the 1960s and 1980s, in the middle of the table. This means that the lifetime employment rate (the bottom row of the table) is biased at the extreme ends of the table. It overrepresents older people at the left end (for the 1940s cohorts) and overrepresents younger people at the right end (for the 1990s cohorts). This is because the earliest surveys for the DHS are in the 1990s, and so we can observe people who were born in the 1940s only when they are about 50 years old, but we have no information about them when they were younger because we do not have surveys fielded before the 1990s. Conversely, since the most recent DHS surveys were collecting data around 2015, we can observe cohorts who were born in the 1990s only when they are about 25 years old or younger, but not when they become older. A similar bias affects the weighted average for the rightmost column of the table. The employment rates for the young ages are overrepresenting the cohorts born more recently, while those for the older ages overrepresent cohorts born in earlier years.

# _Two types of variables: With and without age profiles_

The first issue means that the estimates for the oldest and youngest cohorts (and ages) should be taken with a grain of salt. In fact, when summarizing the long-term evolution of women’s economic engagement

> 9 See table A2 in the annex for details about the availability of DHS and WVS for the countries covered in this study.

8
variety of recent models from the international trade literature that follow a similar path to Wilson's, including the "gravity with gravitas" model of Anderson and Van Wincoop (2003), the Ricardian model of Eaton and Kortum (2002), and the heterogeneous firms model of Chaney (2008). We show that origin and destination fixed effects from a simple gravity regression can be rescaled to produce a measure of connectivity that corresponds to the total pull exercised by each country on the rest of the network. This measure captures the level of costs on bilateral routes, as well as the dispersion of costs across routes. It takes account of all possible nodal connections, even those for which no direct flights are in fact observed. As such, it provides a highly tractable yet informative indicator of air transport connectivity. It has the added advantage of being closely related to the concepts of multilateral resistance and market potential, which are familiar from the international trade and economic geography literatures (Anderson and Van Wincoop, 2003; Head and Mayer, 2011); indeed, our analysis makes it possible to link such measures to the deep structure of networks with bilateral flows, such as air transport or trade.

Although we focus on air transport in this paper, the methods we develop can easily be applied more broadly, in areas such as maritime transport, and even international production networks for manufactured goods. The concept of connectivity that we develop measures "before-the-border" effects, whether endogenous (e.g., transport policies), or exogenous (e.g., geography, and the hub-andspoke structure of the network). In addition to explaining how countries effectively connect to international freight transport and logistics networks, it complements existing indicators of trade and transport facilitation outcomes ("beyond-the-border" metrics such as the _Logistics Performance Index_ or the Doing Business _Trading Across Borders_ data), which are primarily endogenous, and not tied to the geography of the network (Arvis et al., 2007 and 2010).

We implement our
pandemic that lingers for years to come. Consequently, there may be a need for government support to expand opportunities for workers to join formal firms. Third, firm-specific factors, more than structural factors, appear to be associated with whether firms experienced a recovery in employment. This implies that there may be further scope for some firms to fully return their number of employees to pre-pandemic levels by taking actions within their control.

This paper makes two contributions to the existing literature on the impact of COVID-19 on formal firms. First, we draw on two distinct but complementary data sources to track how firms were impacted by and have recovered from the pandemic. Previous studies have either relied on surveys of firms (e.g., Davies et al. 2021; Apedo-Amah et al. 2020; Cirera et al. 2021; Karalashvili and Viganola 2021) or tax administrative data (Angelov and Waldenström 2021; Bachas et al. 2020, 2021; Mascagni and Lees 2022). To the best of our knowledge, this is the first effort to combine these data sources, which allows us to present a far richer picture of how COVID-19 has impacted firms. Extensive efforts are made to identify whether differences (and/or similarities) in the findings across survey and tax administrative data can partly be explained by differences between the samples of firms.

Second, this is one of the first in-depth studies to specifically focus on the recovery of formal firms from the pandemic in a Sub-Saharan African country. The recovery in the region is likely to be distinct from what has occurred elsewhere in the world for several reasons (Aga and Maemir 2021). First, in general, firms in Sub-Saharan Africa received very low levels of government support and it has been argued that this led firms to be more likely to lower wages, lay off workers, and face bankruptcy (Davies et al. 2021). Second, at the outset of the pandemic, the operations of firms in the region relied heavily on face-to-face interactions, which were greatly disrupted by
), and grid-level correlations of urban expansion and food production. Section 4 concludes.

# **2. Data and methodology**

To estimate the relationship of climate-induced urbanization with crop and livestock production, we combine three distinct global spatial datasets— projected urban expansion, crop production, and livestock production— through the process of overlaying them at the grid-level using a mapping software. This allow us to delineate the locations and magnitude of potential urbanization overlap with land used for crop and livestock production, on both a global and regional level. In this case, the potential impact on crop and livestock production is defined as any production located in areas that overlap with future urban expansion.

## **2.1 Shared Socioeconomic Pathways (SSPs) and urban expansion**

## _SSPs and land use change_

The SSP scenarios depict how underlying determinants (i.e. population growth, urban share of population, and income per capita) affect land use (Riahi et al. 2017). First, under SSP1, cropland is projected to barely expand between 2010 and 2100, while pasture, suitable for livestock grazing, is expected to decrease. Meanwhile, forests and other natural land are projected to expand under SSP1. By contrast, under SSP3, cropland and pasture are projected to increase, while forests and other natural land are projected to dramatically decline. Under SSP5, the rapid economic growth 4
, selection and budgeting of road projects and greater expenditure authority. Depending on the region, different government entities are responsible for conducting construction works and also financing of road projects varies. In oil-rich regions, royalties have constituted an important source of revenue for construction of roads. In fact, a World bank study shows that close to 300 municipios had their investment projects financed through royalties. In 1987 and 1988, 15 municipios receiving royalties spent 39 percent of the income on water and sewerage projects, 24 percent on roads and the rest on other services (World Bank, 1989:5). This fact supports the contention that when local authorities have expenditure as well as functional responsibility, they may set priorities in sectors other than roads and choose the appropriate level of road quality for their constituencies. In the coffee-growing region, for example, the quasi-public Federation of Coffee Growers plays an important role in shaping and financing rural development policy in terms of building roads and making infrastructure investments.

The importance of aligning fiscal responsibilities to functional ones is underscored for the case of road maintenance, a finding that supports the "souffle 31
14 Besides suggesting that greater financial intermediary development causes faster economic growth, the results indicate an economically large relationship. For example, India's value of PRIVATE CREDIT over the **1960-95** period was **_19.5_** percent of **GDP,** while the mean value for developing countries was **_25_** percent of **GDP.** The results suggest that an exogenous improvement in PRIVATE CREDIT in India that had pushed it to the sample mean for developing countries would have accelerated real per capita **GDP** growth **by** about **0.6** percentage point per year.<sup>9</sup> Similarly, if Argentina had moved from its value of PRIVATE CREDIT **(16)** to the developing country sample mean, it would have grown more than one percentage point faster per year. This is large considering that growth only averaged about **1.8** percent per year over this period.

# **D.** Sensitivity Analyses

We have conducted a number of sensitivity analyses to gauge the robustness of these findings. For instance, the two-stage least squares estimator gives similar results to the GMM estimator reported above. We have also restricted the sample to those countries for which LLSV **(1998)** collect legal data. This did not alter the results. Furthermore, as control variables, we used measures of the efficiency of the bureaucracy and the level of bureaucratic red tape [Knack and Keefer **1995;** Mauro **1995],** as well as measures of terms of trade changes and population growth. These did not alter our findings. We controlled for the level of stock market development (Levine and Zervos **1998)** without changing the conclusion that financial intermediary development exerts a positive impact on long-run growth. We also experimented with two additional measures of financial intermediary development. One measure equals deposit money
set of price weights. These "prices" were unit values, derived from production censuses by dividing gross value of output by the corresponding quantities. Two sets of binary comparisons were made, i.e. Brazil/USA and Mexico/USA. Each involved (a) unit value weights of country X (Brazil or Mexico) to compare gross volume of output of that country with that in the United States: or (b) unit value weights of the USA to derive the quantity ratio between country X and the United States as follows: v (QX * pU) E (QU * pU) (3.1b) y y with Qy= quantity of product y Py= unit value of product y X = country X U = United States
(B), and our proposed methodology (C). Our methodology outperforms the UNHCR/WFP/CNARR targeting program and the median value of the programs covered by Coady et al. (2004), and the UNHCR/WFP/CNARR program does not perform poorly when compared with the international evidence.

# **VI. Limitations**

The objective of this work was to test how cross-survey imputation methods perform in estimating poverty for refugee populations, using Chad as a case study. Although the results of our crosssurvey imputation exercise show that key demographic variables from ProGres predict well the welfare measure captured in the PDM at the aggregate level, additional work is needed to assess how well this methodology performs in refugee contexts, particularly in poor countries and data scarce environments. To do this, data sets should ideally contain more detailed information on 14
Second, we introduce the concept of allocative efficiencies to the PHC quality improvement literature and use it to quantify losses due to provider misallocation. In our case, the misallocation takes the form of a very low correlation between a provider’s caseload and their competence: More- and less-competent providers were nearly equally likely to be underutilized. In combination with the variation in caseloads we observed, this means it was often true that large patient populations were handled by low-competence providers while facilities with high-competence providers reported few or no patients.

Combining caseloads and competence measures allowed us to determine that half of the countries in this study are experiencing substantial productivity losses from underutilization among high-competence providers, and could improve the quality of care by reallocating better providers to busier facilities (or making other similar allocative improvements). Such improvements were generally comparable to or greater than the impact of training additional providers to the level of local doctors, suggesting the availability of lower-cost and shorter-term paths to quality improvement.

At the same time, the other half of countries had severe shortages of high-competence providers, and therefore had little to no potential gains from reallocation. These countries also demonstrated substantial excess capacity across their workforce, but typically had fewer high-volume facilities where their few high-competence providers could attend to large numbers of patients. Furthermore, due to the very low overall competence of their workforce, these countries would also be unable to improve the quality of care by producing more providers under business-as-usual medical education, suggesting very serious underlying problems in the development of human capital.

Our findings therefore support a revision in perspective regarding the “human resources crisis in health”, at least at the primary case level. We find that this “crisis” is not consistent with a _general_ shortage of health care providers, as aggregate statistics and facility surveys both indicate that many providers in every context have substantial excess capacity. Instead, there are two different workforce crises at play, whose interaction offers a promising new approach to system-wide quality improvement.

The first crisis arises from substantial variation in utilization
we focus on the cross-sectional dimension of the database and look at the year 2017 in the analysis that follows. Details on the coverage of countries, sectors and measures in the OECD’s STRI database are included in Annexes A, B and C, respectively. The database also provides the source of information on each measure, sector and country as well as additional information as comments.

A quick look at some of these measures illustrates how they impose unnecessary costs on Mode 4 service providers and why eliminating such measures or making them less stringent is likely to liberalize (as opposed to facilitate) Mode 4 trade. For instance, the requirement of prior nationality or citizenship to practice or that foreign service providers need to take a local exam or completely re-do the university degree, practice and exam in the domestic country are unnecessarily restrictive and can be eliminated or toned down sufficiently. Similarly, relaxing labor market tests and quotas for contractual service suppliers (CSSs), independent service suppliers (ISSs) and intra-corporate transferees (ICTs) as well as limitations on the duration of their stay in the importing countries is likely to go a long way in liberalizing Mode 4 trade. In contrast, two measures in the STRI MNP data relate to laws or regulations that establish a process for recognizing qualifications gained abroad and these need to be encouraged in the context of Mode 4 trade liberalization.

For the purpose of analysis in this paper, we convert the main Yes/No response to 24 of the 27 qualitative measures in the OECD STRI database for the year 2017 into a binary quantitative index where N=0 and Y=1.<sup>4</sup> To provide an aggregate picture of the MNP restrictive regulatory landscape, we average the constructed index across measures and sectors by country such that the higher the score, the more restrictive is the country (including in a particular sector or for a particular measure). Illustratively, for the measure “foreign providers have to completely re-do the university degree, practice and exam in the