rafmacalaba/data-use-mentions-extended huggingface dataset:
|(7)|0.752***<br>(0.089)<br>0.807***<br>(0.054)<br>-0.088***<br>(0.006)<br>0.002<br>(0.142)<br>-1.041***<br>(0.293)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.582<br>ded in all specifcations. The<br>China_j_ is an interaction term<br>mber of foreign competitors is<br>y the number of other French<br> from the Penn World Tables.<br>-country pairs are reported in|
|---|---|
|(6)|-0.167<br>(0.150)<br>-1.586***<br>(0.312)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.543<br>ar and a constant are inclu<br> _j_ in year_t_. Tariff_fjt ×_ <br>ndahl index, where the nu<br>gn competitors is proxied b<br>og of the GDP of country_j_<br>ard errors clustered by frm|
|ucts<br>(5)|0.758***<br>(0.089)<br>0.806***<br>(0.054)<br>-0.088***<br>(0.006)<br>-0.185<br>(0.129)<br>Yes<br>Yes<br>Yes<br>Yes<br
that control for time invariant factors, such as access to infrastructure, soil quality, or village leadership, and vv v as well as for periods before 2022 capture timeαα varying factors not affected by the war, 2022 or 2023 would provide an upper bound estimate of the vv XX λλ indirect or macro effects of the conflict on area cultivated. λλ λλ Table 4 presents the main coefficient estimates from (1) for winter crops in columns 1 and 2 and summer crops in columns 3 and 4. All the estimated coefficients from the full specification are reported in appendix tables A2 and A3. For each outcome variable, the first column uses data on conflict incidence from publicly available sources, and the second column adds information on damaged area obtained from remote sensing and, for summer crops, an interaction with winter crop area to allow for substitution effects. With R<sup>2</sup> values close to or above 0.9, all the coefficients of interest are significant at 1%. Three conclusions stand out.

First, our results suggest that omitting the measure of damaged area derived from remotely sensed imagery would result in underestimation of the impact of the conflict and reduce the regressions’ explanatory power.

> 8 We include levels and quadratic terms to allow for decreasing marginal effects from rain or temperature or negative flooding or drought effects.

10
, success in raising economic growth may not translate into success at the local economic level, and the existing pattern of spatial development is highly uneven in Africa. Mining projects may have few local economic linkages even though they contribute to higher levels of GDP, export earnings, and government revenues. Accordingly, our aim is to evaluate the impact of China’s WTO accession – via the channel of world mineral prices – on indicators of local development in Africa, namely: economic growth; migration; spatial inequality; and welfare.

Our paper makes four main contributions. First, it provides fresh empirical evidence on the development of China’s WTO accession, as Henderson et al. (2013) notes, there is a shortage of research evidence regarding China’s impact on African economies. Second, previous studies examining the impact of accession have been theoretical or simulation-based, mainly due to a lack of data, especially when the study was undertaken only a few years after 2001 (see for example Wang, 2003; Wong, 2003). As we explain in detail later, the present study utilizes novel night lights and spatial mineral data to circumvent this limitation. Third, the spatial dimension of Africa’s growth, which is a major determinant of the overall income distribution, and whether growth is ‘inclusive’ or not, needs more robust evidence given its high policy importance

> 5 China impacts Africa’s economies through both direct bilateral relations (in trade, finance and investment) and via indirect effects (Henderson et al., 2013). The present paper focuses on the latter category, the impact of which can be much larger than that of direct bilateral relations (Ibid).

2
and/or residents of informal settlements. The analysis utilizes a recently collected household survey, the Cities Baseline Survey, which is representative at both the formal and informal residential areas in Nairobi. Finally, we analyze what trade-off Nairobi residents face in selecting residences by examining the link between job accessibility and housing rents in the city. When housing rents are determined in equilibrium in functioning (even informal) housing markets, the rents reflect job accessibility among other characteristics (Rosen 1974). We estimate hedonic regression models to examine the costs of better job accessibility in Nairobi.

Our analysis illustrates how job accessibility is linked to the spatial distributions of housing rents and poverty in Nairobi. We find that Nairobi’s average job accessibility is indeed limited. By spending 30 minutes traveling, a Nairobi resident can on average reach 2 percent and 4 percent of jobs that exist in the city by foot and by minibus, respectively. She or he can reach 7 percent and 24 percent of jobs within an hour by foot and minibus, respectively. Given the fact that a large fraction of Nairobi residents—particularly the low-income—walk to work, this limited job accessibility must have created considerable inefficiencies in the labor market. Moreover, we find that the job accessibility of poorer households and/or residents of informal settlements is overall worse in Nairobi. Compared to richer households (in the fourth per-capita consumption quartile), poor households (in the first per-capita consumption quartile) can on average reach 20 percent fewer jobs by foot within 60 minutes. Similarly, the number of jobs that residents of informal settlements can reach by foot within 60 minutes is 30 percent smaller than for residents in formal residential areas. Our hedonic regression analysis demonstrates that it is costly to live in housing with better job accessibility in Nairobi, explaining in part why poorer households have such limited job accessibility.

Our findings illustrate the need to assess and enhance access to economic opportunities for disadvantaged workers in African cities, and Nairobi is a case in point. While the poverty headcount ratio
boundaries, especially because the housing reform drastically blurred the rural-urban boundaries.

# **4.2 Descriptive Statistics**

Among the 59 counties (shorthand for counties, or county-level cities) in our study, 11 counties were led by cadres affiliated with the FA3, among which 3 had local guerrilla presence; 48 counties were led by cadres affiliated with YRD, among which 22 had local guerrilla presence. In this subsection, we provide some descriptive statistics.

To control for the differences in the _initial conditions_ across counties, we construct the following variables: (1) _The log of the average agricultural and industrial output per capita_ in 1952 (respectively, in 1978), denoted by Ln_GVOPC52 (respectively, Ln_GVOPC78); and (2) _The log of the total population_ in 1952 (respectively, in 1978), denoted by Ln_Pop52 (respectively, Ln_Pop78).

We construct two variables to measure the county’s geography that may be relevant to economic performance:11

> 10The data is compiled by Kasahara and Li (2018), where they provide the details about the data construction and cross-validation with the other provincial level death stastistics.We are grateful to Bingjing Li for generously sharing the data with us.

> 11We have also tried the distance to Fuzhou – the provincial capital – but it is never statistically significant, and we thus opted not to include it to avoid multicollinearity in light of the few observations that we have. Since Fujian is a coastal province, we have also tried

17
together gives us its length. We use the logarithm of this (in years) as a measure of project duration ( _LNLENGTH_ ). We also have information on the distribution of projects across economic sectors (summarized in Table 1). We use a set of sector dummies to capture any sectoral variation in average project outcomes. Finally, we have data on preparation and supervision costs for each project. We express this as a fraction of the total size of the project, and use the logarithm of this in our regressions ( _LNPREPCOST_ and _LNSUPERCOST_ ).

A second set of project-level variables is obtained from the ISR process of monitoring and eventual evaluation of projects. We have retrieved data from the end-of-fiscal-year ISR for every project, and for each year during the life of the project. This provides us with information reported by the task manager on a variety of interim measures of project performance, on an annual basis over the life of the project. Each year, the implementation status of the project is rated relative to the ultimate development objective, and if this rating is unsatisfactory, the project is flagged as a “problem project”. In addition, task managers indicate with a series of 12 flags whether there are concerns about specific dimensions of project performance, including problems with financial management, compliance with safeguard, quality of monitoring and evaluation, legal issues, etc. If three or more of these flags are raised at any one point in time, the project is identified as a “potential problem project”. Even if a project is flagged as a potential problem project, country management units have the authority to override this classification using a “golden flag”, on which we also have data.

While in principle the flag data are a rich source of information on leading indicators of project performance, they need to be treated with some caution. While some of the flags are automatically triggered (for example, by objectively-measured disbursement delays, or by lags between project approval and the start of work on the project),
Figure B.6: Missing municipalities: budget execution <!-- Start of picture text --> Commitment<br>10.0<br>7.5<br>5.0<br>2.5<br>0.0<br>Verification<br>10<br>5<br>0<br>Payment<br>30<br>20<br>10<br>0<br>CE MG PB PE PR RS SP<br>20032004200520062007200820092010201120122013201420152016201720182019202020212022<br>%<br>%<br>%<br><!-- End of picture text -->

**Notes:** This figure presents the percentage of municipalities by year for which we do not have data in the budget execution dataset. Data from RS are restricted between 2009 and 2020 to avoid distorting the graph. In 2008, commitment data are unavailable for over one third of the municipalities from RS, whereas in 2021 we only have data for 12% of the municipalities.

53
Here we use the experimental data to illustrate four things: (1) implementation of the randomized order within MPL and order within binary choice, (2) MSB and NSB in these data, (3) estimation with a scaled random-effects probit, and (4) alternative approaches in the literature to addressing inconsistent choices and interval identification.

**Implementation.** Three different incentivized MPLs were administered as part of a survey and each subject was randomly assigned to receive two out of the three MPLs (N=767).<sup>20</sup> Here, we focus on one of the MPLs, administered to 506 subjects, which elicited a relative preference for receiving a transfer in the form of an electricity voucher or cash. The other two MPL elicited preferences over one voucher vs. receiving two vouchers on two different days, and two vouchers vs. cash. We include results for estimating WTP in all three MPL in Appendix A (see also below). One binary choice within one of the MPLs was drawn for implementation and the payoffs were immediately realized. We discuss some of our implementation choices in more detail in section 4.

The MPL randomized order both within MPL and within choice. We developed a SurveyCTO template and accompanying randomization files that can be used to carry out an MPL with these features and are described in section 4 and in Appendix S2. The survey module uses a visual representation of the options and shows binary choices one at a time. Subjects select their choice by tapping the screen of the tablet (with the help of an enumerator, if needed).

– The structure of the MPL here with descending value difference ∆ _t_ and showing – options in each choice in the same order is shown in Figure 2. The MPL is designed so that the highest possible value that can be obtained in each choice was 100 Rand. By varying the value of the other option, the value difference increases strictly monotonically between binary choices.

**MSB and NSB in our data
Economic Research, 2020.

- NYFED. 2020.

- OECD. Supporting businesses in financial distress to avoid insolvency during the covid-19 crisis. Technical report, OECD Policy Responses to Coronavirus (COVID-19), OECD, Paris, 2020.

- F. Schivardi and G. Romano. A simple method to estimate firms liquidity needs during the covid-19 crisis with an application to italy. Technical report, CEPR Covid Economics: Vetted and Real-Time Papers, 2020.

- WBG. Covid-19 outbreak: Implications on corporate and individual insolvency. Technical report, Equitable growth, Finance and Institutions, COVID-19 Notes, Finance Series, World Bank, Washington DC, 2020.

- X. Zhang. The Impact of COVID-19 on SMEs in China: Evidence from Two-Waves of Phone Surveys. Technical report, 2020. URL https://www.slideshare.net/ifpri/ the-impact-of-covid19-on-smes-in-china.

16
) which associates cities with dense clusters of population; and, finally, the identification and delineation of metro areas based on the “thresholding of _Night-Time Lights_ data _”_ (NTL), similar to, for example, Ellis and Roberts (2016) and CAF (2017). These latter three approaches all have the obvious advantage that they can be applied to any country in the world using readily available data sets, including those countries for which O-D commuting flow data are not available.

In all our empirics, we focus on the case of a single large developing country – Indonesia. Unlike many developing countries, Indonesia has the advantage that its national labor force survey ( _Survei Tenaga Kerja Nasional_ , SAKERNAS) allows for the derivation of an O-D matrix of commuting flows between sub-national areas (districts), which is the crucial input for the application of the Duranton (2015b) algorithm in defining metro areas. Beyond the main focus of this paper, however, Indonesia’s urban landscape is interesting to study in and of itself. Indonesia has, in recent decades, been one of the world’s most rapidly urbanizing countries and, within the country, there is intense policy interest in the issue of how to delineate metropolitan areas (see World Bank, 2018).<sup>7</sup> By focusing on Indonesia, our paper also contributes to the, to date, limited credible empirical evidence on the strength of agglomeration economies in developing countries. This is a knowledge gap that economists such as Overman and Venables (2005), Duranton (2015a), and Glaeser and Henderson (2017) have made recent calls for the profession to fill.

> 7 The Government of Indonesia has itself attempted to identify metropolitan areas. This began in 2008 with Government Regulation 26/2008 (Peraturan Pemerintah/PP) that specified nine multi-district urban areas (“ _kawasan perkotaan yang bersifat lintas wilayah_ ”). A further four multi-district urban areas were added in 2017 through Government Regulation 13/2017.

3
.00|
|2015|2.74|1.08|1.03|0.99|0.77|0.38|0.01|
|2016|2.75|1.03|1.02|1.00|0.78|0.42|0.00|
|2017|2.75|1.07|1.05|0.96|0.74|0.41|0.02|
|2018|2.79|1.07|1.04|0.98|0.76|0.36|0.01|
|2019|2.78|1.11|1.01|0.95|0.76|0.39|0.02|
|2020|2.79|1.11|1.04|0.92|0.75|0.36|0.03|
|2021|2.76|1.12|1.05|0.94|0.80|0.34|0.00|

_Notes:_ Based on data from EH 2007-2021.

17
that protect property rights and provide a minimum of certainty; on an environment of trust among economic agents; and on the ability of policy-makers to provide stable macroeconomic conditions. Fragile states, where these elements are in short supply, can be expected to have less developed financial institutions and markets—although the evidence on this is mixed (Barajas et al., 2021).

Numerous studies have empirically investigated the links between financial development and economic growth, often uncovering positive and significant correlations (Levine, 1997; Beck et al., 2000; Valickova et al., 2015), with causality running from finance to growth. Financial development has also been shown to reduce income inequality and is strongly associated with poverty alleviation (Beck et al., 2007). However, the relationship between financial development and state fragility (or its opposite, resilience), which may be heterogeneous across groups of countries and measures of financial development, has not been extensively addressed in the existing literature (Barajas et al., 2021). This leaves a gap that this paper aims to fill.

There are at least two major limitations of the current study that we want to mention upfront. First, measuring state fragility is not undisputed; see for instance Grimm et al. (2014). Similarly, proxying financial development is a difficult exercise given its multifaceted nature; see, for example, Svirydzenka (2016). In this paper we use World Bank and IMF measures for state fragility and financial development, respectively. For state fragility we use the Country Policy Institutional Assessment (CPIA) score, a widely used metric to classify fragile countries. This picks up aspects related to the level of institutional capacity and policy environment of a country. For financial development, we use the financial development (FD) index, introduced by Sahay et al. (2015), which is a relative ranking of countries on depth, access, and efficiency of financial institutions and financial markets.

Second, there is an obvious endogeneity issue with the
Mongolia, and Tanzania), which exhibited drops at the peak of 24 to 45%.

Our conditional estimates test whether the gender gap is larger among specific groups of businesses, for example, in particular sectors (e.g. hospitality) or among businesses of a particular size (e.g. micro or small). We also test whether gender differences vary with the severity of the shock (because countries more severely affected could exhibit, for example, larger increases in demand for caregivers which in turn could disproportionately affect women). The conditional specification introduces into (1) controls for the size and the sector of the business and in addition, interacts size, sector, income, and the severity of the shock with the indicator for whether the business is led by a woman: where _δn_ and _δg_ denote size and sector fixed effects.

The estimated _unconditional_ gender gaps _β_ in (1) combine composition effects–which arise from the composition of the sample collected or from men- and women-led businesses operating in different sectors or having different sizes–and gender gaps that exist after controlling for these compositional characteristics (for example, due to some forms of discrimination or because women entrepreneurs were disproportionately affected by the increase in care demands due to school closures). In contrast, the _conditional_ model in (2) estimates gender differences controlling for firm characteristics, hence net of the above composition effect. In addition, (2) allows us to test whether gender gaps are larger in some sectors relative to others and for some firm sizes. To estimate the gender gap in businesses of a specific size, for example, we use the fitted model to

> 10The control for the timing of the survey is the number of weeks between the date of the interview and March 11, 2020 (the date when the World Health Organization declared COVID-19 a pandemic).

> 11For countries without coverage in the Google mobility data, mobility is predicted using data on the stringency of the lockdown restrictions in Hale et al. (
component of domestic absorption in the national accounts.<sup>6</sup> There has been a systematic gap between mean consumption or income measured from the household surveys used to measure poverty and these national accounts aggregates, and there has also been a gap in the growth rates, such that the share of GDP accountable to consumption by households has tended to fall in many developing countries (Ravallion, 2003; Deaton, 2005). The way national accounts are constructed in practice means that there is nothing exactly corresponding to household consumption as measured in surveys, so the comparison is difficult. Differences in the concepts play a role, but so too do measurement errors (in both sources), differences in accounting periods and sampling problems. (It should not be assumed that national accounts data are more accurate than survey data for developing countries.) To investigate this further, I have assembled a data file of the longest available spells between two household surveys for developing countries, using the same welfare indicator (consumption or income) for both surveys. This was possible for 95 developing countries, with a mean (and median) of 16 years between the surveys, ranging from 4 to 32 years. Regressing the annualized growth rate in the survey means on the corresponding growth rate in the survey mean the slope coefficient is 0.968 (White s.e.=0.199; n=95) and the intercept is -0.012 (s.e.=0.006). Figure 1 plots the data points. Imposing the restriction that the slope is unity, the average gap is - 0.012 (s.e.=0.004), indicating that the survey means give a 1.2 percentage point lower growth rate on average.<sup>7</sup> This is significantly different from zero, but there is still a wide variation (Figure 1), with a standard deviation of 4.0 percentage points, and a range from -
WPS3795 economic performance and their association with indicators of market power such as concentration and regulatory restrictions on entry and competition. The empirical results are mixed, with some studies finding generally unfavorable effects from market power (e.g., Elsas 2005, Karceski, Ongena, and Smith 2005, Cetorelli and Strahan 2006), and others finding favorable effects (e.g., Petersen and Rajan 1995, Cetorelli and Gambera 2001, Bonaccorsi di Patti and Dell’Ariccia 2004, Cetorelli 2004).<sup>12</sup>

# **4. The lending infrastructure**

In this section, we turn our attention to the lending infrastructures of nations and how they affect the feasibility and profitability of using the different lending technologies in SME financing. The lending infrastructure includes the information environment, the legal, judicial and bankruptcy environments, the social environment, and the tax and regulatory environments. All of these elements may affect SME credit availability by influencing the extent to which the different lending technologies may be legally and profitably employed. The final element, the regulatory environment, may also restrict SME credit availability by constraining the financial institution structure.

# **A.  The information environment**

An important aspect of the information environment is the accounting infrastructure. Strong accounting standards and credible independent accounting firms are necessary conditions for informative financial statements, which are key to the financial statement lending technology. These are also important for many components of loan contracting associated with financial statement lending and some of the other lending technologies to a lesser extent. For example, covenants based on financial ratios are not feasible if the ratios calculated from the financial statements are not reliable. Indices of global accounting standards indicate considerable variation across countries both between developed and developing economies and among developed economies (e.g., La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1998).

Another important aspect of the information environment is the sharing of information. Commercial and consumer credit bureaus provide formal organizational mechanisms for the exchange of payment performance data