rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3. Cross-country covariates of SME financing**

This section discusses macroeconomic and institutional factors that affect lending to SMEs using cross-country data from the _Financial Access_ database introduced above, and common indicators of growth and development. We use the standard cross-country regression framework, which has both advantages and disadvantages. This framework is useful for obtaining a global picture of SME lending volumes, and for informing the policy debate from a global perspective. However, it is not necessarily the best tool to derive country-specific policy implications, as it does not consider within-country variations in SME lending.

To what extent is cross-country variation in the volume of SME financing driven by differences in the definition? This is an important first question before starting more detailed analyses of the data, since SME definitions vary greatly across countries, as evidenced in Section 2.1 above. This variation in SME definitions might in fact cause differences in levels of SME financing across countries rather than indicators of economic growth and development.

Estimation results are presented in Table 4 and show no consistent and robust correlations between the levels of SME finance and definition criteria. We do not find statistically significant correlations between the value of SME financing and the maximum number of employees used as a criterion to define SME.<sup>10</sup> In a smaller sample the maximum sales volume criteria is positively correlated with the ratio of SME loans to GDP but not with the share of SME loans in total commercial bank loans. Moreover, the correlation between the ratio of SME loans to GDP and sales volume is not statistically significant once we control for income per capita.

These results provide a certain level of comfort and allow us to proceed in analyzing SME finance data using national definitions. Even though the definition introduces a substantial degree of heterogeneity in the data, it does not appear to influence the volume of SME financing reported by countries in a manner that would prevent cross-country comparison.

> 10 All regressions exclude countries where the higher cutoff for the definition of SMEs as defined by number
%|3,580|
|>65|26.0%|208|

Source: Based on 2016/17 Malawi LSMS‐ISA data.

Note:<sup>1</sup> Calculated as the product of the number of hours/day, number of days in the last month, and number of months in the last year. Thresholds are set so that the four groups have a similar number of workers.

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- The common innovation infrastructure which is defined as the broad set of policies affecting innovation including tax incentives, the protection of intellectual property, anti-trust enforcement and openness to competition.  It also includes such purely scientific technological factors as excellence in basic research.  But in general, the innovation infrastructure is more closely related to general business climate issues than to technology measures.

- The cluster-specific environment for innovation.<sup>29</sup> This dimension of innovation capacity recognizes that “the commercialization of new technologies takes place disproportionately in clusters  -- geographic concentrations of interconnected companies and institutions in a particular field.”  This in turn leads to “the presence of high quality and specialized inputs, a context that encourages investment coupled with intense local rivalry, pressure and insight gleaned from sophisticated local demand, and the local presence of related and supporting industries.”  The key point here is that isolated firms are at a competitive disadvantage.  A precondition for success in today’s global economy is a dense web of relationships between suppliers, customers, competitors, researchers and universities. Establishing this dense web is a pre-requisite for innovation and a challenge for innovation policy.

- The quality of linkages is the glue that binds the other elements together.  This variable measures the communications links between these various institutions. But communications links in this context do not refer to such mechanical items as ICT and the internet.  Instead, they refer to the formal and informal social organizations and networks that bind organizations and institutions to each other within the cluster. “Without strong linkages,” the authors explain, a nation’s upstream scientific and technical advances can diffuse to other countries more quickly than they can be exploited at home.”

On the basis of this Innovation Capacity Index, Latvia ranks 41<sup>st</sup> out of 75 countries. Once again, Latvia is behind every CC except Bulgaria and Romania. More interesting than the composite ranking, however, is Latvia’s relative standing on the four components. Latvia,
.54|0.04|-0.13|0.21|
|Woke up fresh & rested last 2 weeks (WHO)|1843|2.58|1.62|1435|2.56|1.53|0.02|-0.14|0.18|
|Daily life filled with things that interest last 2 weeks (WHO)|1843|2.25|1.76|1435|2.30|1.70|0.12|-0.06|0.29|
|Felt unable to control important things last month (Cohen)|1843|2.95|1.26|1435|2.92|1.12|-0.06|-0.17|0.05|
|Felt confident about ability to handle personal problems last month (Cohen)|1843|3.09|1.27|1435|3.05|1.17|0.02|-0.08|0.13|
|Felt confident things were going your way last month (Cohen)|1843|2.81|1.33|1435|2.89|1.25|0.10|-0.02|0.23|
|Felt difficulties were piling up could not overcome them last month (Cohen)|1843|2.39|1.28|1435|2.56|1.20|0.10|-0.03|0.23|
|School attendance(dayspast week)|299|3.21|2.72|226|2.74|2.72|-0.38|-0.82|0.07|

ITT = intention-to-treat effect estimate; HH = household; FIB = fecal indicator bacteria; MPN = most probable number; IHS = inverse hyperbolic spline; WHO = World Health Organization; WVS = World Values Survey. Effects
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“accounting services”), quantities, unit prices and total prices. In some cases, the item will separately describe the “quoted” prices, before the execution of the tender, and the price for the winning proposal. The item dataset also includes the name and document number of the winning entity, which can be a firm or individual. Each item is uniquely identified by the variable id_item_bd and can be connected to tenders by the variable id_licitacao_bd.

Finally, in the _tender-participant_ table each observation is a participant in a given tender. Some municipalities will only provide winners in each tender, so in that case these will be the same as those listed in the tender-item table. Others will include all participants in a tender, including those that did not win any contracts. The data includes names and document numbers of each participant, as well as an indicator of whether they are winners. Each participant is uniquely identified by the pair of variables documento and razao_social, and can be connected to tenders by the variable id_licitacao_bd.

# **2.2 Budget execution**

Budget execution refers to the implementation of the annual budget that is approved by the local legislature. While much of the analytical work presented in this paper focuses on budget execution of public procurement, the tables on budget execution include all activities performed by municipalities, including payment of salaries, transfers, and others.<sup>13</sup> The budget execution process in Brazil is similar to that in other countries.<sup>14</sup> It consists of three distinct steps: commitment, verification, and payment. The commitment ( _empenho_ ) phase is the moment when governments set aside part of the budget appropriated to them for a specific activity, such as buying goods from a supplier or paying salaries of health workers. From a budgetary perspective, this is often seen as the moment an expenditure is recognized, since committed amounts are deducted from the budget appropriation. The second step of the budget execution is the verification (
0178)|(0.0183)|(0.0181)|
|Lag 2 Rain in cm|0.0319**|0.860*|0.0322**|0.0306**|0.0315**|
||(0.0156)|(0.507)|(0.0148)|(0.0150)|(0.0152)|
|Constant|-0.0688**|-1.579|-0.415|-0.142|-0.0719**|
||(0.0290)|(1.199)|(0.264)|(0.164)|(0.0298)|
|Month FE|Yes|Yes|Yes|Yes|Yes|
|Health Post x Month Obs|432|396|432|432|432|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Notes: Augmented version of the Arellano-Bond Generalized Method of Moments estimator used in all specications. In Column 2, I use cases rather than cases per 1000 people. In Column 3, the population of the health post catchment area is adjusted monthly by the number of people entering and exiting based on the mobile phone data scaled by a factor of .7 to account for the population not represented by these data. In Column 4, lag incidence is scaled by the percent of population susceptible to malaria. In Column 5, movements are restricted to those where the SIM card had a call or a text within 14 days of when the movement is counted.
Bank.|
|Freedom From Regulation (lagged)|<br>Freedom from Regulation indicator from<br>Fraser Institute’s Economic Freedom of the<br>World database. Higher values of the variable<br>imply less regulation (of formal businesses).<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of months covered in the calendar<br>year.<br>Source: Economic Freedom of the World,<br>Fraser Institute.|

60
monopoly power may behave like “stationary bandits” in contrast to “roving bandits”, and therefore internalize the benefits of providing a good business environment and property rights protection so that the party has a sustained economic payoff over the long run (Olsen 2000). This stationary bandit interpretation of party monopoly thus implies a positive effect on economic performance rather than the negative effect predicted by the expropriation-risk interpretation. We shall thus allow the empirical results to distinguish between these two opposite interpretations.

In later sensitivity checks, we also include alternative measures of protection of property rights, including voice and accountability, executive competition, and government size. While the first two measures are clearly related to the protection of property rights of citizens from government expropriation, the size of government captures the de facto power of government, and could be viewed as de facto protection of property rights.<sup>12</sup> The third component of political and institutional factors is whether a country experienced any armed domestic conflicts in the previous ten years. This variable is related to the political structure of a country (Acemoglu and Robinson 2012). In countries with no strong centralized power, armed conflicts may develop as challengers attempt to grab power from incumbents by violence. To capture basic domestic safety, we use a dummy variable on whether domestic armed conflicts occurred in the past 10 years, based on Gleditsch et al. (2002) and updates of UCDP/PRIO (2010).

**Business environment** .  The first element of business environment that we measure is the extent of corruption. We use the WBES measure of corruption which is the local average of

> 12 There are alternative interpretations for government size, but here we just mean to use it as a sensitivity check, and the key conclusions do not hinge on our classification.

10
Policy Research Working Paper 10694

# **Abstract**

To what extent do attitudes and perceived norms around household roles hinder the emergence of more gender-equal distributions of labor in Morocco? Moroccan women undertake a disproportionate share of unpaid household and care labor and participate in the labor force at low rates. Yet everyday practices are shifting, and normative expectations may be as well. From an online survey of predominantly urban, employed Moroccans, this paper finds that respondents aspire for men to be equal contributors in care tasks. Yet, unpaid labor burdens remain highly unequal, respondents disfavor men taking primary responsibility for cooking or cleaning, and women’s share of household labor correlates with perceptions of what men prefer more than with individuals’ actual preferences. Results from a conjoint survey experiment measuring preferences around employment and the household division of labor confirm respondents’ interest in more egalitarian relations in principle, but also suggest that strong preferences for a male breadwinner family model will continue to drive an unequal distribution of labor at home.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at carolynbarnett@arizona.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of
parental gender bias affects both relative and absolute mobility when diminishing returns and/or complementarity are important.

A credible empirical analysis of the above ideas, however, depends critically on the quality of the data available.<sup>8</sup> Most of the existing household surveys use coresidency criteria to define household membership and thus miss children from the sample in a nonrandom fashion. This results in truncation bias in the estimates of widely used measures of intergenerational mobility such as the intergenerational regression coefficient (IGRC). As reported by Emran et al. (2018), the truncation bias due to coresidency can vary significantly across gender and countries, making it doubly hazardous to rely on the coresident sample for a comparative study of gender bias in China and India. We take advantage of rich household surveys from China and India that do not suffer from coresidency bias for our analysis. Since the theory yields interesting predictions regarding the effects of gender bias on educational investment, our empirical analysis estimates both the intergenerational persistence equation (using IHDS 2012 for India, and CFPS 2016 for China) and the investment equation (using IHDS 2005 and NSS1995 for India, and CFPS 2010 for China).

The main conclusions from the empirical analysis are as follows. In India, the intergenerational mobility equation is _concave_ irrespective of gender and geographic location, rejecting the almost universally used linear specification in the existing literature. The concavity suggests that the complementarity between financial investment and parental education emphasized recently by Becker et al. (2018) may not be important in India. There are strong diminishing returns to both financial investment and parental direct inputs in education for girls in rural India. Girls face significantly lower relative and absolute mobility when the father is uneducated, but the gender difference becomes negligible when the father is college educated.<sup>9</sup> The relative magnitudes of the estimated parameters of the 8A large literature on intergenerational income mobility in the context of developed countries emphasizes the attenuation bias in the intergenerational income elasticity estimates due to measurement
their bachelor’s degree, and are 35 years of age or younger at the time we observe their wages. These age restrictions ensure we compare individuals at a similar stage in their lifecycle profile and results in 389,749 observations.

Our sample is further restricted to exclude those with missing locations and all observations in 2008 because the geographic identifiers in that year were incorrectly recorded (resulting in _n_ = 310,581). Excluding workers with zero observed wages—either because they are self-employed, long-term unemployed, not in the labor force, or informally employed—we have _n_ = 252,788 individuals.<sup>5</sup> Our next restriction is to exclude workers in the health sector, who are subject to a mandatory rural service requirement for doctors and college graduates in these fields.<sup>6</sup> For ease of interpretation in our results below, we also leave out individuals who moved to a smaller city for college. Only 2.7% of the sample at this stage moves to a smaller city for college, whereas 17.7% of individuals moves to a bigger city. Nonetheless, the inclusion of those who move to a smaller city for college does not alter our results. Our final sample is comprised of _n_ = 212,023 individuals for whom we use their last recorded annual wage if they are observed in multiple years, and we deflate nominal annual wages using the Colombian cpi (Dec 2008 = 100).

# **_Definition of cities_**

For the purposes of the analyses that follow, we define a ‘city’ as the metropolitan area constructed from the aggregation of municipalities that are interconnected through commuting flows, as laid out in Duranton (2015). Duranton (2015) takes all pairs of origin and destination municipalities, and designs an algorithm that flags those pairs for which the share of commuters from the origin is above some chosen commuting threshold. Around each ‘urban core’ or central city, the algorithm then identifies the
occur whenever one tries to estimate population parameters from a nonrandom sample. The sample may be nonrandom because only firms with certain characteristics are selected into the sample (sample selection), or because firms participate voluntarily in the sample (self-selection). Selective samples can also occur because firms fall out of the sample for various reasons, despite an initial random sample (attrition). According to Jaffe (2002), if we cannot control the selectivity problem, we might over- or underestimate the true effects of the policy.

In order to deal with the selectivity issue, we adopt propensity score matching estimators, which have recently been applied to firm-level studies (Yasar and Rejesus 2005; Loof and Heshmati 2005; Oh et al. 2009). Propensity score matching was chosen because: (i) it reduces (although does not eliminate) selection biases, (ii) it reduces the limitation from matching on many observable variables for finite data, and (iii) it is best suited to the structure of the available data. For example, because baseline data was not collected at the outset of the program and detailed secondary source data were not available to construct an appropriate baseline, evaluation techniques such as difference-in-difference, which would have removed non-observable biases between the treatment and control groups, could not be utilized. The difference-in-difference compares the difference between the treatment and control groups before (first difference) and

> 2 An overdraft facility is defined as a flexible account that allows firms to draw upon in the event their account balance becomes negative.

2
accrue to them under two different assumptions about the pass-through from food prices to agricultural wages.

Third, we attempt to shed light on the magnitude and effectiveness of the social policy response of the Brazilian government, in terms of the extent to which it helped mitigate the price increases. And fourth, we present the estimates for each of these effects – the expenditure effect, the market income effect and the transfer income effect – in a distributionally disaggregated way, by means of “price change incidence curves”, which are analogous to the growth incidence curves of Ravallion and Chen (2003). These curves depict each individual effect, as well as the net effect, for each percentile of the income distribution. They are computed and presented separately for the large urban areas where the prices are collected, for rural areas, and for the country as a whole. From the 5 The staples considered are beef, dairy, maize, poultry, rice, sugar, and wheat.
group, had been chosen based on power calculations and cost considerations. A sample size of 400 firms per group allows detecting effects in the order of about 0.20 standard deviations, with at least 80 percent power. Effects of 0.20 standard deviations and less are considered ‘small’ in the literature. If the calculations account in addition for an attrition rate of 20 percent, the minimum detectable effect size increases to about 0.22 standard deviations. If a power of 90 percent is imposed, the measurable effects increase to 0.23 standard deviations and 0.26 standard deviations, respectively. Given, that we have baseline data, at least for the existing firms, we can also estimate Analysis of Covariance specifications (ANCOVA) and use a large set of covariates which further adds to power.

On top of the quantitative surveys, we also used qualitative methods to probe into contextual factors, especially focus group discussions with firm owners and in-depth interviews with staff of MEBF and Bagrépole and also some grant beneficiaries. At baseline, these interviews were conducted 12 months after the quantitative survey and at midline in the same month as the quantitative survey. Moreover, we can draw on monitoring data on program demand, delivery of grants and services, actual program takeup, and process information. All sources taken together provide a rich data set complementing the survey data which can help understand the mechanisms linking program inputs to outputs and ultimate impacts and, more generally, can help validate our results.

# _4.2 Balance tests_

Table A.1 and Table A.2 in the appendix show baseline characteristics and pairwise balance tests across all three groups. Table A.1 focuses on characteristics of the entrepreneur and Table A.2 on characteristics of the firm. Table A.1 shows that in our sample, entrepreneurs are on average about 42 years old, a bit less than half are women, most are married, about 55 percent are the head of their household and have about five to six children, a
served by either a single or very few carriers. Hummels et al. (2009) reported a high degree of the within-route concentration, with the majority of importer-exporter pairs served by three or fewer ships, often owned by a single carrier. Using BlueWater Research data for years 20122021, Ardelean and Lugovskyy (2021), reported that up to 60% of rotations<sup>26</sup> connecting to the US were served by 4 carriers or less. Naturally, we can expect relatively high markups charged by the carriers, especially on the thin routes (see Hummels et al., 2009, for a discussion of the variation in route-specific markups).

> 26 Rotations are defined as ports on a given schedule, similar to having bus stops on a bus schedule. Rotations often include over two ports in multiple countries.

28