rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

A sound business environment (BE) is increasingly recognized as a key driver for economic development (Stern 2002; World Bank 2005; Xu 2011). Governments, international organizations and think tanks have invested heavily in collecting data and finding ways to measure critical elements of the BE. The World Bank has implemented the _Doing Business (DB)_ surveys, an annual survey at the country level, and the _Enterprise Survey (ES)_ , which cover around 130 countries at the firm level. The World Economic Forum (WEF) has its own _Global Competitiveness Indices (GCI)_ , based on the Executive Opinion Survey conducted by WEF in around 140 countries, in cooperation with its partner institutes in each country (and drawing on other external data sources). These three sources of data have been used in various capacity in the literature on the effects of the business environment on firm performance and other development outcomes.

The business environment literature based on _Enterprise Survey_ data is relatively large (see, for example, Dollar, Hallward-Driemeier, and Mengistae 2005, 2006; Hallward-Driemeier, Wallsten, and Xu 2006; Cai, Fang, and Xu 2011; Xu 2011; Clarke, Qiang, and Xu 2015; Reyes, Roberts, and Xu 2017; Knack and Xu 2017). This literature has shown that firm performance depends critically on some basic elements in the business environment, such as limiting government expropriation, the provision of basic and modern infrastructure, access to finance, and agglomeration of sufficiently sized firms together _._ The literature based on the _Doing Business_ data tends to examine specific elements of the business environment one at a time, or to include the aggregate DB index (Botero et al. 2004; Djankov et al. 2002, 2003; Djankov, McLiesh, and Shleifer 2007; Djankov, Freund, and Pham 2010). There are relatively few studies based on Global Competitiveness indicators in the business environment literature, suggesting a research opportunity to exploit to yield further insights
& Tobago||1986|1989|
|Tunisia|1991|||
|Turkey|1982; 2000|1978; 1984; 1991; 1996; 2001; 2018|1978|
|Turkmenistan||2008||
|Uganda|1994|1980; 1988|1981|
|Ukraine|1998; 2008; 2014|1998; 2009; 2014|1998; 2015|
|Uruguay|1981; 2002|1972; 1983; 1990; 2002|1983; 2002|
|Uzbekistan||2000; 2017||
|Venezuela, RB|1994|1984; 1989; 1994; 2002; 2010|1982; 2017|
|Vietnam|1997|1972; 1981; 1987|1985|
|Yemen, Rep.|1996|1985; 1995||
|Yugoslavia, The former SFR|||1983|
|Zambia|1995|1983; 1989; 1996; 2009; 2015|1983|
|Zimbabwe|1995|1983; 1991; 1998; 2003||

Note: Years of crises are taken from Laeven and Valencia (2018), while the list includes currency crises in 2017 and 2018, which are computed with data on nominal exchange rates from the IMF and the methodology described in Laeven and Valencia (2018).

42
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travel-time distance (in number of minutes) connecting any given pair of districts. Origin and destination points are located at the geographic center of each district, and the travel-time estimate is based on the least time-consuming path between the two. Time is computed<sup>8</sup> using Geographic Information Systems (GIS) as the length of the road between two points with assumptions about the speed of travel according to different road categories<sup>9</sup> . The same travel-time measure is also used to compute _Port j_ , the distance of a given district to the closest of the 13 largest trading ports in the country. Access to a major merchandise chipping port should, in theory, positively impact the probability of starting to export.

# **_Infrastructure Variables_**

The next set of variables deals with the general quality of infrastructure within a district, since one would expect that the general business environment would have a positive impact on the probability of a firm to enter export markets. Such variables are also particularly interesting to policy makers since, unlike agglomeration, targeted investments within a location can help to improve infrastructure and make a location more business-friendly. I include the density of roads, i.e. the length of roads per square kilometer within a district, as a proxy for transport infrastructure. I computed these values using ArcGIS data, and the density data is time-invariant<sup>10</sup> . I assess quantitatively the role played by human capital by including the proportion of the population within the district with a high-school education in a given year, captured by the education variable - _Ed jt_ . I define _X jt_ as a measure of ‘natural advantage’ through the embedded quality and availability of infrastructure in the district. I use the availability of power (proxied by the proportion of households with access to electricity) within a location as an indicator of the provision of infrastructure. In addition I also use the proportion of households within a district with a telephone connection as an
# **3.1. Merging and Matching Process**

The Raw Materials Database (RMD) of SNL Metals and Mining inform on the geolocation and the status of several mines in Africa. The database also informs on the year in which the most recent information was recorded. The two datasets are merged by first matching the spatial point coordinates (GPS) of the mines and primary sampling units (enumeration areas or districts or townville) in the survey data; and second, by matching the year of the survey data collection to the reference year for the latest information update on the mine. In cases where the year of the latest update does not correspond to the year of the Afrobarometer survey data collection, this latter is matched with the closest date in the SNL Metals and Mining data which in all accuracy corresponds to the previous or subsequent year. This consideration only applies to a few countries: Senegal (Round 3 and Round 4 of the Afrobarometer), Burundi (Round 5), Benin and E-Swatini (Round 6). In countries where the enumeration area is not coded in the Afrobarometer dataset, the districts or Townsville GPS coordinates are used to set the geographical boundaries.

From a cluster centre point, it is measured the distance to a mine and create a binary variable _‘Active’_ that equals to 1 if the sampling unit or district is located within 50 kilometres radius from an active mine. For the remaining units, it is created another binary variable _‘Inactive’_ which points to all respondents that reside within 50-km radius from an inactive mine. `Inactive’ refers to any mine with one of the following statuses: inactive, care and maintenance, on hold, rehabilitation, and under litigation. The pseudo control group thus include all enumeration areas located farther than 50 km radius from a mine (be it active or inactive). As the individual data are identified by their enumeration areas or districts or Townsville, it is feasible to relate their responses to their residential location at
9

**Expectations of counterpart** Think now for a moment about your husband/ father/ brother, and his views. Does he think or speak badly about women who work outside their homes?

## 2.3 **<mark>Limitations of the Study</mark>**

<mark>The study has several limitations. First, the Social Norms survey sample is representative of urban women and men in KRI; due to the lack of availability of more work options for women in rural areas, we did not include rural women and men in the study. Second, while we were able to oversample working women and were able to speak with a sufficient number of women working in the public sector, we were unable to find larger numbers of women working in the private sector. Future surveys should include more women working both in the formal and informal private sector. Finally, all regression results presented using the Social Norms survey data are correlations and do not bear any causal interpretation.</mark> <mark>In the Rwanga sample, while we started the study with a large number of female j</mark> ob seekers (1,780) registered at the jobs agency, we faced difficulty in regular follow-ups with this sample, and high attrition rates by the end of the study. As such, we only report findings that help to better explain some of the findings from the Social Norms survey.

# 3. Results

## **3.1 Descriptive Statistics**

## **3.1.1 Social Norms Survey**

Table 2 shows descriptive characteristics of the social norms sample broken down by working women, non-working women, men with a working woman counterpart, and men with a nonworking woman counterpart. In total, 1,150 respondents were interviewed, out of which 577 or 50.2% were women, and the rest were men. Nearly all respondents (99.6%) identified themselves as Kurdish. Thirty-six percent
.120|-0.036***|45.558*|
||(0.013)|(23.040)|(0.002)|(4.078)|(0.013)|(23.656)|
|Mean in control|0.148|142.403|0.015|11.865|0.147|130.538|
|Adjusted R-squared|0.041|0.004|0.013|0.001|0.041|0.003|
|Observations|13464|13464|13464|13464|13464|13464|
|_Panel C:_|||_Outside-vi_|_llage Transf_|_ers_||
|Treatment|-0.123***|-144.864*|0.011*|45.021|-0.123***|-197.782***|
||(0.019)|(72.655)|(0.006)|(43.650)|(0.018)|(65.197)|
|Mean in control|0.430|640.652|0.048|132.113|0.420|508.539|
|Adjusted R-squared|0.117|0.027|0.023|0.001|0.112|0.018|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All
owner (Figure 14, Panel B). The rest employ an average of 0.41 (0.36) workers, all of whom are unpaid family member or apprentices (Figure 14, Panel A). By definition, employers hire paid workers, though they also employ a significant share of unpaid workers. The 94.3 percent of employers who are classified as micro firms (defined as firms that employ 1-4 employees), employ an average of 1.85 workers (in addition to the owners), where about 0.8 are unpaid workers. The 5.7 percent of employers who are classified as small firms (defined as firms having five or more employees), have an average of 3.7 paid workers and 2.5 unpaid workers.

**Figure 14. Distribution of workers per firm, by informal firm type**

<!-- Start of picture text --> Panel A: Average Number of Workers per Firm Panel B: Distribution of Labor Force within<br>Type (excluding the owner) Firm Type<br>3.5 100% 5.7%<br>3.0 41.1% 36.3%<br>2.5<br>50% 94.3%<br>1.85<br>2.0<br>58.9% 63.7%<br>1.5<br>0%<br>1.0<br>0.41 0.36 LE-SE HE-SE Employer<br>0.5<br>Owner and 5+ employees<br>0.0 Owner and 1-4 employees<br>LE-SE HE-SE Employer Owner only<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average includes Mozambique, Zambia and Zimbabwe.

The difference in average sales and profits by firm types masks the high variance, and significant overlap, in the
Specifically, we include the logarithm of bilateral distance, along with dummies for country pairs that were ever in a colonial relationship, those that had a common colonizer, and those that have a common language as measured on an ethnographic basis. Finally, we include a dummy for membership of the same regional trade agreement (RTA), taken from De Sousa (2012).

In the discussion above, it is important to note that many of the variables of interest—all except the STRI and the regulatory heterogeneity indicator—vary at the country level; they do not vary at the bilateral level. Without modification, it would therefore be impossible to identify them separately from the importer fixed effects in the gravity model. But given that the gravity model includes intra-national trade data, it is possible to introduce bilateral variation by interacting the country-specific policy variables with a dummy for international trade observations, as per the approach in Anderson et al. (2018). The final element of the data set is therefore a dummy variable equal to unity for international observations, and zero for intra-national observations.

Applying the estimation technique discussed above to these data can provide indications as to which factors are the most important in terms of promoting bilateral services trade, after controlling for the restrictiveness of market access conditions. However, parameter estimates are not easily comparable across measures, due to differences in scale from one indicator to another. We therefore convert statistically significant estimates to ad valorem tariff equivalents (AVEs), by making use of the relationship in the theoretical model between individual factors and overall bilateral trade costs. In other words, we convert meaningful (i.e. statistically significant) results into their trade cost equivalents, compared with a baseline of maximum possible performance, to give an indication at the country level of the impediments to services trade posed by different factors.

14 We have also run our preferred regression specification with Doing Business data on ease of paying taxes, as an additional factor that could plausibly affect cross-border trade in services. However,
78243788|
|210: II.1. Transport and storage|0.16644811|66.45760378|0|56.27007312|2.94492878|27.37850787|
|220: II.2. Communications|0.67516879|0|0.56316173|0|0.03401264|0.02792133|
|230: II.3. Energy|1.95028367|0.02843258|38.94098003|38|0.35716357|28.02851041|

_Source: http://www.oecd.dac.org_ .

# Initial data collection: Practical concerns

SPI data need to be collected from central ministries (finance and economic planning) as well as from key line ministries, autonomous agencies, parastatals and, in a few occasions, nongovernmental organizations (NGOs). The involvement of key official and technical personnel is therefore critical in order to accurately identify sources and resources, and to validate inputs. As part of the data collection effort it is also essential to plan ahead and factor in a given country‘s budget and administrative cycles.

## **Budgetary cycle and budget classification**

Establishing the budgetary cycle of the given country is a prerequisite to identifying the time periods that may be particularly inconvenient for data collection activities, to tailor the fieldwork timetable accordingly, and to get a sense of when budget data will become available (even before being published). Likewise, knowing the budget coding system used by the government is essential. In particular, the country may or may not have adopted the IMF‘s Government Finance Statistics Manual 2001 (GFSM 2001)<sup>5</sup> for reporting budgetary accounts. If the country does not use the GFSM 2001 for its reporting, a code-mapping exercise will be necessary (to be discussed in more detail in the next section).

## **Strategy for fieldwork**

Institutional mapping is followed by establishing the scope of the data collection in terms of the institutions and sectors covered,
this hypothesis.

This study represents a step forward in the comprehension of the role of social capital in child development. In fact, it is the first comprehensive study of the impact of social capital on child nutritional status using data from a developing country. Furthermore, from a methodological point of view it deals with multiple problems in a step-by-step manner, in an attempt to exploit as much as possible the longitudinal setting of the data. Even if the question of causality versus selection bias needs further investigation, this paper establishes the correlation between maternal social participation and child nutritional status, by using fixed effects to control for unobservable heterogeneity at the community level.

Finally, it draws interesting conclusions and has important implications for the designing of programs able to launch poor households on a sustainable pathway out of poverty. This analysis provides suggestive evidences that promoting individual participation to community initiatives might be the way to go to alleviate poverty among the poorest. Even in very deprived context characterized by scarce human and physical capital there is space for effective policy interventions in a relatively short term. Low educated mothers might be helped in growing up healthy children, facilitating the cooperation and association at the community level.

However, further research is required in the analysis of the mechanisms through which maternal social capital might affect child development. If the empowerment channel matters and social 27
**2.2 Asset betas**

The equity beta measures two types of risk; fundamental business risk and financial risk. When making comparisons across countries it is the fundamental risk that should be compared. This is measured through the asset beta. Comparable asset beta values can be calculated as follows: _IAi_ = _PEi_ (1 - _Gi) + GI. fiDi_ where: _PAi_ = asset beta for security i;

**_P_**<sup>**_3_**</sup> **_i,_** = equity beta for security i;

_Gi_ = gearing ratio for security i.

_BDi_ = debt beta for security i.

Correction for gearing differences is particularly important in an international context. For example, firms in the United States tend to be highly geared relative to those in the UK due to differences in tax policy, institutional arrangements, industrial structure and business culture. Changes in gearing ratios over time must also be taken into account. If a company has experienced significant shifts in gearing (or any other factor), beta values must be calculated over fairly short periods, otherwise the resulting estimate is an average of several regimes and is not very meaningful. For this reason, the beta estimates in this paper have been produced using daily data over periods of a small number of years.

A general assumption that is applied is that fiDi = 0; this simplifies the calculation of the amount to: _Ai = Ei (I- G)_ 5
market of Tunisia during the period 2000-2019. Tunisia is an important MENA country that is considered a climate hotspot and for which little evidence is available. Importantly, Tunisia is a good case study to investigate the interacted effects of climate change on the local labor markets in a socioeconomic context characterized by a narrow demographic window, a labor force participation rate higher than the average in the Middle East and North Africa region, and regionally clustered economic activities and employment opportunities. We align nationally representative labor force surveys and weather data that allow to calculate SPEI, a 12
Policy Research Working Paper 5685

# **Abstract**

This study examines recent effective rates of protection across the Egyptian economy, using an ad valorem price wedge introduced by nontariff barriers and energy subsidies, and compares today’s effective rates of protection with those of a decade ago. The study uses 23 aggregated sectors from input-output matrix information. Although trade liberalization since the late-1990s has had a considerable impact in reducing protection of some industries, some sectors, such as the food and tobacco sector, remain relatively highly protected, due to tariff escalation and nontariff barriers, and due to energy subsidies. Energy subsidies are not formally sector specific but do favor sectors that are energy intensive (of particular note is the electricity sector).

It appears that energy pricing is part of a strategy to subsidize and promote certain industries and in effect offset the _dis-_ protection or taxation that results from tariffs on intermediate inputs. The case of the cement sector is notable because energy subsidies appear to almost exactly offset the negative impacts of tariffs and indirect taxes. The fertilizer sector has zero nominal tariffs, benefiting agriculture, and so a negative effective rate of protection due simply to tariffs on intermediate inputs. However, the fertilizer sector ends up with a very high a positive total effective rate of protection due to energy subsidies.

This paper is a product of the Poverty Reduction and Economic Management Unit, 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://econ.worldbank. org. The authors may be contacted at avaldese@uc.cl and wfoster@uc.cl.

_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
# **1. Introduction**

In recent years the term “middle-income trap” has been increasingly used in the development policy arena to refer to the phenomenon whereby rapidly growing economies stagnate at middleincome levels and fail to transition to the status of high-income countries. Several studies confirm this phenomenon. For instance, Larson et al. (2016) note that of 101 middle-income countries in 1960, only 13 countries managed to transition to the status of high-income countries by 2008 based on GDP per capita level relative to the United States. Similarly, Flaaen et al. (2013) note that among the seven countries that could be classified as middle income in 1975, only the Republic of Korea managed to reach high-income status by 2005. The present paper provides fresh and alternative evidence on the middle-income trap by focusing on the difference in labor productivity between firms in upper-middle-income and high-income countries. Firm-level survey data on 22 upper-middle-income countries (henceforth, UMICs) and 11 high-income countries (henceforth, HICs) show that labor productivity in the UMICs is 57.5 percent lower than in the HICs on average. Using the Oaxaca-Blinder decomposition technique, the productivity gap is decomposed into a part that is due to differences in the level of productivity-enhancing factors (endowment effect) and a part due to differences in the returns to the factors (structural effect). We find that the endowment effect and the structural effect contribute roughly equally to the productivity gap. Several potential firm- and country-level determinants of labor productivity are identified as significant contributors to the productivity gap.

The existing literature has largely relied on aggregated data and country-level factors in explaining the middle-income trap (see, for example, Agénor and Canuto 2015; Aiyar et al. 2013; Eichengreen et al. 2013; Flaaen et al. 2013). It is argued that long-term sustained economic growth requires a continued structural transformation of the economy, with the economy transitioning 2