rafmacalaba/data-use-mentions-extended huggingface dataset:
∂ _P_ <u>(</u> _D_ <u>)</u> ∂ _P_ <u>(</u> _D_ <u>)</u> which by definition is negative. Therefore, in addition to , is also of interest. ∂π ∂α

> Although we do not observe _P_ ( _D_ ) directly, we do observe the total number of discoveries, which is an increasing function of the probability of discovery of a particular good _P_ ( _D_ ). The relationship between total discoveries and variables that affect π and _α_ is therefore the object of study. If a significant negative relationship is found, this supports the market failures hypothesis: _q_ is affecting _P_ ( _D_ ), meaning that imitation is hindering discovery.

In section 6 we apply this framework to our discovery data, suggesting variables that affect π and _α_ and determining their relationship with discovery. However, we first describe the data and methodology used to identify discoveries, followed by some stylized facts based on the results.

# **3. Data, Methodology, and Stylized Facts**

_Data_ In the search for economic discoveries, domestic production data would be the first choice. However, as production data are not available at a highly disaggregated level, we use export data. The problem with using export data is obvious: a product emerging as a new export may have been produced domestically for some time, and therefore would not represent an economic discovery. However, the main advantage is that export data are recorded at highly disaggregated levels for customs purposes. In addition, exporting a particular good for the first 11
,446|186,515|
|of which: shares|56|256|652|2,375|3,040|9,086|
|govt. bonds|209|658|1,679|4,580|12,741|85,389|
|T bills|376|1,028|3,479|14,831|56,808|90,729|
|Outstanding securities|||||||
|Total|196|382|729|1,498|3,221|7,025|
|Private|61|112|131|295|441|932|
|of which: shares|49|71|109|224|425|909|
|Public|135|270|598|1,202|2,780|6,093|
||||(in pe|rcent)|||
|Memorandum items:|||||||
|Public sector share of:|||||||
|New issues|86.9|82.1|90.9|90.5|96.6|94.3|
|Trading volume|85.7|83.0|83.7|87.3|94.7|94.4|
|Outstanding securities|69.0|70.7|82.0|80.3|86.3|86.7|
|Public sector outstanding securities|||||||
|As percent of GNP|12.2|13.5|15.4|15.3|18.6|20.6|
|As percent of M2Y|42.7|56.8|50.5|_50.5_|53.8|59.3|

Source: own calculation based on data from Capital Markets Board and Treasury.
currency breakdown in the survey did not include all South currencies. We match these survey allocations more precisely when allocating “other currencies” entirely to the South (yielding allocations to South currencies of 4.7 and 4.4 percent in 2013 and 2014, respectively)

> 34 The COFER data identify the following currencies from North countries: Canadian dollar (from 2012), euro, Japanese yen, pound sterling, Swiss franc, and U.S. dollar. From South countries, the data only distinguish Australian dollars (from 2012 onward) and Chinese renminbi (from 2016 onward).

38
Table 1 The Potential Coverage of Regulations

||Labor force|Formal secto|r employment|
|---|---|---|---|
||(thousand|(thousand|persons)|
|Country|persons)|_||
|||Private|Public|
|Burkina Faso|4167.1|35.6|34.2|
|(1990, 1993, 1991)|[100.0]|[0.9]|[0.8]|
|Central African R.|1384.5|13.0||
|(1990)|[100.0]|[0.9]||
|C6te d'Ivoire|4598.6|131.5|116.0|
|(1990)|[100.0]|[2.81|[2.5]|
|Mali|2958.7||40.2|
|(1990, 1991)|[100.0]||[1.4]|
|Niger|3619.0|15.8|39.7|
|(1990, 1991, 1991)|[100.0]|[0.4]|[1.1]|
|Senegal|2347.6|79.6|66.3|
|(1988)|[100.0]|[3.4]|[2.8]|
|Togo|1396.3|22.4|33.7|
|(1990, 1991, 1991)|[100.0]|[1.6]|[2.4]|

Notes: Figures in parenthesis indicate the years the data refer to, while figures in brackets are percentages of the labor force. The fonnal private sector may include state-owned commercial firms in some countries. Data on the labor force for all countries, except Senegal, are from the
# **Data and hypotheses**

Table 1 presents the list of the 25 countries that have graduated from the Bank since 1970. Six of these nations subsequently “de-graduated” or borrowed again. The goal of this section is to identify the factors that help explain whether and when a country graduates from the IBRD.

As noted above, discussion of graduation is not initiated until a country crosses the IBRD income threshold. We therefore limit our sample to only those nations whose income at the time exceeded the threshold level. As a result, our annual data results in an unbalanced sample, because not every country that exceeded the threshold in a given year exceeded it in all years. The panel is further unbalanced due to some missing data.

Graduation reflects the ability of a country to reach a certain level of development, institutional capacity, and access to capital markets. Since income is obviously a proxy for these factors—as reflected in the threshold trigger—wealthier nations should be more likely to graduate. The IBRD threshold is measured in current-dollar GNI per capita, so we take (the log of) the amount by which each country in the sample exceeds the threshold.

The criterion of “progress in developing key institutions of economic and social development” is not given an explicit and detailed definition within the graduation policy. There are references to “management capacity” and, more narrowly, to “economic management capacity” (Shihata 2000). A reasonably good proxy for this concept is the “quality of the bureaucracy” indicator from International Country Risk Guide (ICRG).<sup>3</sup> In the last 20 years, institutional accounts of economic development have broadened, as recognized in many World Bank reports (World Bank 2001; 2006). World Bank (2006) provides a three-way classification of institutions for development: Bureaucratic Capability, Provision of Public Services (including regulation of firms and resolution of disputes), and National Checks-andBalances Institutions. The ICRG provides two additional indicators – on adherence to
described above. To get a large sample of flood-exposed firms for chapter 3 a methodology was developed to combine flood maps from several sources into a common flood exposure variable. After each firm’s exposure was determined using its city’s respective flood map, a unified binary variable was defined ( _Flood-threatened_ ): It takes the value one if (a) a firm is located in a zone with flood hazard probability greater than 1 percent in any given year according to combined pluvial and fluvial SSBN flood probability maps with return periods of 10, 50, and 100 years, or if (b) a firm is exposed to flooding greater than 5 centimetres in a locally tailored modelled flood with a 50-year return period, or if (c) it is located in the area of the empirical flood map in Dar es Salaam. The variable takes the value of zero for all other firms in the cities for which flood maps were available. This results in a dataset with 3,094 firms from 13 cities of which 329 firms, or about 11 percent, are in flood-threatened areas.

Chapter 3 uses Enterprise Survey weights that are rescaled within each city to account for the firms outside the city boundaries that were dropped. In the pooled regressions, each city is given equal weight: this results in firms in cities with fewer observations having greater relative weight. Consequently, it prevents imbalance in the results due to the differing number of observations in each city. Note that the Enterprise Survey weights were not designed to be spatially representative at the city-level.

# 3. Firms, flood exposure and infrastructure in 13 cities in Africa

This section explores the determinants of infrastructure disruptions for firms in 13 African cities. Using geocoded Enterprise Survey data and flood maps, it demonstrates that infrastructure disruptions should primarily be conceived as system-wide problems that affect all firms in the system.

> 4 <u>https://www.openstreetmap.org</u>

9
financial inputs into children’s education was collected by identifying and interviewing each child’s main caregiver.<sup>12</sup> Financial expenditures include payments and other “gifts” to schoolteachers, tutoring expenditures, and transportation expenditures that we will use as different dependent variables in the analysis.<sup>13</sup> Non-financial inputs include how often the main caregiver helps the child with homework and other school activities in the month prior to the survey interview on a six-point scale ranging from “never” to “every day.” In addition to the household survey, community questionnaires were filled out by local officials, typically in the mayoral office. Finally, we match data from the World Bank’s open budget initiative (BOOST) to provide school-level data on public education expenses in the respective communities and schools (see appendix S.1 for more details). Our baseline sample consists of 2,148 children from 1,448 households.

# _Descriptive Statistics_

A migrant household is defined by the existence of at least one adult who, in the 12 months prior to the survey, has spent a minimum of three months living abroad. In our sample, 29% of children live in a migrant household (table 1).<sup>14</sup> The average student from migrant households is 12.6 years old, five months more than her non-migrant peer. Before accounting for selection into migration, the average grade (GPA) is 0.06 points higher for children in migrant households. Migrant families are slightly larger on average and more likely to come from rural areas. Despite this, their average total income and average per capita income are significantly higher than those of non-migrants.<sup>15</sup> Figure 1 also reflects the underlying effect of migration, showing no difference in assets in 1999 but significantly higher assets for migrant families in 2011.<sup>16</sup>

> 12. The main caregiver is the person responsible for
>Out of labor force 28 28 3 6 34 24% (13%)<br>Private Informal Private Formal Public Unemployed OLF<br>Private informal 39 16 5 2 39 21% (21%)<br>Private formal 17 49 5 2 28 19% (21%)<br>Public formal 12 15 45 26 7% (7%)<br>Women<br>Unemployed 23 26 4 5 42 2% (2%)<br>Out of labor force 17 13 4 2 64 50% (48%)<br>Private Informal Private Formal Public Unemployed OLF<br><!-- End of picture text --> _Notes_ . This figure relies on panel data from the ENOE in 2005 Q1 and 2006 Q1. We restrict our analysis to individuals aged 20 to 53 who were interviewed in both rounds. We use transition matrices to examine individuals’ transition of their work status/sector between the two dates. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2005 Q1, with the shares in 2006 Q1 reported in brackets. Panel weights are used.

Even on an annual basis, transitions are more frequent in Mexico than in Egypt on a sixyearly basis. And even the stickiest private formal sector only retains two-thirds of workers after one year. Those out of the labor force also find employment rapidly. The two largest stocks of 16

**Official Use**
# 2. Data used for the analysis

We used two procurement data sources for the analysis. First, all tenders and contracts were collected from the old national e-procurement portal, http://www.aop.bg. Second, we collected all publications from the new national e-procurement portal, https://app.eop.bg. We collected the data by using automated web scrapers that are adapted to the specificities of the source websites and data repositories. The combined national public procurement dataset includes 148,637 contracts for 2011– 2019, characterized by 129 variables.

We also collected the Bulgarian tenders from the EU-wide Tenders Electronic Daily (TED) portal that was used for cross-checking data availability on the national portal and for the European comparative analysis as TED data are more comparable across European countries than national data. The details of data collection and processing are outlined in Annex 1.

We also collected data on politically exposed persons (PEPs) and linked it to public procurement data to carry out additional analysis. The list of PEPs was compiled by the Center for the Study of Democracy, including information from politicians’ asset declarations, the Panama and Pandora Papers, and the Magnitsky Act (for details see Annex 6). The identified individuals were then linked to relevant companies based on whether a PEP is an owner or official of the company. As a result, 4,566 companies were flagged as politically connected by the Center for the Study of Democracy. We matched these companies to the public procurement data, leading to 197 unique PEP-connected firms, either as a supplier or buyer (for example, a state-owned enterprise [SOE] awarding a procurement contract) in the procurement dataset. The PEP-connected companies were parties to around 36,500 contracts (we considered PEP connection throughout the whole period for matching, that is, we adopted a time-invariant PEP definition).

4
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data

# _A. Estimated MIMIC model_

The MIMIC model eqs. (7), (10)-(12) include vectors and to approximate for price and productivity differences across firms ( and for the unknown parameters Р, , and in the tax compliance model. We include the following variables in and .

First, variables capturing firm size, location (city) and sector are included in both and because they may affect firm productivity and tax reporting behavior (Dabla-Norris, Gradstein and Inchuauste 2008).<sup>26</sup> Larger firms may be more productive than small firms and have more bargaining power to obtain more advantageous product and factor prices; smaller firms may underreport relatively more than large firms because the latter are more formal and subject to more public attention but they may also underreport less if they are more transparent; different cities have different regulation environments that not only affect firm productivity but also the reporting behaviors to the tax office by firms; firms are also expected to form different perceptions of local tax office efficiency especially across different sectors.

We also included proxies for average skill level, management experience and capacity utilization in and as possible determinants of productivity and tax reporting behavior. The skill level and management experience of a firm are included in as underreporting opportunities may vary with the skill intensity of production and managers with more experience may be more adept in reducing the tax burden when dealing with the tax office.

Finally we included proxies for the investment climate in and as investment constraints such as tax burden, corruption, regulation burden and credit constraints have 26 See appendix A for exact variable definitions.

24
# **5. Estimation results**

# **5.1. Program impact**

We start first with examining the local impact of the 30A Program by plotting individuallevel outcomes based on the 2006 poverty rate of the districts where people reside. In Figures 5 and 6, we present RDD graphs both before and after the program's initiation in 2009. The graphs demonstrate that the disparity in self-employed farm work between the treatment and control groups was reduced after the program. Moreover, the treatment group experienced an increasing trend in self-employed farm work as well as wage jobs, compared to the control group. In Figure 6, we find that the treatment group received higher education subsidies than the control group, particularly after the program's implementation.

Using equation (3) we further estimate the effects of the 30A Program on various outcomes for households and individuals. The direct outcomes of the 30A Program are employment. We start with the program impact on employment of individuals aged 15 and older in Table 2.<sup>6</sup> Table 2 shows that there are no significant program effects on the likelihood of working (column 1). However, the program has significant effects on individuals’ probabilities of being self-employed in farm and nonfarm. Specifically, the program decreases the probability of self-employment in farm work by 0.11, while increasing the probability of self-employment in nonfarm work by 0.054 (columns 2 and 3). This suggests that the program encourages individuals to transition from self-employed farm work to selfemployed nonfarm work.

The program impact on the likelihood of having a wage job is positive, although it is not statistically significant (column 4). Similarly, the program effects on monthly wages of

> 6 According to Viet Nam’s Labor Code, the minimum age for working is 15. We also try to use the sample of individuals aged 15-64 (Table A.6 in the Appendix), and the program impact estimates are quite similar to those derived from the
resilience. We generate a binary proxy measure for CSA if a household used at least one or more of hybrid maize seed, inorganic fertilizer, and/or minimum tillage (MT).<sup>14</sup> Before conducting our analysis, we expected a positive correlation between climate shocks and vulnerability, and a negative correlation between climate shocks and resilience. Table 1 defines the remaining variables. We chose all the variables used in the regressions based on the conceptual framework in section 2, drawing from several examples in the literature (see for example Jain, 2007; Thurlow et al., 2012; Wineman and Crawford, 2017; Al Mamun et al., 2018; Braimoh et al., 2018; Alfani et al., 2019; Ngoma et al., 2019; Azzarri et al, 2020).

# 4.3 Empirical strategy

Although the RALS is a longitudinal data set, vulnerability and resilience are defined as single states over all three waves. This implies that most of our estimating equations are crosssectional rather than fully exploiting the panel dimension of the data. We estimate a probit regression model, as specified in Equation 2: where _y_ is a _j_<sup>_th_</sup> dependent variable ( _j_ = vulnerable or resilient) for household _i_ ; **_ClimS_** is vector of exogenous climate shocks described above; **_X_** is vector of socio-economic and demographic factors such as assets, crop diversity, age and education level of the household head; _credit_ is a dummy = 1 if any household member obtained credit from any source; _CSA_ is a dummy =1 if a household used any MT (ripping, basins, and/or zero tillage for main tillage on at least one plot), hybrid maize seed and inorganic fertilizer; _distmkt_ is the distance in kilometers from the homestead to the nearest main market; _dist_ is a dummy controlling for district-specific attributes such as topology and other things, and _μi_ is the idiosyncratic error term. In addition
flexible working hours, telecommuting, childcare facilities, or subsidies.<sup>3</sup> Based on firm panel data for Japan and a rigorous causal approach (propensity score matching difference-indifferences), they show that foreign acquisitions are associated with a 9-10 percentage point increase in the share of female workers, although this effect takes place with a delay.

A second literature focuses on Bangladesh’s ready-made garment sector’s gender outcomes and working conditions. Heath and Mobarak (2015) show that the new employment opportunities brought by the ready-made garment sector in Bangladesh had sizeable effects on female welfare as they delayed marriage, decreased fertility, and led to a rapid increase in girls’ educational attainment over the last 30 years (in absolute terms and relative to boys). These attractive manufacturing jobs were the first large-scale employment opportunities for women in a country with very traditional social values whereby women generally did not have paid jobs outside the home. The expansion of light manufacturing activities such as garments is associated with an increase in female labor force participation (Heath and Jayachandran, 2017). Manufacturing jobs require basic literacy, numeracy, and cognitive skills, thus increasing the returns to education, and they raise the opportunity cost of being married and having children. Garment jobs can be good for women, providing them with steady income that is better than any of the women’s alternative options, but working conditions in the garment sector are far from perfect. High-profile factory disasters such as the Rana Plaza accident in 2013 that killed more than 1,000 garment workers raised concerns about worker safety and the growth of this sector in

> 3  The study shows that these differences are more pronounced in affiliates with a higher foreign ownership share, suggesting that control is essential for the ability of the foreign parent to affect the corporate culture in the overseas affiliate.

6
to be considered, which very high interview frequencies could conceivably increase, risking higher attrition rates (see discussion in Section 3.1). The extent to which a sequence of short but more frequent phone interviews are less burdensome on respondents than a longer in-person sitting remains an empirical question, as there is no ready prior on the number of in-person visits and telephone interviews to optimize the use of survey resources and maximize data quality.

Having an in-person baseline visit makes possible the use of diaries or journals that can be facilitated by phone interviews. Kilic et al. (2021) asked respondents to keep diaries to document cassava harvest and twice weekly phone calls served, among other things, to supervise and support the diary-keeping. The authors conclude that the diary-keeping yields better cassava harvest data than the common end-ofseason recall module. Deininger et al. (2012) use harvest diaries for several different crops in Uganda, which is again deemed more reliable than end-of-season recall. Beegle et al. (2012b) compare personal diaries to several other common methods to measure household consumption and poverty, deeming diaries the most reliable method. These experiences indicate significant scope for improving data quality through diaries which can be readily combined with phone surveys, though survey designers need to consider cost and logistical implications of this method.

Finally, cost considerations and the possible improvements in data quality need to be also weighed against the loss of comparability stemming from possible mode effects. This is a preoccupation that concerns both the comparison with earlier survey years, but also the comparison of different interviews within the same survey round, to the extent that they are implemented using different data collection modes. Longitudinal surveys may attach a different weight to the loss of comparability than cross-sectional surveys (Cernat and Saskaug, 2021). Response burden and attrition patterns may also have features that vary across households with important characteristics of interest (e.g., income and opportunity cost of time, literacy).

25