rafmacalaba/data-use-mentions-extended huggingface dataset:
reports. These detail targets and achievements for individual projects the organization is charged to deliver. Progress reports cover the _entire_ range of bureaucratic activity, including project types that have been much studied, such as procurement and infrastructure, but also areas of activity that have been far less subject to quantitative study, such as policy development, advocacy, human resource management, budgeting and regulatory design. We use these progress reports to identify 3628 projects underway during 2015, and to hand-code each project’s initiation and full completion.

To measure the management practices bureaucrats operate under, we follow the methodological approach of Bloom and Van Reenen [2007], and Bloom _et al._ [2012] (henceforth BSVR). We adapt their management surveys to the Ghanaian public sector setting, taking account of insights from the public administration literature [Rose-Ackerman 1986, Wilson 1989]. We elicited details on management practices in an _individual_ survey administered to bureaucrats. The surveys

> 1There are well-documented reasons why management practices could have di↵erent impacts on middle-tier bureaucrats than for lower-tier frontline public sector workers [Dixit 2002]. The selection, objectives and motivations of middle-tier of bureaucrats might di↵er. The nature of work for middle-tier bureaucrats might also di↵er: they might need to multi-task, and the mapping between e↵ort inputs and observable output is perhaps more uncertain. Finally, there can be specific labor market rigidities applying to middle-tier bureaucrats, leading to di↵erent dynamic selection e↵ects than for frontline workers.

2
.|-..--.......**..**......|.................|
|single|77|85|
|husband and wife|71|93|
|Single working age people:|||
|childless|77|60|
|childless + parents|58|52|
|with children|28|50|
|with children + parents|28|44|
|M'i<br>w''''''i'g ''"age<br>**people:**|||
|childless|71|50|
|childless + parents|35|63|
|with children|31|52|
|with children + parents|54|_55_|
|Average '-.<br>---.<br>::|.:...-4>8|-<br>.._66|

Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

Fig. 1.3 shows the structure of the households living in privatized (including privatized apartments and privately-owned houses) and non-privatized housing in Vladimir and Gorodets. The trends are obviously the same as in Moscow.

"This is probably typical of many small Russian towns, whose housing stock has a similar stiucture.
X represents the particular individual component of the CPI. Y is an element in the set of indicator variables, which for this exercise includes the monetary base (MB); Ml; M2; M3; credit to government (CRG); the Lek/US dollar exchange rate (USD); the Lek/Italian Lira exchange rate (ITL); the Lek/German Mark exchange rate (DM), and the Lek/Greek Drachma exchange rate (GRD).

In the sample, we use monthly data from January 1993 to September 1997. Since we found the variables involved to be integrated of order one, that is stationary in first differences, we have expressed them in first differences. We compute F-tests for the null hypothesis of the non-Granger causality of the relevant indicator variable and calculate the marginal significance levels _(p-values)_ for the bivariate Granger causality tests for lag lengths of 1 to 12. The smaller these values, the stronger the predictive content of the relevant indicator for the particular measure of inflation.

The tables in Appendix 2 present the overall results of this exercise. In this section, we report only the results of Granger Causality tests for tradable and non-tradable goods. The index of tradables excludes prices of public and personal services (food and beverages away from home; rent, water, fuel, and power; medical care; transportation and communication; recreation, education and culture; personal care).'°

> **0** The classification of what is tradable and what is non-tradable is not immutable. Nevertheless, loosely speaking goods included in agriculture, mining and manufacturing are typically the most tradable, while construction, services, and domestic transportation are typically the least.

**18**
a link between financial services and economic development<sup>6</sup> . By transforming informal savings into savings in the formal financial sector, access to financial services helps increase aggregate savings and investments, thus stimulating economic growth.

6. The costs of being unbanked are equally well documented<sup>7</sup> . Financial exclusion is an obstacle to micro-economic development, as it significantly increases transaction and financial costs leading to substantial welfare and efficiency losses<sup>8</sup> . Furthermore, lack of access to financial savings instruments leads to sub-optimal investment choices, as individuals are forced to resort to alternative forms of asset accumulation or income generation<sup>9</sup> . By encouraging asset accumulation, access to financial services provides households with a financial safety net<sup>10</sup> . In addition, access to banking services enhances the probability of credit access by, for example, establishing a credit record.

7. Access to financial services can be good business, aligning private sector interests with the social aspect of access to basic financial services as a public good. The financial self-sufficiency of many microfinance institutions or the experience of community banks in the US demonstrate that working with low income clients can be profitable. In Colombia, recent initiatives such as the launch of BanColombia’s microfinance program reflects the growing interest of commercial banks in doing business with the poor (see Box 1).

8. This report explores the issue of financial access in Colombia in four steps. Chapter 1 provides an overview of Colombia’s financial sector and some basic access indicators. Chapter 2 focuses on three areas of constraints on the supply of financial services in Colombia. Chapter 3 presents key results from a demand side survey and Chapter 4 concludes with initial recommendations on potential policy measures and areas for further work.

> 6 Rostow, 1960, 1962.

> 7 Dunham, 2001; Caskey, 1997; and Stegman 1999; Carr and Scheutz, 2001; Skricki and Fernández
In sum, our primary dataset covers the 134 regions in 11 European countries where BvD, WVS and regional characteristics data are all available.<sup>20</sup>

# IV. MEASURING FAMILY MANAGEMENT

To construct our measures of family management we extract from the Amadeus database (i) the full names of each manager, (ii) the full names of each owner and (iii) the equity share of each owner. We treat each unique surname as a family and match the surnames in the list of owners to those in the list of managers. This enables us to measure the ownership share of the company accounted for by each family in the list of managers. Our baseline measure defines a firm to be family-owned-family-managed (FOFM) if (i) all managers belong to one family and (ii) that family owns more than 20% of the firm.<sup>21</sup> However, in robustness checks we redo our main specifications using three alternative measures. The first uses a 40% equity cutoff for family owners. The second uses a continuous measure defined as the share of managers with the same last name as the largest shareholder with more than 20% equity. The third exploits the information on manager titles provided by BvD and defines a firm to be FOFM if the largest owner with more than 20% equity has a manager with a CEO-like string in their title.<sup>22</sup> While our measure of family ownership is consistent with the previous literature, it is important to mention two main concerns to this method of classifying firms as FOFM. First, larger firms may have a greater propensity to use holding companies within ownership structures, which would lead to a spurious correlation between family management and firm size. We take a number of steps to address this concern. We start by exploiting the fact that BvD computes ultimate owners themselves using the network structure of the raw ownership data it acquires. So if Mr. Jones owns 100% of company A
|2.266|2.266|2.266|2.266|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.0200|0.100|6.220|0.110|0.0100|0.730|0.700|0.0800|17.68|

_Note:_ The table presents the results of equation 1 for our outcomes related to migration. Columns 1 to 5 focus on outcomes for international migrants. All information was obtained from the Population Census of 2007. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.22. Share of Individuals who Work in the Same Place as their Residence

||Work in the Same Place as Residence<br>(Share)|
|---|---|
||(1)|
|Guerrilla control|0.00333|
||(0.00320)|
|Observations|3,647|
|Bandwidth (Km)|2.271|
|Dependent mean|0.987|

_Note:_ The table presents the results of equation 1 for individuals who work in the same place as their residence. All information was obtained from the Population Census of 2007. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the
growth to the inputted actual TFP growth.

> 24 In the study, infrastructure is measured as a composite of several physical infrastructure indicators (as opposed to the monetary value of capital stock). A panel data set is used, comprising 88 countries that cut across different income levels and infrastructure endowments.

> 25 We use the following data from the IMF to calculate the shares: general government capital stock and private capital stock in billions of constant 2011 international dollars.

> 26 Both sources of data use variations of the perpetual inventory method to estimate aggregate capital stocks and do not take account of the destruction/damage of capital from wars/conflicts (which is naturally difficult to measure). Wars/conflicts also reduce output, perhaps faster than they destroy capital, and so the aggregate K/Y ratio may rise as a country enters a war/conflict. In the reconstruction period, it would not be surprising if measured public capital it extremely high because it fails to take account of the public capital destroyed in the conflict.

> 27 To be clear, the exercise is not dependent on the source of assumed actual TFP growth, but just that its distribution across countries is reasonable.

19
- Card, D., A. R. Cardoso, and P. Kline (2015): “Bargaining, Sorting, and the Gender Wage Gap: Quantifying the Impact of Firms on the Relative Pay of Women,” _Quarterly Journal of Economics_ , 131(2), 633–686.

- Card, D., J. Heining, and P. Kline (2013): “Workplace Heterogeneity and the Rise of West German Wage Inequality,” _Quarterly Journal of Economics_ , 128(3), 967/1015.

- Cardoso, A. R., and P. Portugal (2005): “Contractual Wages and the Wage Cushion under Different Bargaining Settings,” _Journal of Labor Economics_ , 23(4), 875–902.

- Danthine, J.-P., and A. Kurman (2006): “Efficiency wages revisited: the internal reference perspective,” _Economics Letters_ , 90(2), 278–284.

- di Giovanni, J., A. A. Levchenko, and I. Mejean (2018): “The Micro Origins of International Business Cycle Comovement,” _American Economic Review_ , 108(1), 82–108.

- (2020): “Foreign Shocks as Granular Fluctuations,” _NBER Working Paper 28123_ .

- Egger, H., and U. Kreickemeier (2009): “Firm heterogeneity and the labor market effects of trade liberalization,” _International Economic Review_ , 50(1), 187–2016.

- Fr ́ıas, J. A., D. S. Kaplan, E. Verhoogen, and D. Alfaro-Serrano (2024): “Exports and Wage Premia: Evidence from Mexican Employer-Employee Data,” _Review of Economics and Statistics_ , forthcoming.

- Garin, A., and F. Silv ́erio (2024): “How Responsive are Wages to Demand within the Firm? Evidence from Idiosyncratic Export Demand Shocks,” _Review of Economic Studies_ , forthcoming.

- Gopinath, G
# **Appendix Tables and Figures**

Table A1: Distribution of sample by previous treatment assignment

||Surveymode experiment|
|---|---|
||Phone survey<br>WhatsApp|
|Treatment|150<br>149|
|Control|150<br>150|
|P-value|0.928|

_Notes:_ This table presents the distribution of the survey experiment sample drawn from the sample of the main impact evaluation being analysed in Amaral et al. (2021). Within each treatment arm, we randomly selected a group of 300 individuals (for a total of 600) and randomly assigned them to one of the survey-mode treatments. “ _P_ -value” indicate the _p-_ value of the test for differences in treatment status (Treatment or Control) in the main impact evaluation by assignment to any survey-mode (Phone-based survey or WhatsApp).

Table A2: Construction of the Comparable Nationally Representative Sample

||Percentage|Observations|
|---|---|---|
||100%|75045|
|Eligibility by age of children|51%|38,097|
|Eligibility by age (own)|47%|17,733|
|Eligibility of main caregiver|98%|17,315|
|Eligibility of access to mobile phone|91%|15,680|
|Eligibility Sample|21%|15,680|

_Notes:_ This table presents the total sample from the 2019 Household Survey (EHPM) that meet the eligibility criteria for the impact evaluation. _Eligibility by Age of Children_ indicates if the individual lives together with at least one child 8 years old or younger. _Eligibility by age_ is met if the individual’s age is between 18-65 years. _Eligibility of main caregiver_ indicates if the person is either a parent or a grandparent of a child within the household. Finally, _Eligibility by access to mobile phone_ was created using the information on whether the person has a mobile phone. Columns
Google searches for “Milk”, which is consistent with the latter as an indicator of economic distress.

The real-time indicators can also predict people’s social distancing and schooling behaviors during the pandemic, as reported in Table 7. The predictive power for social distance is low, however, as the R<sup>2</sup> is only 0.13. The predicting power is slightly higher for quitting school, with four variables explaining 16 percent of the variation. However, none of the predictors is statistically significant in either model.

Tables 3a through 3d in the appendix reports results on models predicting the prevalence of reported income declines and current employment separately for subsamples of urban and rural regions. These results are therefore analogous to those reported in Tables 3 and 4, using models selected and estimated using only data from urban and rural regions. Urban regions are defined as those for which half or more of the weighted survey respondents in the region

> 13 There are three possible values for the income change variables: increase, decrease, and no change. As such, “increase” is not the complement of “decrease”.

20
anomalies publicly accessible, standard approaches to estimating the _human impacts_ of these anomalies are less mature (e.g. Lentz et al. (2019)). Further, expert panels are not always immune to political influence and other sources of local and national bias. This problem is particularly acute in those contexts in which the incentives of local, national and international actors are misaligned (e.g. Alderman (2009), Clarke and Dercon (2016)).

We conduct this analysis for five southern African countries — Malawi, Mozambique, Tanzania, Zambia and Zimbabwe (Figure 1). We have six reasons for selecting these countries. First, all five countries have some segments of their rural populations engaged in subsistence agriculture. Second, stunting rates, particularly for the rural populations, are in all countries. More these countries have low scores on high five broadly, relatively the Human Development Index (see Appendix Figure 2). Third, rural economies and livelihoods in these countries may be characterized as primarily agrarian or agro-pastoral, rather than pastoral. Consequently, harvest cycles determine seasonal patterns. Fourth, in contrast to the countries closer to the equator, there is typically just one harvest cycle every

> 6These weaknesses are particularly relevant to decision trees that have small terminal nodes. The trees we estimate typically have terminal nodes that contain more than a 30 children, and often more than a 100 children.

4
were found to be successful in reducing poverty and child labor. Alzua, Cruces, and Ripani (2012) examined the welfare programs of Mexico, Nicaragua, and Honduras and found small and insignificant effects on the labor supply, and Abdulai, Barrett, and Hoddinott (2005) examined Ethiopian household-level data and concluded that the food aid program had no disincentive effects.

On the interaction between social protection and informal employment, the empirical literature is almost silent except for a few recent studies in Latin American countries. For example, Bosch and Campos-Vasquez (2014) and Aterido, Hallward-Driemeier, and Pages (2011) found that the public health insurance program in Mexico (Seguro Popular) had significantly reallocated labor toward the 4
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investors are reluctant to invest in the Energy and Basic Materials’ corporate bonds, even when they are of higher ratings. The sectors whose investment grade bonds benefit the most are relatively less cyclical: Utilities, Industrials, Healthcare and Communications. For a 10% decline in the WTI oil price, the indices for these sectors’ investment-grade bond indices increase from 0.32% to 0.56%.

# **V. Impact on Emerging Markets**

This section considers the impact of oil price declines to emerging markets. Table 5.1 shows that oil price declines hurt the dollar-denominated MSCI Emerging market index. For every 10% decline in WTI oil price, the dollar value of MSCI Emerging Market index drops by 1.32%. The MSCI Emerging Markets Index consists of 21 emerging countries,<sup>15</sup> most of them are not oil exporters. Nevertheless, we still see the declines in these markets. This reinforces the concern that this time, lower oil price could carry global systemic risk.

Table 5.1 MSCI Emerging Market index

|VARIABLES<br>∆log (MSCI<br>Emerging)|∆log (MSCI<br>Emerging)|
|---|---|
|∆_Log( Oil Price)_<br>0.142**<br>(0.0709)|0.132**<br>(0.0534)|
|Lags<br>No|Yes|
|Constant<br>9.41e-05|0.000181|
|(0.000358)|(0.000339)|
|Observations<br>699|696|
|R-squared<br>0.068|0.168|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1||

> 15 <mark>Brazil; Chile; China; Colombia; Czech
. Importantly, we often derive several additional statistics from different indicators. For example, data on temperature is used to construct average temperature, maximum temperature, and minimum temperature during the year, while data on pollution is used to generate many distinct indicators.<sup>8</sup> In addition, we also create features by aggregating to higher levels by taking means. For example, we calculate mean urbanity at the admin3 level for all countries, and create predictors with other variables in similar ways. By combining these features in different ways and across different levels of aggregation, we end up with several hundred different predictive features. While we include all of these features in the machine learning methods, we use lasso to select features for the EBP model, following others (Engstrom et al., 2022; Masaki et al., 2022; Newhouse et al., 2025).<sup>9</sup> We pull the most recent data available for all countries. Different geospatial features have different time coverage, meaning we sometimes pull data from after the census. For example, our preferred land classification data only goes back to 2019, so we extract 2019 data for the 2012 Tanzania census. We recognize that this is not ideal and can result in additional stress-testing for the proposed methods (e.g., by introducing noise into the data).

> 8More concretely, we construct weather variables separately by month throughout the year of the census, maximum and minimum values throughout the same year, as well as climate-related variables that are long-run means and standard deviations from the 10 years prior to the census. We construct similar variables (except the long-run averages) for pollution.

> 9All three machine learning methods have their own regularization methods which make lasso redundant.

10
27 frequently used (and, we would argue, most sensible) recall period for inpatient care. Concretely, when a survey reported pap-smear (mammogram) utilization data for a recall period other than 5 (2) years, we transform the reported utilization rates to a 5 (2) year recall using the formula for the probability of an event over multiple trials, 1 ��1 �x�<sup>�/�</sup> , where _x_ is the percentage of women obtaining pap smears (mammograms) over the survey’s reported recall period _z_ (in years), and _y_ is our preferred recall period of 5 (2) years.<sup>30</sup> To obtain the adjustment factors for inpatient care data with less than 12 months recall, we exploit the availability of both 4-week and 12-month recall inpatient utilization data in the MCSS. Using the observed 4-week and 12-month inpatient utilization rates across 54 MCSS countries, and assuming zero utilization at time zero, we fit a nonlinear model of the relationship between time and utilization.<sup>31</sup> We then use the model to estimate hypothetical utilization rates for 2-week, 3-month, and 6-month recall periods. The adjustment factors are obtained by dividing the observed 12-month utilization rate by the respective estimated rates (and the observed 4-week rate).<sup>32</sup> Finally, the observed rate for the respective shorter recall period is multiplied with its adjustment factor to estimate the 12-month utilization rate.

# <u>Data processing process</u>

Figure 6 summarizes the data-processing steps. Whenever possible, we first generate from the raw household survey microdata. In these microdata sets, we generate a standardized or harmonized microdata set that contains our standardized indicators. The rates for each survey are then compared to the rates reported elsewhere in a quality-control exercise (see below for further details). Rejected datapoints are dropped. The remaining datapoints are consolidated into a ‘meso data set’, which