rafmacalaba/data-use-mentions-extended huggingface dataset:
_Relative Firm Size._ To test Prediction 3, that family-managed firms are relatively smaller as contractual institutions improve, we run the following regression The key parameter of interest is _γ_ , which our model predicts will be negative. Columns (4) and (5) of Table II show that family-managed firms are about 37% smaller than their professionally-managed counterparts. This is also reflected in the first three columns, where a 10% increase in firm size is associated with around a 1% reduction in the probability that firm is family-managed. Column (6) estimates the interaction term in the regression above, and confirms that family-managed firms tend to be relatively smaller in contexts where contractual institutions are stronger.

# **_V.B. Within-Country_**

We now exploit variation in our regional proxy for contractual institutions - trust, as measured in the WVS - to more carefully test the predictions of the model in our primary dataset spanning 134 regions in 11 countries. As discussed in Section III.C., trust correlates with measures of rule of law and contracting enforcement at the country-level, motivating our use of it as a proxy for contractual institutions.

While our results should be interpreted as associations consistent with the model rather than causal relationships, our data allow us to control for country-level unobservables and region characteristics (such as GDP per capita and the share of college-educated workers) when examining patterns of trust and firm behavior. We show that failing to account for these differences would lead us to substantially overestimate the effect of trust on management modes. Nevertheless, these relationships we previously documented at the country-level still exist once we purge the data of these sources of endogeneity.

_Management Mode Choice_ . We begin by testing Prediction 1, that an improvement in contractual institutions is correlated with a decrease the probability of being family-managed, conditional on firm size, by running the following regression Here _i_ indexes firms, _r_ indexes regions, _c_ indexes countries and _k_ indexes 3-digit industries. _β_ is identified off variation in trust and management modes across regions within
in Ukraine where women outscore men, using both comparison methods. The results for the other countries are less clear. In no country do women outscore men in risk-taking behavior; this male advantage is significant in Kenya, Ukraine and Vietnam, according to both comparison methods.<sup>49</sup> This gender pattern comports with the results of a meta-analysis of 150 risk experiments which find that women are significantly more averse to risk (Byrnes, Miller and Schafer, 1999) as well as with other studies (Croson and Gneezy, 2009; Charness and Gneezy, 2012).<sup>50</sup> With respect to time preference, there is no significant gender difference in six of the seven countries.

> 49 Note that there are no data for hostile attribution bias, risk-taking and time preference in the Serbia data set, and no data on time preference also for Colombia.

> 50 Note that Nelson (2015) challenges the conclusion reached by the latter studies.

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conflict has already started. Without question, humanitarian aid targeting households and the regions most affected by violence is needed. However, these results suggest that development and food assistance is also necessary for households and regions that are not necessarily the most affected by violence.

There are a number of issues that we are unable to explore here. In particular, we only have access to a subset of the unit‐level data for three WFP mobile phone surveys that have been conducted since August 2015 (September 2017‐November 2017), and we only have access to the complete survey for one month (November 2017). Although we are able to make a number of inferences using the variation in food security at the governorate level that is publicly available, there are a number of additional inferences that would be better answered by household‐level data that identify the exact date of the survey, the district of the respondent, and other household‐level information. In particular, this includes issues related to the decision to migrate, distributional issues that might affect aid‐receiving, IDP, or poor households differently from other respondents, and so on. We leave these issues to future collaborations with the WFP.

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In estimating and projecting tax expenditures, a reliable database is a major factor in ensuring the reliability of the estimates. The best initial source for the database is tax returns filed by taxpayers. Other sources for the database include national accounts, population survey, industry surveys, and trade and production statistics. Those data sources are also used to cross-check tax expenditure estimates. The methods used in tax expenditure estimates and projections include the aggregate model, the distributional model, or the microsimulation model, based on the sufficiency and reliability of the database<sup>16</sup> .

A database with sufficiently detailed data must be developed over time. It takes at least two years to develop a basic tax database for initial estimating tax expenditures. Therefore, any development relating to tax database for this purpose must be planned and begin as soon as possible.

> 16 The detailed methodology and techniques for estimating tax expenditures could refer to related technical books.

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Figure 8. Cumulative government resource revenues for selected resource-rich African countries, 2004-2014 <!-- Start of picture text --> 500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Billion US$<br>Nigeria Angola Equatorial Guinea Sudan Congo, Rep Gabon Ghana Congo, Dem Rep Zambia Chad Cameroon Cote d'Ivoire Mali Guinea Botswana<br><!-- End of picture text --> Note: sum of government revenues from resources of selected resource-rich countries ordered by cumulative production value of oil, gas and minerals (Figure 7) with available resource revenue data. Source: authors’ calculations based on UNU-WIDER (2021).

There exists a widespread perception that governments have systematically failed to capture their fair share of revenues– thus allowing companies to make larger than necessary profits (Laporte and Quatrebarbes 2015; Mansour 2014). An economic shorthand for this fair share is the rents. Conceptually, rents are the fraction of the resource value that is left as excess and therefore should accrue to the resource owners – invariably government – once reasonable costs of extraction and a reasonable rate of return on capital have been deducted.

Figure 9 presents a comparison between government revenues per capita according to our best measures, against the World Bank’s calculation of rents per person for each country during this same period. In most cases, the value of natural resource rents exceeds the value of revenues, except in Botswana, Nigeria, Sudan, and Uganda. This implies that there 16
cooking oil, and tea leaves, and are almost 18% for dried sardines. These are staple goods: kerosene is the primary lighting fuel in much of Tanzania, cooking bananas are a staple carbohydrate in the two districts where they are commonly purchased, dried sardines are a key source of protein, cooking oil is the main source of cooking fat. Most households purchase one or more of these goods: 85% purchase kerosene, 78% purchase cooking oil, 70% purchase dried sardines, and 22% purchase cooking bananas (Table 2). The consumption losses implied by columns 1–3 of Table 4 are substantial at face value.

The money-metric measures of loss tell a similar story. In columns 5–7 of Table 4 we report summary statistics for _e_ ˆ _hi_ , _Lhi_ , and _L_<sup> ̃</sup> _hi_ . Items are listed by decreasing values of column 7, so that high loss items are at the top (from now on we will usually display items in that order.) On average, losses represent 8.7% of total expenditure at the householditem level. For a number of frequently purchased items – kerosene, onions, cooking bananas,

> 14In Appendix S3 we also argue that bulk discounts cannot be the manifestation social capital or buyerseller relationships, because the prices collected by enumerators in the market price surveys also reflect bulk discounts.

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achieve (such as full labor market flexibility).

Kambourov also studies the case of Mexico, a country with a less flexible labor market than Chile. The trade reform was, in turn, gradual and thus the author simulates an economy in which tariffs are initially halved and later eliminated completely after three years. The results are qualitatively very similar to the Chilean experience. Eliminating trade distortions increases welfare by 5.8 percent while eliminating trade and labor market distortions, by 6.1 percent. As before trade reforms seem to have larger impacts on the steady state than labor market reforms. Further, there are foregone benefits from not liberalizing the labor market jointly with the international trade market.

# **4.2.4. Labor Mobility Costs and Worker Sector-Specific Experience**

Dix-Carneiro (2010) estimates a dynamic structural model of the labor market with labor mobility costs and worker sector-specific experience using Brazilian data. Labor demand is given by the value of the marginal productivity of labor in perfectly competitive sectors without frictions. The labor supply side features overlapping generations of heterogeneous workers who, as in Artuc, Chaudhuri and McLaren (2010) can switch sectors at a cost. Dix-Carneiro offers a very rich model with several nice additions to the labor mobility cost model of Artuc et al. Workers are heterogeneous in various dimensions, they can

> 9 If the increase in welfare, after 2 years, with both labor and trade reforms is 6.4 percent and the increase in welfare, after 2 years, with only trade reform is only 5.7 percent, then the labor reform brings about additional gains of 0.7 percent, after 2 years, or 11 percent of 6.4.

> 10 Note that fully addressing the role of human capital requires modifying the model to eliminate human capital specificity. This, in turn, requires recalculating the steady states and the transition. Kambourov (2009) does this in section 6.3 of the paper: ``The results from this experiment suggest that both firing
trade effects of trade agreements (Baier and Bergstrand 2007; Piermartini and Yotov 2016), our empirical specification lacks bilateral fixed effects, because our variables of interest –indicators of protectionism in government procurement laws – are time-invariant (their interaction with the internal trade dummy would be absorbed by the country-pair fixed effects). This limitation of our empirical setting prevents us from credibly identifying causal effects. Besides adding an exhaustive set of control variables as explained above, we try to lessen potential omitted variable bias by further controlling for interactions between year dummies and the internal trade dummy in additional specifications. As Bergstrand, Larch, and Yotov (2015) argue, controlling for trends in the border effect can net out important variation, which in our case might well correlate with the interaction between internal trade and protectionism in government procurement laws.

Table 2 reports the results of the gravity estimations on the yearly sample, using the composite indicator for protectionism in government procurement laws – equal to one if the laws lack a national treatment provision (“discrimination”), they provide preferential

> 7 Mulabdic and Rotunno (2022) employ a similar empirical specification to estimate the impact on bilateral government procurement of provisions in PTAs that are likely to be multilateral (that apply equally to members and non-members).

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, and the decline is more pronounced for women. One possible explanation is that a higher proportion of people in this age group are in school in later years, rather than in the labor market (Figure 3). In 2003, about one-third of young women and 35% of young men in this age group were in school, compared to 64.5% of young women and 59% of young men in 2013.<sup>10</sup> Based on data from the micro-census, the share of 15-17 year olds in rural areas with some upper secondary attainment increased from 43% in 2005 to 70% in 2015 (Wang et al. 2019). This school attendance expansion may be partly attributed to a supply factor: the consistent government policy priority over a decade to promote vocational education and training (VET) in upper secondary education. Even though

> 8 Similar gender and age patterns in migration trends, as shown in Figure 2, have been observed in an early time period (1995-2003) (Giles and Mu, 2018) and with an alternative data source (Mu and van de Walle 2011). 9When we use a 10% sample of the 2017 RCRE household data and the rural sample of the 2018 CFPS data to examine the age and gender specific migration rate (Appendix Figure A1), we see a similar pattern overall, but the migration rate of the younger age group in the RCRE sample is distinctly lower than that in the CFPS sample. 10According to CFPS 2018, the school enrollment rate for the 16-20 age group is 74.8% for women and 68.5% for men in the rural areas. The respective numbers seen in the 10% sample from 2017 RCRE data are 76.8%, and 70.7%.

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and earnings improve with time in Ecuador.<sup>14</sup> Table 5 classifies Venezuelan migrants on the basis of their arrival in Ecuador. Let us first describe the size of each arrival cohort. We estimate that 11,140 Venezuelans arrived in 2016 (and remained in the country until the time of the survey). The flows increased 6-fold in 2017 to 66,305 arrivals in that year. Once again the annual inflow tripled in 2018 to an estimated 184,075 individuals. The inflows probably plateaued in 2019 and we estimate that about 63,664 Venezuelans settled in Ecuador in the first half of 2019.

The top panel of the table also shows that the average age of migrants has been falling over time. The average migrant arriving in 2016 was 32.3 years old whereas the average age of the most recent arrivals (2019) has fallen to 24.6 years. The data also show a 10 percentage-point increase in the share of females between 2016 and 2019 and a marked change in the skill composition of the Venezuelan arrivals in Ecuador. Among those who arrived in 2016, 70% had a college degree. In contrast, that proportion is only 29% among those arrived in 2019. In sum, these observations suggest that the most educated migrated first. But, as economic conditions deteriorate in Venezuela, individuals with a lower propensity to migrate, such as women and lower-educated workers, are also choosing to leave their home country.

The remaining panels in the table focus on labor market indicators for working-age individuals. It is helpful to focus on the two most recent arrival cohorts, which are fairly similar in terms of average age and educational attainment. The data show that the 2018 arrival cohort has a higher employment rate, higher earnings and a higher hourly wages than the 2019 arrival cohort. Thus, Venezuelan migrants do seem to experience a substantial improvement in their work conditions through the first year in Ecuador. However, the improvement appears to stall when comparing to the 2017 arrival cohort
services to consumers in that economy.<sup>5</sup> Various domestic regulations that apply to both domestic and foreign agents are also covered insofar as they affect trade. The database focuses on economies’ Most-Favoured Nation (MFN) policies and contains only limited information on their relatively elusive applied preferential policies.<sup>6</sup> It is important to delineate the policy data covered by this database from other information that is not (yet) covered. First, the database does not include information on the existing market structure – e.g. the number of firms, their market share – across sectors and economies, which means that our policy measures capture the restrictions on entry into markets but do not capture the extent of competition among domestic and/or foreign firms. Second, we do not collect data on outcome variables such as prices or quality, although we include data on whether those variables are subject to regulation in various sectors. Third, we capture only limited information on the implementation of policies. For instance, we identify certain aspects of the process by which services providers are granted licenses, e.g. whether the requirements are publicly available or whether the regulator has been given discretion to reject authorizations even if criteria are complied with, but some features of the process remain opaque. For example, information on how discretion is exercised in practice, or on the number of license applications received versus the number of licenses granted, is not generally available; the lack of such information renders it hard to determine whether the licensing/authorization process in itself offers protection to domestic providers.

# _Economies covered in the database_

The database encompasses data on services policy and regulation in 68 economies – 41 high-income, 15 upper-middle and 12 lower-middle income economies. The selection of economies was based on two parameters: size of the economy, using Gross Domestic Product (GDP) as a basis, as well as participation in world services trade on a balance-of-payment (BoP) basis.<sup>7
indicators that characterize these two areas for both the poorest households and the least poor are often different, which is an important finding for policy purposes.

Overall, we found that the multidimensional approach largely confirms previous results on monetary poverty. Monetary and non-monetary indicators are positively and significantly correlated but this correlation is by no means unitary suggesting that multidimensional and monetary poverty do not describe the same phenomenon. However, the multidimensional approach corroborates monetary results in various respects. In 2007, poverty is found to be higher in rural areas as compared to urban areas and according to all FGT poverty indicators. And the decline in poverty observed between 2001 and 2007 has 18
(0.67)|0.00147|(0.49)|
|Mother’s years of schooling|-0.00139|(-0.40)|-0.00155|(-0.44)|0.00140|(0.26)|0.00134|(0.25)|
|Mother’s age|-0.000491|(-0.35)|-0.000538|(-0.38)|0.000217|(0.13)|0.000190|(0.11)|
|Number of siblings|0.00290|(0.65)|0.00278|(0.63)|0.00570|(0.82)|0.00559|(0.81)|
|Poverty incidence rate|||0.00527*|(1.97)|||0.00634**|(2.15)|
|Infrastructure index|||0.0573|(0.45)|||-0.0133|(-0.08)|
|Number of students per classroom in high<br>school|||-0.0000552|(-0.11)|||-0.000189|(-0.39)|
|If phone interview|||0.00434|(0.15)|||0.00127|(0.04)|
|Othervariables and school fixed effects|yes||yes||yes||yes||
|Number of observations|2,776||2,776||2,585||2,585||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000.

t values are in parentheses.

* significant at the
# **5.2 Alternative Measures of Management Practice**

Our measures of management practice at the organization level are all aggregated from individual responses on management to our civil servant survey. The measure used for our core analysis averages management scores over the most senior divisional-bureaucrat reports. We now consider alternative constructs for management practices, based on all the senior bureaucrats in an organization, or as elicited from non-senior bureaucrats, namely those that are being managed. We can thus compare and contrast top-down versus bottom-up views of management practices, an underexplored element of the BSVR approach to measuring management.

The results are in Table 10, where we focus on project completion. Column 1 repeats the baseline specification from Column 3 in Table 3. Column 2 shows the findings to be robust to constructing management practice scores from all senior bureaucrats in the organization, while Column 3 shows them to be weaker if bottom-up views of management are used based on all non-senior bureaucrats only. Column 4 constructs management scores based on all bureaucrats in each organization, and these closely replicate the baseline findings in Column 1 in terms of the magnitudes of the coefficients of interest ( _γ_ 1, _γ_ 2) and the precision with which they are estimated.

Given these results, we probe further where the top-down and bottom-up views of management diverge. Figure 5 shows the CDF of management scores for CS-autonomy and CSincentives/monitoring elicited from senior and non-senior bureaucrats. We see that non-seniors generally report there being more autonomy than seniors (the average of the CS-autonomy score for non-seniors is _._ 021, while for seniors it is _−._ 003); and they also report their being more incentives/monitoring (the average of the CS-incentives/performance score for non-seniors is _._ 004, while for seniors it is _−._ 018 and seniors often report the lowest scores in this dimension).

We then examine what individual characteristics of non-senior bureaucrats predict the difference between their raw individual management score, along each dimension of autonomy and incentives/monitoring
Figure 38. Adjusted net savings share in GNI vs control of corruption score in Sub-Saharan African countries, 2004-2014 average _Notes: Adjusted net savings as % of GNI vs control of corruption estimates measured as units of standard normal distribution changes between 2004 and 2014. The control of corruption variable captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, or influence of the elites and private interests. According to this, Liberia is the country that has better improved the perception of control of corruption. Oil and gas rich countries are Chad, Nigeria, Cameroon, Equatorial Guinea, Gabon, Republic of Congo and Angola; while mineral-rich countries are the remaining resource-rich countries, as defined by Lundgren, Thomas, and York (2013). It excludes Burundi and Liberia which have ANS of -32 % and - 61 % of GNI, respectively. Source: authors’ calculation using World Bank data and the Worldwide Governance Indicators database._

# **Investment efficiency**

Finally, we examine the effects of the end of the super-cycle and prospects for resource-rich countries- including the vulnerability to the price crash, the future threat of depletion and the prospect that prices may not hit similar highs again. While resource-rich countries saw strong economic performance during the boom, the vulnerability and lack of fiscal buffers became apparent after prices fell, particularly in petroleum rich countries following 2014. Low rates of adjusted savings and poor public investment management during the boom in resource-rich African countries contributed to this vulnerability. For example, although resource-rich countries like the Republic of Congo and Mauritania were some of the largest recipients of natural resource rents during the boom, their adjusted savings rates and public investment management index are poorer than other non-resource-rich Africa countries, such as Ethiopia or Benin, which did not enjoy the same windfall from commodities (Figure 39).

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