rafmacalaba/data-use-mentions-extended huggingface dataset:
1; Source: Author’s elaboration on the European Social Survey (ESS).

# Table A.7: Robustness check: Sport clubs and share of refugees in 1923

|Dep. var:||Presence o|f at least o|ne sport clu|b||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Share of refugees in 1923|0.066|0.120*|0.078|-0.015|0.039|0.023|
||(0.051)|(0.062)|(0.070)|(0.049)|(0.068)|(0.075)|
|Share of refugees in 1923 * small community||||0.266***<br>(0.100)|0.265***<br>(0.097)|0.187*<br>(0.100)|
|_R_<sup>2</sup>|0.288|0.295|0.327|0.288|0.295|0.328|
|Observations|4,393|4,393|4,393|4,393|4,393|4,393|
|Average outcome|0.712|0.712|0.712|0.712|0.712|0.712|
|Geography controls|Y|Y|Y|Y|Y|Y|
|Population 1991|Y|Y|Y|Y|Y|Y|
|Socio-Economic Development 1991|Y|Y|Y|Y|Y|Y|
|Region FE||Y|
data from 10 countries and predict an annual payroll reduction of 5%-10%.

A smaller set of global studies explicitly considers distributional effects. Bargain and Aminjonov (2020) document that poorer regions across nine countries in Latin America and Africa were less likely to comply with stay-at-home orders, and therefore more likely to spread the disease. Dang, Huynh and Nguyen (2020) use surveys from China, Italy, Japan, Korea, and the United Kingdom to analyze the unequal effects of the pandemic by income 7
In addition to the basic antidumping data described in section 2, additional data for Turkey is as described in the following table

**Table 3.12.1 : Additional Turkish Data Included in AD-TUR-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-TU**|**R-Master :**||
|**U.**|**P_AD_DUTY**|Description of the preliminary antidumping duty imposed|
|**V.**|**F_AD_DUTY**|Description of the final antidumping duty imposed|
|**W.**|**F_AD_GP**|_Official Gazette_government publication number documenting the final<br>antidumping determination|

# **3.13 United States (USA)**

The United States’ antidumping process is a two-track procedure handled by two distinct government agencies – the dumping investigations are administered by the International Trade Administration within the Department of Commerce, and the injury investigations are handled by the quasi-judicial United States International Trade Commission. Information on United States’ antidumping investigations is reported by these agencies to the public through the _Federal Register_ . The US antidumping process has been studied extensively, see for example, Boltuck and Litan (1991), Staiger and Wolak (1994), Blonigen and Prusa (2004), Horlick (2005), and Nelson (forthcoming).

In addition to the standard antidumping information described in section 2, the AD-USA workbook contains much additional information<sup>13</sup> that may be of use to researchers.

> 13 Much of the information in the workbook on US domestic and foreign firms is graciously provided by Bruce A. Blonigen as posted at his website, http://darkwing.uoregon.edu/~bruceb/adpage.html. The data can also be compiled from notifications to the _Federal Register_ (
(0.059)|
|No. Obs.|5,317|5,317|5,317|5,317|5,317|5,317|
|R-Squared|0.687|0.652|0.706|0.716|0.658|0.730|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients in columns (1) to (3) using OLS on a set of firms that had positive patenting during the sample period. In columns (1) to (3), the dependent variable is the number of collaborators in each year. In columns (4) to (6), the dependent variable is the total number of collaborators up to the current year. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

38
Appendix Figure B4 shows that _PV_ - _DV_ differences are larger after the first couple of clinic observation days. At the same time, we find that only 56 percent of patients prescribed an antimalarial on Control days received a malaria test, and there is no significant difference in this “test given prescription” rate across treatment arms, even though testing before treatment was a major focus of the training and a requirement for using the voucher. Overall this suggests the implicit monitoring that took place during the experiment had limited effects on behavior that, if anything, bias us away from detecting effects.

A related worry is that _PV_ - _DV_ differences are driven by experimenter demand effects on the patient side (e.g. if patients interpreted receiving the voucher as a signal that “buying an antimalarial is encouraged by an outside NGO”). While this would not invalidate our tests of gatekeeping costs and reluctant compliance by doctors, it is important for interpretation. We believe experimenter demand effects were minimal in our setting for two reasons. First, study staff were carefully trained to explain that the vouchers were not valid without a provider’s signature, and that the doctor was the one who would make the malaria diagnosis. Second, data from the patient information treatment ( _PI_ ) suggests patients were not influenced by experimenter demand effects. The _PI_ video communicated that suspected malaria cases should only be treated if they received a positive test. However, Appendix Table B14 shows that _PI_ actually _decreased_ the share of total patients and antimalarial prescription-holders who took a malaria test, while leaving the share of patients who were prescribed an antimalarial unchanged. Panel B shows there is no significant interaction effect with the voucher treatment arms, which suggests that effects are not simply driven by patients refusing tests in the hope of redeeming a voucher. These results are inconsistent with experimenter demand effects changing patient preferences, but are consistent with patients having preferences of their own about whether to get tested.

# **4.3 Do Doctors Use Vouchers to Increase Revenue?
three continents.<sup>4</sup> Table 1 shows the descriptive statistics by country, ranked in increasing order of GDP per capita. Countries’ GDP ranges from 643 USD per capita in Uganda to 12,027 USD in Costa Rica. Population size also varies substantially, from 1.5 million inhabitants in Eswatini to 110 million in Ethiopia. Similarly, the number of firms in the data ranges from 3,791 in Eswatini to over 80,000 in Costa Rica. The number of firms by inhabitants is generally higher in richer countries.

Our analysis relies on a few key variables: a firm’s sector of activity, its revenue, profits and total costs, and the breakdown of costs into fixed costs (machinery, buildings, etc.), material costs (inventory, inputs into production) and labor costs (the wage bill). The profit rate is defined as profits (the tax base) divided by turnover. We use a firm’s sector to determine its revenue loss from the lockdown<sup>5</sup> : we assign firms to the

> 4These data are not publicly available, except the data for Ecuador which are published here. The Ecuadorian data contains the tax declarations of firms required to file balance sheets (hence excluding very small firms).

> 5As mentioned above, we use the term lockdown here and thereafter to refer to the reduced form of the shock. The 100, 50 and 20% shocks can correspond to a period of total or partial inactivity for 3 or 5 months or more, with imperfect compliance of firms to lockdown restrictions, or an extended period of reduced activity.

5
technical aspects that readers are referred to, the technical publications cited in this paper provide the necessary details.

The four country-based studies reveal difficult tradeoffs among various policy objectives, including weights put on different sectors, regional advantages, and general economic efficiency gains versus social impacts. The results underscore that no one policy is able to achieve a balanced set of outcomes for the various objectives **.** The comparison demonstrates the usefulness of the macro-micro linkage framework in addressing the effectiveness of various policy interventions and providing information that policy makers can use to rank the policy interventions according to their preferences among the objectives.

The paper starts by identifying the importance of irrigated agriculture as the sector that uses most of the available water in a country. Then the theoretical framework of the macro-micro linkage approach is set and explained and contrasted against the various economy-wide studies in the literature that are reviewed in Annex 3 so that the advantages and drawbacks of each are fully understood.. The results of the analyses of the macro-micro linkage approach that was applied to Morocco, Mexico, South Africa and Turkey are presented and contrasted so that the tradeoffs between different social allocation preferences are clearly identified in terms of their impact on sectoral productivity, on welfare distribution, using economy-wide performance indicators. The paper is concluded by identifying areas (e.g., climate change, globalization, food crisis, migration, distributional effects) in need of more research and will provide a proposed framework for their inclusion in such analysis. The paper provides a set of recommendations for a balanced policy intervention options that take into account the competing needs by various sectors and the existing institutional arrangements in several countries, including the countries analyzed (Saleth and Dinar 2004:Chapter 7).

# **Special attention to irrigated agriculture**

Irrigated agriculture is an important sector in many developing, as well as developed countries. It uses a major share of the available water resources in the country, provides employment opportunities to rural population, and supports, in some countries, their food security
overidentifying restrictions is not rejected for all conventional levels of significance, thus we cannot reject the null that all the instruments are valid. We also reject that the estimation is underidentified at 5% level of significance.

However, the results should be treated with caution. It is possible that the instruments may not satisfy the exclusion restriction. Selection of right-wing politicians may not be random; consequently preferences of citizens for right-wing politicians may be correlated with unobservables that are related to crime, since crime typically ranks high in the list of voter concerns. Similarly, a higher migrant stock may be correlated with several country-level unobservables that are also correlated with crime. Although the IV results are retained after controlling for corruption and the quality of government, the potential endogeneity of our instruments cannot be ruled out completely.

# **5. Panel Estimation**

Thus far, all estimates provided have been cross-sectional. The implication is that it is hard to account for certain country-specific of firm-specific unobservable variables. However, panel data are available for a few surveys. The Enterprise Surveys typically re-interview a subset of firms across multiple waves of surveys. This is typically done for two waves of surveys given the especially high attrition rates that occur in a span of 3 to 4 years. In order to account for firm specific unobservables, we use panel data for 644 firms in a subset of 12 Eastern European economies for 2004 and 2007. Column 1 of table 4 presents these results using country and year fixed effects, while column 2 of table 4 presents firm and year fixed effects estimation. We use 20
Eq. (11) captures the distortions in input choice relative to the optimal combination of factor input. More specifically, it states that a firm faces a high capital distortion (larger _τk_ ) when the ratio of labor to capital compensation is high compared to the efficient allocation of input. It is worth emphasizing that _τk_ measures capital market distortion relative to labor market distortion. Thus high capital distortion (larger _τk_ ) should be interpreted as a low labor distortions, and vice versa. Eq. (12) states that a firm faces a high ‘output’ distortion (higher _τy_ ) when the labor compensation of the firm is low compared to what one would expect in a frictionless environment.

The establishment-level productivity can be inferred as: <u>1</u> where _κ_ = ( _PsYs_ )<sup>_−_</sup> _σ−_ 1 _/Ps_ , which is normalized to 1, as in HK.

It is worth emphasizing that the HK framework allows to obtain physical output _Y_ using the CES demand relationship.

<u>1</u> The industry TFP would be _A_<sup> ̄</sup> _s_ = _Mi_ =1 _s_<sup>_A_</sup> _si_<sup>_σ−_1</sup> _σ−_ 1 , if marginal products were equalized across �� � establishments within industry. HK show that the ratio of the actual TFP in 9 to the efficient level of TFP<sup>7</sup> : Eq. 13 shows how within industry misallocation of resources leads to a lower measured TFP.

# **5 Data Description**

Our analysis exploits census data for manufacturing firms in each of the four SSA countries we study: Cˆote d’Ivoire (2003-2012), Ethiopia (2011), Ghana (2003), and Kenya (2010). These countries provide comprehensive and comparable census data. The censuses are nationally representative and both small and large firms in the formal sector
03|1.51|1.18|1.51|
|HEI characteristics (PCA Score)|32.42|0.81|0.63|0.73|
|Student administrative variables (PCA Score)|0.41|14.33|24.23|18.09|
|Peer administrative variables (PCA Score)|1.83|18.17|15.36|18.03|
|Field of study fixed effects|23.92|7.34|5.67|7.64|
|State fixed effects|0.16|36.71|25.79|33.51|
|Graduation year fixed effects|-|0.29|0.90|0.27|
|R-squared|0.134|0.135|0.122|0.149|
|Obs.|22,663|7,177|6,827|7,089|

_Source_ : Own estimations using data from the WBSCPS and Brazil individual-level data. For variable definitions, see Section 3 and Appendix 1.

_Notes_ : This table present results from the R-squared Shapley-Owen decomposition for the regressions reported in Table A13, estimated with individual- and program-level data for Brazil (states of Ceara and Sao Paulo). For each regression, the table shows R-squared (net of the variation explained by survey noise variables) and the percent of the (net) explained variation attributable to each set of variables. “Obs.” indicates number of students (equal to the number of observations in the underlying regression).

45
age 6–18.<sup>11</sup> In addition to socioeconomic characteristics of household members, detailed information on private financial and non-

> 9. This problem was so widespread that, sometime after our survey took place, the education minister introduced video surveillance during the final high school exam, a move that lead to a spike in failure rates. Something similar has recently been studied in Romania; see <u>Borcan et al.</u> (2017).

> 10. Heyneman et al. <u>(2008), for example, discuss survey data that indicate that about 80% of university students</u> in Moldova, Bulgaria, and Serbia were aware of payments of illegal bribes in university admission. 11. The response rate was above 80%. For detailed information on the survey, see <u>Böhme and Stöhr</u> (2014) and Böhme et al. <u>(2015).</u>

7
1

# **1. Introduction**

The number of motor vehicles in the world is growing rapidly. Between 1980 and 1995-just fifteen years-the global fleet of cars, trucks, and buses increased by 60 percent, with a third of the increase in developing countries. The increasing number of vehicles brings many benefits but is associated with worsening negative externalities including congestion and air pollution. In addition, motor vehicles need roads, and road networks have been expanding to accommodate the increasing number of vehicles. Roads are costly to build and maintain, and they also produces externalities. For example, road network expansion is a major determinant of development patterns, particularly in urban areas. Opinions are divided on the desirability of increased motorization. The effects of the growth of motor vehicle use and road networks are viewed by some commentators as inevitably adverse,' while others argue that national patterns of motor vehicle use are sustainable.<sup>2</sup> The analysis presented here reviews past trends in vehicle ownership and road network expansion. It analyzes the determinants of past growth so that we can better understand what future patterns of motor vehicle and road network growth are likely to be, and how policy interventions can influence future growth. Because motorization raises different issues at the urban and national levels, city-level and national patterns are analyzed using data from a sample of cities and countries covering all income levels.

Motor vehicles are central to policy debates on urban transport because their increased use causes congestion, contributes to low-density development, and reduces transit use.<sup>3</sup> Some analysts have argued that urban development densities must be increased to reduce auto dependence and promote transit use.<sup>4</sup> Moreover, vehicular emissions have contributed to the degradation of the air quality in many cities, particularly in developing countries.

Motor vehicle use and road provision are concerns at the national level because large investments are needed for road infrastructure, and because countries want to ensure that their national transport policy reflects the comparative
. Mullainathan, 2001. “Do People Mean What They Say? Implications for Subjective Survey Data”, _American Economic Review_ , 91 (2): 67–72.

- <mark>Blattman, C., J. Jamison and M. Sheridan, 2016. “Reducing crime and violence: Experimental evidence on adult noncognitive investments in Liberia“,</mark> _<mark>American Economic Review</mark>_ <mark>, forthcoming.</mark>

- <mark>Blattman, C., and S. Dercon, 2016. “</mark> Occupational Choice in Early Industrializing Societies: Experimental Evidence on the Income and Health Effects of Industrial and Entrepreneurial Work”, _IZA discussion Paper_ 10255.

- Borghans, L., A. L. Duckworth, J.J. Heckman, and B. ter Weel, 2008. "The Economics and Psychology of Personality Traits," _Journal of Human Resources_ , 43(4): 972-1059.

- Bowles, S., H. Gintis, and M. Osborne 2001. “The determinants of earnings: a behavioral approach.” _Journal of Economic Literature_ , 39(4), 1137–76.

34
Reform programs 2 and 3 generate better outcomes in terms of growth and unskilled employment, but the largest effects (unsurprisingly) are achieved with Program 4. The growth rate of real GDP increases by 2.7 percentage points per annum (or, equivalently, to 6.3 percent, given a base value in the model of 3.6 percent), the unskilled unemployment rate falls by about 5.3 percentage points, and the skilled unemployment rate by 0.5 percentage point. Accounting for the relative weight of each type of worker in the labor force, the aggregate unemployment rate falls by about 4.5 percentage points, that is, given an initial unemployment rate of 9.4 percent, a drop to 4.9 percent. The reason why the unemployment for the skilled does not fall much is the same as before: the increase in demand is matched by an increase in supply, because more unskilled workers choose to become skilled.

These results suggest that far‐reaching structural reforms could boost Morocco’s potential growth by up to 2.7 percentage points over the long term. Are these growth rates high enough for the country to avoid falling into a middle‐income trap and achieve high‐ income status within the next two decades? To address this question, consider first Reform Program 1.

According to World Bank data, in 2017 GDP per capita in constant 2010 US dollars was 3,292 in Morocco and 42,568 in France. Suppose that France grows on average at the rate of 1.1 percent in the next decades. Suppose also that the full implementation of the reform program takes 4 years, and that during that period, Morocco continues to grow at the average rate observed during the period 2010‐2017, that is, 3.6 percent (Figure 2.3). Thus, if the package is implemented in 2019, France’s income per capita in 2022 will be 42,568*(1+0.011)^5
cluster 8 in stratum 3. This is also true of split-off households, which should be classified as part of the cluster where the source member was surveyed in round 1, even if they have never lived in that cluster as a complete household.<sup>10</sup>

> 10 In terms of using the later rounds of a panel survey in sample design for other data collection efforts, some caution should be used with the intracluster correlation coefficient (icc).  The icc for later rounds of the panel survey represents the correlation amongst persons or households based on their cluster location at the time the original sample was selected.  As time passes, members may have shifted out of the original cluster location physically but retain the original identifiers for the purpose of correctly estimating standard errors in panel analysis.  There would

10
panel data (e.g., Andrews, Bellmann, Schank and Upward, 2009; Martins, 2004, 2011; Heyman, Sjöholm and Tingvall, 2007) and many of them find much reduced foreign wage premiums. However, few studies trace the same individuals in developing countries, perhaps due to data constraints. Exploiting the panel component of the VHLSS 2002 and 2004, this study investigates how the wage levels of identical individuals changed if they “switched” between domestic and foreign sectors.

While Vietnam’s “formal” enterprise employment has been growing rapidly (Ramstetter and Ngoc, 2007b), Vietnam’s “formal”<sup>5</sup> sector in terms of employment represents still only a fraction of its economy (e.g., about 17 percent of total employment in 2004 (VHLSS, 2004)) and the remaining workers belong to large agricultural and other informal sectors. One of the roles of foreign firms in Vietnam is to create “formal” job opportunities, drawing workers from the informal sectors. Such an employment effect of FDI has been documented in other developing countries, especially in the context of the expansion of export-oriented manufacturing production.<sup>6</sup> However, previous studies based on firm-level data as well as those using matched employer-employee data ignore the wage effects on workers who are moving from informal sectors. Another advantage in using the VHLSS is that the data enable to extend the wage comparison analyses to the informal sector, filling the gap in the literature.<sup>7</sup>

> 5 Throughout the paper, jobs in foreign, private and state enterprises as well as those in government are referred to as “formal” employment. “Informal” employment is defined to include “informal wage” workers and “self-employed”. The impacts of foreign ownership on income for self-employed are not considered in this paper due to the lack of wage data.

> 6 See Anderson and Dimon (1999) for Mexico, Fu and
**ANNEX A Literature review: Fan chart construction methodology**

|**Author**|**Country/Variable**<br>**of interest**|**Methodology**|**Variables included**|
|---|---|---|---|
|Banco Central do<br>Brasil, Inflation<br>Report, Dec. 2015|Brazil. Inflation,<br>real GDP growth|Asymmetric fan chart based on historical forecast errors.|External and domestic<br>developments: global demand,<br>commodity prices, financial<br>market, and GDP growth.<br>Inflation.|
|Bank of Canada,<br>Monetary Policy<br>Report, Jan. 2016|Canada. Inflation,<br>core inflation|Fan chart based on Bank of Canada forecast errors and<br>survey professional forecast data.|Inflation, real GDP growth.|
|Canada,<br>Parliamentary<br>Budget Office,<br>Aug. 2010|Canada. Budget<br>balance|Uses the procedure proposed by Kannan and Elekdag (2009)<br>to construct the forecast distribution of Canadian Parliament<br>budget projection in 2010.|Real GDP, U.S. growth, U.S.<br>term spread, oil prices, budget<br>deficit.|
|Banco Central de<br>Chile, Monetary<br>Policy Report,<br>Feb. 2015|Chile. Inflation, real<br>GDP|Fan chart based on historical forecast errors and allowing<br>subjective assessment of risk.|Global demand, commodity<br>prices, financial market, GDP<br>growth, inflation.|
|Michal et al.<br>(2014)|Czech Republic.<br>Inflation, real GDP<br>growth, interest<