rafmacalaba/data-use-mentions-extended huggingface dataset:
Turning to the results themselves, the effects of both NECESSITY and NOWAGE are positive and significant for the biprobit estimations; though with the caveat that only the rho for NOWAGE is significant, suggesting that an endogenous selection effect requires a two-stage estimate. The ATE then bears some interpretation. This estimate shows that, all else equal, the bribery rate faced by informal operators is 26% greater if no households had a member under contract (NOWAGE=0 for all) compared to the counterfactual where all households included such a steadier income source (NOWAGE=1 for all). The effect among those specifically without a contract income in the household (that is the LATE from 2SLS) shows a magnitude of 52%. Such magnitudes are substantial and have suggestive economic conclusions: that is the lack of an alternate, steady income such as under-contract employment has a notably large and significant effect on the overall incidence of corruption.

Appendix V shows the first- and second- stage results for these estimations, where the first stage is a probit estimation of each opportunity cost measure (D). The biprobit estimates for NOWAGE are most appropriate, as discussed above, though all estimates for all measures, including 2SLS and biprobit are given for completeness. The relative “uptake” probabilities implied by Eq. �5� (27)
in aggregation.<sup>10</sup> Throughout this paper, we use a threshold of zero, implying that we sum over all pixels with a value greater than zero.<sup>11</sup> Third, satellite sensor recalibration and decay may affect the nighttime lights captured across countries. For example, a recalibration of satellite sensors in the first quarter of 2017 reduced background light and resulted in a worldwide increase in recorded nighttime lights.<sup>12</sup> Countries across the planet all show an increase in nighttime lights, albeit to different extents. Since sensor recalibration and decay pertain to all countries, controlling for time and country fixed effects helps to mitigate the issue.<sup>13</sup> Last but not least, despite all the noise removal, nighttime light growth rates still exhibit large fluctuations. For example, year-on-year quarterly nighttime light growth can exceed 200 percent in some countries in some quarters. Those countries are either very poorly lit because of low electricity access or their nighttime lights are poorly captured because of atmospheric conditions and interference by the sun. A small change in the sum of nighttime lights can then lead to large percentage changes. To mitigate this issue, we winsorize the data by removing the tails of the nighttime light growth distribution. In the following, we report results after removing the top and bottom 5 percent of nighttime light growth rates.<sup>14</sup>

# **3.2 GDP Growth and Other Data**

# _GDP Growth, GDP per Capita, and Sectoral Shares_

For quarterly GDP growth, we use all the available data from the World Economic Outlook database from the International Monetary Fund (IMF), which covers mostly advanced economies. We complement these data with national accounts data for EMDEs compiled by Haver Analytics. We remove the top and bottom 1 percent of

> 10Panel (a) in figure B.1 in the appendix shows that the threshold of zero reduces the volatility of the sum of
12 in Web Appx. Section A.3, this issue only affects a few older individuals in the earliest surveys of 12 countries. Using returns based on the 0-25 bins, as we estimate later, also eliminates the issue. Finally, results hold if we exclude the 12 countries or exclude only the problematic surveys that number 12 in total and involve 7 countries.<sup>17</sup> An alternative approach is to construct for each country periods based on important years, e.g. for the United States, 1923-1928, 1929-1932, 1933-1940, 1941-1944, 19451953, 1954-1961, 1962-1963, 1964-1967, 1968-1973, 1974-1979, 1980-1989, 1990-1993 and 1994-2000, due to important events in 1929 (Great Depression), 1933 (New Deal), 1941 (Pearl Harbor), 1945 (World War II ends), 1954 (ends of racial segregation in schools), 1962 (Cuban Missile Crisis), 1964 (Civil Rights Act), 1968 (Martin Luther King assassinated), 1974 (Watergate), 1980 (Reagan), 1990 (Gulf War starts, Cold War ends), 1994 (NAFTA), 2001 (9-11), and 2007 (Great Recession).<sup>18</sup> We identify important events using information from standard sources such as Wikipedia. For each country, we read the top of the Wikipedia webpage of the country (in English as well as in the main language of the country) as well as the “contemporary/modern period” and “colonial period” sections.<sup>19</sup> We cross-check this information using other sources, e.g. the BBC Profile of each country.<sup>20</sup> We then include in the regressions estimating the returns country-specific period dummies equal to one if the individual was aged between 18 and 67 during the period(s), for countries with at least two years of data. Doing so captures the fact that each person was affected by multiple
# **3 Data**

## **3.1 Data sources**

Table A3 in the Appendix presents summary statistics of all variables used in the analysis.

_A. Formal remittances_ : Data from formal remittances come from quarterly reports produced by Mexican authorities starting in 2013 that contain information about remittances received by each municipality during that time. They also provide information on the origin of remittances by US state. The reports are produced through declarations from credit institutions and legal entities that provide international transfer services and primarily reflect remittances received electronically. Since quarterly remittances at the municipal level tend to be noisy, we aggregate the data at the biannual level in our main analysis.

_B. Registry of Mexican immigrants:_ To provide a comprehensive measure of the bilateral connection between Mexico and the United States, we use a confidential version of the Mexican government’s _Matricula Consular de Alta Seguridad_ (MCAS) database. The _matricula consular_ is a document (card) that Mexican consulates issue to Mexican citizens who live in the United States. These data provide robust measurement of trends in Mexican immigration (both authorized and unauthorized) in the United States. (Allen et al., 2018). We restrict our analysis to employed Mexicans living in the United States whose ages range from 18 to 69 years. Thus, we observe around 3.1 million individuals from 2009 to 2013. For each, we observe their birth municipality in Mexico, the US county of residence, and demographic characteristics such as age, gender, education, and occupation. This subnational information lets us exploit variation in labor market patterns over time and control for state-level shocks and policies that may impact remittance flows.

_C. Unemployment of Mexicans in the United States:_ To test the relationship between remittances and unemployment in the United States, we use unemployment data from the CPS, which comprise a monthly sample survey of 60,000 eligible households conducted by the US Census Bureau for the Bureau of Labor Statistics. We use quarterly (seasonally adjusted)
**Table 2. Variable definition and Data source (Continue)**

|Variable Name|Data Source|Definition|
|---|---|---|
|GC Corporate Ethics|GC, WEF Executive<br>Opinion Survey|Ethics/corruption score: “How do you rate the corporate ethics of companies<br>(ethical behavior in interactions with public officials, politicians, and other<br>firms)?”|
|GC Police System|GC, WEF Executive<br>Opinion Survey|Reliability of police service: "To what extent can police services be relied<br>upon to enforce law and order?"|
|GC Electricity Quality|GC, International<br>Energy Agency|Electric power transmission and distribution losses as a percentage of<br>domestic supply|
|GC Broadband|GC, International<br>Telecommunications<br>Union|Broadband coverage, number of fixed-broadband internet subscriptions per<br>100 population|
|GC Education System|GC, WEF Executive<br>Opinion Survey|Quality of education system: "How well does the education system meet the<br>needs of a competitive economy?"|
|GC Financial<br>Development|GC|Financial development score (GC indicator pillar 8)|
|Population|WDI|Country total population|
|GDP PC|WDI|Country GDP per capita|
|Pop Density|WDI|Country population density|

_Note:_ DB = _Doing Business_ ; ES = Enterprise Survey; GC = Global Competitiveness index; HLO = Harmonized Learning Outcome; LP = labor productivity; TFP = total factor productivity; WDI = World Bank World Development Indicators; WEF = World Economic Forum.

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# Impact Evaluation of Trade Interventions: Pavin the Wa <u>g y</u><sup>1</sup>

Olivier Cadot<sup>*</sup> Ana M. Fernandes<sup>+</sup> Julien Gourdon<sup>§</sup> Aaditya Mattoo<sup>++</sup> JEL classification codes: F13, F14, L15, L25, 017, 024, C23 Keywords: impact evaluation, trade competitiveness, trade facilitation, aid for trade, export promotion, randomized control trials, propensity-score matching.

> 1 We thank Vivian Agbegha for excellent research assistance, Christina Neagu for help with tariff data and Mohini Datt for help with data on World Bank aid for trade. We thank Daniel Lederman, Martin Ravallion, and participants at the December 2010 workshop on ―Impact Evaluation of Trade Interventions: Paving the Way‖ in Washington, DC for comments. Support from the governments of Norway, Sweden and the United Kingdom through the Multi-Donor Trust Fund for Trade and Development is gratefully acknowledged. This paper is the result of collaboration between the World Bank and Switzerland’s NCCR on the evaluation of trade-related interventions.

> * University of Lausanne, CEPREMAP and CEPR.

> + Trade and International Integration Unit, Development Economics Research Group, World Bank. § CEPII.

> ++ Trade and International Integration Unit, Development Economics Research Group, World Bank.

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preference, it would be captured by _ηcFirstGirli_ , _ηsFirstGirli_ , and _ηtFirstGirli_ .

Given the fixed effects included in specification (1), our coefficients of interest are identified from (a) changes in expected dowry payments within state-caste groups over time, (b) differences in expected dowry payments across castes within a state-year, and (c) differences in expected dowry payments across states within a caste-year. The literature on dowry variation in India suggests a few potential sources of such variation. During our study period (1986-2007), the states of Andhra Pradesh (1986), Tamil Nadu (1989), Maharashtra (1994), and Karnataka (1994) amended the laws governing female inheritance rights for Hindus, Buddhists, Jains, and Sikhs, thereby generating variation in bequest-motive for dowry by state-year of birth-religion. Roy (2015) has shown that these amendments affected dowry payments. Another potential source of variation is macroeconomic changes such as those brought about by trade liberalization. Using an older (1999) round of REDS, Chakraborty (2015) has shown that tariff cuts resulting from the 1991 trade reforms in India also affected dowries differentially across districts and castes/ religions via effects of tariff cuts on within-caste wealth inequality across districts, consistent with the theory proposed by Anderson (2003). Chiplunkar and Weaver (2017), also using the 1999 REDS data, demonstrate that the dowry variation in India during 1975-1999 can be explained by greater differentiation in groom quality over time and marriage market competition within state-caste-religion for more educated grooms; this explanation analyzes dowry as groom-price. However, given the small size of our estimation sample, we do not explicitly model or utilize the dowry variation generated by these channels.

Any remaining threats to identification of the coefficient of interest, _β_ 1, as the causal effect stem from omitted caste-state-year specific factors that may be correlated with _Dowrycst_ and that differentially affect FG and FB parents. To address the former concern, we replace the main effect of _Dowrycst_ with caste _×_ state _×_ year fixed
<!-- Start of picture text --> 10.0 32,000<br>Actual<br>5.0<br>16,000<br>0.0<br>8,000<br>-5.0<br>4,000<br>-10.0 Forecast<br>-15.0 2,000<br>HL GDP growth GDP PC growth GNI Per Capita Simulated GNI per capita */<br>Forecast HL GDP growth Forecast GDP pC growth High Income Threshold Baseline<br>Source: World Development Indicators and WB staff`s Source: World Development Indicators.<br>forecasts */ Simulated with a constant growth rate of 5% (2022-19<br>Note: The series are the 3-year centered moving average. average).<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text --> We use the World Bank Long-Term Growth Model suite (LTGM, Loayza and Pennings 2022) to evaluate ways to boost growth and achieve high-income status more quickly. The standard LTGM is a Solow-Swan 2 Methodology used for Figure 2: Calibrate the levels to the GNI per capita Atlas method (NY.GNP.PCAP.CD) for 2018, and then cast backwards using real GNI growth (NY.GNP.PCAP.KD.ZG). The horizontal line shown is the 2022-23 high income threshold.

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102 countries, including 23 in South and East Asia, and the Pacific. They find that improvement of a country’s infrastructure base correlates significantly with higher economic growth. Similarly, using a large dataset on infrastructure stocks covering 88 developing and industrial countries and the period from 1960 to 2000, Calderón, Moral‐Benito, and Servén (2014) show that the output contribution of infrastructure is highly significant. According to their estimates, an increase in infrastructure provision from a typical lower‐ middle income country (e.g. Bolivia in 2000) to an upper‐middle income country (e.g. Uruguay) increases output per worker by 5 percent.

The same positive impact of infrastructure investments is found in studies investigating individual countries at different income levels. A prominent study by Aschauer (1989) finds that public investment in US infrastructure has a significant positive effect on total factor productivity. In particular, investments in ‘core’ infrastructure such as transport, electricity, gas, water, and sanitation are found to have the strongest explanatory power for productivity. For a 30 year‐long panel of South African manufacturing firms, Fedderke and Bogetić (2009) find a positive and significant impact of investments in different types of transport, telecommunication, and power generation infrastructure, on measures on productivity, output, and growth. Similarly, Mitra, Sharma, and Végantonès‐Varoudakis (2016) analyze Indian manufacturing firms and find a strong positive impact of the availability of transport, information and communication technology infrastructure on firms’ productivity. The size of this effect is larger in industries with greater exposure to foreign competition, thus highlighting the link between competitiveness and infrastructure stocks.

Further studies offer evidence of the economic contribution of specific infrastructure systems, in particular power, transport, ICT, and water.

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<sup>_,_11</sup> These rides can be completed any weekday between 6-9AM or 5-8PM (rush hour window when the reserved space policy is in effect), up to twice per day (once in the morning, once in the evening) and

> 9Our survey of a random sample of commuters indicates that 85 percent of them own a smartphone.

> 10This payment covers the SuperVia transit fare.

> 11Even though the sub-tasks are priced separately, riders must complete all three sub-tasks in the correct order to receive payment.

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1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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contribution to the index of crops which are less sensitive to rainfall. The idea is to prevent the influence of other production risks, such as pests or diseases, which are not covered by rainfall insurance. We use monthly rainfall data drawn from the Climatic Research Unit, and simply define _fkm_ as the correlation between total rainfall over the three wettest months and the yield of crop _m_ .<sup>22</sup> Overall, this index captures how insurance provision affects farmer crop portfolio choices in light of the traditional riskproductivity trade-off outlined in the literature.

Table A1, taken from our pre-analysis plan, provides more details on how we derived each

> 19We view the allocation of land to each crop as perhaps more appropriate than the allocation of other inputs such as fertilizer because it is more ex-ante with respect to rainfall realization, and because different crops may have different fertilizer needs.

> 20FAOSTAT provides annual statistics on harvested areas, production quantities and yields for 173 crops over the 1961-2017 period, covering the production of all primary crops for all countries – see http://www.fao.org/faostat for more details.

> 21See https://aps.dac.gov.in/ for more details. To our knowledge, India is the only country in our sample providing yearly sub-national crop production statistics over a sufficiently large time frame.

> 22As a robustness check, we also add a quadratic term to take into account the possibility of non-linear relationships and find similar results.

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learn how to improve their product quality, use standardization, improve marketing skills, and enhance the reliability of their shipments (see, for instance, Haskel et al. (2007), Keller and Yeaple (2009), Poole (2013), Balsvik (2011)). FDI inflows also increase competition in the domestic market, which may force local firms either to improve or to exit.

Domestic firms may learn about the profitability of various export opportunities by observing their foreign peers’ exports, and this could make them invest in quality upgrading, or developing new products. Thus, the presence of foreign affiliates may lead to reduction of export costs (Aitken et al., 1997), an increase in the amount and unit value of trade transactions (Chen and Swenson, 2007), help develop new export connections (Swenson, 2008), and expand export varieties (Sheng and Yang, 2016).

This study examines the impact of FDI on the quality of products exported by local firms in a country entered by FDI. The analysis focuses on the Russian Federation. I use detailed customs data recording Russia’s exports at the level of the firm, 10-digit HS (Harmonized System) product classification, destination country and year. Due to the data I have at hand, the time period I examine is 2012-2016.<sup>5</sup> The export data are combined with firmlevel balance sheets and ownership information. Although the balance sheet information covers 2007-2016, the ownership information is available to me only for 2012 and 2015. Thus, my analysis focuses on a long difference. I measure quality upgrading, defined as within-product improvements in export quality, in two ways. First, I focus on unit values of exports.<sup>6</sup> Second, I use the approach in Khandelwal et al. (2013), which builds on the work of Khandelwal (2010).<sup>7</sup>

> 5This time period includes trade sanctions imposed primarily
- 27 -

The inconsistent mix of government policies in 1993 triggered devaluation expectations, a domestic demand led boom, and worsened export performance and trade deficit. When the crisis erupted in the first quarter of 1994, there was a massive outflow of capital, and the Turkish Lira clepreciated by almost half in nominal terms. Turkey managed to cut some of the expenditures and a sizeable primary surplus was achieved in 1994. While in 1995 and 1996 the Government was able to maintain a primary surplus position as shown in chart 6 above, by 1997 these were largely eroded. Preliminary data suggests primary surplus will be around 4 percent of GNP in 1998 thanks to higher revenues due to a new tax law. While this is a welcome development as it is a key condition for fiscal sustainability, it is not clear how long it could be maintained.'<sup>0</sup> Structural reforms to maintain a sound fiscal 10 An important issue that needs to be mentioned here is the measurement of public sector deficits. The fiscal data discussed above refers to the consolidated budget which excludes quasi-fiscal operations of public banks. As shown by Atiyas et.al (1998), consolidating these into public sector accounts and netting out intra-governmental debt change the primary balance situation significantly. This strongly suggests that policies for fiscal adjustment to ensure consistency between debt and inflation targets should be based on properly defined consolidated public sector accounts.

continued
# **D. Robustness: Simulation Using Data on Individuals**

**Table D.1 – Cigarettes Price Elasticities, by Decile**

|_Price Elasticity_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|-0.032|-0.014|0.005|0.015|0.020|0.017|0.018|0.026|0.029|0.036|
|Medium|-0.232|-0.214|-0.195|-0.185|-0.180|-0.183|-0.182|-0.174|-0.171|-0.164|
|Upper bound|-0.432|-0.414|-0.395|-0.385|-0.380|-0.383|-0.382|-0.374|-0.371|-0.364|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles were created using individual expenditure. Lower- and upper-bound elasticities show on average differences of −0.2 and +0.2, respectively, with the medium-bound elasticity. Demographic controls include: area, macro region and year fixed effects.

**Table D.2 - Direct Effect of Price Increase through Taxes (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|
neither of which proved significant, but data on informality are very noisy. To the extent that such factors are important, our estimates will tend to understate the informal sector. Set against this, a reduction in the cost of sending and receiving remittances through the formal sector, as well as in the presence and severity of other formal restrictions affecting remittance senders and recipients, could in principle increase recorded remittances by either: (i) increasing _total_ remittances; or (ii) increasing the share of remittances that flow through the formal as opposed to the informal sector. To the extent that the first channel is active, our estimates of informal remittances would be biased _upwards_ . However, as discussed earlier, we find the second channel to be relatively more plausible. Overall, we view the estimates in Table 5 as plausible ballpark estimates of the magnitude of informal flows. However, we now check this conclusion by turning to some detailed country case-studies.

# **_VII.  Household Survey Estimates_**

Household surveys are likely to be the most accurate means of estimating the informal sector for particular countries. The main issue is that remittances must be fairly widespread or it is difficult to get a representative sample of recipients. As a result, we use only surveys from countries where remittances are known to be large and widespread, where there is more than one year of data, and which include questions about the channel through which money was transferred. Three household surveys satisfy these criteria: the (2000–2003), El Salvador (1995–1997), and the Philippines (1992–2000). We also report results from a handful of remittance surveys done by IOM and other agencies.

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