rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Robustness: subsamples of rich and poor countries_**

To assess better the relative importance of institutional quality versus differences in legal origins we split the data into two subsamples. The first, “Rich Partner” subsample includes all observations in which at least one country in the pair has PPP GDP per capita _greater_ than 10,000 dollars in 2000. This effectively excludes all pairs in which both countries have less than that income. The second, “Poor Partner” subsample includes all observations in which at least one country in the pair has PPP GDP per capita _less_ than 10,000 dollars in 2000. This effectively excludes all pairs in which both countries have more than that income<sup>33</sup> . The GDP data were taken from the World Development Indicators<sup>34</sup> . A list of 31 countries that define the Rich Partner subsample is reported in the appendix.

We estimate the same baseline specifications of Tables 4-6 on the two subsamples of country-pairs. The results are reported in Tables 14-16. In all three tables the first two columns replicate the first two columns of Tables 4-6 for convenience. The next two columns report results for the same specification for the Rich Partner subsample, while the last two columns report the results for the Poor Partner subsample. We keep here the practice of using IV estimators for all subsamples to correct for potential endogeneity and measurement error.

The estimates in Tables 14-15 for Bureaucratic Quality and Control of Corruption exhibit a similar pattern. First, differences in legal origin are more important when at least one trading partner is rich than when at least one trading partner is relatively poor. Second, the opposite is true for institutional quality; it is more important when at least one trading partner is relatively poor. Moreover, the _relative_ importance of institutional quality versus different legal systems is

> 33 The results reported below hold for a broad range of cutoff incomes. These results are available upon request. 34 These are published by the World Bank.
the main estimations, I use tariff rates as a measure of protection for import-competing sectors. I also have access to effective rate of protection (ERP) data which I use to augment my results with tariffs. The effective rates take into account the tariffs on inputs, and they are based on the value added. They are considerably higher than the nominal rates but show a similar pattern with the tariffs as can be observed from Figure 1.

Before I discuss my empirical methodology and the results, in the next section I briefly review the existing empirical literature to the extent that it relates to this paper.

# 4 Empirical Literature Overview

Tybout (1991) briefly reviews the literature that contains implications for the linkages between trade and productivity, and he indicates that the net effect of a liberalization episode is ambiguous.

> 24Note that the reductions are computed as percentages to account for the variation in the initial tariffs.

> 25Coefficient of variation is the mean divided by the standard deviation of a given group.

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National data collection systems have been simplified. And evaluators are no longer faced with the problems associated with massive federal education programs and have turned their attention to activities at the individual school level. This reflects not only the accountability pressures and demands from both state and federal levels, and from communities, that are being placed on local school districts and schools, but also the lessons derived from extensive input-output research on education undertaken within the past several years. School effectiveness studies have clearly pointed to the importance of the educational experience at the local school and to the critical role played by the human resources there -- the students and teachers (Murnane, 1982). This new interest in the educational program at the relatively smaller educational unit of the local school district or individual school, has increased the importance of monitoring and routine information collection at that level: The object of traditional evaluation designs is changing, from a "programmatic intervention" focus to a focus on "schooling" itself. In other words, the "program" for evaluation focus is now the ongoing constellation of daily activities and outcomes constituting the program of the local school. This suggests the need for ... a considerable variety of information ... designed to facilitate any number of evaluative purposes from appraising the impact of specific programmatic interventions, to informing organizational and instructional planning and development activities, to, perhaps most importantly, monitoring the periodic health of the school work and learning environment. (Sirotnik, 1987, p.77) The establishment of information bases at the school and district level has been supported by the rapid diffusion of computer technology and software.

The purposes of the collection and development of information have also changed significantly. Accountability has become the paramount concern at the local district level and the school principals are coming under increased pressure to produce demonstr...ble successes (see Fetler, 1986). As one evaluator described the current situation: ... the data for program improvement emphasis of the late sixties and seventies
AMCs as part of that strategy.

Two main types of AMCs can be distinguished: AMCs set up to help and expedite corporate restructuring and AMCs established to dispose of assets acquired/transferred to the government during the crisis-rapid asset disposition vehicles. According to a survey of 26 banking crises (Caprio, Klingebiel 1997b), centralized AMC structures were set up in nine cases. Out of the nine, seven cases, where data was publicly available for a more thorough analysis, were selected. In three out of the seven cases (Finland, Ghana, Sweden), the government set up restructuring vehicles. In four cases (Mexico, the Philippines, Spain and the US) governments set up rapid asset disposition agencies.

The results of the analysis of the seven cases can be summarized as follows: Two out of three corporate restructuring AMCs did not achieve their narrow goals of expediting corporate restructuring. These experiences suggest that AMCs are rarely good tools to accelerate corporate restructuring. Only the Swedish AMC successfully managed its portfolio, acting in some instances as lead agent in the restructuring process. It was helped by some special circumstances, however: the assets acquired were mostly real estate related, not manufacturing that are harder to restructure, and were a small fraction of the banking system which made it easier for the AMC to maintain its independence from political pressures and to sell assets back to the private sector. Rapid asset disposition vehicles fared somewhat better with two out of four agencies, namely Spain and the US, achieving their objectives. The successful experiences suggest that AMCs can be effectively used, but only for the purpose of asset disposition including resolving insolvent and unviable financial institutions. But even achieving these objectives required many ingredients: a type of asset that is easily liquifiable-real estate, 2
although these coefficients are not statistically significant once I control for those characteristics that most differ between the two groups of households. Indeed, the inclusion of control variables seems to amplify the difference in adult goods consumption revealed in table 8 and to reduce the difference in terms of food consumption.

**Table 9.** OLS Estimation. Household Level.

|||**Shares of h**<br>|**ousehold incom**<br>|**e spent on:**<br>||
|---|---|---|---|---|---|
||(1)<br>Food|(2)<br>Education|(3)<br>Health|(4)<br>Durables|(5)<br>Adult|
|**A. No controls**||||||
|Migrant mother|-0.206**|-0.036|-0.034|-0.024|0.057***|
||(0.096)|(0.029)|(0.023)|(0.018)|(0.017)|
|Size of household|-0.402***|-0.078***|-0.105*|-0.004|-0.019|
||(0.104)|(0.023)|(0.062)|(0.005)|(0.020)|
|Observations|337|337|337|337|337|
|R<sup>2</sup>|0.053|0.019|0.033|0.001|0.045|
|**B. Controls included**||||||
|Migrant mother|-
parenthesis (clustering at community level). Mothers with no formal education as reference category. All variables are measured at wave1. Controls: paternal education, cognitive social capital, wealth index, child's age and age squared, birth weight, gender, ethnicity, maternal and paternal age and age squared, mother's height, maternal employment status, maternal depression, rural/urban, negative economic shocks, breastfeeding length, migration background, language, single parent, only child, household size and age composition, household member with disability).

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.1948**|**0.2041**|**31.9**|**4.6**|
|**Informal**|**1,373**|**33**|**106**|**18**|**0.3327**|**0.533**|**11.0**|**37.6**|

Note: Each loan is counted as an observation. Com. Bank includes Finance and Insurance Companies. Village-level Institutions include loans from Village Funds, Rice Banks, Buffalo Banks and Production and Credit Groups. “Informal P” includes Moneylenders, Store Owners, Landlords and traders. “Informal R” includes friends and relatives. Column _L_ reports the average loan size and column _σ_ ( _L_ ) its standard deviation in 1,000 Baht. The figures in both columns are in Baht. The length of the loan is in months. The interest rates _r_ and _rc_ are net and yearly compounded. Column “Collat.” reports the percentage of loans that required collateral. Finally, column “Z. Int.” reports the percentage of loans given interest-free. Source: Townsend-Thai data.

Table 2 reports the variables by source of credit that will be used in the estimation. From the sample of 2,612 households, 34 percent of the sample self-finance, 36 percent borrow from a formal institution, 17 percent borrow from an informal lender only and 13 percent of the sample borrow from both a formal and an informal lender. These numbers are large if compared to those of Aryeetey (1997) where only 16 percent of all households interviewed in the Ghana Living Standards Measurement Survey reported borrowing from the formal sector.

Observed loan size _k − b_ is large for clients of a formal institution that requires collateral and for those households who resort to both a formal and informal lender. This fits well with the prediction of the model that
A firm as defined here may have many factories/facilities in the same or different locations. Each facility may have various pollution endpoints such as stacks for air pollution or discharge points for water pollution. Although facilities are supposed to be inspected at least once every six months or a year, because of the limited capacity of the OSPCB, there is an element of randomness to how many times a facility gets inspected during the five-year period for which data were obtained. It is not obvious if the inspectors go more frequently to the facilities that do not usually comply or to the most polluting facilities or any other metric such as availability of an inspector in the regional office that determines the frequency of inspections. In addition to the inspected data, the firms are also required to submit self-monitored data. For example, the Air Act requires facilities to monitor pollutants such as sulfur dioxide, carbon monoxide, nitrogen oxides, particulates, and metals. Under the Water Act, facilities are required to monitor parameters such as total coliform, turbidity, pH, color, odor, taste, and pollutants such as phenolic compounds, pesticides, metals, fluoride, nitrate, chlorides, and sulfate. The monitoring frequency and methodology vary for each industry and type of pollutant. But the facilities are generally required to maintain records and report the information periodically to the pollution control board.

The following data were recorded from the OSPCB database (i) company id; (ii) monitoring point id; (iii) year started (age); (iv) industry; (v) products; (vi) company name; (vii) location name; (viii) sample date; (ix) inspection/self-reported; (x) environment category (air/water); (xi) indicator monitored (PM 2.5, Sox etc.); (xii) data value; (xiii) standard; (xiv) minimum; (xv) maximum; (xvi) monitoring frequency; (xvii) compliance ratio; (
screen credit card data. Using cash or precious metals for certain transactions over amounts specified in the law is prohibited. An example of a general major cash payment reporting system is the Form 8300 system in the United States. Any person engaged in a trade or business who receives more than US$10,000 in one transaction or several related transactions must file a cash payment report to the Internal Revenue Service (IRS). The system, developed jointly by the IRS and the Financial Crimes Enforcement Network (FinCEN), is used by the government to track tax evaders and individuals profiting from criminal activities.

Considering the difficulties of ensuring the correct reporting of cash income, tax administrations are simultaneously aiming at tracing the deposit and use of cash revenues. Cash income will eventually be deposited in a private bank account, spent, or transferred abroad. Matching data on the wealth and spending behavior of a shadow economy business operator with data in his income tax return can reveal major discrepancies and hidden sources of income. Developing 360‐ degree profiles of high net‐worth individuals is useful to track this hidden income and wealth.

Increasing tax revenues by broadening the tax base and further reducing the shadow economy has been at the center of tax policy and administration in the Republic of Korea since the National Tax Service (NTS) was established as an independent agency in 1966. The tax incentive regime to promote use of electronically traceable payments made through credit cards, debit cards, and electronic cash receipts that the Korean government introduced in 1999 was a pioneering policy to broaden the tax base in business‐to‐consumer (B2C) transactions. It is now believed that these measures contributed to broadening the tax base to a great extent by curbing cash transactions. This paper will examine how Korea successfully reduced the shadow economy using this methodology.

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Figure C3: DID estimates - NTL measured at district level <!-- Start of picture text --> Control<br>Year<br>(a) Means (2000-2018)<br>12<br>10<br>8<br>NTL<br>6<br>4<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>Note: Sample size is N = 741<br>(b) difference-in-differences estimates<br>4<br>2<br>0<br>-2<br>-4<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> _Note:_ Panel A presents mean NTL for districts in ULGSP treatment and comparison districts. Nighlights are measured as average in each of the study districts. Nighlight data excludes pixels with NTL < 7. Panel B presents results from DiD estimates. Coefficients obtained estimating equation ntlwy = α + γw + θy + 2018 �y=1999<sup>βy(ULGSP</sup><sup>_∗_year)wy + Xwy + εwy.StandarderrorsareclusteredattheLGA,theleveloftreat-</sup> ment.

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output by the less productive firms actually reduce industry productivity. And finally, at the level of separate size groupings, the largest increase in allocative efficiency has occurred among large firms, with resources moving from smaller to larger firms within this size group; large firms have also benefited from the largest increase in average technical efficiency, a 36 percent increase. In contrast, medium-sized firms are characterized by the smallest increase in both allocative and technical efficiency.

# **VII. Firm-level Innovation Analysis**

# **Data sources**

For the firm-level innovation analysis, we compare the full 2011 WBES, including both manufacturing and services firms, to the baseline of the Sri Lanka Longitudinal Survey of Enterprises (SLLSE) – a survey collected between January and May 2008 and used by de Mel et al. (2009). This survey was designed to obtain a representative sample of micro, small and medium-size manufacturing and services enterprises in urban Sri Lanka, irrespective of their registration status. We use the booster sample of 610 small and medium enterprises collected for this survey because it is more representative of the size distribution of establishments in Sri

> 17 Our large city sample comes from Colombo, Gampaha, Kurunegala, and Kandy.

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nology diffusion and thus persistent productivity externalities among local firms in the host country or if more difficult behind-the-border structural policy reforms are indispensable. Second, it matters for the design of developing FDI policy because it indicates whether it is enough to simply attract the MNE for a limited time (via, for example, a tax holiday) or whether the government must aim to attract and retain foreign firms such as by maintaining low corporate tax rates, strengthening the domestic absorptive capacity, or promoting technology sharing between foreign and local firms (for instance, through joint ventures). Third, if growth spillovers stem primarily from leaning rather than learning effects, FDI-led growth increases a country’s vulnerability to adverse global shocks in that the productivity gains of domestic firms will be partly reversed with the disinvestment of multinational firms.<sup>3</sup> This paper contributes to the existing literature by exploring whether there are asymmetric spillover effects from FDI increases and decreases using Jordanian firm-level data. Jordan is a particularly good example to study (spillover) effects of FDI. First of all Jordan has seen a massive increase in FDI over the last decade cumulating to a share of FDI relative to GDP of 10.1 percent in 2008, becoming one of the most attractive countries for FDI.<sup>4</sup> Furthermore, given the market oriented reforms undertaken, it has also generated sufficient absorptive capacity to take advantage of FDI effects. Our estimates do indeed reveal significant asymmetries between MNE entries and exits which point to significant temporary components to spillovers. We find that FDI in downstream industries (i.e., those that purchase inputs from local firms) are particularly important in generating these transitory leaning effects among local suppliers, with smaller local firms being especially impacted by exiting MNEs. Our results suggest that the significant FDI spillovers to local suppliers in upstream industries which have been found in several developing countries do not necessarily capture persistent learning effects (such as technology transfers) but rather transitory leaning effects reflecting the higher demand for
# **Do Global Trade Distortions Still Harm Developing Country Farmers?**

While developing country farmers contribute less than 3 percent of global GDP, they account for 43 percent of global employment, 64 percent of global agricultural value added, and a similarly large share of global poverty as measured by earnings of less than $1 a day. Raising net farm incomes is therefore a key to meeting the Millennium Development Goal of halving global poverty by 2015. If that can be done by policy reforms that also boost the efficiency of resource use and raise developing country and world GDP, so much the better. This paper asks if reducing trade policy distortions (including agricultural subsidies in high-income countries) could provide such a magic bullet.

Two decades ago, the answer to that question was unequivocally affirmative. A number of studies provided a clear picture of the adverse effects of government policies on farmers’ incentives in developing countries in the 1980s. Farm subsidies and import restrictions of developed countries depressed the international prices of farm relative to non-farm products (Tyers and Anderson 1986, 1992), while developing countries’ own trade and exchange rate policies further depressed their farmers’ incentives (Krueger, Schiff and Valdes 1988) – as they had since at least the 1960s (Little, Scitovsky and Scott 1970; Balassa and Associates 1971). Time series data for developed and newly industrializing countries up to that time also indicated a clear tendency for national governments to gradually change from taxing to subsidizing agricultural relative to industrial production (and from subsidizing to taxing food consumers) in the course of their economic development (Anderson and Hayami 1986, Lindert 1991). Had that
|**Table A5: Impact of the"leaflets intervention"**|(1)<br>(IV) Formalized<br>after May 2016<br>(Admin Data)|
|---|---|
|Received a leaflet|0.010|
|(Instrumented by assignment to leaflet group)|(0.006)|
|Observations|1,197|
|R‐squared|0.050|
|Share of firm selected in Leaflet group that received it|0.706|
|Formalization rate in control(no leaflet) group<br>Note: Administrative data from GUFE: May, June and Jul<br>survey folow up data 2016. IV regressions, controlling f<br>dummies (used to randomize the "leaflets intervention<br>indicate statistical significance at 1, 5 and 10%.|0.003<br>y 2016) and<br>or strata<br>"). ***, **, *|

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using the newly constructed model and database.

A few more innovations were introduced in the model; first, we introduce a multi-level nest in the labor demand, wherein perfect substitution happens between male and female composite labor while the different types of skilled labor substitute each other based on the standard GTAP elasticities; second, we introduce a labor supply curve based on calibrated elasticities from Indian data from 1981-82 to 2017-18, as well as other literature.

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# **Appendix 2: Details of Sampling Procedure**

Sampling protocols for inside and outside the market were different:

- For Danktopa market, we used a precise map of the market made by the public company managing markets in Benin (SOGEMA). This map allowed to divide geographically the market in small areas. We then randomly selected areas in the markets in which 50% of the businesses (with fixed location) where sampled for the survey.<sup>16</sup>

- For other neighborhoods of Cotonou, we were able to obtain detailed maps of each of the 144 neighborhoods in Cotonou. Those maps allowed the easy identification of _ilots_ (blocks), the official administrative unit within a neighborhood. We used this administrative unit as a reference for the listing survey sampling. We then used information given by the tax administration (and confirmed by the survey company) in order to characterize neighborhoods as high or low firm density areas. We randomly sampled 38% of _ilots_ in high density neighborhoods and 10% of the _ilots_ in low density neighborhoods. In each _ilots_ 68% of businesses where sampled for the survey in average.

Overall, 19,246 businesses were listed. The listing survey allowed us to estimate the total number of businesses operating in Cotonou (with a fixed location, excluding international and nationwide businesses and liberal professions) to approximately 68,500, including around 5,000 in Dantokpa market.<sup>17</sup> Among those 19,246 businesses, 9,938 businesses were randomly selected to be surveyed. 7,945 (80%) businesses were successfully surveyed, 1,000 (10%) businesses refused to be surveyed, and 995 (10%) businesses were dropped because the business owner was not available or not reached after 4 attempts. Figure A1 details the listing survey results inside and outside the market.

From the 7,945 businesses surveyed, a population of 3,596 businesses was then selected to participate in the study based