rafmacalaba/data-use-mentions-extended huggingface dataset:
revenues, capital investment, the region it is located in, and the sector in which it operates. Together, these firms comprise 53% of employment in Jordan as compared to Labor Force Survey (LFS) data.<sup>15</sup> In studying the spillover effects of FDI, these Jordanian data have several attractive features. First, prior to 2000, FDI inflows were very low (below 1% of GDP). Since then, the government removed restrictions to FDI in manufacturing and several service sectors including banking, telecommunication, and retail trade.<sup>16</sup> As a result, net FDI inflows tripled from US$ 0.9 billion in 2000 to US$ 2.8 billion in 2008. On average, these FDI inflows amounted to 10.1 percent of GDP over the decade, a share which ranks Jordan among

> 14We use sample weights for each firm and year making the subset of firms observed in both periods representative for the entire population of more than 100,000 firms in the 2006 and 2011 firm census. As we focus on changes over this five-year period, we can only use firms for which data are available in both years.

> 15Note that LFS data typically exceed firm census employment data significantly in most countries, since they usually better cover informal employment, illegal foreign workers, family workers, etc.

> 16Note that, transport and professional (accounting and legal) services remain restricted to foreign investors (Schiffbauer et al., 2015). This motivates our sample splits between services and manufacturing.

8
Table 7: LONG-RUN EFFECTS ON MATCHING IN TRANSFERS

||Whether gave<br>transfers to any<br>ultra-poor<br>household|Fraction of<br>ultra-poor<br>households who<br>weregiven transfers|
|---|---|---|
||(1)|(2)|
|Treatment|-0.004|-0.057|
||(0.005)|(0.130)|
|Treatment_×_other poor|0.017*|0.322|
||(0.010)|(0.240)|
|Treatment_×_middle class|0.019***|0.566**|
||(0.007)|(0.230)|
|Treatment_×_upper class|0.034***|1.206**|
||(0.011)|(0.532)|
|Treatment effect for other poor|0.013|0.265|
||(0.006)|(0.163)|
|Treatment effect for middle class|0.015|0.509|
||(0.004)|(0.176)|
|Treatment effect for upper class|0.030|1.149|
||(0.009)|(0.494)|
|Control mean|0.012|0.441|
|... for ultra poor|0.008|0.280|
|... for other poor|0.007|0.313|
|<br>... for middle class|0.012|0.510|
|... for upper class|0.034|1.066|
|Observations|21547|21547|

**Source:** Author’s analysis based on original survey data. **Notes:*
|0.087||0.353|0.359|
||[0.034]|[0.344]||[0.001]|[0.001]|
||_{_0.037_}_|_{_0.247_}_||_{_0.001_}_|_{_0.001_}_|
||(0.055)|(0.388)||(0.001)|(0.002)|
|API Original = API Plus|[0.001]<br>_{_0.001_}_|[0.056]<br>_{_0.056_}_||[0.029]<br>_{_0.158_}_|[0.001]<br>_{_0.001_}_|
||(0.002)|(0.036)||(0.036)|(0.001)|
|Number of clusters|224|224||224|224|
|Observations|1045|1045||1045|1045|

_Notes_ : This table shows OLS estimates and the associated _p_ -values on survey-based measures of parental behavior measured after two years of exposure to the API program. Panel A refers to the first experiment run by the government. Panel B refers to the second experiment designed and implemented by the authors in collaboration with the government. For detailed descriptions of the individual components of the summary measures of parental engagement used in this table, see Appendix A.2. _p_ -values reported in brackets refer to the conventional asymptotic inference. _p_ -values reported in braces are computed using randomization inference (randomization- _t_ ). _p_ -values reported in parentheses are adjusted for testing each null hypothesis (null impact of API _Original_ , API _Plus_ , and the comparison) for the two different families of outcomes through the stepwise procedure described in
# **1.1. Key Labor Market Indicators**

Similar to many countries of the region, Serbia has experienced a painful transition from a centrally planned to a market economy. Restructuring of economic activities resulted in massive job losses and the unemployment rate remained high over the past decade. Reform processes in Serbia were delayed by the political turmoil of the 1990s and the country still lags behind other Eastern European economies on key employment outcomes (Table 1). Only half of Serbia’s working age population reports being employed, which is much worse than the EU average and far from the European Union Lisbon target of 70% to be achieved by 2010. The unemployment rate in Serbia is almost double the EU-27 average and much higher than in other Eastern European economies.

**Table 1. Labor market indicators for working age population (15-64) in Serbia and selected countries of Eastern Europe, 2009**

||ActivityRate|Employment Rate|Unemployment Rate|
|---|---|---|---|
|EU 27|71.1|64.6|8.9|
|Bulgaria|62.5|62.6|6.8|
|Czech Republic|70.1|65.4|6.7|
|Hungary|61.6|55.4|10.0|
|Romania|63.1|58.6|6.9|
|Croatia|62.4|56.6|9.1|
|Poland|64.7|59.3|8.2|
|**Serbia**|**60.5**|**50.0**|**17.4 **|

Source: for Serbia, LFS, 2009; for other countries Eurostat LFS Database <u>http://epp.eurostat.ec.europa.eu/portal/page/portal/employment_unemployment_lfs/data/database</
types of structures, there is a clear incentive to use them to legally avoid a domestic charge to tax. Introducing a charging provision will bring this income back within the tax base of the jurisdiction concerned. However, the use of these structures is not confined to legal tax avoidance. It is common for it to be associated with deliberate tax evasion and other criminal activity, including corruption. One of the ancillary benefits of an effective charging provision, coupled with increasing international exchange of information, is that it should help deter corruption, which often involves cash payments into offshore accounts. The link between tax evasion and other criminal activity including corruption is well established.<sup>25</sup> Having an effective charge to tax on income diverted into offshore structures is therefore a necessary part of the wider fight against corruption and other forms of criminality. It is the policy corollary to the improved access to information about such structures that is resulting from the Common Reporting Standard and increased international cooperation between tax administrations and between tax administrations and other arms of law enforcement.

Anglo-Saxon jurisdictions (US, UK and Australia) have enacted specific rules, particularly targeting trusts. Typically, a charge may arise on the settlor (person putting wealth into the trust) and/or the beneficiaries. To be effective, charging provisions should not be limited to trusts but should apply generally to arrangements that may be exploited to exclude income from a charge to tax. So, for example, the UK has provisions known as the “settlements

> 24 The leaked data contained in the Mossack Fonseca and Paradise Papers illustrate the lengths some tax avoiders and evaders will go to disguise their ownership of assets and minimize their taxes. As discussed in Alstadsaeter et al. (2018) several developing economies that feature prominently among the countries that generated a lot of shell companies relative to the size of their economy also have high offshore wealth-to-GDP ratios. Similar observations apply to corrupt officials who commonly rely on corporate vehicles to conceal their identities. These include shell companies in
level (village Panchayat), while offering the prospect of lower basis risk for farmers and therefore providing improved benefits, creates both challenges and costs for the state governments in the implementation of the crop cutting experiments (CCEs).  These need to be addressed in the medium term. Moving to a lower insurance unit for major crops significantly increases the number of CCEs to be conducted, as at least 8 CCEs have to be conducted for each of these crop in each Gram Panchayat. While mNAIS is currently being piloted in only 50 districts, issues related to the costs, potential outsourcing of CCEs, quality control including possible applications of technology, and sampling framework need to be addressed in advance of wider expansion.

To deal with the challenge of significantly increased number of CCEs, new technology and partial outsourcing of the yield estimation process could be piloted to increase the efficiency of the CCE process and to reduce the number of CCEs. Technology could be utilized in the conduct of official CCEs, for example by requiring that all CCEs are video recorded on inexpensive mobile phones. Insurers and state governments could watch a random selection of CCE videos, to verify that the correct procedure was being followed and to highlight areas for future training of field staff. CCE data could be transmitted to the insurer on the day each CCE is conducted to allow real time monitoring by local staff and comparison with other data sources, such as remote sensing imagery. Remote sensing imagery could also be used to reduce the number of CCEs needed: a higher number 16
very similar. There are no significant differences in terms of urban status, education levels, business or farm ownership, employment shares, weekly earnings, or mobile phone access. This is likely because the sample already restricts to low-income households with poverty scores just below and just above the 4Ps eligibility cutoff (in 2010). In addition, the lack of updates to the 4Ps beneficiary list means that changes in income over time could have led to a narrowing of the gap between the 4Ps and non-4Ps households in the sample. We also report statistics on job types: the share of households with at least one member working as a private sector employee, government employee, freelance agricultural worker, freelance non-agricultural worker, and self-employed worker. These are also similar across the two groups.

We note, however, two important differences between 4Ps and non-4Ps households, which are a result of the 4Ps program itself. In wave 1 (this information was not collected at baseline), a much larger share of 4Ps households were part of the national health insurance program, PhilHealth (83% compared to 69% of non-4Ps). This is because 4Ps beneficiaries are eligible for free PhilHealth membership. It is also the case that 4Ps beneficiaries are significantly more likely to report having a bank account (a difference of 63 percentage points). This could be due to their use of the Land Bank ATM card and bank account to receive 4Ps payments (even though this is not a regular bank account that supports financial transactions).

Table 1: Baseline Household Characteristics by 4Ps status

||(1)<br>Non-4Ps|(2)<br>4Ps|(3)<br>Diference||(1)<br>Non-4Ps|(2)<br>4Ps|(3)<br>Diference|
|---|---|---|---|---|---|---|---|
|Urban|0
(Victora, Adair, Fall, et al., 2008). In other words, these inequalities tend to persist and even worsen over time. Third, inequalities experienced in early childhood play a crucial role in the intergenerational transmission of poverty and inequality (Case, Lubotsky, & Paxson, 2002; Currie & Moretti, 2007).

The data used to evaluate the impact of the INDH on economic outcomes and on ECD come from three impact evaluation surveys (2008, 2011, and 2013) carried out by the Moroccan National Human Development Observatory ( _Observatoire National du Développement Humain_ , ONDH). The surveys provide panel data for households in rural communities just above and below the cutoff for inclusion in the INDH. For this reason, this analysis makes it possible for us to examine the levels of and gaps in development within a dynamic framework, in terms of economic development and ECD in these rural areas.

The analysis begins with an evaluation of the impact of the INDH on income, expenditure, and the assets of households for the various communes studied. Previous work evaluating the INDH program’s impact on economic and human development using the first two rounds of the survey suggested substantial impacts (Observatoire National du Developpement Humain, 2012), and we update those findings adding the 2013 data. Subsequently, indicators for child health, nutrition, and early childhood education are examined. Since the impact evaluation surveys conducted by the ONDH allow us to do so (they are in fact designed for this purpose), we use a method known as Regression Discontinuity Design (RDD or simply RD) to identify program impact. We also exploit the panel data to estimate fixed effects models.

We begin the paper with a discussion of the general framework for the INDH and its operations, especially in terms of ECD. Next, we describe the ONDH surveys that we have used in this study. Before presenting the results of the various analyses, we discuss the econometric methods used to estimate the impact of the INDH. The findings are
of early learning environment quality.

We used the data collected under the observational tool to develop a _Classroom Observation Index_ with the purpose of measuring classroom quality. At each round, the index computes a weighted average of a set of underlying observational variables (31, 34, and 32 variables at baseline, 18-, and 36month follow-up, respectively) using the Inverse Covariance Weighting (ICW) methodology in Casey et al. (2012), which is described in detail under _Kaufman Assessment Battery-Children, 2_<sup>_nd_</sup> _Edition (KABCII)_ above.

63
collected. Research reported in this publication was supported through the Research Support Budget and the Knowledge for Change Program (KCP) at World Bank, the NIA grant 1K01AG066999-01A1 (Carvalho), the ESRC for CEMMAP (ES/P008909/1) and the European Research Council through grant ERC-2015-CoG- 692349 (Carneiro). Cardim gratefully acknowledges financial support by FCT – Fundação para a Ciência e Tecnologia - SFRH/BD/131707/2017. This study was approved by the Committee of the Secretaria Municipal de Saúde in Rio de Janeiro, Brazil (Plataforma Brasil) on 3/8/2016, (Study ID:1.466.676) and by the IRB at the University of Southern California University Park, on 2/8/2016 (Study ID: UP-15-00641) and registered in the AEA RCT Registry ( <mark>AEARCTR-0004536).</mark> The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or the National Institutes of Health. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
Policy Research Working Paper 9149

# **Abstract**

Regulation often creates opportunities for public officials to extract bribes. If this is true, deregulation offers a simple way to combat corruption. However, empirical evidence on the corruption and regulation nexus is limited. Further, the corruption indices used are based on experts’ opinions, which may suffer from perception bias. The present paper attempts to address these shortcomings using firm-level survey data for 131 mostly developing countries on the experiences of the firms with bribery and regulatory burden. Exploiting within-country and industry-level variation in regulatory burden, the analysis finds a large, positive effect of regulatory burden on corruption. For the baseline results, the bribery rate is higher by about 0.03 percentage point for each percentage point increase in the regulatory burden. The finding is robust to several endogeneity checks.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mamin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1 Introduction**

The recent backlash against globalization and resurgence of protectionist tendencies have renewed interest in the distributional impacts of trade protection. To inform trade and social protection policy reform, identifying who gains and who loses from trade and quantifying by how much is of crucial policy interest.

Trade reform impacts households as consumers, producers, wage earners, and, possibly, taxpayers. As a consequence, how a particular household is impacted by a trade reform depends on its income and consumption portfolios. This implies that trade reforms typically have very heterogeneous effects on household well-being. These heterogeneous impacts operating at the household level are difficult to measure with readily available data.

This paper presents a new cross-country _household survey_ data set, the Household Impacts of Tariffs (henceforth HIT) data set, that enables researchers to investigate how tariff changes impact the real incomes of households across the income distribution. The data set covers 54 developing countries, and was constructed by harmonizing representative household surveys with import tariff data from UNCTAD. The sample comprises all low-income countries for which relevant nationally representative household survey data—i.e., data with information on both households incomes and consumption spending is available—and a number of middle-income countries. In addition, we use the HIT database to assess trade policy and establish stylized facts about agricultural trade protection using a simple agricultural household model and a first-order effects approach as in Deaton (1989). In mostly agrarian economies, as the ones covered in our data set, agriculture is a major source of gains and losses from trade, especially for the poor.

We find substantial gains from own agricultural tariff liberalization, amounting to 2.50 percentage points of real household income per capita across our sample of 54 countries. Because of differences in consumption and income portfolios as well as in initial tariffs, there is huge heterogeneity in the gains from trade both across countries and across households within countries. For example, the average standard deviation of the gains from trade across countries is 1.01 percentage points
more hours)|0.02|0.03|0.50|0.01|0.01|0.88|
|<br>Total duration of school day (hours)|4.31|4.34|0.53|4.30|4.30|0.83|
|Number of observations|611|385||546.00|319.00||

_Sources:_ MEC/FNDE, INEP, _Prova Brasil,_ and School Census.

_Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2008 and remained until 2011 (columns (1) and (4)) and for the control group selected by propensity score matching (columns (2) and (5)). Columns (3) and (6) report the _p_ value of the _t_ test performed to check mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. In control group, the means are weighted using propensity score matching weights. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample. Our sample include only schools that have information for all variables over all the analyzed period.This table compares treated to control schools in 2007, after propensity score matching.
mechanisms, as these may provide effective substitutes for electoral control. To do so, the paper uses the Index of Executive Oversight, an interval indicator (ranging from 0-9) constructed by Hamilton and Stapenhurst (2009) using data on the following factors to determine to what extent the non-elected elements of a polity are under the supervision of elected officials/voters at large: (1) the existence and powers of a parliamentary public finance committee (0-3); (2) the ability of elected officials to summon and scrutinize the actions of un-elected officials (0-2); (3) the ability of elected officials to censure and dismiss members of the executive (0-2); (4) the existence of an Ombudsperson (0-1); and (5) the existence of an Access to Information Law (0-1). In addition to this measure, it is also possible to use the World Bank’s indicator for Government Effectiveness as a potential measure of the existence of oversight mechanisms faced by NEDDs. While, this measure is usually used as a measure of rent extraction (e.g. Persson and Tabellini, 2003), the index is in fact supposed to be measuring the extent to which non-elected policy-makers are independent of political control (World Bank, 2010). Therefore, while caution needs to be exercised in interpreting the results, it is possible to use this measure as a robustness check, to see whether the existence of an efficient non-electoral mechanism of oversight, associated with an independent non-elected public sector, is in fact associated with less perceived rent extraction.

As Table 1.04 shows, controlling for the existence of non-electoral oversight mechanisms does not alter the basic results, which remain the same: the EDD/NEDD ratio exhibits a highly statistically significant and negative effect on rent extraction, especially in high-income democracies. Consistent with the large literature on the importance of oversight, both the Index of Oversight and the measure of Government Effectiveness are also associated with reduced rent extraction. These findings suggest that while non-electoral
Thus, it appears that a greater emphasis on supply-side policies may be more effective in stimulating more radical frontier innovation when local capabilities are in place, a greater emphasis on demand-side policies may be more effective in spurring firms to introduce incremental environmental innovations (both frontier and catch-up). The available evidence does not contradict a judicious combination of supply-side and demand-side policies, appropriate to the local context.

**Figure 10. Benefits of environmental innovations, 2006-08**

( _As a percentage of innovative firms)_ <!-- Start of picture text --> Reduced energy use per unit of output (producer benefits)<br>%<br>60<br>Reduced energy use by end-user (end-user benefits)<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Source: OECD, based on Eurostat (CIS-2008) and national data sources, June 2011.<br><!-- End of picture text -->

# **3.3 Developing absorptive capacity**

At the level of the firm, the ability to ̳learn‘ is seen as essential for innovation. Firm learning or ̳absorptive capacity‘ refers to a firm‘s ability to understand, adapt and use technologies. According to Cohen and Levinthal (1989), a firm‘s learning capacity is enhanced by its R&D activities, so that R&D has important roles in both frontier and catch-up innovation.

Griffith, Redding, and Van Reenen (2004) find empirical support in a panel of industries across 12 OECD countries for these ―two faces of R&D‖, this notion that R&D, in addition to stimulating the generation of new frontier technologies, also improves the firm‘s ability to globally search for and adapt existing technologies, and allows firms to more easily