rafmacalaba/datause-encoder-data huggingface dataset:
||NLD|Netherlands|13,114|76|36|
|---|---|---|---|---|---|
||NOR|Norway|11,349|79|36|
||POL|Poland|10,385|74|35|
||PRT|Portugal|7,756|77|35|
||ROU|Romania|9,373|79|23|
||SRB|Serbia|864|19|24|
||SVK|Slovak Republic|8,972|79|26|
||SVN|Slovenia|11,910|76|30|
||SWE|Sweden|7,459|79|36|
||XKX|Kosovo|359|38|26|
|US BEA|USA|United States of America|2,970|15|33|
|Total|||255,875||945|

# **3.2 Data on IPA Characteristics (WB-WAIPA)**

To study the specific characteristics of IPAs, we rely on a new survey conducted in 2019 by the World Bank and the World Association of Investment Promotion Agencies (WAIPA) that included 91 IPAs – 87 national and 4 sub-national IPAs. This information is then combined with the new FDI database to explore the effect of IPA sectoral targeting on inward FDI. This leads to a study sample of 36 national IPAs from middleand high-income countries around the world (Table 3). Of these, 26 IPAs have sectoral targeting (group 1, the core sample), while 10 do not target any priority sectors (group 2, used for control only). We align the two data sets by adapting the potential priority sectors of the WB-WAIPA survey to 28 NACE sectors, corresponding to either NACE1 (e.g. for agriculture, mining and services) or NACE2 level (for manufacturing sectors).

**Table 3: Study sample from WB-WAIPA Survey**

|**Study**|**sample**|**Excluded from**|**the study**|
|---|---|---|---|
|**Group 1: Sectoral FDI data**<br>**available, with sectoral targeting**<br>**(CORE SAMPLE)**|**Group 2: Sectoral FDI data**<br>**available, no sectoral targeting**<br>**(CONTROL ONLY)**|**Group 3: No sectoral**|**FDI data available**|
|Austria<br>Brazil<br>Bulgaria<br>Chile<br>Cyprus<br>Czech Republic<br>Denmark<br>Egypt, Arab Rep.<br>Estonia<br>Greece<br>Hong Kong SAR, China<br>Hungary<br>India<br>Ireland<br>Italy<br>Poland<br>Romania<br>Korea, Rep.<br>Slovak Republic<br>Slovenia<br>South Africa|Canada<br>Finland<br>France<br>Germany<br>Iceland<br>Nigeria<br>Portugal<br>Russian Federation<br>United States<br>Australia*|Albania<br>Andorra<br>Angola<br>Antigua and Barbuda<br>Azerbaijan<br>Bahrain<br>Bangladesh<br>Belarus<br>Bosnia<br>and<br>Herzegovina<br>Botswana<br>Cambodia<br>Cameroon<br>China<br>Comoros<br>Congo, Rep.<br>Cook Islands<br>Costa Rica<br>Curaçao<br>Djibouti<br>Dubai- UAE<br>El Salvador|Haiti<br>Iraq<br>Jamaica<br>Jordan<br>Kazakhstan<br>Kuwait<br>Lebanon<br>Lesotho<br>Madagascar<br>Mali<br>Mauritius<br>Mongolia<br>Montenegro<br>Mozambique<br>Namibia<br>Nicaragua<br>North Macedonia<br>Pakistan<br>West Bank and<br>Gaza<br>Rwanda<br>Scotland, UK|
|Spain||Eswatini|Serbia|

10
|**Abbreviatio**|**ns and Acronyms**|
|---|---|
|CAGR|Compound annual growth rate|
|CIT|Corporate income taxes|
|CONT|Contributing|
|ISIC|International Standard Industrial Classification|
|OPEX|Operational expenditures|
|PAYE|Pay as you earn taxes|
|PIT|Personal income taxes|
|RDB|Rwanda Development Board|
|RRA|Rwanda Revenue Authority|
|Rwf|Rwandan francs|
|TIN|Taxpayer identification number|
|VAT|Value‐added tax|
|YoY|Year‐on‐Year|

3
(2010), and Round 4 (2012). Figure I presents an illustration of the study design, and a more detailed description of the experiment can be found in (Baird, McIntosh and Özler 2011).<sup>11</sup> Girls receiving UCTs simply had to show up at a local distribution point each month to pick up their transfers. Monthly school attendance for all girls in the CCT arm was checked and payment for the following month was withheld for any student whose attendance was below 80% of the number of days school was in session for the previous month. However, participants were never removed from the program for failing to meet the monthly 80% attendance rate, meaning that if they subsequently had satisfactory attendance, their payments would resume. Other design aspects of the program were kept identical so as to be able to isolate the marginal effect of imposing a schooling conditionality on outcomes of interest among _baseline schoolgirls_ .<sup>12</sup>

# **2.3 Data Sources and Outcomes**

The focus of this paper is data collected in Round 4, which took place in 2012, more than two years after the end of the intervention. However, to provide context to these results, we also present impacts on the same outcomes, when available, for data collected during Rounds 2 and 3. Focusing on the core respondent, the data sources include household surveys (all rounds), biomarker data collection on HIV (Round 2-4) and Anemia (Round 4), and competencies (Round 4). In Round 4, data collection also included anthropometric data (children under 60 months of

> program. Towards the end of the first year, upon successfully obtaining additional funding, we circulated new offer letters informing the beneficiaries that the program would be continued for one more year, but not more. This message was repeated regularly at the cash distribution points by the program staff during the second and final year of the intervention.

> 11 The size of the transfers, the identity of the recipients, or the intensity of treatment within the cluster did not prove to be influential on the primary outcomes of interest. Because these were randomized across the control, CCT, and UCT arms, estimates of average treatment effects remain highly robust to these controls.

> 12 For households with girls eligible to attend secondary schools at baseline, the total transfer amount was adjusted upwards by an amount equal to the average annual secondary school fees in the conditional treatment arm. This additional amount ensured that the average transfer amounts offered in the CCT and UCT arms were identical and the only difference between the two groups was the “conditionality” of the transfers on school attendance.

11
<!-- Start of picture text --> Benchmark Counterfactural Benchmark Counterfactural<br>3 10<br>2.5 8<br>2 6<br>1.5 4<br>1 2<br>0.5 0<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>(a) Entry Costs (b) Average Ability<br>Benchmark Counterfactural Benchmark Counterfactural<br>500 5<br>400 4<br>300 3<br>200 2<br>100 1<br>0 0<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>(c) Total number of attacks (d) Average ransom<br>Benchmark Counterfactural Benchmark Counterfactural<br>0.3 4.5<br>4<br>0.25<br>3.5<br>3<br>0.2<br>2.5<br>2<br>0.15<br>1.5<br>0.1 1<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>(e) Success rate (f) Average size<br>Benchmark Counterfactural Benchmark Counterfactural<br>4.5 0.7<br>4<br>0.6<br>3.5<br>0.5<br>3<br>2.5 0.4<br>2<br>0.3<br>1.5<br>0.2<br>1<br>0.5 0.1<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>(g) Average conditional size (h) Average duration of captivity<br>Entry Costs<br>Average Ability<br>Number of Attacks Average Ransom<br>Success Rate Average Size<br>Average Conditional Size Avg. Duration of Captivity<br><!-- End of picture text --> Figure 9: Counterfactual: No private maritime security Note: The figures plot the entry costs, average pirate ability, total number of attacks, average ransoms, success rates, average size attacked, average size hijacked, and relative delay in the counterfactual analysis 32 where we shut down private maritime security (dash line) and benchmark model (solid line). All other parameters in the counterfactual simulations are kept the same as in the benchmark model.
Policy Research Working Paper 6518

# **Abstract**

The authors create a standards restrictiveness index using newly available data on maximum residue levels of pesticides for 61 importing countries. The paper analyzes the impact that food safety standards have on international trade of agricultural products. The findings suggest that more restrictive standards are associated, on average, with a lower probability of observing trade. However, after controlling for sample selection and the proportion of exporting firms in a gravity model, the analysis finds that the effect of standards on trade intensity is indistinguishable from zero. This is consistent with the assumption that meeting stringent standards increases primarily the fixed costs of exporting. Once firms enter the market, however, standards do not impact the level of exports. The analysis also finds a greater marginal effect of BRICS (Brazil, Russia, India, China, and South Africa) standards on the probability of trade, relative to other countries’ standards, keeping in mind however that on average BRICS standards are less restrictive. The analysis also suggests that exporters in low-income countries are more adversely affected by stricter standards.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at eferro@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
22

# **FIGURES**

Figure 1: The World Is Urbanizing <!-- Start of picture text --> 70<br>world less developed regions 59.9<br>60<br>55.8<br>52.1<br>50 46.5<br>40 36.6<br>29.4<br>30<br>25.3<br>20 17.6<br>10<br>0<br>1950 1970 2011 2030<br>urban population (%)<br><!-- End of picture text --> Source: United Nations, 2011
-8-

now evolved into a permanent World Bank development effort. The original Living Standards Unit of the Development Research Department has become the Welfare and Human Resources Division of the Population and Human Resources Department, which in turn is part of the Policy, Research .nd External Affairs (PRZ) complex of the World Bank. The transition from. a research project to a permanent entity highlights the need for a inore systematic process for starting new LSMS surveys.

Future LSMS work will focus on: (1) refinements and diversification of the LSMS survey system, and (2) expanded resea-ch activities. Regarding the former, new questionnaires are being developed based on LSMS experience in several countries in the late 1980's. Attention will be given to adding flexibility, so as to allow for adaption to the special characteristics of individual countries, and to refinements in computer software and hardware systems, to allow for easier and faster implementation of surveys. In terms of the latter, emphasis is being given to developing re5carch capability in the countries in which LSMS su-veys are done and in undertaking new types of research at the World Bank in Washington.
39

# Table 10. An internaticinal comparison of the income transfers from Egyptian food subsidies with those of food subsidy programs in other countries

|||Percent o|f IicomeTran|sfers Going to|Quintile Grou|p|
|---|---|---|---|---|---|---|
|Country|Program|Poor<br>1|2|3|4|Rich<br>5|
|**(a) Self-tarpete**|**d proarams**||||||
|Egypt (1997)|SELF-SELECTION:<br>Baladi bread||||||
|Urban|and wheat flour, edi"ble oil, and sugar|20|21|22|19|18|
|Egypt (1997)|SELF-SELECTION:<br>Baladi bread||||||
|Rural|and wheatflour, edi,ble oil, and sugar|19|18|19|22|22|
|Tunisia (1993)|SELF-SELECTION:<br>Food price<br>subsidies, food quality differentiation|21|20|21|20|18|
|Morocco (1991)|SELF-SELECTION:<br>Food price||||||
||subsidies, high extraction rate flour|23|24|22|18|13|
|**(b) Administrat**|**ively**<br>**targeted proarams**||||||
|Jamaica|Food starnps at health clinic|44|31|18|5|2|
|Chile|Food supplement a-t health clinic|41|28|18|10|3|
|Peru|Food supplement by neighborhood|42|30|20|6|3|

Source: Egypt figures are calculated from Table 5 and 8; all other data from Laura ruck and Kathy Lindert, From Universal Food Subsidies to a Self-Taraeted Program: A Case Study of Tunisian Reform, Wcrld Bank Discussion Paper 351 (Washington, DC: World Bank, 1996), Table 20.
Figure 2: Running sum of signs from rnormal() with different seeds <!-- Start of picture text --> Running sum of first ten thousand signs from rnormal()<br>(normalized by sign of the first draw)<br>0 2000 4000 6000 8000 10000<br>Draw number<br>Seed 1 Seed 1001 Seed 1000001 Seed 1000000001<br>150<br>100<br>50<br>Running sum<br>0<br>−50<br><!-- End of picture text --> Note: this problem was resolved in the March, 2011 Stata update.

12
-5-

# 1982).

The second area of work was at the more theoretical level. To judge whether people are poor requires conceptual definitions of poverty. To reflect the multi-dimensional nature of poverty, special studies were undertaken on different aspects of living standards. Among the papers written were a study on the economic theory of welfare measurement (Deaton, 1980), studies on employment and living standards (Mehran, 1980; Grootaect, 1982, Acharya, 1982), reports on the collection of nutrition and health data in household surveys (Martorell, 1982; Ho, 1982; Sullivan, Cochrane and Kalsbeek, 1982), a similar study on the collection of education data (Birdsall, 1982), and several other papers looking at topics ranging from collection of price data to the usefulness of collecting panel data.

The end product of five years of work was the development of a household survey methodology which was first implemented in Cate d'Ivoire in 1985. The questionnaires used are explained in Grootaert (1986) and the implementation of the survey in Cate d'Ivoire is explained in Ain- rth and Munoz (1986).

# C. 2resent LbM' Survey Activities

As seen in Table 1, six LSMS surveys have been put in the field, and there are plans to begin three more in the next few months. The Cate d'Ivoire Living Standards Survey (Enquete Permanente Aupres Des Meages) began in February, 1985, and by 1989 4 years of data had been collected. The Peru Living Standards Survey (Encuesta Nacional de Hogares Sobre Medici6 de Niveles de Vida) went into the field in July, 1985 and collected one year of data. Future surveys in Peru are now under consideration. Field work for the Ghana Living Standards Survey began in September, 1987, and so far 2 years of
11 _Number of prior actions by sector as a fraction of all prior actions; and dummy variables by sector affiliation of the task team leader._ The former set is needed because different sectors may be characterized by differing levels of risk or difficulty in achieving development results. The latter tries to gauge if either more experience doing policy-based loans or better quality control of certain sectoral departments within the Bank have an impact.

# **_3.4 Control variables_**

_The log of per capita GDP._ This would likely proxy for several country development conditions, such as the level of skill and education of government employees, strength of institutions to implement reforms, and the predictability of the political process, among others.

_The macroeconomic cluster and/or the governance component of the Country Policy and Institutional Assessment (CPIA)_ . The justification for the former is that weaker macroeconomic policies and institutions jeopardize macroeconomic outcomes, which in turn may have a serious impact on many development outcomes. The justification for the latter is that poor governance – corruption, patronage, weak fiduciary systems – may hamper development outcomes even if the reforms, on paper, are undertaken.

_Force majeure._ We codified this dummy variable to indicate if major natural disasters or coup d’états occurred between the approval of the operation and the evaluation of results. These shocks are not under the control of the government that negotiated the loan or the lending organization. Yet they may have a significant impact on short to medium term development outcomes/results that could be expected as a consequence of certain reforms.

_IBRD_ .<sup>9</sup> The higher level of development of IBRD member countries may make for more successful loans. This may happen because governments may be more stable or have increased capacity to implement reforms, which has produced their higher incomes in the first place. On the other hand, IBRD loans are likely to have more ambitious reforms and more ambitious result

> 9 The World Bank’s lending falls into two parts: the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA).  The former lends to countries with per-capita income above a cut-off level at terms slightly more attractive than commercial. The latter lends to countries below that cut-off level at highly concessional terms, including through grants.
The use of panel data may have many benefits but their use also raises a number of potential econometric problems including:

# 1) _Coefficient heterogeneity across countries_ .

We have strong prior views that countries will differ consistently in their intercepts according to persisting, largely time invariant local factors. For this reason our maintained hypothesis is that a fixed effects model is more appropriate than a random effects approach. In addition, the fixed effects static model avoids the potential biases which could arise in the random effects model owing to correlation between the included exogenous variables and omitted country attributes.

# 2) _Dynamic structure_

Static models, which assume that all adjustment to disequilibrium occur within the period defined by observation frequency may be inappropriate. In particular, investment in electricity is not usually completed in a year so we would expect that scope for some adjustment process would need to be incorporated into our model. Such processes can be modelled generally by a combination of lags on the dependent variable (autoregressive) and on the explanatory variables (moving average).

However, the presence of a lagged dependent variable in a fixed-effects model can result in a biased estimates for the lagged dependent variable coefficient. The size of the bias will depend on the number of time series, _N,_ the length of the time series, _T,_ and the influence of other exogenous variables in the determination of the dependent variable.<sup>19</sup> The problem is mainly significant in short panels. For T=21 we have estimated the asymptotic bias (as N increases) to be of the order of 3%. This is an upper limit given the presence of other major influences on the dependent variable.

There is also the potential problem of spurious correlation eg if both electricity capacity and GDP per capita were both strongly trended across our countries. In fact, they are not in our data set, but we have considered carefully how the dynamics should be modelled and we report a selection of the key results.

# 3) _Endogeneity and Causality_

There has been much discussion of the need to take account of the endogeneity of regulatory agencies. This has been a major theme in the ICB literature where the introduction of an ICB (particularly the _early_ introduction) may be interpreted as a signal of strong commitment to anti-inflation policies. Similarly, the early introduction of an autonomous regulator may also be a signal of a strong commitment to commercialisation and the enforcement of property rights<sup>20</sup> .

> 19 See Hsiao (1986)

> 20 See Gual and Trillas (2002)

17
_State Dispute Settlement Cases_ .<sup>5</sup> By definition, all 394 cases from 1987-2010 experienced contractual disputes. The data set also has information on investing country, investor’s originating country, and sectoral affiliation, as well as venues for arbitration. It reports the year when disputes were filed, but does not list data on when original contracts were signed. The contract year was manually identified by looking through each arbitration case document, which is used to define the contract age.

The PPI and UNCTAD data sets form the baseline sample at project level for my empirical analysis. Appendix 1 provides lists of the host countries that are included in my sample. I later provide country-year regressions as well. For this purpose, the project-level data are also merged with cross-country panel data that contains various macroeconomic variables and country-level political and institutional indicators from various sources.

# **3. Background**

# _3.1.  Recent Trends in Breach of Contract_

Figure 1 depicts the historical trend of BOC risk (number of contract breaches divided by total surviving contracts).<sup>6</sup> Due to a boom in private participation in greenfield power generation projects in the mid- to end-1990s (as a result of increased demand in PPP financing), the total number of surviving contracts has increased over time (Woodhouse, 2006). At the same time, the risk of BOC has also risen since the late 1990s and peaked in 2000-05. The risk keeps rising in two regions (i.e., ECA and LAC).

This trend shows a clear contrast with the historical trend for outright expropriations and nationalizations, which peaked in the 1970s and 1980s (World Bank, 2012). This raises the question of how the nature of BOC risk differs from outright expropriation risk.

# _3.2.  Countries and Sectors Prone to BOC Risk_

Given an increase in BOC risk, the next question is how it is regionally clustered. Figure 1 provides a crossregional picture of breach of contract events; disputes tend to be clustered and are recently increasing in the ECA and LAC regions. The Appendix map shows the distribution of total disputes that occurred in my sample from 1985 to 2012, confirming the descriptive facts in Figure 1. Political economy theories suggest

> 5 The data set is publicly available at http://iiadbcases.unctad.org/.

> 6 It can be defined as pure breach of contract (BOC) vs. contract renegotiation (CR) depending on the way the disputes will be resolved.  Disputes are categorized as BOC for any repudiation or breach by the host government of a contract agreement with investors, including non-honoring of arbitral awards, which is sought to be resolved formally through local or international arbitrations.  On the other hand, disputes are defined as CR for any repudiation or breach by the host government of a contractual agreement with investors, which is sought to be resolved through informal renegotiation on the contractual terms or obligations between the parties.

5