rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Share of firms with ‘diverse’ connections by country, entity and number of connections

||Connections with:|Eac|h frm|’s nu|mber o|f connec|tions:|
|---|---|---|---|---|---|---|---|
|||1|2|3|4|_>_=5|tot|
|Russian Federation|Corporate|0|74|93|98|88|77|
||Individual|39|50|70|73|95|70|
||Pol. Individual|23|10|5|7|21|13|
||Pol. Party|0|0|0|0|0|0|
|Spain|Corporate|0|39|33|33|50|34|
||Individual|39|74|89|67|97|77|
||Pol. Individual|9|22|44|33|43|31|
||Pol. Party|0|0|0|33|0|1|
|Slovak Republic|Corporate|0|8|17|20|42|22|
||Individual|70|92|83|100|100|90|
||Pol. Individual|22|24|33|20|28|25|
||Pol. Party|0|0|0|0|0|0|
|Serbia|Corporate|0|42|40|71|28|26|
||Individual|69|100|100|100|100|93|
||Pol. Individual|25|0|10|0|24|20|
||Pol. Party|0|0|0|0|0|0|
|Bulgaria|Corporate|0|74|40|67|76|59|
||Individual|33|63|80|100|76|68|
||Pol. Individual|50|42|0|0|52|41|
||Pol. Party|0|0|0|0|0|0|
|Romania|Corporate|0|21|39|47|40|24|
||Individual|23|100|96|87|98|72|
||Pol. Individual|66|45|48|47|47|53|
||Pol. Party|0|0|0|0|0|0|
|Hungary|Corporate|0|38|81|61|69|56|
||Individual|65|98|100|100|100|95|
||Pol. Individual|21|11|12|4|34|23|
||Pol. Party|0|0|0|0|0|0|

Source: _PEPData_ with Firm name (FN) listing 30
# **Table A4: Impacts of mental health on labor outcomes – Non-standardized mental health score**

|Dependent variable:|Employ<br>|ment status<br>|Weekly labor<br>A<br>|income ($1,000<br>UD)<br>|
|---|---|---|---|---|
||Logit-FE|IV-Logit-FE|FE|IV-FE|
||(1)|(2)|(3)|(4)|
|Mental health scores|-0.008***|-0.011***|-0.0008***|-0.032***|
||(0.002)|(0.001)|(0.0002)|(0.009)|
|_First stage of 2SLS (depende_|_nt variable is non_|_-standardized mental_|_health scores)_||
|Trauma experience*Year<br>after arrival||-0.229***<br>(0.053)||-0.347***<br>(0.096)|
|Kleibergen-Paap F stat.||17.692||19.635|
|AR 95-CIs||[-0.023, -0.007]||[-0.067, -0.021]|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|3,687|3,687|
|Number of individuals|1,609|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results in Columns (1) and (2) are presented as marginal effects. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

36
Policy Research Working Paper 8539

# **Abstract**

Misreporting is a well-known challenge for researchers in social sciences and even more so for policymakers who rely on accurate data to design effective relief strategies. This issue is especially prevalent if incentives for misreporting exist, for example, if there are perceived social or material implications to the answer reported. Due to the vulnerabilities that internally displaced people face and their dependency on aid support, this challenge is particularly acute when trying to measure economic welfare. In contexts of emergency, the need for accurate information is acute, but many approaches to improve data are intrusive or impractical. To address this challenge, this paper proposes a light touch method to improve the accuracy of consumption data: the inclusion of checks and primes to emphasize the importance of accurate reporting. The study assesses the effectiveness of this method, by randomly distributing the bundle of primes across a survey of internally displaced persons in South Sudan. In line with the main hypothesis, positive and significant effects arise for low consumption quantiles, especially consumption quantities that are more susceptible to manipulation. The findings suggest that light touch approaches to improving survey design can act as a cost-effective tool to induce more accurate reporting. Further research is needed to validate the measure against a gold standard approach and to understand what mechanisms are at play.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at upape@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
<!-- Start of picture text --> ∑ ai(t)<br>Eabat<br>B<br>E0<br>t time<br>/yrGtCO2<br>Emissions<br><!-- End of picture text --> Figure 1: An illustration of the climate constraint. The cumulative emissions above _E_ 0 should be lower than an intertemporal carbon budget _B_ (this requires that the long-run emissions tend to _E_ 0 ). Dangerous emissions _Eabat_ are measured from _E_ 0 .

absence of abatement (Fig. 1). We assume that all dangerous emissions are abatable, and, without loss of generality, that doing so requires to use all the abating potential in every sector. Denoting _Eabat_ the emissions above _E_ 0 , this reads: Using another representation of climate policy objectives would not aect the qualitative results of this analysis.

The social planner determines how and when to abate in order to minimize abatement costs discounted at a given rate _r_ , under the constraints set by the sectoral potentials and the carbon budget: Under these assumptions, the classical result holds: the optimal strategy is to implement abatement options such that marginal abatement costs are equal in all sectors and equal to the current carbon price. With the objective to maintain cumulated emissions below the carbon budget, the (present value of the) social cost of carbon is constant, we denote it _μ_ . As its current value _μe_<sup>_rt_</sup> increases at the discounting rate _r_ , abatement eorts are increasing over time, and each sector _i_ is progressively decarbonized until it reaches the sectoral potential at a date denoted _Ti_ . After this date, the sectoral marginal abatement costs remain constant: These results (proved in annex A) are a straightforward application of the equimarginal principle: unless the sectoral potentials are binding, optimal marginal eorts are equal, in all sectors, to a unique carbon price.

# 3 Introducing economic inertia leads to dierent marginal costs across sectors

The classical framework assumes that the amount of abatement is decided (and paid) at each period independently. This is a simplifying assumption. For instance, replacing coal-red plants with gasred plants actually reduces annual emissions over a long period of time, while abatement costs 4
**Croatia’s turnover rate has been increasing due to the rising entry rate but is still below the regional average.** Firm turnover has increased from 3 percent in 2000 to 5.7 percent in 2007. The increase is accounted solely by a steady rise in entry rates from 2 percent to 5.5 percent over the same period, as the exit rate decreased from the high point of 1.5 percent in 2001 to 0.2 percent in 2007 (fig. 1). The dynamic of Croatia’s turnover rate resembles closely that of Greece, but differs greatly from the regional one. Romania, Slovenia, Bulgaria and Turkey are all characterized by an above 10 percent turnover rate between 2000 and 2007 (fig.2). In 2007, Croatia’s turnover rate was 5.7 percent, whereas those of Slovenia, Bulgaria, Romania and Turkey were 13, 15.3, 16 and 18.3 percent respectively (fig.3).

**Figure 16: Firm Turnover in Croatia**<sup>**7**</sup>

<!-- Start of picture text --> 7%<br>6%<br>5%<br>4% Entry Rate<br>Exit Rate<br>3%<br>Turnover rate<br>2%<br>1%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source: World Bank, Entrepreneurship Survey 2008_

**Figure 17: Firm Turnover**

<!-- Start of picture text --> 35%<br>30%<br>25% Croatia<br>Turkey<br>20%<br>Bulgaria<br>Greece<br>15%<br>Slovenia<br>10% Romania<br>5%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source: World Bank, Entrepreneurship Survey 2008_ 7 Firm turnover is the calculated as the sum of entry and exit rates, where the entry rate is the ratio of entering firms to the total population and the exit rate is the ration of exiting firms to the population of origin.

17
Figure 2: Tariffs on digital goods in Africa and rest of the world, 2012-2019 <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>Africa Rest of the world<br>2013 2014 2015 2016 2017 2018 2019<br>Tariffs on digital goods (%)<br><!-- End of picture text --> Notes: Tariff is a simple average by country and product. Authors’ calculations based on TRAINS.

19
19 Columns 3 and 4 of table 3 show the same expenditure regressions at the household level. Using the household level data we get very similar results on social capital, a coefficient of .56 or .34 depending on the instrument set compared to .49 using the cluster level. Not surprisingly, the estimates on the control variables, such as education, are more reasonable using the household data.

The standard test for the exogeneity of the instrument set (which is essentially a test that trust does not itself cause higher incomes except through its effect on social capital) does not reject the instruments in the cluster data, but the test does reject the instrument set in the household data. The variable 'trust in strangers' causes the instrument set rejection, which is puzzling as it is the most plausibly exogenous a _priori_ as we thought trust in strangers would be the least likely to be affected by income or associational activity. Estimation using the household data without "trust in strangers' as an instrument provides a quantitatively similar (.34) but less precisely estimated (t-stat 1.27) coefficient on social capital and the exogeneity test is not rejected.

_Is "social capital"social?_ The second question is whether participation in associational life raises incomes only of those who directly participate or whether social capital produces spillovers to other individuals. We can address this question in two compelling ways.

cluster level average social capital will contain a substantial component of measurement error in measuring the true cluster social capital. If this were a univariate regression, the ratio of the OLS to IV estimates is an estimate of the ratio of the true signal to the total variance. The estimates suggest the noise is very large. The correlation in repeated measurements is also a measure of the noise to signal ratio. While we do not have repeated measurements in the same villages for social capital, the correlation of the two village level estimates of expenditures per person is .45, so if the magnitude of measurement error between the two variables is similar these are consistent.
interesting, those issues are beyond the scope of this review. See Baldwin and Venables (1995) for an early authoritative survey that takes into account this and some other issues that we do not consider here, like the effects of RTAs on growth.

This paper is organized as follows. The next section discusses trade creation and trade diversion, and how these affect the formation of an agreement, assuming external trade policy is given. Section 3 reconsiders these issues taking into account that external tariffs are endogenous. Section 4 examines linkages between regionalism and the multilateral trade system. Section 5 discusses design issues that shape the impact of trading blocs: rules of origin and the depth of integration. Section 6 concludes and discusses future research.

# **2. Trade Creation and Trade Diversion**

A key question raised by the formation of a regional trade agreement is whether it will make member countries better off. In seminal work, Viner (1950) shows that an RTA does not necessarily improve members’ welfare. The preferential removal of tariffs may lead to trade diversion, where imports shift away from the most efficient supplier to the country receiving preferential treatment. This generates an inefficiency in world production, which is harmful to bloc non-members. It can also hurt members, if the change in consumer prices, and therefore in consumer surplus, is too small to outweigh the costs from the inefficiency. In contrast, if the RTA leads to greater imports from the efficient suppliers within the bloc, consumer gains outweigh the costs from production inefficiency and the agreement necessarily improves members’ welfare. Such welfare analyses in the context of regional trade agreements highlight 5
Emran, M. Shahe, A. Islam, and F. Shilpi (2013), “Admission is Free only if Your Dad is Rich! Distributional E¤ects of Corruption in Schools in Developing Countries”, SSRN working paper. Emran, M. Shahe and Z. Hou (2013), “Access to Markets and Rural Poverty: Evidence from Household Consumption in China,”Review of Economics and Statistics, May, 2013.

Ferraz, C. and Finan, F. (2008) “Exposing Corrupt Politicians: The E¤ects of Brazil’s Publicly Released Audits on Electoral Outcomes." The Quarterly Journal of Economics, 123(2): 703–745. Ferraz, C., Finan, F. and Moreira, D.B. (2012).“Corrupting Learning: Evidence from Missing Federal Education Funds in Brazil”, Journal of Public Economics 96 (9-10): 712-726.

Fisman, R. and Svensson, J. (2007). “Are corruption and taxation really harmful to growth? Firm level evidence." Journal of Development Economics, 83(1): 63-75.

Hertz, T, Jayasundera, T., Piraino, P., Selcuk, S., Smith, N. and Verashchagina, A. (2007), “The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends”, The B.E. Journal of Economic Analysis and Policy. Volume 7, Issue 2, Pages 1935-1682, Hsieh ,C.R and Moretti, E. (2006).“Did Iraq Cheat the United Nations? Underpricing, Bribes, and the Oil for Food Program.’The Quarterly Journal of Economics, 121(4):1211-1248.

Hunt, J. and S. Laszlo (2012): “Is Bribery Really Regressive? Bribery’s Costs, Bene...ts and Mechanisms", World Development, 40 (2), 355-372.

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Hunt, J. (2008): “The Contrasting Distributional E¤ects of Police and Judicial Bribery”. working paper, McGill University, May 2008.

Hunt, J. (2007).“How corruption hits people when they are down," Journal of Development Economics, vol. 84(2), pages 574-589.

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Kaufmann, D., S. Pradhan and R. Ryterman (1998), New Frontiers in Diagnosing and Combating Corruption". World Bank PREM Notes: Public Sector, October (1998).

Kaufmann, D., Judit M. and F. Recanatini (2005). “How Does Bribery A¤ect Public Service Delivery? Micro-evidence from Service Users and Public O¢ cials in Peru”. World Bank working 38
# _<u>Research agenda</u>_

The upcoming research agenda should comprise the following elements: (i) an update on the depth of the gender gaps; (ii) documentation of constraints for which, as shown in this note, the evidence is mixed or limited; (iii) test through impact evaluations possible innovations that can overcome gender specific underlying constraints; and (iv) answer a group of broader research questions requiring a combination of multiple studies on household and non-household related issues.

(i) Update on depth of gender gaps in enterprise development in Africa The first element of the research agenda going forward should comprise an update of the extent of the gender gaps in performance in Africa. The last comprehensive reports on gender gaps in enterprise development in Africa used data from 2010 or earlier, but there is now a growing number of more recent datasets available due to: (a) the increased number of household and MSME surveys conducted in Africa as countries' interest in and capacity to run such surveys has increased, (b) the implementation of various impact evaluations generating panel data on control groups, and (c) the push for open data. The analysis of gender gaps can include:

- Documenting the gender gaps not only in terms of business outcomes (revenues and profits), but also in terms of returns to factors of production (capital and labor).

- Estimating the share of these gaps that can be explained by firm and entrepreneur characteristics.

- Estimating the size of these gaps at different points of the productivity distribution (is the gender gap larger at the 25th or 75th percentile?).

- If there is sufficient overlap in the activities of male and female entrepreneurs, estimating these gaps within specific sectors.

- If there is a share of the gender gap that is not explained by firm and entrepreneur characteristics, assessing what could explain that remaining gap.

This work on documenting gender gaps should also include extensive descriptive analysis of gender differences in business operations. This could include looking at gender differences in firm or business-owner characteristics, business practices, and spatial dimensions of enterprises (using geographic information systems).

(ii) Documenting gender specific constraints A second critical element of the research agenda should comprise documenting through qualitative and observational studies the depth of the constraints where evidence is more limited. As discussed above, there are areas where the evidence even at the constraint level is mostly anecdotal, or sometimes contradictory.

21
Migrant Remittances by 2015.” Indian Institute of Management Calcutta.

Reyes, Fat. 2012. “14 mall-based passport offices to serve public by end of year – DFA **.”** _Philippine Daily Inquirer. 30 July._ Sjaastad, Larry A. 1962. “The Costs and Returns of Human Migration.” _Journal of Political Economy_ , 70(5): 80–93.

Stark, Oded, and David E. Bloom. 1985. “The New Economics of Labor Migration.” _American Economic Review: Papers and Proceedings_ , 75(2): 173–178.

Wilson, William Julius. 1987. _The Truly Disadvantaged_ . Chicago: University of Chicago Press.

World Bank. 2006. Global Economic Prospects 2006: Economic Implications of Remittances Washington D.C. : World Bank.

World Bank Group. 2011. “Global Bilateral Migration Database.” Last accessed 28 August 2013.

Yang, Dean. 2006. “Why Do Migrants Return to Poor Countries? Evidence from Philippine Migrants’ Responses to Exchange Rate Shocks.” _Review of Economics and Statistic_ s, 88(4): 715–735.

25
_Simply focusing on stemming inefficiencies, could greatly accelerate the achievement of infrastructure targets._ Even if it were not possible to close the funding gap any further, simply by capturing the identified inefficiencies Uganda could still meet the postulated infrastructure targets within a 20-year period, even at current levels of spending. Without stemming inefficiencies, however, the existing resource envelope would not suffice to meet power infrastructure targets in the medium term.

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project Web site: <u>www.infrastructureafrica.org. For papers go to the document page</u> (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Country Diagnostic). 2010. “East Africa’s Infrastructure: A Regional Perspective.” Africa Region, World Bank, Washington, DC.

Banerjee, Sudeshna, Quentin Wodon, Amadou Diallo, Taras Pushak, Helal Uddin, Clarence Tsimpo, and Vivien Foster. 2009. “Access, Affordability, and Alternatives: Modern Infrastructure Services in Africa.” AICD Background Paper 2, Africa Region, World Bank, Washington, DC.

Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank. www.infrastructureafrica.org.

- World Bank. 2006. _Beyond Recovery:  Investment and Behavior Change, for Growth. Country Economic Memorandum., Volume 1: Summary and Recommendations_ . Washington, DC: World Bank.

- World Bank and IFC (International Finance Corporation). 2006. _Enterprise Survey. Uganda Country Profile_ . Washington, DC: World Bank.

Adam and Bevan 2003, Garber 2004, Same 2008

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. “Financing Public Infrastructure in SubSaharan Africa: Patterns and Emerging Issues.” AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

51
# **Figure 3 China’s Quarterly Growth Rates by Sector, 2004–09** (percent)

<!-- Start of picture text --> 18<br>GDP primary secondary tertiary<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2004.12004. 2 004.32004.42005.12005. 2 005.32005.42006.12006.22006.32006.42007.12007.22007.32007.42008.12008. 2 008.32008.42009.12009. 2 009.32009.4<br><!-- End of picture text --> Source: NBS (2010b). Note that the circle highlights the period of the global financial crisis.

_Source_ : NBS **(** 2010b **).** Note that the circle highlights the period of the global financial crisis.

21