rafmacalaba/data-use-mentions-extended huggingface dataset:
experts would have a limited knowledge of the range of corrupt dealings ongoing in a country at a given time. So, when ranking countries they might draw on observable information that in their view is indicative of corruption rather than direct experience or knowledge.

With regard to noise, a case from Peru gives reason for doubting the power of general indices to reflect the underlying extent of corruption. In 2000 the government released tape transcripts on the Montesino corruption scandal, which involved the bribery of over 1,600 senior government figures. Following the release of the tapes, the Corruption Perception Index of TI moved fairly dramatically from 4.4 to 3.5 (on a scale of 10 - 1). This seems to suggest that the experts did not really understand corruption in Peru prior to the release of the tapes –before the scandal broke, when the corruption was ongoing, Peru was judged as clean as the Czech Republic.

Comparing firm-level surveys and perceptions of aggregate corruption Knack (2006) shows for Eastern Europe and Central Asia that firm-level data on petty corruption are only weakly correlated with perceptions on aggregate corruption. Both sets of data are not well correlated with firm data on procurement related corruption. Olken (2008) and Donchev and Ujheliyi (2008) also find weak correlation between micro-data on corruption and aggregate perceptions.

Kaufmann, Kraay and Mastruzzi (2006) (hereafter KKM) explore whether biases are reflected in perceptions indicators. First, they find that expert surveys do not seem to exhibit systematic biases compared to surveys based on the observations of samples of individual firms or households. Essentially, they showed that ratings among commercial risk-rating surveys were not more correlated with each other than with firm surveys of corruption. They cast doubt on a study by Razafindrakoto and Roubaud, which shows that for eight African countries, experts significantly misjudged what households would report about corruption. For example, households were asked whether they had been “victims of corruption”. KKM argued that some may have paid bribes, but may not have felt victimized
the deep trade agreement dummy as the trade-creating effect of the agreements is not appropriately accounted for by the mismeasured tariff variable. Taking Specification (3) at face value, non-tariff provisions in TAs do not _per se_ increase bilateral trade. In principle, this can be due to two reasons: either the non-tariff provisions in TAs do not lower trade costs at all, or they lower trade costs in a non-discriminatory (MFN) fashion so that trade costs fall with all trade partners (not just the ones in an FTA). Since we include domestic trade flows in our analysis, so that different treatment of domestic versus international trade flows is possible, the failure to find a significant TA effect would imply that TAs put foreign trade partners at par with domestic ones. In contrast, tariffs—by their very definition discriminatory—have a strong and precisely identifiable effect. One interpretation of this finding is that if policy makers want to use trade preferences for geostrategic purposes, they should focus on tariff reductions. Deep non-tariff provisions tend to diffuse and benefit all trade partners, potentially including those that the agreement does not directly address.

The larger sample used in Specification (4) resuscitates a marginally statistically significant effect of deep TAs, which now have an independent effect of increasing bilateral trade by 15%.

> 10 (1 + 3 _/_ 100)<sup>_−_</sup> (<sup>_−_</sup> 1 _._ 08) _−_ 1 = 0 _._ 0324

14
of the Palestinian Authority: Towards Enhanced Public Finance Management and Improved Fiscal Sustainability. World Bank Report No: ACS18454. [http://hdl.handle.net/10986/25100]. Administrative data of pension contributors from the Public Expenditure Review (PER) for 2014 (the closest year available).

> 37 According to the program rules, the scheme applies to anyone working in the public sector, a local authority, an International Organization (IO), the United Nations Relief and Works Agency (UNRWA), or a non-Governmental Organization (NGO).

26
7<sup>th</sup> repetition rate, FEPBA score in Language, school district –or location). The number of observations for regression for the sub-sample of students with low skills (high skills) in column (1) is 544 (561), in column 2 is 545 (565), and in column (3) is 543 (584). All the regressions exclude the classrooms where the Human Body Unit was not taught. Author’s analysis based on experimental data generated for this study **.** 26
assess, understand, and gauge the actual effectiveness of these vital programs in alleviating poverty and enhancing the livelihoods of disadvantaged individuals and families.

# **Considering the abovementioned constraints and limitations, this paper adopts a distinct approach to**

**address the issue.** Instead of relying solely on survey data, we employ a simulation method to assess the performance of the Monthly Social Allowance (MSA) and heating allowance programs. This simulation draws upon eligibility criteria and administrative data to accurately represent the benefits provided to eligible households. The simulation involves utilizing several household-level variables in the Survey on Income and Living Conditions (SILC)<sup>16</sup> to determine potentially eligible households using the program's parameters specific to 2020<sup>17</sup> . These are used to calculate the DMI coefficient factor. Subsequently, this factor is multiplied by the base amount for 2020/2021, which stood at 75 Bulgarian leva (BGN) before the reform. The result is a more precise estimation of the benefits that these social assistance programs would provide. This approach allows us to access valuable information about DMI for various categories of individuals, ensuring a more nuanced and accurate evaluation of the MSA and heating allowance programs. By mitigating the reliance on survey data and introducing administrative parameters, we enhance our ability to understand and analyze the impact of these programs on the targeted beneficiaries, providing a more comprehensive and reliable assessment of their effectiveness. With this approach, we can identify information about DMI for the following categories of persons:

- people over the age of 75 living alone

- people over the age of 65-75 living alone

- people over 65 years old cohabiting with others

- people under 65 years living alone

- people under 65 years old living with others

- people with reduced working capacity (50% or more)

- people with reduced working capacity (75% or more)

- all children and youth in education

- for children attending school

- for children not attending school

**Since
migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014).

# **_Administrative records and registers_**

Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylumseekers could be used to generate statistics on both flows and stocks of refugees.<sup>79</sup> For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014).

# **_General population registers_**

In a small but growing number of countries, information from the central population register is the main source of migration statistics.<sup>80</sup> While population registers may generate statistics on both internal and international migration (if they record changes of residence, and international arrivals and departures) they do not typically record reasons for movement. However, it may be possible to link data from the central population register to those from immigration or border authorities to identify refugees and asylum76 Using standard ILO definitions, Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators. They are typically conducted monthly in developed countries and quarterly or annually in developing countries.

77 Supported by USAID and implemented by ICF International, the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries.

78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women.

79 Many refugee hosting countries issue a form of identification
better management practices, and this occurs simultaneously with the decentralization of decisions.

Finally, recently a number of other field experiments in developing countries (for example Karlan and Valdivia 2010, Bruhn et al. 2010 and Drexler et al. 2010) have begun to estimate the impact of basic business training and advice in micro- and small enterprises. This research has found significant effects of some forms of training on performance in smaller firms, supporting our results on in larger firms.

# **II. MANAGEMENT IN THE INDIAN TEXTILE INDUSTRY**

## **_II.A. Why work with firms in the Indian textile industry?_**

Despite rapid growth over the past decade, India’s one billion people still have labor productivity that is only 15 percent of U.S. productivity (McKinsey Global Institute, 2001). While average productivity is low, most notable is the large variation in productivity, with a few highly productive firms and a lot of low-productivity firms (Hsieh and Klenow, 2009).

In common with other developing countries for which data is available, Indian firms are also typically poorly managed. Evidence from this is seen in Figure 1, which plots results from the Bloom and Van Reenen (2010) surveys of manufacturing firms in the US and India. The Bloom and Van Reenen (BVR) methodology scores firms from 1 (worst practices) to 5 (best practices) on specific management practices related to monitoring, targets and incentives. Aggregating yields a basic measure of the use of modern management practices that is strongly correlated with a wide range of firm performance measures, like productivity, profitability and growth. The top panel of Figure 1 plots these management practice scores for a sample of 751 randomly chosen US manufacturing firms with 100 to 5000 employees and the second panel for similarly sized Indian ones. The results reveal a thick tail of badly run Indian firms, leading to a lower average management score (2.69 for India versus 3.33 for US firms).
.

The search for political accountability based on the performance of a sector is not trivial, however; Deininger and Mpuga (2005) show that such use of the term “accountability” is possible. Indeed, they find a positive correlation between the performance of public service delivery and political accountability.<sup>11</sup> But the evidence suggests that the link is hard to achieve systematically. Users who are also voters may have a perception about political performance that deviates from actual performance. For example, there is evidence that infrastructure prices in some countries and sectors went down while quality and access increased as a result of energy reforms in Latin America during the 1990s (see, for instance, McKenzie and Mookherjee 2003; Fay and Morrison 2006; Bonnet et al. 2009). However, surveys on public perceptions showed discontent with the results of reform in infrastructure despite these improvements in sector performance (see Straub and Martimort, 2005; Latino Barometro).<sup>12</sup> A possible explanation could be that voters may not manage to unpack the implications of separate elements of reforms if the outcome for the overall economy is perceived to be negative and/or if reforms have led to high unemployment rates (see, for instance, Huber, Pribble, and Stephens, 2009). Clearly, democracy has many benefits, but it is not necessarily sufficiently effective to ensure accountability when it comes to the performance in a sector as complex as infrastructure—which has delayed and quite opaque payoffs on some dimensions, and high visibility in some others, such as access. A more transparent link between sector performance and policy choices is needed for accountability to be effective.

Ultimately, the main message from this discussion of accountability specific to the infrastructure sector is a common one. The devil is in the details, and hence the details about the risks of political distortions need to be understood much better than they commonly are. They also need to be much more transparently handled if users and hence voters are
concern given the distribution of the main dependent variable.

The fundamental determinants regressions were run using: (a) instrumental variables (IV); and (b) system GMM. Since both of these methods are designed to address endogeneity issues, we are more confident about attributing causality in these cases. The IV estimates are performed for the 1999–03 cross section, and deployed using the following instruments: for institutions, settler mortality (IV-1) and fraction of European-language speaking population (IV-2) (Acemoglu, Johnson & Robinson 2001);<sup>14</sup> for integration, the gravity-predicted trade volume (Frankel & Romer 1999); for human capital, historical enrollment data from 1900 (Glaeser, La Porta, L ́opez-de Silanes & Shleifer 2004); and for democracy, the predicted level of democracy (Decker & Lim 2008). Both geography and social capital were treated as (plausibly) exogenous.

The same set of instruments is used for the dynamic panel estimates. Here, we exploit additional information available from the panel structure of the dataset to draw additional insight. We choose system GMM as the preferred estimation method, for two reasons: first, since we only have instruments available for one period, panel IV could not be used; second, the inclusion of geography as a primary time-invariant determinant rules out the use of difference GMM.

# **4.2 Benchmark results**

Table 4 reports the results of the benchmark regressions for the proximate correlates of the aggregate growth polarity measure, computed from real GDP growth rates and excluding migration flows. The first three columns report the pooled OLS ( _P1_ ), fixed or random effects ( _P2_ ), and system or difference GMM ( _P3_ ) estimates for the augmented Solow specification proposed in Mankiw, Romer & Weil (1992). The following three columns, ( _P4_ )–( _P6_ ), report coefficients from the same three estimation methodologies using a full specification that includes the major proximate variables of importance as identified by
**Figure 7. Credit-to-GDP ratio and HP trend (quarterly data)**

<!-- Start of picture text --> Credit to GDP HP trend (lambda = 400000)<br>% HP trend (lambda = 1600)<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>19971998 1999 20002001 20022003 20042005 20062007 20082009 2010<br><!-- End of picture text --> Notes: lambda = 400000: BCBS proposal; lambda = 1600: typical value of smoothing parameter in business cycle analysis. Source: authors’ calculation.

**Figure 9. Credit-to-GDP ratio gap (quarterly data)**

<!-- Start of picture text --> HP trend (lambda = 400000) HP trend (lambda = 1600)<br>p.p.<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: authors’ calculation.

**Figure 11. Structure of loans for nonfinancial sector**

<!-- Start of picture text --> Households Corporates<br>%<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text -->

**Figure 8. Housing loans-to-GDP ratio (quarterly data)**

<!-- Start of
certain number of non-wage benefits. In this area, there are no distinctions between domestic and foreign invested companies. Since the adoption of the Labor Code in 1995, contributions to the social insurance fund are mandatory in all enterprises that employ more than 10 workers: employees must pay 5% of their wages and employers a further 15% of workers' wages. For businesses that employ fewer than 10 people, or for employees with contracts shorter than 3 month, social insurance allowances have to be directly included in take-home pay. The social insurance fund benefits workers in case of occupational disease, workplace accidents, maternity, retirement or for death allowances (there is no unemployment insurance in Vietnam so far). The replacement rate is generally 75% of wage levels. In addition to social insurance, employers and employees are also subject to mandatory health insurance: employers must contribute 2% of workers wages and employees 1%.

# _b) Evidence:_

In Vietnam, compliance with non-wage regulations is very poor. According to data from the Vietnam Social Security (VSS), out of about 40 million workers only 4 million are covered by social insurance. And out of these 4 million, only 380,000 are private sector workers. Such low compliance is a typical problem in developing countries, where too few 22
relatively low yields in Figure 4.

The georeferenced IFPRI data have higher spatial resolution (0.083 degrees) than our spatial grid for CH4 anomalies (0.25 degrees). To incorporate them, we map each IFPRI pixel to a grid cell. Then we add IFPRI pixel information to obtain total production and irrigated rice land in each grid cell, and divide the former by the latter to calculate the yield.

In this exercise, we use the CH4/IFPRI data to address four related questions about the irrigated rice production areas displayed in Figures 3 and 4. First, do CH4 concentration anomalies from the database align with the scale of irrigated rice production areas? Second, what is the alignment between the same area scale and EDGAR’s “bottom-up” emissions estimates that combine detailed sectoral activity data with regionally-tailored emission factors? Third, what is the “value added” by the S5P CH4 data in identifying methane emissions from irrigated rice production that are not identified by area scale and EDGAR data in combination? Finally, what can the S5P data tell us about the global distribution of CH4 intensities, or methane emissions per unit of irrigated rice production? The following sections address these questions in turn.
**Figure 3 Difference Between Actual and Projected Poverty Gap for LAC Countries, by Model**

<!-- Start of picture text --> 4.00<br>2.00<br>0.00<br>2017 -2.00<br>-4.00<br>ARG BOL BRA CHL COL CRI ECU SLV HND PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>4.00<br>2.00<br>0.00<br>2018<br>-2.00<br>-4.00<br>ARG BOL BRA COL CRI DOM ECU SLV HND MEX PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>4.00<br>2.00<br>0.00<br>2019 -2.00<br>-4.00<br>ARG BOL BRA COL CRI DOM ECU SLV HND PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>4.00<br>2.00<br>0.00<br>2020<br>-2.00<br>-4.00<br>ARG BOL BRA CHL COL CRI DOM ECU MEX PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

Note: actual data excludes COVID-19- related mitigation measures when the survey includes the program beneficiaries and the amount received.

19
>0.8<br>-0.2<br>-1.2<br>ECA<br>World<br>ECA (excluding high income)<br>(1991=100)<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>GDP (Constant 2010 US$)<br><!-- End of picture text -->

**Figure 2. Labor Productivity Growth**

<!-- Start of picture text --> 14<br>9<br>4<br>-1<br>-6<br>ECA<br>World<br>ECA (excluding high income)<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>Labor Productivity (1991=100)<br><!-- End of picture text --> _Source_ : World Development Indicators (WDI).

_Source_ : WDI.

This paper attempts to identify the structural determinants of growth in ECA. The analysis builds on the approach introduced by Bouis and Duval (2011), Barnes et al. (2013), and Johannson et al. (2013) and elaborated by Egert and Gal (2016). The methodology allows to identify the main structural determinants of potential GDP growth by studying separately capital, labor, and productivity—the channels through which those determinants may work. As in Lusinyan (2018), productivity here is 2 See Cusolito and Maloney (2018), Arteta and Kasyanenko (2019) and World Bank (forthcoming) for a recent analysis of productivity trends in ECA.

2
invention of customs and traditions that backdated the triumph of men. African chiefs and male elders argued alongside missionaries and colonial officials that African women did not belong in towns, collaborating from Sierra Leone through the Gold Coast, the Copperbelt and the Witwatersrand to deny women space in towns in the face of long-standing traditions of female industry and entrepreneurship (Little (1973), White (1987), Allman (1996), Parpart (2001), Bradford (1992)). Longue durée studies of poverty (Iliffe (1987), Kalu (2006)), violence and warfare (Reid (2007, 2010)), disease (McNeil (1976), Packard (1989, 2007), Akyeampong (2006), Iliffe (2006)) and other major themes have deepened our understanding of these phenomena in contemporary Africa. This paper is a similar undertaking in the sphere of women (gender), production and development in Africa. The pre-colonial and colonial installment privileges historical and ethnographic works with some statistical data becoming available over the course of colonial 4
Policy Research Working Paper 7728

# **Abstract**

Climate change damage (or, more correctly, impact) functions relate variations in temperature (or other climate variables) to economic impacts in various dimensions, and are at the basis of quantitative modeling exercises for the assessment of climate change policies. This document provides a summary of results from a series of meta-analyses aimed at estimating parameters for six specific damage functions, referring to: sea level rise, agricultural productivity, heat effects on labor productivity, human health, tourism flows, and households’ energy demand. All parameters of the damage functions are estimated for each of the 140 countries and regions in the Global Trade Analysis Project 9 data set. To illustrate the salient characteristics of the estimates, the change in real gross domestic product is approximated for the different effects, in all regions, corresponding to an increase in average temperature of +3°C. After considering the overall impact, the paper highlights which factor is the most significant one in each country, and elaborates on the distributional consequences of climate change.

This paper is a product of the Development Prospects 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://econ.worldbank.org. The authors may be contacted at roson@unive.it and martina.sartori@unive.it .

_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