rafmacalaba/data-use-mentions-extended huggingface dataset:
advances in empirical micro-econometrics, such as, in particular, mediation analysis to investigate the mediating role of a variable of interest (the so-called ‘mediator’) in explaining a causal relationship of interest appear promising, although not yet picked up by scholars in the migration field.</mark><sup>7</sup> <mark>The analysis of migration-asadaptation, i.e., of how the decision (not) to migrate fits within a broader analytical framework on the full pool of adaptation options available to farmers in response to climate change, is also still underexplored. I</mark> n this respect, while the ‘rare event’ nature of migratory flows is a drawback for household surveys, the potential to carry out empirical analyses of the full range of intervening factors, mediating channels, and adaptation options available to households in response to

> 7 Recent examples of works leveraging the potential of this methodology to unpack the black box of causal relationships in the development field are Azzarri et al. (2022), Pace et al. (2022), and Prifti, Daidone, and Davis (2019). For a comprehensive review of the use of mediation analysis in economics, see Celli (2021).

17
private firms using matching techniques.

The results have been mixed. Using propensity score matching on a sample of 477 partially privatized SOEs, Lazzarini and Musacchio (2018) find that SOEs outperformed similar private firms. In their study of dozens of fully owned SOEs in Latin America, Musacchio and Pineda Ayerbe (2019) find the opposite result.

# **3 The Data**

The World Bank Infrastructure SOE Database was compiled as part of a broader research project to analyze the fiscal costs and risks of infrastructure. The data come from financial statements from SOE websites, government websites that include SOE financial statements, annual reports, and other sources, such as the EMIS Intelligence database and stock exchange websites. The database covers all SOEs operating infrastructure assets in the power (generation, transmission, and distribution) and transportation (roads, railroads, and airlines and airports) sectors for 19 4
of the paper, we use KIHBS 2015/16 with the full consumption module as a benchmark. The previous round of 2005/6 is used to define the core items, and for two of the alternative approaches it is used to adjust the poverty line and to build the consumption model for the cross-survey imputation. The 2015/16 pilot is used as a real-world example for the implementation of the rapid approach. We harmonize the data sets to ensure comparability across the different surveys. The harmonized data sets include 133 food and 81 non-food items. The consumption aggregate is exclusively based on these 214 items. In addition, we use harmonized location and household characteristics for the various models, including a binary and a categorical location variable as well as 6 additional binary, 9 additional categorical and 9 continuous variables for household characteristics (Table 5 in the Appendix).

Consumption shares differ markedly between the 2005/6 and 2015/16 surveys, which is unsurprising given the 10-year gap (Kenya National Bureau of Statistics 2018).<sup>10</sup> For example, the top 10 food items in 2005/6 capture 58 percent of the food consumption share, but only 39 percent of the consumption share in 2015/16. Non-food consumption changed to a lesser degree. The top 10 items in 2005/6 represent 64 percent of non-food consumption shrinking to 59 percent in 2015/16. These differences will impact the

> 8 Balance mean tests are shown in Table 6 indicating similar households in both surveys with similar although not always statistically indistinguishable characteristics.

> 9 The assignment of optional modules to households was balanced with 4,222 households assigned to module 1, 4,192 to optional module 2, and 4,248 to optional module 3.

> 10 See Table 4 for the shares of the items with the top 20 consumption shares for KIHBS 2005/6 and KIHBS 2015/16.

8
A validation exercise which is typically conducted to check the overall quality of a welfare measure based on consumption expenditure is to rank households based on such metric and then check whether Engel’s Law is satisfied, i.e. check if poorer household devote a higher share of total consumption to food.<sup>19</sup> As shown in Table 5 below, the expected negative relation between food shares and total household expenditure is only confirmed in 2022 data, possibly suggesting that the changes apported to the questionnaires improved the correspondence between the measure of welfare based on expenditure and households’ living conditions.

_Table 5: Analysis of relation between food share and total household expenditure_

|**Dependent variable: share of total con**|**sumption devoted to food**<br> <br>|
|---|---|
||**2021 data**<br>**2022 data**|
|(Log) Total monthly expenditure|0.0253<br>-0.0205|
||(13.51)<br>(-13.15)|
|Household size|-0.002<br>0.0014|
||(-2.75)<br>(2.38)|
|Nr. Observations|23166<br>26074|
|R<sup>2</sup>|0.0079<br>0.0068|

_Note_ : Model include a constant term _Source_ : World Bank staff calculations, based on HNAP 2021 and 2022 summer round of the Demographic and WASH survey Despite such improvements, the measure of consumption based on the 2022 Demographic and WASH survey is still far from being an accurate welfare metric for poverty estimation in Syria, particularly for the purpose of assessing the evolution of poverty over time using pre-conflict estimates from the HIES as a baseline.

In fact, the consumption module in each of the three rounds of HIES conducted prior to the conflict
.65||||||
|_Main market_||||||||
|Local|3,647|0.41||||||
|National|3,647|0.48||||||
|International|3,647|0.12||||||
|_Sector_||||||||
|Manufacturing|3,647|0.27||||||
|Retail|3,647|0.20||||||
|Other services|3,647|0.53||||||
|**_b. Firm performance (Round 3)_**||||||||
|Percentage change in monthly sales relative to one year earlier|3,507|-1.99|33.91|-40.00|25.00|-100.00|400.00|
|Percentage change in number of permanent full-time workers since December 2019|3,546|-14.60|51.38|-80.00|28.57|-200.00|200.00|
|Reduced number of permanent full-time workers since December 2019|3,546|46.96||||||
|Anticipate fallinginto arrears on outstandingliabilities in the next 6 months|3,247|20.85||||||

_Sources_ : Enterprise Surveys and Enterprise Surveys COVID-19 Follow-up Surveys.

_Note_ : In our sample, the percentage of firms who have state ownership (10%) is 0.17 percent.

15
poverty reduction in Kagera seem to be representative for rural Tanzania as a whole: only some pockets in Tanzania, such as Dar es Salaam, have had substantial growth and poverty reduction, but this has not spread to other areas. This reflects the typical problem of land-locked, agriculture-based economies: how to deliver poverty reduction if the main engine of growth appears to be elsewhere.

In 2004 and 2010 follow-up surveys aimed to re-interview all individuals that were ever considered a household member in the baseline survey and were alive at the time of re-survey. A full household questionnaire was administered in a household where a panel respondent was found residing. Due to household dynamics, the sample size increased to more than 3,313 households by 2010.<sup>10</sup> Excluding households for which all previous members were deceased (17 households and 27 respondents), the KHDS 2010 field team managed to re-contact 92 percent of the initial households. Table 1 gives further details on the interview status at individual level. Starting from the baseline sample, 11.2% of the individuals were not found, while 0.5% refused to be interviewed. The attrition rates in KHDS are extremely low, even compared to much shorter term panels (Alderman et al., 2001). The average baseline household spawned 4.1 households by 2010, out of which 2.4 were non-migrant and 1.7 were migrant households.

There is occupational data for 1991/94 and 2010. Because the analysis is conducted at the household level, households are classified into farm<sup>11</sup> and non-farm based on the self-reported occupation of the head. In 2010, households were found in three cities: Dar es Salaam, Mwanza and Kampala. This is defining cities as locations with more than 500,000 inhabitants. There are a further 21 respondents who moved to areas that, while administratively recognized within Tanzania as cities, have a population below 500,000 (Arusha, Tanga and Mbeya). Results
some impact on the overall income distribution. The estimated impact is also large enough to explain 20‐40 percent of the gap observed during the 1990s between average consumption growth in NSS data and national accounts data.

15 URP, or uniform reference period, is collected for a 30‐day recall period for all consumption items. MRP, or mixed reference period, records consumption by combining a 30 day recall period for most items and a 365 day recall for items that are purchased with a lower frequency. The MMRP (modified‐mixed reference period) aggregate introduced in 2009‐10 and 2011‐12 has further closed the gap. The MMRP method which reduces the recall period for some food items to a 7‐day recall (compared to a 30‐day recall in MRP) brings NSSO’s methods closer to the more common practice of a two week or less recall period for food items in other countries.

9
# **Tables and Figures**

Table 1: Randomization Verification: Clinic Census and Health Worker Pre-Training Survey

||(1)<br>Basic<br>Training<br>Mean|(2)<br>Extended<br>Provider<br>Training|(3)<br>N|
|---|---|---|---|
|_A. Provider-Reported Clinic Characteristics_||||
|Average Patient Load|30.371|-1.192|57|
||[22.172]|(1.207)||
|Clinic Has Microscopy|0.897|-0.111|57|
||[0.310]|(0.094)||
|Fraction Patients Tested with RDT|0.648|0.065|57|
||[0.221]|(0.057)||
|Fraction Patients Tested with Microscopy|0.473|-0.134|57|
||[0.306]|(0.083)||
|Fraction Tested Malaria Positive|0.543|0.062|57|
||[0.196]|(0.048)||
|_B. Provider Attendance_||||
|Number of Staf Attended per Clinic|3.862|0.000|58|
||[0.441]|(0.158)||
|Pre-Tests Submitted per Clinic|2.793|-0.061|58|
||[1.424]|(0.558)||
|Post tests submitted per clinic|3.897|-0.172|58|
||[0.900]|(0.293)||
|_C. Provider Pre-Test Knowledge_||||
|Knows Malian Malaria Policy|0.519|-0.045|160|
||
H., & Zhang, J. (2007). Do high birth rates hamper economic growth? _The Review of Economics and Statistics_ , _89_ (1), 110–117.

- Liberia Institute for Statistics and Geo-Information Services. (2015). _Household Income and Expenditure Survey (HIES) 2014._ Monrovia, Liberia.

- Liberia Institute of Statistics and Geo-Information Services - LISGIS, Ministry of Health and Social Welfare/Liberia, National AIDS Control Program/Liberia, and ICF International. (2014). _Liberia Demographic and Health Survey 2013._ Monrovia, Liberia: LISGIS and ICF International.

- Liberia Institute of Statistics and Geo-Information Services - LISGIS/Liberia, Ministry of Health and Social Welfare/Liberia, National AIDS Control Program/Liberia, and Macro International. (2007). _Liberia Demographic and Health Survey 2007._ Monrovia: LISGIS and Macro International.

- Liberia Institute of Statistics and Geo-Information Services (LISGIS), Ministry of Health, and ICF. (2021). _Liberia Demographic and Health Survey 2019-20._ Monrovia, Liberia: Liberia Institute of Statistics and Geo-Information Services (LISGIS), Ministry of Health, and ICF.

- Liberia Institute of Statistics and Geo‐Information Services (LISGIS). (2010). _Report on the Liberia Labour Force Survey 2010._ Monrovia: ILO & Liberia Institute of Statistics and Geo‐Information Services (LISGIS).

- Löfström, Å. (2006). _Gender equality, economic growth and employment._ Stockholm: Swedish Ministry of Integration and Gender Equality.

- Lucas, R. (1988). On the Mechanics of Development Planning. _Journal of Monetary Economics_ (22), 3-42.

- Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. _Quarterly Journal of Economics_ , _107_ (2), 407.

- Miller, G. (2010). Contraception as development? New evidence from family planning in
0.256|0.255|
|Department fixed effects|Yes|No|No|Yes|No|

Sources: 2018 National Census of Guatemala, 2017 and 2018 municipality statistics (FUNDESA and INE), 2017 social spending data (ICEFI).

Notes: Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves. Robust standard errors clustered at the municipality level in columns 1 and 4 and at the department level in columns 2, 3 and 5 between brackets. *** p<0.01, ** p<0.05, * p<0.1.

35
affect both sectors, with firms slightly more affected than households.

# <u>Foreign Banks during the Crisis</u>

Political as well as financial integration with Western Europe including a major presence of multinational banking groups had been the predominant model of development in most of the transition region since the mid-1990s. This model of cross-border finance had stimulated longterm growth (EBRD (2009)) and attenuated the effects of host country crises as foreign greenfield banks had kept their lending stable whereas domestic banks had been found to decrease lending (De Haas and Van Lelyveld (2006)). Thus, the availability of funds from internal capital markets made foreign banks in the transition region more resilient to host country crisis. However, the very same linkages included the risk of the transmission of a crisis from these banks‟ home countries to emerging Europe. Apart from that, foreign banks had been the drivers of an enormous credit boom including many foreign currency loans to often unhedged borrowers (EBRD (2010)). So how did foreign banks react to the 2007-2009 crisis? Did multinational banking groups contract their lending in the transition region because of financial problems at their parent banks thereby reinforcing the real effects of the crisis?

The empirical evidence so far is mixed. Popov and Udell (2010) find that the financial distress at foreign parent banks was indeed transmitted cross-border to emerging Europe leading to a reduction in lending to firms during the early stage of the crisis in 2007-2008. Their data on firms come from the 2009 wave of BEEPS (see section 4.1. for details). They match this data with information on bank presence in the localities of firms and bank balance sheet data from Bankscope. Although their dataset does not allow them to directly match firms with their banks, they can provide evidence that firms in localities served by foreign banks whose parents experienced financial distress have a higher probability to be credit constrained. Explicitly controlling for demand shifts and selection of firms out of the application process, they also find 37
# **II. Input data**

The best predicting model assessment consists of evaluating which of the previously proposed model variations best capture the observed changes in the income distribution for 15 LAC countries from 2017 to 2020, given the availability of perfect inputs. In other words, this paper attempts to identify the model with the lower bias to estimate changes in the income distribution under the availability of perfect information about the sectoral and total output growth, the total population growth, and the changes in labor market structure and earnings; and to test if allowing for more flexibility in the simulation of labor income results in a more accurate estimation. For this purpose, this paper uses as inputs the World Bank's Macro Poverty Outlooks (MPOs) actual growth in sectorial and total GDP rates and remittances growth rate for the years 2015 – 2020.<sup>19</sup> It also uses the harmonized SEDLAC dataset<sup>20</sup> to compute actual changes between the baseline year and the estimated year in total population growth, labor market structure, and average formal and informal labor income.<sup>21</sup> The simulations performed to test the model's variations use 2016 SEDLAC household survey data for each country (or the most recent household survey data available before 2017) as the baseline for the estimation. It is important to note that formality (informality) has been defined as contributing (not contributing) to work-related retirement insurance for most countries. Table 1 presents the countries considered, the baseline year used for each country, the simulated years, and the informality definition used. It is important to note that, for this work, all inputs are in real terms in 2017 USD PPP, so they already account for inflation changes.

**Table 1 SEDLAC Country Data Used in the Simulations**

|**Country**|**Baseline**<br>**Survey**|**Simulated**<br>**
help others. In corrupt societies, however, otherwise altruistically motivated officials may participate in corruption because:

- Officials believe that their careers can be advanced by corrupt practices.

- Corruption acts as an insurance against risk in an unstable and uncertain political environment.

- Officials may believe that their marginal contributions to the burden of corruption are insignificant.

- Corruption may be reinforced by prejudice or tradition.

- Officials may believe that their actions are an appropriate "fend for yourself' response in an inhospitable environment with large income inequalities.

Policies which promote altruistic behavior face significant hurdles. Wide spread corruption is likely to be influenced by multiple factors. Consequently, an anticorruption program which focuses on officials' concerns for others is unlikely to be effective unless it simultaneously addresses these factors -- awareness or selective enforcement campaigns may fail if officials do not believe most other officials will not change their ways, creation of ethics offices and ombudsmen may actually lead to greater entrenchment of underlying prejudices or traditions, and public opinion surveys may confirm officials' views of their clients.

While policies that promote altruism seem unlikely to be successful by themselves, programs which influence officials' cost-benefit analysis may also reinforce altruistic motivations. Efforts to improve service delivery or policies which reward performnance should be effective from either a cost-benefit or altruistic perspective.

# _Adapting OED Methodology to the Evaluation of Anti-corruption programs_

The above discussion provides us with some background to apply the OED methodology in evaluating anti-corruption programs. In the following, the key criteria used in OED methodology are discussedl'.

_Relevance._ OED defines relevance as to "the extent which the project's objectives are consistent with the country's current development priorities and with current Bank country and sectoral assistance strategies and corporate goals."'"1 Thus anti-corruption programs are judged to be relevant if they have the potential to achieve their objectives given a country's existing institutional and policy environment.

Schacter and Shah (2000) argue that judgement about relevance combine two distinct factors: technical relevance and welfare relevance. Technical relevance
(2009), using Chinese data, find that total bank assets are not significant for banks’ decision to lend to SMEs. Yet, more local lending authority, more competition, carefully designed incentive schemes, and stronger law enforcement encourage commercial banks to lend to SMEs. Macroeconomic instability in developing countries and competition in developed countries are perceived as the biggest obstacles to SME finance. Rocha _et al._ (2010) provide supportive evidence from banks in Middle East and North Africa, noting lack of SME transparency and underdeveloped financial systems as the main obstacles.<sup>5,6</sup> At the same time, a recent study by Beck _et al._ (2008b) indicates that most commercial banks perceive the SME sector as profitable.

The lack of consistent indicators restricts extensive cross-country analyses of SME lending. Most countries either do not collect data on SME lending or do so on an _ad hoc_ basis. Even when SME data are available, it is extremely difficult to perform a cross-country analysis in the absence of a standard definition as to what constitutes an SME. Though SMEs are commonly defined as registered businesses with less than 250 employees (IFC, 2009), the definition still varies from country to country and even from bank to bank. In this respect, an OECD conference on SMEs in 2004 made two key policy recommendations to both member and non-member economies: (i) develop greater international comparability of SME statistics, and (ii) develop a common definition of an SME.<sup>7</sup> Without reliable SME data, it is difficult for policy makers to implement programs aimed at expanding and strengthening the SME sector.

> 5 One strand of the literature on SME financing indicates that SMEs pay higher interest rates on formal bank credit. For example, Cressy and Toivanen (2001) find that collateral provisions and loan size reduce the interest rate paid and that better borrowers get larger loans and lower interest rates. Hernandez-Canovas and Martinez-Solano (2007), using firm-level data from
# **Figure 2: Quantile Treatment Effects on Profits After Three Years**

<!-- Start of picture text --> Treatment Effect Spillover Effect<br>5 10 20 30 40 50 60 70 80 9095 5 10 20 30 40 50 60 70 80 9095<br>Percentile Percentile<br>1000 1000<br>500 500<br>Weekly Profits 0 Weekly Profits 0<br>-500 -500<br><!-- End of picture text --> Notes: Intent-to-treat effects from quantile regressions controlling for randomization strata and baseline profits, combining round 4 and round 5 survey data. Straight dashed line shows OLS estimate. 95 percent confidence intervals displayed allowing for clustering at market level.

26
Migali, S. & Scipioni, M., 2019. Who's About to Leave? A Global Survey of Aspirations and Intentions to Migrate. _International Migration._ Mussa, E. C., 2020. Youth Aspirations, Perceptions of Farming, and Migration Decisions in Rural Sub-Saharan Africa: Further Empirical Evidence From Ethiopia. _ZEF-Discussion Papers on Development Policy No. 296._ <mark>National Bureau of Statistics. Nigeria COVID-19 National Longitudinal Phone Survey (COVID19 NLPS) 2020-2021, Phase 1. Dataset downloaded from</mark> <u>https://microdata.worldbank.org/index.php/catalog/3712</u> <mark>on January 2, 2024</mark> Nguyen, T., 2008. Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar.. _Working paper, MIT, Cambridge, MA._ OECD, 2021. _PISA 2025 Foreign Language Assessment Framework,_ s.l.: OECD Publishing.

Pinquart, M., Juang, L. P. & Silbereisen, R. K., 2004. The role of self-efficacy, academic abilities, and parental education in the change in career decisions of adolescents facing German unification.. _Journal of Career Development,_ 31(2), pp. 125-142.

Rao, V. & Walton, M., 2004. _Culture and Public Action._ Stanford, CA: Stanford University Press.

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