rafmacalaba/data-use-mentions-extended huggingface dataset:
enforcement. Since these regulatory and informational characteristics determine the efficiency of financial intermediaries and markets, we regard the legal origin of countries as good instruments for financial development. LaPorta, Lopez-de-Silanes, Shleifer, and Vishny (1999a) also show that the dominant religion of a country influences institutional development.

# 3. The Data

This section describes the measure of external dependence, the indicators of financial development and structure and the industrial growth data. The data are for 42 countries and 36 industries. All industries are in manufacturing as in RZ.

## **_3.1._** _External Dependence_

The industry-level data on external dependence are from RZ (1998). The underlying assumption in RZ - and our work -- is that for technological reasons some industries depend more heavily on external finance than others. Scale economies, gestation period or intermediate product intensity might constitute some of these technological reasons. Unfortunately, we can only observe the actual use of external finance, but not the demand for it. If financial markets were relatively frictionless, the actual use of external finance would represent the equilibrium of supply and demand. For countries with very well developed financial systems, RZ note that external funds will be supplied very elastically, so that the actual use of external finance would primarily reflect the demand for external finance. Assuming that the variance of the need of external finance across industries persists across countries we can thus use the actual external dependence of industries as observed in a country with a very well developed financial system as a proxy for the "natural" dependence of industries on external finance. As discussed in RZ, we 11
<br>2000-10 2011-20<br>F. Transparency of policy making<br>Index<br>LAC EMDEs<br>4.2<br>4.0<br>3.8<br>3.6<br>3.4<br>3.2<br>2007-13 2013-18<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Population Prospects; World Economic Forum, Global Competitiveness Index; World Bank.

Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.B. Period averages of annual GDP-weighted averages. Potential growth estimates based on production function approach. Sample includes 53 EMDEs (16 from LAC).

C.-F. Cross-period simple averages of annual GDP-weighted averages. Samples include: for C, 112 EMDEs (23 from LAC); for D, 53 EMDEs (11 from LAC); for E, 101 EMDEs (18 from LAC); for F, 112 EMDEs (23 from LAC).
VPL and the proportion of households who are vulnerable are as presented in table

# 2.9

Table 2: Estimates of Vulnerability and Proportions of Vulnerable HH's When Headcount Poverty is 20 Percent of the Population (May 1997 Prices)

||Mini S<br>(December 199|usenas<br>8 - August 1999)|100 Villa<br>(May 1997 -|ge Survey<br>August 1998)|
|---|---|---|---|---|
||Ignoring<br>measurement<br>error|Net of<br>measurement<br>error (300%)|Ignoring<br>measurement<br>error|Net of<br>measurement<br>error (50(10)|
|Mean of log percapita expenditures<br>in the initial period|<br>10.901|<br>10.901|<br>10.730|<br>10.730|
|Inverse of mean of log percapita<br>expenditures (Rp/month)|54,251|<br>54,251|<br>45,687|<br>45,687|
|Standard deviation of changes in log<br>expenditures durmig the period|<br>0.412|<br>0.288|<br>0.462|<br>0.231|
|Yearly standard deviation of<br>changes in log expenditures|0.617|<br>0.432|<br>0.370|<br>0.185|
|Yearly coefficient of variability|_0.057_|<br>0.040|<br>0.035|<br>0.017|
|Yearly probability of falling to<br>below poverty line|0.238|<br>0.154|<br>0.
<u>Table 1a. Average household statistics in 2008 by household migration status</u>

||<br>Overall<br>average|<br>Migrant<br>average|Non-migrant<br>average|Difference<br>(migrant -<br>non-migrant)|
|---|---|---|---|---|
|Total expenditures (Quetzales)|35161.01|47143.97|31314.63|15829.34***|
||(30319.71)|(38190.96)|(26250.58)|(3925.61)|
|Per capita expenditures (Quetzales)|5074.35|6000.77|4776.98|1223.79**|
||(4069.83)|(4548.96)|(3864.32)|(495.26)|
|Per capita food expenditures (Quetzales)|2753.62|2641.30|2789.67|-148.37|
||(2695.92)|(1790.15)|(2929.31)|(234.96)|
|Food share (food / total expenditures)|0.57|0.51|0.59|-0.07***|
||(0.19)|(0.20)|(0.18)|(0.02)|
|Poor = 1|0.83|0.76|0.85|-0.10**|
||(0.37)|(0.43)|(0.35)|(0.04)|
|Extremely poor = 1|0.49|0.38|0.52|-0.14**|
||(0.50)|(0.49)|(0.50
0.00252)|(0.0279)|(0.0196)|
|VIX Index|-0.0608|-0.276|-0.000433|0.120|-0.00684|-0.0152|-0.0535|-0.296|
||(0.0755)|(0.328)|(0.0317)|(0.287)|(0.0146)|(0.0391)|(0.0765)|(0.327)|
|US Economic Policy|0.00135|0.00323|-0.000382|-0.00105|6.99e-05|0.000113|0.00167|0.00330|
|Uncertainty|(0.00370)|(0.00276)|(0.00155)|(0.00227)|(0.000715)|(0.000304)|(0.00375)|(0.00280)|
|Commodity Terms of|-0.114|0.0428|0.211|0.00239|-0.0649|0.00290|-0.261|0.0333|
|Trade (in logs)|(2.000)|(0.0584)|(0.840)|(0.0348)|(0.386)|(0.00380)|(2.026)|(0.0660)|
|<br>Oil Price|0.0664|-0.0218|0.00307|0.0111|0.00197|0.00109|0.0613|-0.0228|
|(in logs)|(0.172)|(0.123)|(0.0723)|(0.0872)|(0.0333)|(0.0102)|(0.174)|
# **_Networks and SME Entry_**

Table 5 presents the results of a regression analysis that examines the power of the ethnic network in determining firm size at start up. Size at entry is important because it is a predictor of future prospects, such as survival and ability to grow in the SSA environment (Biggs and Shah 2002). The hypothesis is that members of a network are able to start larger firms, because the network provides connections, information, and governance capabilities, which facilitate access to credit and other inputs, access to technologies and markets, or access to equity investments.

We examine first the effect on size at start-up of belonging to a minority ethnic network without controlling for other possible determinants of size at start. Model I looks at whether networked entrepreneurs start firms in a different size class compared with indigenous-African firms. We know from the RPED surveys that indigenousAfrican SMEs in manufacturing in these countries lack the strong business networks enjoyed by ethnic minority entrepreneurs (Biggs and Srivastava 1996; Fafchamps 2004). In particular, they lack effective multilateral reputation mechanisms able to share information on payment histories and enforce contracts through group sanctions. The results of model I confirm that the coefficients for Asian and European networked firms are highly significant and positive, indicating that firms belonging to these stable and cohesive business groups start at twice the size of indigenous-African firms.

But this result could be influenced by other factors, such as education of the entrepreneurs, their initial assets or their access to start-up finance. Model II controls for human capital. The results demonstrate that educational attainment matters. Entrepreneurs with university or technical degrees start bigger firms. The coefficient for university education, for example, suggests that entrepreneurs with a university degree start firms approximately 50 percent larger than entrepreneurs with only primary education. Nevertheless the network coefficient remains positive and highly significant 15
|**Table 5: Impact on Employment**|Firm|Data||||PILA Data||||
|---|---|---|---|---|---|---|---|---|---|
||Jan 2013|‐Dec 2017|||Jan 2013‐|Dec 2017||Jan 2013|‐Dec 2018|
||||Firm|Cond|itional|Uncon|ditional|Cond|itional|
||Level|I.H.S.|Survival|Level|I.H.S.|Level|I.H.S.|Level|I.H.S.|
|Individual Treatment*During Intervention|‐3.012|‐0.018||‐1.987|‐0.058*|‐1.048|0.001|‐2.056|‐0.063*|
||(2.912)|(0.040)||(2.339)|(0.035)|(2.279)|(0.045)|(2.129)|(0.033)|
|Individual Treatment*Post Intervention|‐2.150|0.040|0.019|1.222|0.027|2.563|0.113|||
||(3.741)|(0.052)|(0.049)|(4.253)|(0.066)|(4.103)|(0.094)|||
|Group Treatment*During Intervention|3.837*|0.101**||4.685|0.097*|3.081|0.081|4.815|0.104**|
||(2
# **1. Introduction**

How does an individual’s perception of their position on the income distribution influence their beliefs about inequality and their demand for redistribution? What shapes this perception of one’s relative position in the first place? A growing literature examines these questions (Alesina, Glaeser, and Sacerdote 2001; Alesina, Stantcheva, and Teso 2018; Benabou and Ok 2001; Benabou and Tirole 2006; Bussolo, Ferrer-i-Carbonell, Giolbas and Torre 2021; Iversen and Soskice 2020; Mo and Conn 2018). However, existing research has studied them mainly in high income countries. India remains surprisingly understudied, considering that it is a populous democracy that harbors a deeply unequal society.

We present results from the first nationwide study of perceptions of the income distribution in India. Our data comes from a survey of 116,061 households, spanning all major states of India. We examine people’s beliefs about which decile their household occupies on the income distribution, and compare this to the household’s actual position based on income data collected on a monthly basis over nearly two years. We find that the difference between perceived and income-based decile in India is large, exceeding two deciles on average. Moreover, over 70% of the sample perceive themselves as poorer than they are based on income. While the presence of misperceptions has been widely documented across contexts (Cansunar 2021; Cruces, Perez-Truglia, and Tetaz 2013; Gimpelson and Treisman 2018; Hoy and Mager 2020), the magnitude we observe is striking, as is the widespread underestimation of one’s position.

We find that household nominal income (the _objective_ position of the household in the income distribution) does not predict support for reducing inequality in India. Rather, where people believe their household stands on the income distribution (the _subjective_ perception of the position) is a significant predictor of the belief that the government should narrow the gap between the rich and the poor. Given this finding and the highlighted large differences between 2
This version: 15 Feb 2024 57

# **Appendix Data Note A**

The data collection effort was jointly supervised by a World Bank team in collaboration with the University of Malaya’s economics department where 56 openended focus groups were conducted across Malaysia, in which a total of 473 individuals participated. In this note, we provide additional information on site and sample selection, research team, participant recruitment and moderation. **_Sites and sample_** We selected three broad regions in Malaysia to cover peninsular as well as East Malaysia - the broader Kuala Lumpur (KL) region in western peninsular Malaysia, Terengganu state in eastern peninsular Malaysia and Sabah state on the island of Borneo. The states and specific in-state locations were chosen after consultative meetings in each region with local stakeholders (academics and representatives of government and civil society organizations). <mark>The selection of specific in-state locations was also informed by district-level analysis of the 2016 Household Income Survey to help ensure adequate numbers of participants in each income group.</mark> Of the 56 focus groups, 27 were held in the greater KL region (in Selangor state), and as ethnicity is an important source of variation in western Malaysia, we conducted nine focus group discussions each among Malaysians of Bumiputera, Chinese, and Indian descent. Urban-rural differences are important in Terengganu and Sabah; hence, 14 discussions in Terengganu and 15 discussions in Sabah were split between urban and rural areas. We further split the groups by income and age. Participants between 25-40 years were classified as “young” and those 41 and above were classified 57
imagery pre-loaded on enumerators’ tablets as a base map.<sup>18</sup> The listing identified 8,301 farms in 1,729 enumeration areas (EAs) of which 6,782 were mapped (and 6,703 had a mapped area above 5 ha). To generate a sample frame for a more detailed farm performance survey, we dropped all wards<sup>19</sup> with fewer than 30 farms as these would have been too costly to visit, resulting in a frame of 5,620 farms in 1,066 EAs from 62 wards. To attain our target sample size of 3,000, we include all farms greater than 20 ha and a random sample of farms below 20 ha.<sup>20</sup> This provides the basis for studying the extent to which farms’ productivity, level of land-related investment, and self-assessed land values are affected by access to formal documentation.

> 17 In principle, the Zambia Integrated Land Information and Management System (ZILMIS) created in 2003 on an Oracle platform should allow fully digital document processing. However, the system is not operational, and the Ministry has neither access to source code nor capacity to even execute basic queries. The largely textual information in the system is of poor quality. While all spatial records for Eastern Province were digitized with World Bank support, the Ministry of Lands continues to run paper-based and digital systems in parallel so that absence of a competing claim in the electronic system cannot provide conclusive evidence of property ownership, failing the basic requirement of a titling system.

> 18 Mapping was limited to farms located in the same district as loading imagery beyond the district would have exceeded tablets’ storage capacity. 19 After provinces, districts, and constituencies, wards are the fourth tier in Zambia’s system of administrative divisions. They are used for census and election purposes.

> 20 We include all 1,451 farms above 20 ha from the listing and randomly select 1,549 of
# **_V.A Quality_**

Our measure of quality is the Quality Defects Index (QDI), a weighted average score of quality defects, which is available for all but one of the plants. Higher scores imply more defects. Figure 3 provides a plot of the QDI score for the treatment and control plants relative to the start of the treatment period. This is September 2008 for Wave 1 treatment, April 2009 for Wave 2 treatment and control plants.<sup>18</sup> This is normalized to 100 for both groups of plants using pre-treatment data. To generate point-wise confidence intervals we block bootstrapped over firms.

It is clear the treatment plants started to reduce their QDI scores (i.e. improve quality) significantly and rapidly from about week 5 onwards, which was the beginning of the implementation phase following the initial 1 month diagnostic phase. The control firms also showed a mild downward trend in their QDI scores from about week 30 onwards, consistent with their slower take-up of these practices in the absence of a formal implementation phase.

Table 2 in columns (1) to (4) examines whether management practices improve quality using regression analysis. In column (1) we present the fixed-effects OLS results which regresses the weekly log(QDI) score on plant level management practices, plant fixed effects, and a set of weekly time dummies. The standard errors are bootstrap clustered at the firm level to allow for any correlation across different experimental plants within the same firm. The -0.561 coefficient implies that increasing the adoption of management practices by 1 percentage point would be associated with about a 0.6% reduction in defects, although this is not statistically significant.

In Table 2 column (2) we report the first stage from using the experimental intervention to identify the causal impact of better management on quality. The coefficient on log cumulative treatment is extremely significant, reflecting the fact that the intervention substantially increased the adoption of management practices. In column (3) we
Akerman, Anders, Ingvil Gaarder, and Magne Mogstad. 2015. “The Skill Complementarity of Broadband Internet.” _Quarterly Journal of Economics_ 130 (4): 1781–824.

Albanesi, Stefania, Antonio Dias da Silva, Juan Francisco Jimeno, Ana Lamo, and Alena Wabitsch. 2023. “New Technologies and Jobs in Europe.” CEPR Discussion Paper No. 18220, Centre for Economic Policy Research, Paris.

<mark>Antón, José-Ignacio, David Klenert, Enrique Fernández-Macías, Maria Cesira Urzì Brancati, and Georgios Alaveras. 2022. “The Labour Market Impact of Robotisation in Europe.”</mark> _<mark>European Journal of Industrial Relations</mark>_ <mark>28 (3): 317–39.</mark> Aranda-Jan, C.B., Mohutsiwa-Dibe, N. and Loukanova, S. 2014. “Systematic Review on What Works, What Does Not Work and Why of Implementation of Mobile Health (mHealth) Projects in Africa.” _BMC Public Health_ 14: 188.

Aron, Janine. “Mobile Money and the Economy: A Review of the Evidence.” _World Bank Research Observer_ 33 (2): 135–88.

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Asongu, Simplice A., and Jacinta C. Nwachukwu. 2018. “Comparative Human Development Thresholds for Absolute and Relative Pro-Poor Mobile Banking in Developing Countries.” _Information Technology & People_ 31 (1): 63–83.

Asongu, Simplice A., and Nicholas M. Odhiambo. 2019. “How Enhancing Information and Communication Technology Has Affected Inequality in Africa for Sustainable Development: An Empirical
Policy Research Working Paper 8683

# **Abstract**

This paper explores the methodological differences underlying the construction of the national consumption aggregates that are used to estimate international poverty rates for all countries in the South Asia region, including Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. The analysis draws on a regional dataset of standardized consumption aggregates to assess the sensitivity of international poverty rates to the items included in national consumption aggregates. A key feature of the standardized aggregate is that it includes the reported value of housing rent for urban Indian homeowners. Using the standardized consumption aggregates reduces the international poverty rate in South Asia by 1.3 percentage points, or about 18.5 million people. Comparing standardized and non-standardized monetary welfare indicators to other nonmonetary indicators suggests that the latter are more consistent with the standardized consumption aggregates. Overall, the results strongly suggest that harmonizing the construction of welfare measures, particularly the treatment of imputed rent, can meaningfully improve the accuracy of international poverty comparisons.

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 Myanezpagans@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
absence of panel data, some researchers have simply relied on cross‐sectional estimates alone, though Ligon & Schecter (2004) caution that such results can be quite biased. In other cases, researchers have used “pseudo‐panel” techniques, where repeated rounds of cross‐sectional survey data are collapsed at a supra‐household level where there are repeated observations (Christiaensen & Subbarao, 2005).

A recurring theme in the literature is the choice of the poverty line, and the choice of a probability threshold on which to categorize household vulnerability. Due to data constraints, few studies have been able to empirically test how these two choices affect the ability of estimated vulnerability to capture realized poverty in future periods. Zhang & Wan (2008), using three‐period panel data from China, estimate poverty and vulnerability using different poverty lines for the first two periods of the panel to get an estimate of vulnerability with which they then compare to observed poverty in the third period. The authors find that a relatively high poverty line closer to the mean observed income per capita does a better job at predicting the percent of households that stay in poverty and transition into poverty than a lower poverty line, as does setting the probability threshold at .5.<sup>3</sup> Cruces et al. (2010) also discuss the sensitivity of vulnerability classification depending on the threshold probability. They use two different threshold probabilities, the common “more likely than not” .5 probability, and the proportion of the population currently classified as poor. It is difficult to understand why the authors’ use the proportion currently classified as poor, but it does highlight difference in results, especially when comparing across countries. For countries with high poverty rates, a relatively large fraction of non‐poor households will be considered vulnerable, while in countries with relatively low poverty rates, relatively few non‐poor households will be classified as vulnerable. While these results are to be expected given the log‐normal distribution, as
distortions and find jobs, the success stories may just crowd out other jobseekers, and it would be better for governments to concentrate their policy efforts in addressing the distortions directly.

In the absence of these government-introduced distortions, the simplest introductory model of labor supply and labor demand, in which the market works to equilibrate the supply and demand for jobs through changes in the wage is actually not a bad approximation for many types of labor in less-skilled jobs in urban labor markets in developing countries. Many firms have no trouble filling the job openings that they have. For example, Groh et al. (2015) conducted a panel survey of employers in Jordan to track how long it took firms to fill vacancies. Most vacancies are filled fast, with only 6 percent requiring more than two weeks. In a field experiment conducted in Sri Lanka, De Mel et al. (2019) find that only 12 percent of microenterprises said they found it hard to find the right worker for routine, physical jobs. Many governments are particularly interested in boosting the workforce in formal jobs in large manufacturing firms. But several studies have found these manufacturing firms do not appear to be that discerning about who they hire. They find workers very quickly and many workers voluntarily quit these jobs – suggesting that these jobs are neither that hard to fill, nor so rationed and sought after that no one leaves these jobs after securing such a position (for example, Blattman and Dercon 2018).<sup>4</sup> So then what are some better reasons for government involvement in the provision of job training and search assistance? There are three main reasons why the private market allocation of labor may be inefficient or inequitable, thus providing a potential justification for the government to get involved. First, search and matching frictions are likely to exist for some jobs, and they not only inhibit individual jobseekers from finding work, but can slow down or prevent the reallocation of workers across sectors and geography that is such an integral part of structural transformation