rafmacalaba/data-use-mentions-extended huggingface dataset:
penditure surveys that adopt different recall periods.<sup>6</sup> We consider two approaches to imputing NSS-compatible consumption into the CPHS. Our preferred approach identifies the relationship between CPHS- and NSSconsumption, and then use this relationship to convert observed CPHS consumption into NSS-type consumption (within the CPHS survey). As a robustness check, we also impute NSS-type consumption on the basis of non-expenditure predictors of consumption that are shared between the CPHS and NSS (i.e. demographics, education, employment, dwelling characteristics, and asset ownership). Both approaches yield qualitatively similar results. We validate our estimates of the levels and trends in poverty and inequality by means of an inclusive set of corroborative evidence that brings in every available source of official and non-official data that could help rationalize the trends in mean consumption, poverty and inequality in India over the last decade.

Our findings are as follows. First, the poverty headcount rate in India is estimated to have declined by 12.3 percentage points since 2011.<sup>7</sup> Our preferred estimates suggest that the poverty head-count rate is 10.2 percent in 2019, down from 22.5 percent in 2011. Second, reductions in rural areas are more pronounced than in urban areas. Rural and urban poverty dropped by 14.7 and 7.9 percentage points during 2011-2019. Third, urban poverty rose by 2 percentage point in 2016 (coinciding with the demonetization event) and rural poverty rose by 10 basis points in 2019 (coinciding with a slowdown in the economy). Fourth, we observe a slight moderation in consumption inequality since 2011, but by a margin smaller than what is reported in the unreleased NSS-2017 survey.<sup>8</sup> Finally, the extent of poverty reduction during 2015-2019 is estimated to be notably lower than earlier projections based on growth in private final consumption expenditure reported in national account statistics. Our analysis stops just before the 6Similar methods have been applied to estimate consistent poverty measures when recent household
|_Type of estimation_<br>This table presents stress tests to estim<br>outliers out, and US Holdings are norm<br>opposed to the 03/2008-03/2009 crisis<br>any local stock market returns. *, **, *<br>US Treasury.|_Time du_<br>ations in Didier et<br>alized by substract<br>period used in this<br>** Denote significa|_mmies, Lehman inte_<br>al., 2010. First three c<br>ing its sample averag<br>paper. In turn, column<br>nce at the 1%, 5%, an<br>**Financial globali**|_raction_<br>olumns presents<br>e and dividing it<br>s 4-10 are prese<br>d 10% levels, re<br>**Ta**<br>**zation and fin**|data from Didier<br>by its sample sta<br>nted for the sam<br>spectively. In ita<br>**ble 9**<br>**ancial recoupli**|_Rep_<br>et al., and their<br>ndard deviation<br>e data but with o<br>lics appears the<br>**ng around the**|_lacing time dummie_<br>methodology. Return<br>. Additionally their c<br>ur methodology. Ret<br>correspondent the p-<br>**crisis**|_s by S&P retu_<br>s are normal lo<br>risis period are<br>urns are dlog(s<br>value. Source:|_rns_<br>cal returns, a<br>defined as 6/<br>tock_index) a<br>Didier et al,.|
|---|-
1|2009-2009|Other|
|**Burkina Faso**|Sub-Saharan Africa|5|2006-2010|Central Bank|
|**Burundi**|Sub-Saharan Africa|5|2007-2011|Central Bank of Burundi|
|**Cameroon**|Sub-Saharan Africa|1|2005-2005|Other|
|**Central African Republic**|Sub-Saharan Africa|1|2005-2005|Other|
|**Chad**|Sub-Saharan Africa|1|2005-2005|Other|
|**Congo, Rep.**|Sub-Saharan Africa|1|2005-2005|Other|
|**Gabon**|Sub-Saharan Africa|1|2005-2005|Other|
|**Ghana**|Sub-Saharan Africa|2|2006-2008|Other|
|**Guinea**|Sub-Saharan Africa|1|2005-2005|Other|
|**Kenya**|Sub-Saharan Africa|5|2006-2010|Other|
|**Malawi**|Sub-Saharan Africa|2|2006-2007|Central Bank|
|**Namibia**|Sub-Saharan Africa|1|2011-2011|Hofinet|
|**Nigeria**|Sub-Saharan Africa|2|2006-2008|Other|
|**Senegal**|Sub-Saharan Africa|1|2004-2004|Other|
|**South Africa**|Sub-Saharan Africa|12|2000-2011|South African Reserve Bank|
|**Tanzania**|Sub-Saharan Africa|1|2009-2009|World Bank Mission April 2009|
|**Togo**|Sub-Saharan Africa|5|2006-2010|Other|
|**Uganda**|Sub-Saharan Africa|2|2010-2011|Central Bank|

_Source_ : Authors’ construction based on Badev et al. (2014) database on housing finance.

**Table A.5: Housing Loan Penetration by Country** **<u>(2011)</u>**

|Country|%|Country|%|Country|%|
|---|---|-
estimator (Silva & Tenreyro 2006) demonstrated by Fally (2015): the solution of the GE system of equations derived from a gravity model can indifferently be estimated (Anderson, Larch & Yotov 2018, Fontagn ́e & Santoni 2021) or computed with a solver Head & Mayer (2014). And when the error term is in a multiplicative form (Anderson et al. 2018) this is equivalent to the so-called “hat algebra” resolution in line for instance with the approach coined as “trade theory with numbers” (Arkolakis, Costinot & Rodr ́ıguez-Clare 2012) – and thus not fundamentally different from what a resolution of a CGE implies.

A natural extension of such modeling approach is to assess the uneven impacts of PTAs (Baier, Yotov & Zylkin 2019), provided that the “ambition” of signed agreements differs strongly. In this paper, we explore the uneven economic impacts deriving from the diverse provisions contained in PTAs. Indeed, competition policy, by allowing foreign competitors to benefit from a level playing field should have more trade creation and less trade diversion effects compared to a simple phasing out of tariffs among members of the PTA. Alternatively, a provision on government procurement might facilitate trade between members of the agreement but increases trade costs vis-a-vis third countries that do not receive the same access to members’ public procurement markets. In other words, the varying content of PTAs may be an important determinant of their uneven trade effects. Providing a new method to assess these effects and offering a first application using new data on the content of PTAs is the purpose of this paper.

Our starting point is to rely on an exhaustive description of the provisions included in PTAs based on a new database on the content of PTAs that has been collected by the World Bank (Mattoo, Rocha & Ruta 2020). We use information on all policy areas (except tariffs) encompassing objectives, substantive commitments and enforcement procedures present in legal texts and available annexes of the 278 PTAs in force and notified to the
_Brazilian spatial labor dynamics Page 21_

**Table 7C. Summary statistics, Non-metropolitan Brazil without North Region**

|**Variable**|**Obs**|**Mean**|**Std.Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Delta ln Wage (2000/1991)|3300|0.0000|0.2901|-1.3942|0.8495|
|ln Teacher qualification in 1991|3300|1.3156|2.1187|-4.6052|4.2370|
|Years of Schooling in 1991|3300|3.6858|1.3320|0.4746|7.1707|
|Total rainfall|3299|1251.7990|367.9959|368.6636|2532.2020|
|Government Accountability|3300|0.9124|0.2827|0.0000|1.0000|
|Delta ln Employment (2000/1991)|3300|0.1103|0.2035|-0.6577|1.2464|
|lnTransport Cost to São Paulo in 1995|3299|7.0422|0.7355|4.1589|8.3051|
|ln Transport Cost to State Capital in<br>1995|3299|5.8691|0.6163|2.1972|7.7619|
|Delta ln Transferences (2000/1991)|3300|0.8448|0.3073|-0.2986|2.3336|
|Delta ln Market Potential (2000/1991)|3299|0.4101|0.1179|-0.2664|1.5442|
|ln Population in 1991|3300|9.2337|0.8054|6.6214|11.9770|
|Dependency Ratio in
for measuring social norms in the context of an important development challenge: reducing open defecation among rural households. The measurement approach disaggregates social norms into two types of beliefs—</mark> beliefs about <mark>what others do (social empirical expectations), and beliefs about what others believe to be normatively appropriate (social normative expectations). In this approach, social norms are understood to be socially conditional preferences to behave in a given way (Bicchieri 2012, 2016). We also draw on a conception in which social norms are more internalized and drive behavior through their incorporation into central cultural concepts (Brennan et al., 2013).</mark> <mark>In rural Uttar Pradesh, between January‐March, 2017, we first measured the relevant cultural and normative barriers to latrine use, conditional on latrine ownership. We then developed and tested behavioral interventions aimed at weakening those social norms and beliefs to increase latrine use. Our data were gathered across three rounds—focus group discussions to inform the development of the relevant norms and belief measurement instrument, a baseline norms measurement survey, and an endline survey following the pilot intervention.</mark> <mark>Findings from the first two rounds confirmed our hypothesis that social normative and empirical expectations were, in fact, associated with individual behavior and influential elements of behavior change. People’s, especially men’s, normative beliefs were closely linked to what they expected others in their reference group to believe, even though they slightly underestimated the extent of pro‐latrine norms (beliefs) among others. At the same time, narrative analysis revealed that reducing open defecation in the context of rural UP also requires direct confrontation with ‘dirtiness” or “gandagi” (Coffey et al., 2016), and their subsequent associations with latrine use.</mark> <mark>To address these barriers, we piloted two simple informational interventions and evaluated their impact,</mark> at least in the short term, on
# Figure 5. International trade costs relative to domestic trade costs

A. Average trade costs in 1995 and 2019 <!-- Start of picture text --> B. Average trade costs in EMDE regions<br>Percent 1995 2019<br>250<br>200<br>150<br>100<br>50<br>0<br>EAP ECA LAC MNA SAR SSA<br>D. Average trade costs for agriculture for<br>EMDE regions in 1995 and 2019<br>Percent 1995 2019<br>400<br>300<br>200<br>100<br>0<br>EAP ECA LAC MNA SAR SSA<br>F. Average trade costs for EMDE regions for<br>manufacturing in 1995 and 2019<br><!-- End of picture text --> <!-- Start of picture text --> Percent 1995 2019<br>200<br>150<br>100<br>50<br>0<br>Advanced economies EMDEs<br><!-- End of picture text --> C. Average trade costs for agriculture in 1995 and 2019 <!-- Start of picture text --> Percent 1995 2019 Percent 1995 2019<br>300 400<br>250<br>300<br>200<br>150 200<br>100<br>100<br>50<br>0 0<br>Advanced economies EMDEs EAP ECA LAC MNA SAR SSA<br>E. Average trade costs for manufacturing in F. Average trade costs for EMDE regions for<br>1995 and 2019 manufacturing in 1995 and 2019<br>Percent 1995 2019 Percent 1995 2019<br>200 250<br>200<br>150<br>150<br>100<br>100<br>50<br>50<br>0 0<br>Advanced economies
One manner of clarifying the theoretically ambiguous relationships between banking system competition and the provision of external financing is to investigate how the degree of competition affects the growth of firms that vary in their dependence on external financing. This can be done using the empirical setup developed by Rajan and Zingales (1998; hereafter RZ). Using sectoral growth data for a large sample of countries, RZ assess the relationship between financial development and growth in value added of sectors that vary in their external financial dependence. The methodology of RZ has been adapted in a number of ways to assess the impact on industrial growth of banking system concentration, the development of trade finance, and the strength of property rights.<sup>1</sup> This paper further adapts the methodology to explore the relationships between banking system competition and growth.

We find that our competitiveness measure is positively associated with countries’ industrial growth, suggesting that more competitive banking systems are better at providing financing to financially dependent firms. We find no evidence that market structure—that is, concentration in the banking system—helps predict industrial sector growth. Thus, the view that market power in banking is good for firm access to financing finds no support. Rather, there is support for the view that more competition may reduce hold-up problems and lower the costs of financial intermediation, making financially dependent firms more willing to seek (and more able to obtain) external financing. These effects are robust to a number of sensitivity tests using different measures of financial sector development, different time periods and

> 1 Other papers that use this approach include, among others: Cetorelli and Gambera (2001), which investigates the effects of bank concentration on sectoral growth; Fisman and Love (2003), which investigates the effects of trade credit usage on sectoral growth; and Claessens and Laeven (2003), which investigates the role of property rights in growth.

5
rate, employment to population ratio, and unemployment rate for the periods January-April 2019 and January-April 2020 to see differences in trends before and during the lockdown.<sup>11</sup> We also use the LFS data to identify how other aspects of the Greek labor market have been affected by the COVID-19 crisis. In addition, to assess the degree of labor market slackness, we calculate the extended labor force indicator which is simply the active labor force plus the “potential additional labor force” (PALF), which takes into account persons seeking work but not immediately available and persons available for work but not seeking work.

> 10 <mark>Information regarding fiscal responses to the economic fallout from the coronavirus was provided by the Bruegel data sets; see</mark> <u><mark>here.</mark></u>

> 11 It should be noted that the pandemic and mitigation measures affected the LFS data collection process, to some extent. From mid-March 2020 onwards, the LFS data collection switched from a blended style of personal and telephone interviews to solely telephone interviews. This decreased the response rate compared to previous months, especially in urban areas. The relevant ELSTAT press release is here.

15
the Mid-Age dummy (6-10 years) is negative and significant suggesting that young firms ≤ 5 years have higher productivity growth than mid-age firms (6-10 years) and as high or higher productivity growth than the mature firms (11+ years).

# **_F. Discussion_**

In this section, we discuss our findings in the context of the existing literature on firm size, and growth. The empirical literature on firm size and growth has largely focused on understanding the role of firm size and age for growth dynamics, and why Gibrats Law, the proposition that firm growth is independent of size, does not hold.<sup>19</sup> In the most recent evidence on this subject, Haltiwanger, Jarmin, and Miranda (2010a, 2010b) study U.S. census data and find that over the period 1992-2005, the large and mature firms (over 500 employees and 10+ years) account for about 45% of employment and most job creation (and destruction). They find that while size is inversely related to growth without controlling for age in the US, there is no systematic relation between size and growth once age is controlled for.

Our results, on the other hand, suggest that in developing economies small firms are significant contributors to employment and job creation. We do not have data on job destruction. In employment growth regressions, we find that size remains a significant predictor for employment growth even after controlling for age. The importance of small firms in developing economies is of significance since we know that in these countries, small firms face many institutional constraints such as limited access to finance (e.g. Demirguc-Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Beck, Demirguc-Kunt, and Maksimovic, 2005), poorly

> 19 See Sutton, 1997 for a review. Early studies such as Birch (1979, 1981, and 1987) found an inverse relation between growth and size and found small firms to be particularly important in job creation. Evans (
Table 6: Analysis of classroom quality, teacher, and structural characteristics on children’s emotional maturity

||no|DV:Emoti|onal maturity|(Raw score)<br>w/ teacher||
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.141***|0.072|0.063|0.063|0.072|
||(0.048)|(0.051)|(0.051)|(0.051)|(0.062)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.191|0.191|0.183|
||||(0.410)|(0.411)|(0.415)|
|% of teachers w/ post secondary edu.|||0.271<br>(0.429)|0.271<br>(0.431)|0.268<br>(0.434)|
|Mean years of teaching|||0.010|0.010|0.009|
||||(0.008)|(0.008)|(0.008)|
|% of teachers w/ no ECED experience|||0.064<br>(0.103)|0.063<br>(0.101)|0.055<br>(0.100)|
Hungary - - - - - - -

-

After documenting that CAP funds tend to reach regions with high poverty rates and with a high share of a country’s poor, the question of whether CAP has actually contributed to poverty alleviation remains pending. It is worth highlighting that by answering this question, we are in no way trying to evaluate the CAP’s overall performance since, as specified earlier in this paper, the program’s original objective is not related directly to reducing poverty in the areas where it is allocated. Nevertheless, this question is important for further policy recommendations, and more importantly, to assess whether CAP is an instrument that supports the successful structural transformation of a country.

# _Poverty and inequality dynamics and the CAP_

With this in mind, we use panel data to determine the impact, if any, that the CAP has had on poverty rates. Despite heterogeneity in the allocation of the CAP across EU member states, on average a positive impact of the CAP on poverty has been observed. Table 10 documents that total per capita CAP payments are associated with a decrease in the poverty rate over time in the EU. The total per capita payments are further disaggregated into the program pillars to explore potential differences in each pillar’s contribution to poverty alleviation. In the individual analysis, the per capita payments of both pillars remain significant and negative in their association with poverty growth. However, when both pillars are jointly analyzed, it seems that pillar 2 is more significant in its contribution to poverty reduction.

**Table 10.** Per capita CAP payments are linked to regions with higher poverty reduction.

||(1)<br>Povertyrate|(2)<br>Povertyrate|(3)<br>Povertyrate|(4)<br>Povertyrate|(5)<br>Povertyrate|(6)<br>Povertyrate|(7)<br>Povertyrate|(8)<br>Povertyrate|(9)<br>
**4 Data**

For estimating the potential gains, our data consist of 3817 randomly sampled rural households from the 1991/92 Household Expenditure Survey (HES) of the Bangladesh Bureau of Statistics. The consumption measure is comprehensive (including all market commodities, with market-based imputed values when they do not entail market transactions). Regional price differences were taken into account by deflating nominal consumption by regional poverty lines giving the estimated cost of basic consumption needs; for further details see Wodon (1996).

We use the primary occupation of the household head (whether male or female) to classify households into the two sectors. The HES provides data on occupations according to 31 categories. To obtain sufficient sample sizes, these categories have been aggregated into five farm groups and seven non-farm groups. The farm groups are: (1) landless agricultural workers, (2) agricultural workers with land, (3) workers in fisheries, live stock, and forestry, (4) tenant farmers, and (5) owner farmers. The non-farm groups are: (1) servants and day-laborers, (2) transport and communication workers, (3) salesmen, service workers, brokers, and middlemen, (4) factory workers and artisans, (5) petty traders and other small businessmen, (6) executives, officials, professionals, and teachers, and (7) non-working heads (retired, unemployed, or students). Table 1 gives summary data by sectors of employment.

Using the head's primary occupation as the criterion for the farm/non-farm classification has its limitations. It does not take into account the secondary occupation of the head, or the occupations of the spouse and other household members, who may well be part of the non-farm sector when the head is in the farm sector, or of the farm sector when the head is in the non-farm sector. However, the extent of such secondary "cross-sectoral" effects is small; only 19% of 12
WPS3694

# **Governance in the Gullies: Democratic Responsiveness and Leadership in Delhi’s Slums***

Saumitra Jha, Department of Economics, Stanford University Vijayendra Rao, Development Research Group, The World Bank Michael Woolcock, Development Research Group, The World Bank

## _Abstract_

We use detailed ethnographic evidence to design and interpret a broad representative survey of 800 households in Delhi’s slums, examining the processes by which residents gain access to formal government and develop their own, informal, modes of leadership. While ethnically homogeneous slums transplant rural institutions to the city, newer and ethnically diverse slums depend on informal leaders who gain their authority through political connections, education and network entrepreneurship. Education and political affiliation are more important than seniority in determining a leader’s influence. Informal leaders are accessible to all slum dwellers, but formal government figures are most accessed by the wealthy and the well-connected.

## World Bank Policy Research Working Paper 3694, September 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We are grateful to the World Bank’s Research Department for funding the survey upon which this study is based. Valuable comments were received from two anonymous referees, Susan Athey, Anthony Bebbington, Liran Einav, Deon Filmer, Ravi Kanbur, Jessica Leino, Michael Lokshin, Pedro Miranda, Paul Shaffer and seminar participants at the American Planning and Geographical Associations, Oxford, Stanford and the World Bank. The data was collected cooperatively between
Finland|25+|Avail.|Citizenship estimated on EC. Educ. attainment & share of 25+ on LFS|
|**France**|Census|Count. Birth|Nat. Inst. of Stat. and Econ. studies|25+|Avail.|-|
|**Germany**|Microcensus|Citizenship|German Mircocensus|25+|Avail.|Citizenship estimated on EC. Educ. attainment & share of 25+ on LFS|
|**Greece**|Census|Citizenship|European Council + Labor Force Survey|Estim.|Estim.|-|
|**Hungary**|Census|Count. Birth|Hungarian Central Statistic Office|25+|Avail.|-|
|**Iceland**|Admin. register|Count. Birth|Stat. Iceland|Total|Not Avail.|-|
|**Ireland**|Census|Count. Birth|Central Stat. Office|25+|Avail.|Count. birth estimated on Irish Census. Educ. attainment & share of 25+ on LFS|
|**Italy**|Census|Citizenship|National Statistical Institute|25+|Avail.|Citizenship estimated on EC. Educ. attainment as the rest of OECD|
|**Japan**|Admin. register|Citizenship|Ministry of Justice|Total|Not Avail.|-|
|**Korea**|Population register|Citizenship|National Statistical Office|Total|Not Avail.|-|
|**Luxemburg**|Census|Count. Birth|Nat. Inst. of Stat. and Econ. studies|25+|Avail.|-|
|**Mexico**|Census|Not Avail.|None|Total|Not Avail.|-|
|**Netherlands**|Census|Count. Birth|Statistic Netherlands|15+|Estim.|Citizenship estimated on EC. Educ.