rafmacalaba/data-use-mentions-extended huggingface dataset:
In this atmosphere, new firm formation and investment are challenging for entrepreneurs that are not members of existing networks. Connections matter for access to financial resources, quality inputs, skilled labor, and information on technology and markets. Network “insiders” have better access to these productive resources and therefore have larger size at entry, higher productivity and faster growth rates, as we have shown. Relationships also matter for entering certain business activities. New entrants have to deal with established market participants and create new business relationships. Some way has to be found to establish trust-based relationships and to enforce contracts. New investors with contacts in these business activities, because of referrals from other network “insiders” or relatives, have a distinct advantage. As a consequence, entrepreneurs in SSA are inclined to enter businesses where they are known and connected.<sup>9</sup> Ultimately, patterns of network specialization are established in specific activities, and information sharing, referrals, and existing relationships cause these patterns to persist over time. Distinct patterns of ethnic concentration in particular businesses are quite evident in the RPED enterprise data (Biggs and Srivastava 1996; Bigsten et al 2000). This zoning of economic activity by business networks has distributional consequences. Networks controlling highly profitable activities do better, and the resulting differentials in income that arise between groups can persist. Non-members of the network are in effect excluded from many opportunities. Exclusion and the persistence of income differences can lead to conflict, reduced investment, and capital flight.

Partition of economic activity by networks also influences the allocative efficiency of financial and human capital. Where business activities are controlled by

> 9 This has some similarities with what studies find in more developed countries. Entrepreneurs tend to start up businesses in industries where they have “experience” (Audretsch 1995).  Experience implies connections, as they are similarly useful in advanced countries for learning and getting access to resources and markets.  But in developed countries, where there are strong market institutions and more generalized trust, experience relates more
# Meta-analysis data

The primary focus of our paper is a meta-analysis of agglomeration productivity elasticities differentiating between developing and developed countries, where our paper follows the approach outlined in Stanley et al (2013) to construct the sample of studies (further details are given in the Appendix).

We build on the meta-analysis data of Melo et al. (2009) and Ahlfeldt and Pietrostefani (2019) by extending their sample on elasticity estimates for developing countries. We use a combination of keywords, such as “agglomeration” or “density” or “urban”, and “elasticity” or “productivity” or “wages”, applied to the academic databases EconLit, Web of Science, and Google Scholar. We supplement the keyword search by conducting an analysis of citation trees of key published and working papers by eminent researchers in the field and papers that inspired the study.<sup>4</sup> We also consulted with experts in academia (including the University of Pennsylvania, Johns Hopkins University, and Oxford) and practice (including the World Bank) to identify ongoing work in the field. The initial sample included peer-reviewed papers, working papers of universities, research institutes and international organizations (World Bank, NBER, CEPR, CESIfo, and IZA), chapters in books and conference proceedings.

We restrict the initial sample to papers with one of four outcome variables --- labor productivity, wages or earnings, TFP and output using a production function -- and four agglomeration measures: population size, population density, economic density and market potential. To ensure comparability, we only retain studies that either estimate unit-free agglomeration elasticities, or those that could be converted to an elasticity (e.g. semi-elasticities). Estimates reporting analysis pooled across several countries are excluded; country specific estimates are retained.

To test and correct for publication bias, we also collect standard errors of estimates either from the papers or by requesting the authors. Studies without the standard errors needed for precisionweighting are excluded from the meta-analysis.
in primary school can have a serious impact on a child’s cognitive development.<sup>7</sup> We use the children’s residence (specifically communities known to have unimproved sanitation), and the primary school enrollment data at the time of the survey. The goal of the paper is to determine whether exposure to unimproved sanitation early in life negatively affects the likelihood of being enrolled in primary school between ages 6 and 9. Are children exposed to unimproved sanitation facilities early in life less likely to attend primary school?

Our findings indicate that unimproved sanitation has a negative and significant impact on primary school enrollment. A child’s early exposure to unimproved sanitation decreases that child’s likelihood of being enrolled in primary school by 6 to 9 percentage points on average compared to a child with access to improved sanitation. The effect is particularly strong – a difference of 8 to 10 percentage points – for children ages 6 to 7. It is also strong for children living in rural areas. We find that the results are statistically robust to errors due to potential omitted variable bias.

> 5 We define “community” in the data set as a household cluster.

> 6 We define school enrollment as primary school enrollment for children between 6 and 9 years.

> 7 See, for example, Sclar et al. (2017) and Cole and Cole (1996), who reviewed findings from several studies conducted in lowand middle-income countries where schooling is not universal. For example, the memory performance of schooled children is comparable to their American counterparts, unlike unschooled children from the same village (Cole et al., 1971).

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settings in which some but not other components of the labor calculation face considerable variability (for example, see Dupas, Robinson, and Saavedra 2015).

Clearly, survey designers should tread lightly when asking questions about the frequency of nonsalient, irregular events. But what is the alternative? The benchmark weekly visit approach used here is an expensive one that is unlikely to be a realistic prospect at the larger scale necessary for national labor surveys. A result that comes out forcefully in this study is the strong performance of the phone surveys, which show little difference from the benchmark weekly visit survey. Crucially, given the significantly lower transportation costs involved, phone surveys are also, by design, likely to be less expensive to implement than face-to-face high-frequency alternatives, but how much cheaper?

We use the cost data available through our survey experiment to mimic a scenario whereby an existing household baseline survey adds either short face-to-face surveys or short phone surveys. The results of this costing exercise are presented in table 11. We assume that all fixed costs related to training and preparation have been subsumed in the baseline interview and focus instead on the increase in the variable costs of conducting 1, 10, 20, 25, or 30 visits or phone calls. Phone calls are much less expensive that revisits. .The cost of a single round of phone surveys is 6 percent of the cost of the baseline survey. This estimate is close to the 7 percent reported by 19
, the reduction in standard errors from more data is offset by the smaller average effect than is given by the point estimates in round 2, and so we do not detect find significant results from pooling that were not significant in round 2 before pooling. The last two columns use a different approach to pooling data to improve power. In the round 2 survey, firms were asked for the last month’s sales and profits, and then also asked for monthly profits for October, November, and December 2018, and monthly sales for each of the six months from July 2018 through December 2018. We stack this data in a panel, and estimate the pooled average impacts on 4 months of profits and 7 months of sales. The results are again similar to those in Table 5 for round 2 only.

Appendix Table 6.4 considers two additional checks. The first is to examine whether treatments changed the reporting of profits and sales, by examining the number of reporting errors. We measure five such errors, which arise from firms reporting profits larger than sales, monthly totals larger than annual totals, or total wage and salary bills greater than total sales. Since the main impacts of insourcing and outsourcing were on marketing, rather than accounting, practices, we do not expect reporting accuracy to have changed with treatment. Column 1 shows marginally significant evidence of reporting improving with insourcing at the one year follow-up, but column 2 shows there are no significant treatment effects on reporting errors in the two year follow-up. We conclude that our results are unlikely to be driven by changes in reporting. The second check is to examine levels of profits and sales, rather than the inverse hyperbolic sine. As noted in the text, we think a proportional rather than level treatment effect makes more sense, and levels are 72
**3.3 Delivery modes and market access channels**

Delivery of services often has an element of ”jointness in production” in the sense – – that complementary inputs including other services are needed to allow effective exchange of (trade in) a service to occur. While this is often also true for – goods various intermediate inputs are needed to produce a unit of final output – jointness in production for services goes beyond this in the sense that often the buyer or consumer is part of the production process. Think for example of management consulting or a doctor’s visit: the quality or value of the transaction will be determined in part by the information that is provided by the client or patient. These complementary inputs can be related to addressing the location or proximity requirements that must be satisfied for many service transactions to be feasible. Where there is a need for proximity in exchange, factors like distance place a cost burden on certain forms of services delivery. Christen and

> 12The figure is taken from Francois, Manchin, and Norberg, 2012. Productivity calculations are based on EU-KLEMS data. In Section 7 we use these data in an illustrative policy experiment. This includes regression analysis of the data in the figure.

> 13Noteworthy contributions include Arnold et al. (2011) and Fernandes and Paunov (2012).

13
. For example, the SCI includes no indicators of some important surveys such as the labor force surveys

> 4 There is also a key methodological difference between the SCI and other assessment tools. The latter methods collect data directly from national statistical offices’ staff or local experts. While this procedure may provide more in-depth analysis and uncover finer details in the organization of an NSS, it incurs high costs and is (far) more time-consuming. Furthermore, even the best evaluator can bring personal biases, non-uniform conceptions of capacity, or other subjective elements. Direct interviews of government officials might bias responses and complicate comparability across countries. On the other hand, since the SCI is based on publicly available data, it can provide an assessment of a country’s statistical capacity in an internationally comparable and cost-effective manner.

3
nationally representative probability sample of 18,671 households in 2008 and 2009. In 2008, the survey was conducted monthly with 720 and annually with 8,640 sample households between January 1 – December 31. That is, survey data of the first 720 households were collected in January. In February, the survey was carried out on a different set of 720 households. This rotation continued until the end of December. The 2009 HBS followed the same methodology but the size of the survey was expanded to contain monthly 1,050 and annually 12,600 sample households. Because each household appears in the dataset only once, the dataset supports a pure pooled cross-section analysis.

The survey is conducted through in-home interview. Each interviewer recorded the data on consumption expenditures during eight visits to the household throughout the survey month, including one visit prior to the survey month, twice during the 1<sup>st</sup> and 2<sup>nd</sup> weeks, once during 3<sup>rd</sup> and 4<sup>th</sup> weeks and once following the end of the survey month. Interviewers also inventory the households’ appliances, physical characteristics of the residence and collect demographic information. Further details about the HBS data and survey design are available in Turkish Statistical Institute (2007).

The survey data are then combined with the actual monthly rate schedule. The Turkish electricity tariff data have two advantages that are fairly unique to the study. First, in the retail sector, Turkey has applied a price equalization mechanism to maintain a nationwide uniform tariff until 2011. Under such a system, all residential consumers face a uniform flat-rate price schedule. The simplicity of the rate structure avoids specification difficulties in identifying marginal electricity prices – a common challenge for demand studies. Second, the tariff reform in 2008 introduced three price hikes for residential electricity that jointly increased the price by more than 50 percent from the previous year. The price change by virtue of its magnitude and exogeneity
24

**30. The impact of deleveraging could be particularly significant for those countries that lack well-developed capital markets and alternative sources of stable nonbank financing to substitute for bank credit.** The CEE region, in particular, has been experiencing a tightening of credit standards and a sharp slowdown in loan growth, given strong ownership links with Euro Area banks, an overreliance on nondeposit funding to finance credit expansion (figure 17f), and relatively shallow capital markets. Constrained credit could particularly affect SMEs because only the largest companies would likely be able to deflect some of the impact by relying on bond market financing.

**Figure 18. Foreign Bank Ownership, 2010**

<!-- Start of picture text --> AS CEE MENA SSA WH AE OA<br>Source: BCL Survey<br>KORSGPPAK IDNMYSPHL IND THA BTN ESTSVK BIH HRVROM HUNBGRLTU YUGLVA ARM POLUKR MDA SVNBLR RUSTUR EGY LSOMOZ NAMUGASYC MUSSLEKEN ZAF SLV MEX PANURY PER PRYCHL CRIARGCOL BRAGTMDOM LUX MLT FIN IRL BEL CYPNORGRC DNKGBRITA AUT FRACHE DEUESPISR ISL AUS<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: World Bank. AS: Asia; CEE: Central and Eastern Europe; MENA: Middle East and North Africa; WH: Western hemisphere; AE: Advanced economies; OA: Other advanced economies.

# **V. IMPACT ON EMERGING MARKET CREDIT CONDITIONS**

**31. Euro Area problems are already producing ripple effects around the globe.** Recent lending surveys by the Institute of International Finance (IIF) suggest that emerging markets have also been facing tighter credit conditions since the third quarter of 2011. Moreover, survey respondents explicitly stated that they had been adversely affected by the fallout of the euro debt crisis. Although less drastically than in the last quarter of 2011, the deterioration in lending conditions has continued for the third consecutive quarter in 2012:Q1 (figures
These district-level results do not account for changes in school attendance patterns that are the product of changes in the characteristics of children and their families. We now turn to multivariate analysis based on the 1994 and 1997 LSMS to see whether the positive effects of FONCODES expenditures are still apparent when we control for other factors.

We consider four measures of educational outcomes in the multivariate regressions: the school attendance rate for children aged 6-13, the school attendance rate for children aged 14-16, the probability that children aged 8-10 will be at least at the appropriate grade for their age, and the amount of time (in minutes) it takes children aged 6-13 to get to school1<sup>9</sup> In each case (except for the measure of school proximity) we run regressions at both the "household" and "individual" levels. The "individual" regressions take a given child as the unit of observation, while the "household" regressions take a given household which has at least one child in the relevant age group as the unit of observation: for example, the first set of individual regressions estimates the probability that a child aged 6-13 is attending school, while comparable household regressions estimate the probability that all of the children aged 6-13 in a household are attending school. All of the results we present for regressions with dichotomous dependent variables (that is, those for the probability of attending school or being on track for a given age) are based on the linear probability model. The results from probit regressions (not reported) are very similar throughout.

We start by showing results of "naYve" regressions in Table 6.2. These specifications correspond to equation (2) above, when the sample is limited to observations in the 1997 LSMS (similar regressions were estimated using the 1996 INEI, with similar results). The "naYve" estimates illustrate the problems with drawing inferences from bivariate relationships between treatments and outcomes based on a single cross-section of data. The coefficients on FONCODES expenditure when no controls
undeclared revenues, opening the door to penalties or informal payments required by tax officials. Furthermore, our findings suggest that the magnitude of the relationship between the demand side bribe payments and tax evasion is twice as large as found in previous studies not differentiating between bribe behavior categories (Ufere et al., 2012; Gauthier and Goyette, 2014; Alm et al., 2016).<sup>7</sup> In public procurement, our results suggest on the contrary that corruption tends to be mainly a supply-side phenomenon, with bribe transactions generally initiated by firms in order to secure public contracts. Firms supplying a bribe without previous request by officials (active bribery) is associated with a 17% increase in the bribe paid to secure a government contract, more than four times the effect observed on the demand side of bribery (passive bribery). The relationship between bribe levels and public contract is also twice as large as the one found by D’Souza and Kaufmann (2013) who did not differentiate between bribe-behavior types.

This paper contributes to the burgeoning literature on corruption at the firm-level by identifying empirically who initiates the act of corruption in different settings, the demand and the supply sides, using a large sample of firms in developing countries.<sup>8</sup> Given the informal nature of corruption, it is very difficult to document and study the contracts between the initiator and the receiver of a corrupt deal. As suggested by Tanzi (1998), identifying whether an act of corruption is briber-initiated or bribee-initiated (i.e. active or passive) has important implications for anti-corruption policies. As far as we know, our paper is the first to examine empirically who initiates corruption in different contexts and across countries. Clarke and Xu (2004) discuss supply and demand of corruption by bribe takers and payers, using WBES data but do not separate the two sources of bribery. More recently, Freund et al. (2015), similarly make use of WBES data, examining how
0.183 0.247 2 57.46<br>health 0.126 3 0.098 0.220 6 0.074 0.145 4 66.11<br>financial assets 0.076 4 0.054 0.288 4 0.147 0.141 6 48 88<br>edu cation 0.043 5 0.032 0.260 5 0.117 0.143 5 54.99<br>mobile phones 0.010 6 0.007 0.324 3 0.169 0.155 3 47.96<br># of Households 6770<br># of Interviewers 428<br><!-- End of picture text -->

||||Panel A: C<br>|ambodia<br>||||||
|---|---|---|---|---|---|---|---|---|---|
|ee<br>|||R<br>|esults from <br>|odelsWi<br>|th Covariate<br>|s<br>|||
||Variance|Rank|of<br>Residual|ICC Total|Rank|ICC-HH|ICC-I|Rank|ICC-I in<br>ICCTotal|
|Individual Ox Module (Equation 5)||||||||||
|land|4481|1|2.509|0.440|4|0.368|0.072|7|16.31|
|migration|0.481|2|0.326|0.323|6|0.252|0.071|8|21.95|
|time use|0.381|3|0.141|0.631|1|0.345|0.285|1|4526|
................................................................................... 28|
|Table 1: Official poverty rate and national poverty lines ............................................................................. 6|
|Table 2: Poverty estimates using income class international poverty lines ................................................. 7|
|Table 3:Comparative Overview of Post-Conflict Poverty Estimation Studies in Syria ................................ 9|
|Table 4: Expenditure module of the 2021 and (revised) 2022 Demographic and WASH survey ............... 14|
|Table 5: Analysis of relation between food share and total household expenditure ................................ 15|
|Table 6: Spatial price deflator based on WFP Standard Food Basket (SFB)
wages in the _factory sector_ and the differences in wages among highly skilled workers and in some geographic areas. But evidence to date that would describe labor market opportunities for the public sector, as a whole, is surprisingly scarce.

In this paper, we use nation-wide, representative survey data to ascertain labor market opportunities for workers in the public sector. We employ a set of econometric techniques to control for the differences in human capital and other characteristics of the workforce in the public and private sectors and control for unobserved characteristics that could be correlated with wages and individuals’ chances to join a particular sector.

Examination of wages in public, formal-private and informal-casual sectors shows that differences in wages between workers in the public and private-formal and informal sectors are positive and high. In 1999-00, the average real wage in the public sector was about 2.1 times that in the organized private sector. The difference in real wages between the public and private-informal sector is even larger at 3.8 times. The public sector tends to employ workers with more human capital. Since the labor market places a premium on more education and experience, adjusting for these differences in characteristics between public and private sector workers reduces the size of the wage differential. To obtain robust estimates of the adjusted wage differential, we employed three methods, each relying on a different set of assumptions about the process of job selection and wage determination. The public sector wage premium remains, ranging from 62% to 102%, over private-formal, and from 164% to 259% over casual-informal sectors, depending on the choice of the methodology.

The wage differentials tend to be higher in rural as compared to urban areas, and are higher among women than among men. The wage differential also tends to be higher for low-skilled workers. Moreover, there is considerable evidence of an increase in the wage differential between 1993-94 and 1999-00, no matter which of the three adjustment methods is used.

This paper is organized as follows. Section 2 presents the salient features of India’