rafmacalaba/data-use-mentions-extended huggingface dataset:
documented by Ceritoglu et al. (2017); Rozo and Winkler (2019); Altindag et al. (2020). Returnee inflows may be fully absorbed by the informal sector, increasing the informal market share, as informal businesses are able to offer lower wages than the formal sector, as found in Rozo and Winkler (2019) and Altindag et al. (2020).

There are at least three reasons that justify a separate analysis for the informal sector in the context of El Salvador (Banegas and Winkler, 2020). First, informality in El Salvador is high. About 70 percent of workers are employed in the informal sector and most jobs are created by micro firms and self-employed in the informal sector. Second, informality limits the growth of formal firms. Unfair competition from informal businesses is claimed as a barrier for formal firms to grow. This is part of a vicious cycle that is difficult to break, as informal businesses provide most of the jobs, but still pose a negative externality to firms that are the most productive. Moreover, micro firms in the informal sector do not tend to grow to become medium-sized firms or to formalize and among the higher productivity sectors, those that expanded the most are those with higher informality levels. Finally, informal employment is considerably more sensitive to economic growth than formal employment: while a 1-percent growth in GDP was accompanied by a 0.8 percent increase in the number of informal jobs, such figure with respect to formal jobs was only about half that value.

To test this channel, we separate our sample of formal firms by the probability that a sector is more or less prone to informality competition. We use data from the household surveys and split the sample according to industry codes in two groups of firms using the 2011 STEP survey. If a firm was operating in a sector that tends to have high levels of informality (e.g., agriculture, construction, wholesale and retail, restaurants, and others) or low levels of informality (
explained by the variables. In general, obstacles to trade are higher in countries that are landlocked, poor, vast and unpopulated.

The SADC dummies indicate that SADC countries do not face unusually severe obstacles to trade (conditional on geography, population and income) relative to the rest of the world. The obstacles to trade are high, but these reflect particular geographical constraints and correspond closely with their level of development. In fact, the results based on the ETI reveal that the overall trading environment and market access and border administration in particular (but not transport and communications infrastructure) are on average better in the SADC region than the rest of the world, conditional on GDP, population and geography. SADC performs poorly in terms of the timeliness with which its shipments reach the consignee.

Regressions including individual country dummies reveal relatively high obstacles associated with logistics in Angola, Botswana, Namibia, Mauritius and Zambia (see Table 10 and Table 11 in Appendix 2). Further, the SACU members, Angola, Zambia and Malawi perform poorly relative to their peers in terms of the required documents, time and cost of exporting and importing. In contrast, Madagascar, Mauritius, Mozambique and Tanzania perform well relative to their income levels.

This benchmarking analysis suggests that trade is not particularly constrained in SADC compared to other countries at similar levels of development, although there is variation at the country level. This does not imply that further investments in reducing obstacles to trade will not enhance trade flows from SADC countries. However, investments in these areas will also require an analysis of the comparative costs and (trade) benefits relative to improvements in other institutions rather than those specific to the trade environment. The results are likely to vary by country, which motivates country-level analysis that ideally uses firm level data.

Furthermore, trade-related reforms require consideration of the complementarities between the various policy constraints. Building multilane highways will not raise trade if trucks must wait at the border. Port improvements would have limited impact if the problem is getting goods to the
**Figure 4: Oil prices and perception of standard of living (% of adults)**

<!-- Start of picture text --> Iraq Export Prices Standard of living is<br>(US$/bbl) getting better<br> 120 60%<br> 100 50%<br> 80 40%<br> 60 30%<br> 40 20%<br> 20 10%<br> - 0%<br>2013 2014 2015 2016 2017 2018 2019<br> Average Iraq Export Price (US$/bbl) Standard of living is getting better<br><!-- End of picture text --> Sources: Gallup World Poll, 2019, and Iraq Ministry of Oil.

# **_3.3 Job prospects_**

Consistent with perception of declining living standards, adults in Iraq are pessimistic about their job prospects. By 2019, only 3 in 10 believed that it was a good time to find a job. Job perceptions vary by ethno-religious groups. Only 17 percent of Shia adults believe that it is a good time to find a job, while the share is 24 percent among Kurds and 34 percent among Sunnis. There are no significant age or gender gaps. Almost all Iraqi youth (95 percent) believe that personal connections, or wasta, shape one’s employment opportunities—the highest rate in the region (World Bank 2020, Raz 2019). Public concern about the government’s capacity to address this demand for jobs may be aggravated by a general feeling that one’s “situation can only improve with government assistance or salaries,” expressed by 70 percent of respondents (National Democratic Institute 2019).

# **4. National Institutions**

Frustration with perceived corruption, insecurity, and economic instability can be key in determining accountability and confidence in the state. Certain populations, especially those who are traditionally 8
# **3. Data description and main variables**

# 3.1 _Data sources_

Our primary data source consists of firm-level survey data collected by the World Bank's Enterprise Surveys (WBES). The WBES focus on formal or registered private firms with five or more full-time permanent workers. The survey incorporates the entire manufacturing sector but excludes agriculture, extractive industries, and certain service industries like finance and health care. The WBES is nationally representative of the targeted private sector. All the surveys use a common sampling methodology, stratified random sampling, and a common questionnaire. The sampling stratification is done on firm size, industry, and location within the country. Sampling weights provided by the WBES are used in all the regressions.<sup>1</sup> Our baseline sample includes all SMEs in the manufacturing sector. Following the definition used by WBES for stratification purposes, SMEs are all firms with fewer than 100 fulltime permanent workers. Large firms (100 or more workers) have very different financing needs and access than SMEs and therefore they are analyzed separately. The focus on the manufacturing sector is to keep the sample relatively homogenous. Our empirical strategy is based on change over time in credit supply in a country. Thus, our sample includes all developing and emerging countries with multiple rounds of WBES administered across years. We note that our sample is a repeated cross-section and not a firm-panel. The WBES tracks firms over time but the observed attrition rates are quite high to generate sizeable panels. Our baseline sample includes 15,460 manufacturing SMEs in 47 countries. Table A1 in the Appendix provides the list of countries covered along with the survey years.

> 1 The firm-level WBES data that we use are publicly available at <u>https://www.enterprisesurveys.org/en/enterprisesurveys. All the other data sources that we use are also publicly</u> available.

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11 investment to the capital stock) is lower for this group of firms. The ratio of liquid assets to total capital is no different for firms that never had access to long term credit and those that always had it, although it is higher for companies that have had long term financing during some years. The results obtained when firms are partitioned according to quartiles are similar with two interesting exceptions. Firstly, the (operating) profit rate is the lowest for the group with a maturity length in the top quartile relative to the other firms. Secondly, the liquid asset to total asset ratio seems to be monotonically decreasing with maturity. The results for the SCI and the SC2 sample are very similar. The only difference is that profit is always positively correlated both to the access and length of maturity.

# **111.2. ECONOMETRIC EVIDENCE ON** ACCESS **AND MATURITY**

We now discuss some of the econometric evidence on the access to long term debt. In Table 9 we report in the first two columns the results obtained using probit and logit models for the probability of obtaining long term credit for the SC 1 sample. In order to take into account of the panel data nature of the data, in the last two columns we also report the results for the probit random effects model (see Butler and Moffit (1982)) and for the fixed effect logit model (see Chamberlain (1980)). In order to minimize endogeneity problems, at least in the logit models with fixed effects, we have included all regressors as beginning of period (if stocks) or last period values (if flows). All equations in this subsection include also sector and year dummies (not reported here for brevity
because of male unemployment rate. The female unemployment has increased since 2009.. Labor force participation rate is special concern among the female population. Decreasing from 28 percent in 2000 to 22 percent in 2018, FLFP reflects the absence of urban job creation for women, and probably discourages many women from FLFP altogether (Figure 2.9).

**Figure 2.8: Unemployment rate has decreased for men, not for women** Percentage of the active population

**Figure 2.9: Labor force participation rates** Percentage of the working age population

<!-- Start of picture text --> 16 80%<br>12 60%<br>Total Total<br>8 Male 40%<br>Male<br>Female<br>Female<br>4 20%<br>Source: Estimates based on the Enquête Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force Nationale sur l’Emploi (ENE; Labor Force<br>Survey) Survey)<br>2000 2003 2006 2009 2012 2015 2018 2000 2003 2006 2009 2012 2015 2018<br><!-- End of picture text --> The sectorial distribution of female employment may help explain the declining FLFP rate. In 2000, the manufacturing sector represented 18 percent of total female employment. In 2014 it accounted for just 11 percent (Figure 2.10) as women lost jobs in apparel. This is different from the experience of many developing countries (Artuc et al., 2019). Female employment in agriculture, however, remained about the same. The share of women in the service sector overtook manufacturing. The main point is that urban job creation has not been enough to offset rural migration and the increasing working-age population. Women encountered fewer working opportunities while opportunities for men in a few capitalintensive sectors expanded.

# **Figure 2.10: Share of manufacturing in female employment considerably decreased, while agriculture
analysis relies on the 2019/20 Cambodia Socio-Economic Survey (CSES) and 2019 administrative data on taxes, transfers, and social spending. The CSES is collected by the National Institute of Statistics (NIS) and the fiscal administrative data is compiled by the Ministry of Economy and Finance (MEF).

The paper seeks to answer the following questions: How much are social spending, subsidy, and tax policies contributing to redistribution and poverty reduction goals? How are specific taxes and government spending contributing to equalization and poverty reduction? How would fiscal policy reforms that change the size and/or progressivity of a particular tax or benefit affect inequality and poverty? Within the limits of fiscal prudence, what could be done to make taxes and transfers more “propoor”? Such evidence can inform policy makers and other stakeholders in assessing existing fiscal instruments and in designing reforms.

The 2019 fiscal system in Cambodia, and many of its elements, reduced inequality but also left poor households out-of-pocket in the short term.<sup>7</sup> According to the Fiscal Incidence Analysis using the CEQ methodology, the modeled fiscal system in Cambodia decreases the Gini index by 0.95 percentage points. Spending on primary education shows the largest benefit on inequality, complemented by direct taxes,

> 6 See Lusting and Higgins (2018) for a description of the CEQ methodology.

> 7 Note, some of the findings of this paper are featured in the 2022 Cambodia Poverty Assessment.

5 World Bank (2022a).

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numerous empirical papers which examine the trade-migration nexus. Gould distinguishes an _information_ channel through which migrants reduce the transaction costs of trade, from a _preference_ channel via which migrants foster trade flows through demanding domestically produced goods. Collectively, these two channels may be termed _direct immigrant links (direct links_ henceforth), since they pertain to the effects of migrants whose country of birth relates to either the importing or the exporting nation. In other words migrants which directly affect trade flows either to or from their country of origin.

Rauch (2001), an advocate of the „network‟ view of trade, stresses the role of business contacts and social networks that promote „trusting‟ contractual arrangements and overcome informational asymmetries and informal trade barriers. These arguments are akin to Gould‟s transaction cost mechanism or information channel. Rauch and Trindade (2002) examine the extent to which concentrations of ethnic Chinese<sup>4</sup> – and not the absolute levels - foster trade. The key additional insight proffered by Rauch and Trindade therefore is that _third-party_ migrants, the ethnicity of which pertains neither to the importing or exporting nation, may also promote bilateral trade flows. This is what Felbermayr, Jung and Toubal (2009) refer to as indirect effects.

The majority of papers in the trade-migration literature, implement gravity models and build upon Gould‟s insight to test these links in a variety of (predominantly OECD-centric) geographical settings, most commonly focusing upon a single country and her trading partners.<sup>5</sup> Head and Ries (1998) investigate immigrant-links in Canada, Dunlevy and Hutchinson (1999, 2001) examine historical data for the United States, Girma and Yu (2002) study the impact of migration on trade in the UK, Bryant et al (2004) for New Zealand, Blanes-Cristobel (2003) in Spain, White (2007b) for Denmark and Hatzigeorgiou (2010) in Sweden. At the _country_ level, only three papers examine direct-links amongst groups of countries. Hatzigeorgiou (
this exercise for India’s is 29.9 percent. Unlike the remarkably high rates reported by some studies, this figure is quite close to the corresponding official estimate. However, we also find important gaps with the official estimates at more disaggregated levels. Consistent with those other, recent studies, we show that many places administratively defined as villages have urban characteristics. But we also find that a significant share of “statutory towns” and “census towns” would be better classified as rural. And there are differences across states as well.

While these subnational gaps with official estimates could be interpreted as the outcome of prediction error, we show that the sign of the gaps is consistent with the fiscal incentives and statistical innovations characterizing the administrative classification of places in India. We also show that our estimate of the urbanization rate is not an outlier relative to estimates based on global urban layer products, as the main discrepancy with them arises from the population data used, rather from the underlying land use classification.

Beyond offering new insights into the debate on urbanization in India, the exercise in this paper illustrates several strengths of the proposed methodology. First, the methodology is holistic. Because it relies on human judgment to assess the “urbanness” of places, it allows the multiple characteristics of a place to be evaluated jointly. Second, it makes the most of existing data. A growing number of studies are using satellite imagery data to identify urban areas, contributing to urban studies (Donaldson and Storeygard 2016). However, our analysis shows that relying on built-up cover or nighttime lights alone can be insufficient. And third, our methodology is scalable. High-quality Google images are becoming available for an increasing number of places, and human judgment can be crowd-sourced efficiently nowadays.

A legitimate concern with our methodology is its reliability, in the sense that small changes to its design could result in important changes in its outcomes. However, the variants tried in this paper suggest that 4
1), 105-118.</mark> O’Brien Ed., Konrath S.H., Gruhn D., and Hagen A.L. (2013). Empathic concern and perspective taking: linear and quadratic effects of age across the adult life span. _The Journals of Gerontology: Series B, 68(2),_ 168–175. <u><mark>https://doi.org/10.1093/geronb/gbs055</mark></u> <mark>Organisation for Economic Co-operation and Development.</mark> (2021). Beyond Academic Learning: First results from the survey of social and emotional skills. _OECD Publishing._ <mark>Organisation for Economic Co-operation and Development.</mark> (2015). Skills for Social Progress: The Power of Social and Emotional Skills. _OECD Skill Studies._ <mark>Onyeizugbo, E. U. (2003). Effects of gender, age, and education on assertiveness in a Nigerian sample.</mark> _<mark>Psychology of women quarterly</mark>_ <mark>,</mark> _<mark>27</mark>_ <mark>(1): 12-16.</mark> <mark>Organisation for Economic Co-operation and Development. (2015). Skills for social progress: The power of social and emotional skills.</mark> _<mark>OECD Publishing.</mark>_ <mark>Paola, M., Gioia, F., and Scoppa, V. (2018).</mark> Teamwork, Leadership and Gender. _IZA Discussion Papers_ , No. 11861.

Park, G., Yaden, D.B., Schwartz, H.A., Kern, M.L., Eichstaedt, J.C., Kosinski, M., et al. (2016). Women are Warmer but No Less Assertive
GHGE.<sup>13</sup> The OECD IOTs contain information about how each sector’s gross production is distributed among all economic sectors (intermediate demand) and the final demand. The latter comprises households’ domestic demand, non-profit institutions’ domestic demand, government’s domestic demand, capital formation, change in inventories, exports, and imports. The sector classification corresponds to 2-digits of the 4<sup>th</sup> revision of the International Standard Industrial Classification of Economic Activities (ISIC Rev. 4), with some sectors grouped according to their nature. This information generates the matrix of coefficients and the vector of final demand. The IOTs matrix has the structure described in Table 3: i represents the demand from sector _j_ for production of sector _i_ , represents the added value generated by sector _j_ , and represents the gross production of sector _j_ . Notice that the sum ii yy vv of the intermediate demand plus added value equals the sector’s gross production. Then, the technology ii YY coefficients are calculated by dividing each sector’s intermediate demand from a specific sector by that sector’s gross production. In other words, each one of the technology coefficients represents the ratio i . In this paper, information about imports is not included in the final demand vector since they do not relate directly to the production process of each sector. Then, only domestic demand and exports are ii yy /YY considered for the estimations.

**Table 3 – Input-Output table structure**

||**_Sector 1_**<br>|**_Sector 2_**<br>⋯<br>|**_Sector n_**<br>|**_Final demand_**<br>|
|---|---|---|---|---|
|**_Sector 1_**|y11<br>|y12<br>⋯<br>|y1n<br>
And last, how much would such an intervention cost in terms of dollars per life-year saved and what does this imply in terms of cost-effectiveness?<sup>4</sup> To answer these questions, we analyze 38 developing countries where recent Demographic and Health Surveys (DHS) are available. Building on a previous paper (Günther and Fink, 2010) we first estimate a logistic model to quantify the effect of improved water and sanitation on child mortality. Based on the estimated relation between water, sanitation and child mortality, we compute counterfactual child mortality rates under various water and sanitation improvement scenarios in a second step. In a third step, we compare the predicted mortality changes to the country-specific child mortality goals set in MDG 4. In a last step, we price the infrastructure investment needed for each of the relevant scenarios, and calculate country-specific estimates of cost per life-year saved.

Our empirical estimates suggest that, on average, access to “basic” improved water and sanitation technology (such as public water pumps and ventilated improved pit latrines) lowers child mortality by 8 deaths per 1000 children born, while high-end technologies as private water connections and flush toilets lead to an average mortality reduction of 25 deaths per 1000 live births.<sup>5</sup> Compared to the current gap between child mortality and the 2015 mortality targets this implies that basic improved water and sanitation could on average reduce the gap by about 13 percent, while private piped water and flush toilets would reduce the mortality gap by about 41 percent. Given the high degree of heterogeneity across countries in terms of current infrastructure and child mortality, the variations in the potential progress towards achieving MDG 4 are substantial across regions and countries.

Our calculations show that the cost associated with water and infrastructure improvements is substantial in absolute terms, but not necessarily large when compared to the number of lifeyears saved. Across all Sub-Saharan African countries, we estimate the average cost per lifeyear saved at around US$ 1000, with similar estimates for basic technologies and
0.04, -.011 and -.061 (significant) for the former, and 0.04, -0.02 percent and -0.07 (none of them being significant) for the latter. In fact, the De Loecker estimates will be generally small and insignificant throughout the exercise, supporting the point that productivity gains are unlikely to be large. Only for Chile, our highest income country, does the elasticity enter positively and significantly at .03 suggesting some productivity gains from agglomeration. As detailed in Annex table C.1, the pattern of results remains largely robust to alternative estimation techniques and function forms.

Consistent with these findings, prices and marginal costs also rise with agglomeration and increasingly so in poorer countries.<sup>13</sup> In Indonesia, the output price elasticity is .033, the mark-up elasticity -.016 and the marginal cost elasticity is .047. In Ethiopia, the output price elasticity is slightly higher at .038, but the marginal costs elasticity is much higher at .11, driven by a negative mark-up elasticity of -.07. Colombia follows roughly the

> 13 This result is in line with that of Handbury and Weinstein (2015), who use detailed barcode data and find a positive elasticity of price with respect to population.

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