rafmacalaba/data-use-mentions-extended huggingface dataset:
India’s dramatic transformation since 1989, there has only been a small decline, if any, in the share of workers in the unorganized sector for manufacturing.<sup>3</sup> The patterns also hold in Table 1b’s account of the establishment size distribution in services. The estimated size of the unorganized sector for services depends upon definitions. Using our simple cut-off of five employees, the unorganized sector would still account for 76% of services employment in 2001, well above the comparable 9% for the United States. If using a 20 employee cut-off, the unorganized sector accounts for 90% of services employment in 2006. The share of services in the unorganized sector declines 3% from 2001 to 2006. Our panel is unfortunately too short to identify whether this decline is a short-term fluctuation (similar to the 2%-4% jumps in manufacturing in Table 1a) or part of a long-term trend.

Table 2 lists the 20 states that are in our sample and their unorganized sector shares. These 20 states are a subset of all 35 states/union territories. The 15 exclusions were due to three potential factors: 1) the state was not sampled across all of our surveys, 2) the small sample size for the state raised data quality concerns, or 3) persistent conflict and political turmoil existed in the region. Our explicit criteria with respect to size are that the district has a population of at least one million in the 2001 census and has 50 or more establishments sampled. The exclusions are minor in terms of economic activity, and the resulting panel accounts for over 90% of employment in the manufacturing and services sectors throughout the period of study.

Among major states, the unorganized sector shares are particularly high in Bihar, Orissa, Uttar Pradesh, and West Bengal. These four states have an unweighted average share exceeding 85% across our six surveys. In contrast, low unorganized sector shares are evident in Gujarat, Haryana, Maharashtra, and Punjab, as well as
7 suffer from a substantial increase in their borrowing cost.<sup>5</sup> Even in the case in which this increase is generated by the desirable removal of previous subsidization schemes, it appears important to devote particular attention to the phasing-in process of any regulatory change in the domrain of minimum bank capital ratios. Such caution is needed in order to avoid undesired systemic implications, which could increase rather than reduce the fragility of banking systems.

More generally, all other things being equal, any risk-related criterion for the definition of minimum capital ratios would substantially decrease the cost of credit for internationally diversified fitms and could result in an increase in the share of credit to the domestic sector provided by internationally diversified firms. Although the equilibrium allocation should arguably benefit from the removal of an implicit subsidization scheme to the riskiest firms in the market, the potential consequences of a large reallocation of credit flows among different categories of financial interrnediaries would need to be analyzed carefully.

# **4. A tale from the 1997-98 crises: downgrading sovereigns, banks and corporations**

The experience of soviereign and private sector ratings in emerging economies in 1997-98 may provide an example in which the revision of ratings could have had some undesired macroeconomic consequences had it been related to banks minimum capital requirements. Indeed, the East Asian crisis and the other crises that hit emerging economies during 1997-98 induced markets and rating agencies to a delayed and large set of revisions of their assessment on crisis countries. Ratings were sharply downgraded for sovereigns. The astounding downgrading is depicted in the rating-transition Figure 3. Based on Moody's data for a. large set of countries,<sup>6</sup> Figure 3 reports on the x axis 1996 (pre-crisis) sovereign ratings and on the y axis the relative 1998 (peak-of-the-crisis) ratings. The rating-transition graph can be read as follows. Figure 3 is divided into different regions by three lines: the **450** continuous line contrasts 1996 with 1998 ratings; the vertical dotted line separates
growth of town in-migration has been staggering over the last five years, hovering around 1.5 percent in average each year during the 1996-2000 period. During whole period of 1995-2000, in-migration only accounted for 7.3 percent of town population. In Sichuan, which was economically in the middle range among three provinces, in-migration into towns has shown an upward trend during this period although growth was not been as pronounced as that in Zhejiang province.

Table 5 Migration in towns as percentage of total population:2000

||live<br>in tow<br>since<br>born|before<br>nOct31<br>1995|11/1- 1996<br>12/31<br>1995|1997|1998|1999|2000|total of<br>last five years|
|---|---|---|---|---|---|---|---|---|
||||(|%)|||||
|Zhejiang|61.70|19.71|0.38<br>1.64|1.96|3.21|4.91|6.49|18.21|
|Henan|70.51|21.88|0.33<br>1.15|1.28|1.73|1.79|1.34|7.29|
|Sichuan|57.65|26.99|0.38<br>1.69|2.16|3.22|4.01|3.84|14.92|

________________________________________________________________________ Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table L7-2.

A main expectation underlying the advocacy for town development is to have the towns to absorb excessive laborers who would otherwise head for large cities. In the following two tables, we present
%, 5% and 10% levels, respectively. We use simple ols. Standard errors are clustered at the industry level and given in parentheses. Our measures of input and output distances, as well as average minimum distance, are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. All industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

Second, one may worry that import entry points are geographically localized, so that increasing imports could lead to the geographic concentration of industries around those entry points. This may, in turn, bias the transport cost coefficient. To address this potential concern, we include as a control the average minimum distance of plants in each industry from one of the five major Canadian container ports.<sup>23</sup> As can be seen from **Model 8** in Table 6, although 23Those ports are Halifax (NS), Saint John (NB), Montreal (QC), Vancouver (BC), and Prince Rupert (BC).

22
was limited and spillovers were mostly expected in rural areas with a relatively higher coverage.

For all specifications, we use double-selection Lasso to include relevant baseline controls, starting with the full set of variables in balance Table A5 (for direct effects) or Table A6 (for local spillovers). To account for multiple hypothesis testing, we also calculate p-values controlling for the false discovery rate (FDR) following the step-up approach (Benjamini and Hochberg, 1995). The adjusted p values for outcomes in the main tables are presented in Table A9. Results are generally robust, but we note deviations in the text.

Lastly, we analyze treatment heterogeneity by estimating specifications in equations 1 and 2 with an added linear interaction between the treatment variable and the group of interest (and a control for the group of interest). We do so for rural areas (as this dimension is used in the stratified randomization), as well as for key subgroups of program participants such as women, youth and natives.

# **3.2 Timeline and Surveys**

**Enrollment and Baseline survey.** Figure 2 summarizes the study timeline. Enrollment took place in the 207 sample localities between December 2015 and January 2016. The NGO visited each locality at least twice. First, it organized a public meeting to describe the program and explain eligibility criteria. Second, the NGO collected enrollment (baseline) data for individuals interested in the program.<sup>24</sup> Baseline data was collected for 14,880 (interested) individuals, and 12,692 fulfilled the eligibility criteria.

As explained above, and key to the integrity of the experimental design, baseline data was collected prior to assigning localities to the three treatment and control groups. The baseline questionnaire included simple variables on employment, assets, education, and household characteristics.

The second public lottery took place in March 2016. The list of the 5,116 selected beneficiaries was publicly released in each locality between July and September 2016.<sup
Table 2 shows the number of countries and associated populations in the GMD sample used for the analysis. In terms of child population, the GMD sample has high regional coverage of developing countries in South Asia, Latin America, Europe and Central Asia, and East Asia and the Pacific (above 84 percent); partial coverage of Sub-Saharan Africa (74 percent); very low coverage of Middle East and North Africa (3.9 percent); and no coverage of North America. As a result, the distribution of children by region is close to that of the world, but the sample heavily underrepresents the Middle East and North Africa (0.4 vs 6.9 percent) and slightly underrepresents Sub-Saharan Africa (20.7 vs 23 percent). South Asia is moderately overrepresented in the sample, as it accounts for 35.7 percent of the sample as opposed to 30.2 percent of the developing world child population. Because of underrepresentation in Middle East and North Africa we follow the World Bank and do not report that region separately in the results. However, this region accounts for a very low percentage of the extreme poor and therefore has a minor impact on the results.<sup>8</sup>

**Table 2: Population and Child Population distribution in Sample of Global Micro Database, by income group, region and welfare type**

||**Number**<br>**of**<br>**countries**|**Population**<br>**(millions)**|**Share of**<br>**sample**<br>**population**<br>**(%)**|**Population**<br>**of children**<br>**(millions)**|**Share of**<br>**sample**<br>**child**<
also shows the result of the same analysis for the preferred design. For our preferred design, security, the dimension least correlated with monetary poverty has the largest impact on the fraction of omitted poor.

_Table 3: Impact of each non-monetary dimensions on other-dimensions poverty rates_

|||**Nationalpoverty headcounts**<br>||
|---|---|---|---|
||**Other-dimensions poor**|**Omitted poor**|**Consistently poor**|
||**(%)**|**(%)**|**(%)**|
|||**Baseline Scenario**<br>||
|All dimensions kept|27.9|11.4|16.5|
|Health|18.1|6.5|11.6|
|Housing|14.9|6.9|8.0|
|Security|21.4|7.6|13.8|
|||**Preferred Scenario**||
|All dimensions kept|34.9|15.7|19.1|
|Education|30.5|14.0|16.5|
|Health|27.0|11.2|15.8|
|Housing|22.8|11.4|11.4|
|Security|27.7|10.9|16.8|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table shows, other-dimensions poverty rates when all non-monetary dimensions are taken into account and when one of them is removed, for the baseline and the preferred scenario. The overall other-dimensions poverty rate is decomposed into omitted poverty and consistent poverty.

Second, we look at the impact of considering both extreme and moderate deprivation statuses for nonmonetary dimensions, as suggested by the theory presented in section 2.1.
Table B7: Mechanism at play, controlling for Share of services in GDP

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|
|---|---|---|---|---|
|Uncertainty (standardized)|-0.404|1.430***|1.274***|1.750***|
||(0.605)|(0.501)|(0.432)|(0.608)|
|Uncertainty _×_ Developing|1.805***<br>(0.688)||||
|Share of services in GDP (standardized)|0.749|0.507|0.397|1.385|
||(1.215)|(1.181)|(1.019)|(1.543)|
|Observations|2,283|1,460|1,789|1,158|
|R-squared|0.835|0.826|0.847|0.803|
|Data Coverage|All countries||Developing||
|Minimum # of|||||
|Foreign Investors|3||1|10|

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database
innovation. While these are unlikely to be captured in international patent data,<sup>10</sup> they are reflected in the production and trade of ̳green‘ goods and services, to the extent that green technologies are embodied in a good or service.<sup>11</sup> As shown in Figure 5, environmental goods constitute a non-trivial and rising share of high-income country exports. The share of green exports is slightly lower in most developing regions - but the gap is nowhere near as large as with frontier innovations. However, with the exception of EAP, the share of green exports has not been rising, suggesting that new firms are not entering these sectors.<sup>12</sup> The policy implication of this observation depends on the extent to which this reflects some under-exploited comparative advantages in specific developing countries accounting for lower levels of home production and export of green goods and services. Any policy intervention should be predicated on better information on the sources of this under-exploitation, whether driven by specific market or policy failures. For instance, information on the extent to which the relatively less developed state of environmental regulations in many developing countries may be accounting for these differences could suggest appropriate policies.

**Figure 5: Export of Green Goods and Services** (as % of all exports)

<!-- Start of picture text --> 7%<br>6%<br>5%<br>4%<br>3%<br>2% 2000<br>1%<br>0% 2005<br>2010<br>Source: COMTRADE + OECD List of environmental 6-digit HS categories<br><!-- End of picture text --> Figure 6 shows that green imports are as important (as a share of all imports) in developing regions as they are in high-income countries. This indicates the international transfer of green technology as embodied in green consumption goods. Further, inasmuch as some
*|0.74|
|**_Access to Finance_**|<br>||||
|Finance from internal sources(%)|69.9|59.6|0.111|1.17|
|Finance from banks(%)|19.0|23.1|0.445|0.82|
|Finance from trade credit(%)|5.6|11.1|0.082*|0.51|
|% of firms with line of credit or loans from financial institutions|41.7|36.8|0.443|1.13|
|% of firms usingbanks to finance investments|24.1|27.7|0.581|0.87|
|% of firms usingbanks to finance expenses|31.0|37.5|0.502|0.83|
|%of firms identifyingaccess to finance as a major constraint|26.8|32.6|0.405|0.82|

Source: Own calculations. Data: Enterprise Survey Indicators. NB:<sup>+</sup> t-test of difference in means. p*<0.1, p**<0.05, p***<0.01.

Infrastructure-related indicators confirm the existence of congestion costs in agglomerations for services firms, especially with regards to electricity. The number and duration of power outages per month is higher in the core. Moreover, the time to obtain an electrical and water connection is almost four times as high for services firms in the core compared to firms in non-core regions. As a result, the percentage of services firms producing their electricity from a generator is almost 9 14
income markets. Lelarge et al. (2010), provide evidence indicating that the obtention of a loan guarantee helps newly created French firms grow faster and pay a lower cost for their capital, though it also increases their probability of default.

Inadequate pricing of guarantees has been identified in the literature. A study covering 76 PCGs across 46 developed and developing countries finds evidence that while governments have a crucial role in funding and management of PCGs, their involvement in risk assessment and recovery is limited (Beck et al., 2008b). Risk-based pricing and limited use of risk management mechanisms have been largely absent in the design of the PCG schemes. This finding is also confirmed in this paper.

The World Bank Principles for Public Credit Guarantee Schemes for Small and Medium Enterprises offer a sound approach for assessing the soundness of the PCG schemes. Following the launch of the _World Bank Principles for Public Credit Guarantee Schemes for Small and Medium Enterprises_ , Calice (2016) reviewed its implementation across the globe. The paper found the space for reform and actions in terms of the accountability of funding sources, corporate governance and risk management, prudential regulatory recognition of guarantees, program/product calibration, and reporting and disclosure. More recently Goffe and others (2021) provide a set of good practices for policy makers in the design of PCG schemes.

# 3. Methodology

The paper is largely based on information from three surveys of PCG schemes in LAC, conducted by the World Bank in the second half of 2020. The surveys cover almost all the operating PCGs in LAC by examining 19 PCGs in 12 countries: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Honduras, Mexico, Paraguay, Peru, and Uruguay.<sup>2</sup> In that regard, the project benefited from a network that brings together PCGs in the region and programs in Spain and Portugal: _Red Iberoamericana de Garantías_ (REGAR).<sup>3</sup> The information was collected over three survey
countries and at borders. While demand for some food products may decrease, for instance as restaurants cannot operate, this effect might be outweighed by government policies, including stimulus packages, aimed at supporting demand and by fears of food scarcity that can generate an increase in demand due to consumers’ hoarding. As the global supply of food shrinks and inventories decline, world prices would eventually rise as the gap with global food demand widens. Evidence based on high-frequency data points to an increase in export prices of rice since February 2020 for several exporters (Figure 2).

**Figure 2: Evolution of prices for rice (January 2020 – April 2020)**

<!-- Start of picture text --> 600<br>550<br>500<br>450<br>400<br>350<br>300<br>China Thailand United States of America Viet Nam<br>export price by exporter (USD/tonne)<br><!-- End of picture text --> _Source_ **_:_** ITC Market Price Information.

We provide a first assessment of the initial shock that Covid-19 could have on world food markets, which considers its potential heterogeneous effects across food products. Intuitively, supply conditions will impact food products differently, as workers’ morbidity and the need for social distancing will affect more the supply of products that are more labor intensive, such as paddy rice, or the supply of products that have crucial labor-intensive stages, such as the processing of fish. For these products, Covid-19 may be expected to cause shortages to be more severe than in food sectors that do not require workers to be in close proximity, for instance due to more extensive automation (World Bank, 2020a). Moreover, as the extent of automation to produce the same food product varies widely across countries, we would expect for the same product larger reduction in supplies in countries with less automation.

To account for the heterogenous impact of Covid-19 on
Policy Research Working Paper 10054

# **Abstract**

A series of civil conflicts in Mali has generated more than 346,000 internally displaced people (UNHCR, 2020). This study estimates the effect of conflict-generated internal displacement on consumption, poverty, and inequality in host communities. Using comprehensive nationwide household survey data this study finds that wealth at the commune and household level is non-decreasing in IDP hosting communes relative to non-IDP host communes. This study also finds some partial evidence of increasing consumption at the household level although inequality and poverty at the commune level remain the same. The evidence suggests a fairly successful hosting and aid process in Mali for IDPs in terms of mitigating economic disruption for host communities.

This paper is a product of the Social Sustainability and Inclusion 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/prwp. The authors may be contacted at jdfoltz@wisc.edu.

_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 the governments they represent._ Produced by the Research Support Team
# **Tables and Figures**

Table 1: Stock of Refugees, Population and Share of Refugees in Total Population for the Top 10 Receiving Countries in Europe, 2019

|**#**|**Country of**<br>**Asylum**|**Number of**<br>**Refugees**|**Country**<br>**Population**|**Rank**<br>**Population**|**Share of**<br>**Refugees (%)**|
|---|---|---|---|---|---|
|1|Sweden|253 787|10 285 453|8|_2.47_|
|2|Austria|135 951|8 877 067|9|_1.53_|
|3|Germany|1 146 682|83 132 799|1|_1.38_|
|4|Switzerland|110 162|8 574 832|10|_1.28_|
|5|Greece|80 454|10 716 322|7|_0.75_|
|6|France|407 915|67 059 887|2|_0.61_|
|7|Netherlands|94 417|17 332 850|5|_0.54_|
|8|Belgium|61 662|11 484 055|6|_0.54_|
|9|Italy|207 602|60 297 396|4|_0.34_|
|10|United Kingdom|133 083|66 834 405|3|_0.20_|

_Note: World Bank data on population and refugees: 1960 – 2019. Data refer to the total stock of refugees on the territory of the country of asylum in 2019. Share refers to the share of total number of refugees in total population. These countries are top ten countries in Europe in both the total population and
32|
|Portugal|1,188|53|0.02|0.42|15.32|9.84|22.09|15.57|15.53|21.66|
|Spain|1,745|28|0.02|1.89|24.18|20.8|16.53|12.77|14.46|11.25|
|China|1,962|51|0.02|0.53|5.71|13.9|20.75|19.52|15.59|24.53|
|Germany|2,113|2|0.19|7.9|39.62|24.5|16.01|10.85|5.61|3.4|
|Belgium|1,470|10|0.23|3.84|27.81|20.3|15.55|11.26|12.12|12.96|

i) Figures are based on the averages of export values for 2001‐2007. ii) Top 10 and Top 100 correspond to the most complex products; products are divided into six groups, 1 is the most complex product group. An RCA >1 implies that the product accounts for a larger share of a given country’s export basket than it does in the world export basket.

Source: Felipe and Kumar (2011) based on Abdon et al (2010) using a measure of complexity based on Hidalgo and Hausmann (2009).

Structurally different, at different levels of GDP per capita, yet GIPS demonstrate decisively the primacy of debt, _whether public or private_ in effecting economic /financial crises. Large increases in either one are cause for concern, no matter how rich or structurally flexible is the country.

# **_Lesson 2. Signaling policy commitment at the national and supranational levels has value in financial markets