rafmacalaba/datause-encoder-data huggingface dataset:
To capture the relative strength of output relationships, we also define a consolidated metric Outputdi = ∑k=1,...,IEdk/Ed∙Outputi→k. This metric multiplies the national share of industry i's output sales that go to industry k with the fraction of industry k's employment in district d. By summing across industries, we take a weighted average of the strength of local industrial sales opportunities for industry i in the focal market d. This Outputdi measure takes on higher values with greater sales opportunities. Unlike our input measure, this output metric pools across industries that normally purchase goods from industry i. By measuring the aggregate strength of industrial sales opportunities in district d, the metric assumes that selling to one large industrial market is the same as selling smaller amounts to multiple industries.

For most of our estimations, we employ a combined input-output market strength measured as Input-Outputdi = mean(Inputdi, Outputdi). We take the mean value after transforming both Inputdi and Outputdi to have zero mean and unit standard deviation. Our use of a consolidated metric is primarily motivated by the instrumental-variable specifications that use the 1994 incumbent conditions. The consolidated metric reduces the number of endogenous regressors that we need to simultaneously instrument for. We find similar results to those reported below when instead considering the max values over Inputdi and Outputdi.

Beyond material inputs, labor is perhaps the most important input into any new firm, and entrepreneurship is quite likely to be driven by the availability of a suitable labor force (e.g., Combes and Duranton 2006). While a district’s education and basic demographics are informative about the suitability of the local labor force, these aggregate traits can miss the very specialized nature of many occupations. As an extreme example, Zucker et al. (1998) describe the exceptional embodiment of human capital in specialized workers in the emergence of the US biotech industry. These specialized workers are often tightly clustered together.<sup>9</sup> We unfortunately lack Indian data to model direct occupational flows, so we instead take a very simple approach. Greenstone et al. (2010) calculate from the Current Population Survey the rate at which workers move between industries in the United States. Using their measure of labor similarity for two industries, we define Labordi=∑k=1,...,IEdk/Ed∙Mobilityi←k. This metric is a weighted average of the labor similarity of industries to the focal industry i, with the weights being each industry’s share of employment in the local district. The metric is again by construction mostly orthogonal to city size. We calculate this connection for both manufacturing and services.

9 Theories to explain the agglomeration of specialized workers and firms include Marshall (1920), Diamond and Simon (1990), Helsley and Strange (1990), Krugman (1991), Rotemberg and Saloner (2000), and Overman and Puga (2010).

12
to the observed patterns of middle-income countries during the seventies, these previous CGE studies stop short of explaining why investments (hence debts) were high after the first oil shock in 1974 and low after the second shock in 1979. Investment levels were either exogenousty fixed at their high levels or d,iven low by arbitrarily setting them equal to a lower level of domestic and foreign savings as required by the macro-dosure of the model. One effect that these models were designed to measure - the intersectoral reallocation of resources caused by the market price shifts - was often swamped by exogenous changes in investments.<sup>4</sup> To examine these dynamic issues, a forward-looking behavior in investment and consumption is embedded in a multisectoral and general equilibrium representation of a developing economy. Unlike the static case, the solution of the model is a dynamic competitive path in the sense that it is an intertemporal path along which the economy is always in general equilibrium. It is characterized by three key features - the simultaneity of investment and saving decisions, the intertemporal and forward-looking behavior, and a general equilibrium applicable to developing countries.<sup>5</sup> First, savings and investment decisions are not only intertemporal but separate and simultaneous. Investment is neither fixed as in static CGE applications nor dynamically passive as in studies concerned mainly with optimal borrowing, for example, Kharas and Shishido's (1985) study of Thailand and Devarajan's (1986) examination of Korea. The 'While it is possible to set the various elasticities of substitution very low to have strong reallocation effects, convergence problems are often encountered first as the models become rigid and approach Leonitf's fixed-coefficient world. The effects of relative price shocks in a static CGE generally run their course in a single period and have less impact on real income over time than changes in factor supplies.

'There are some recent studies that utilize intertemporal models with forward-looking investment behavior, but they are not applied to development problenm. For examples, Bruno and Sachs (1985) and Summer(1985) have forward looking investment incorporated in very macro simulation models with applications to the OECD countries or the United States. Wilcoxen (1986) discusses forward looking investment in general equilibrium models and their numerical calculation using linearization. Goulder ani Summer (1989) incorporates this type of investment behavior iu a multisectoral general equilibrium model for the United Statez and examines tax incentive issues for investment.

3
# **The Impact of Regional Liberalization and Harmonization in Road Transport Services: A Focus on Zambia and Lessons for Landlocked Countries**

**Gaël Raballand**<sup>**1**</sup> **,**<sup>**2**</sup> **Charles Kunaka**<sup>**3**</sup> **Bo Giersing**<sup>**4**</sup>

**Keywords: Transport, Trucking, Services Liberalization, Zambia, South Africa, Landlocked.**

> 1 The authors would like to thank Aaditya Mattoo, Lucy Payton, Jos Verbeek, Ben Gericke, Marouane Ameziane, Jean-Francois Marteau and one anonymous referee for their comments.

> 2 World Bank Africa Transport Unit.

> 3 Sub-Saharan Africa Transport Policy Program (SSATP). 4 Portfutures.
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|---|---|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|Rep. of Congo - Water,Electricity& Urban Development SIL(LEN)|Anas Benbarka|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|DRC-Growth with Governance in the Mineral Sector|Kirsten Lori Hund|
|Croatia Stat Master Plan Support|Sanja Madzarevic-Sujster|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Yemen-Water Sector Support|Amal Talbi|
|Carbon Finance Assist(Central America)|Marcos Xavier Castro Rodriguez|
|Carbon Finance Assist(Central America)|Marcos Xavier Castro Rodriguez|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
Policy Research Working Paper 10300

# **Abstract**

This paper examines the welfare impacts of the COVID-19 pandemic, using harmonized data from 343 high-frequency phone surveys conducted in 80 economies during 2020 and 2021, representing more than 2.5 billion people. The analysis focuses on the scarring effects of the initial losses of employment and income by examining their evolution over time across and within countries, as restrictions on mobility and economic activity were introduced and then gradually relaxed. The employment and welfare outcomes of some groups that were impacted to a greater degree initially—including women, informal workers, and those with less education—have been improving at a slower pace. The social protection response in lower-income economies was largely insufficient to protect households from the pandemic shock. Unmitigated welfare losses, as seen for example from the large share of households indicating income losses well into 2021, are highly correlated with food insecurity, which likely led some households to sell physical assets and deplete their savings. Without proper remediation, the uneven welfare impacts associated with COVID-19 may be amplified over the medium to long term, leading to future increases in poverty and inequality.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bbrunckhorst@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> 1,400 30.0%<br>1,200 25.0%<br>1,000<br>20.0%<br>800<br>15.0%<br>600<br>10.0%<br>400<br>> - ttt oo<br>SseageseseesBBe2eSssequgsz4eSSSSSSSSSSSSSSSSSSESESESANNANNNNKRKRRR RR NANAsssasgAN AAD<br>ms OSR (LHS) gms Transfers (LHS) mam Others (LHS) < OSR's proportion (RHS)<br><!-- End of picture text -->
the degree to which the global response should include a development element. We find that the average stood at around 10.3 years at the end of 2015, with a median duration of 4 years, and significant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations.

To produce these numbers, we rely on the Population Statistics Database compiled and maintained by UNHCR. The database records the number of “persons of interest” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a _no-turnover_ assumption, whereby a decrease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such approach tends to over-estimate the true duration of exile, the lack of individual-level data on registration precludes refining the estimate further.

Attempts to estimate similar statistics have been limited. In a 2004 note to its Executive Committee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner’s Programme 2004). This number has been widely quoted by media, activists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015).

The rest of the paper is organized as follows. Section 1 gives some definitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our analysis are presented in section 4. Section 5 concludes.

# **1 Background: Definitions and Data**

Under the terms of the 1951 Convention Relating to the Status of Refugees –henceforth the Convention– later amended by the 1967 Protocol, a refugee is a person, who “owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country.” Data on refugees and asylum seekers are collected by individual countries, international orga3
of 3.2 and 0.4 percent, respectively. These results provide further evidence that lending to local public governments in the pre-reform period was crowding out lending to private firms.

Fourth, we further test whether private-sector borrowers operating in sectors less dependent on public spending (i.e., less affected by the contraction in government spending) benefit relatively more from the reform. Following Belo, Gala, and Li (2013), we use the Mexican Input-Output table at the 4-digit NAICS level to calculate the share of revenues that comes from sales to the government for each sector. The results of this exercise are displayed in Table A7. They suggest that private borrowers operating in sectors less dependent on government spending benefit relatively more from the reform curtailing local public debt. More concretely, a one-standarddeviation decrease in the level of government exposure is associated with a 0.1 percent increase in total loan volume, and a 0.7 percent increase in loans destined to investment.

Fifth, along with the debt ceilings, the Law also introduced procurement rules requiring state-owned entities to carry out a competitive procurement process with multiple lenders and select the offer with the lowest cost. One potential threat to our identification is that by potentially reducing the cost of credit of local governments, the new procurement rules might have induced local governments with ex ante lower debt (i.e., the states that belong to our control group) to increase their indebtedness. In such case, the gap in public debt that widened across states of varying indebtedness after the Law would be driven not exclusively by the debt contraction of ex ante more indebted governments (due to the new debt ceilings), but also by the rise in debt of ex ante less indebted governments (due to the procurement rules). We rule out this concern by examining changes in bank lending of ex ante less indebted local governments before and after the Law. As Figure 2 shows, while local governments with ex ante higher public debt contracted considerably their bank liabilities ex-post, bank lending of ex ante less-indebted local governments did not increase. If anything, the liabilities of local governments with ex ante lower debt decreased after the Law, albeit at a slower pace than their more indebted peers. Similarly, as Panel B of Figure IA1 shows, even state governments of ex ante lower indebtedness saw an increase in the cost of credit in the years following the FD Law. In addition, we rely on an alternative identification strategy to estimate the impact of the debt ceilings. Under this strategy, we exploit differences in the alert system classification (i.e., _sustainable_ vs _under-watch_ ) of 10 states with ex ante similar debt-to-net-income ratios around the threshold splitting five _sustainable_ and five 28
investment incentives are systematically lower on sharecropped than on owned land, the total productivity effect of _barga_ tenure will be the sum of Marshallian inefficiency and investment effects. For observable investments such as irrigation that enter into the production function, multiplying relevant coefficients is one way to estimate total productivity impacts.

# **3. Data and descriptive evidence**

We use basic information at the household level that is available for all households in the village before focusing specifically on plot-level outcomes for owner-cum cultivators only. For the full sample, our data support the notion that issuance of share contracts virtually came to a halt after 1978 and that, while effectively targeting the poor, land reform did not trigger a massive exodus out of poverty. At the plot level, data for owner-cum tenants are consistent with the hypothesis of tenancy-induced differences in effort supply between owned and sharecropped parcels. Levels of input use, productivity, investment, and exogenous plot characteristics (e.g. salinity) that change slowly over time in response to cultivators’ effort are all lower on tenanted as compared to owned parcels, justifying econometric investigation.

# **3.1 Household-level evidence from listing data**

Our data are from a 2008/9 World Bank/FAO survey in 200 randomly selected villages from 10 districts in West Bengal. Villages were selected randomly with probability of selection proportional to the number of beneficiaries from 1978 land reforms, based on official lists obtained from the State Institute of Panchayats & Rural Development (Chakraborti, Mukhodpadhyay, and Roy 2003). The data is thus representative of the universe of West Bengal’s land reform beneficiaries. A complete listing of all village residents provided, for the approximately 96,000 households in our 200 sample villages, basic information on current and past household structure, key asset endowments, and the extent and nature to which households were affected by land reform. This was followed by an in-depth follow-up survey of some 1,800 owner-cum-tenants to compare productivity, intensity of input use, and investment between owned plots and those for which inheritable _barga_ rights were received during the 1978 reforms.

The village-wide sample highlights that, after the reforms, few if any new sharecropping contracts were concluded. The majority (82%) of share tenants in our sample thus either held their land since 1978 or inherited tenancy rights to a given plot of land ( _barga_ ) from their parents who benefited from land reform. In the remaining 18% of cases, land already encumbered with a tenant had been sold to a third party without interrupting pre-existing tenancy relationships. In line with descriptive studies (Hanstad and Nielsen 2004), the data include 484 cases where residual claims by the other party were bought out by one of the parties, either by the tenant (289 cases) or the landlord (195 cases).

8
# Table &8 Comparison<sup>of gerformance and policy amone African county erouns.</sup>

||Countries<br>average|with . <sup>favorable</sup><br>average|<sup>policies</sup><br>growth rate|Countries wi<br>average|th unfavorable<br>average<br>|<sup>policies</sup><br>growth rate<br>|
|---|---|---|---|---|---|---|
||<br>198082|<br>198548|198248|1980482|1985488|198248|
|Economic Performance<br>||1215|415|1018|102.8|-1.80|
|Volume of agricultural<sup>exports a/cf</sup><br>|99.1<br>|.<br>1183|.<br>255|.<br>1029|<br>1091|0.89|
|Total agricultural production<sup>a/</sup><br>|104.2<br>|.<br>1165|.<br>233|.<br>1033|.<br>107.7|0.62|
|Food production<br>|103.9<br>1015|.<br>1197|.<br>350|.<br>1017|<br>99.4|-0.91|
|Agricultural Value-added<sup>a/c/</sup><br>|.<br>|.<br>1256|.<br>325|.<br>1041|118.4|2.14|
|Gross domestic product<sup>a/</sup>|103.9|.|.|.|||
|Polc Performance<br>||956|-94|1682|112.4|-5.3|
|Real effective exchange<sup>rate b/</sup><br>|133.0<br>|.<br>1049|.<br>437|.<br>958|96.5|0.85|
|Real producer prices<sup>for exports</sup><sup>_a/cl_</sup><br>|94.3|.|.|.|||
|Nominal protection<sup>coefficient</sup><br>||114||096|0.99||
|(export commodities)<sup>c/</sup><br>|0.84|.||.|||
|Real protection<sup>coefficient</sup><br>||112||075|0.97||
|(export commodities)<sup>c/</sup>|0.65|.||.|||
|Food Sector<br>||1006||104|108||
|Real producer food<sup>price cd</sup><br>|98.4|.|||||
|Nominal protection<sup>coefficient</sup><br>||_1A_||074|1.27||
|(tradeable foods) d/<br>|2.9|||.|||
|Real protection<sup>coefficient</sup><br>||197||063|1.03||
|(tadeable foods) d/<br>|1.18<br>|.<br>133|_55_|.<br>112|160|4.5|
|Food importscl|106||_._||||
|ogenous Factors<br>||871||978|89.8||
|Index of export prices<sup>a/</sup><br>|99.1<br>|.<br>005||.<br>002|-005||
|Weather effect|-0.06|.||.|.||

a/ Index, 1979-81<sup>= 100.</sup>

- b/ Index, 1970-75<sup>= 100.</sup>

c/ Period is 1985-87<sup>only, no 1988 data are available.</sup> d/ Periods are 1981-83,<sup>and 1987 only. Reduced samples are 6 and 4 for FPE and UPE, respectively.</sup>