rafmacalaba/data-use-mentions-extended huggingface dataset:
Harmonized Survey on Households’<br>Consumpton and Informal Sector|Consumpton|2.15|2018|
|Colombia|Gran Encuesta Integrada de Hogares (GEIH) Survey ;<br>Encuesta Pulso de la Migracion|Income|6.85|2021|
|Costa Rica|Encuesta Nacional de Hogares (ENAHO)|Income|6.85|2021|
|Ethiopia|Socioeconomic Survey of Refugees in Ethiopia (SESRE)|Consumpton|2.15|2023|
|Jordan (*)|Vulnerability Assessment Framework (VAF) survey for<br>refugees|Consumpton|6.85|2018|
|Kenya|Kalobeyi Socioeconomic Survey|Consumpton|3.65|2019|
||Kakuma Socioeconomic Survey|Consumpton|3.65|2021|
||Kenya Integrated Household Budget Survey|Consumpton|3.65|2016|
|Lebanon (**)|Lebanon Vulnerability Assessment Panel (LVAP)|Consumpton|6.85|2022|
|Niger|Enquête Harmonisée sur le Conditons de Vie des<br>Ménages (EHCVM)|Consumpton|2.15|2018|
|West Bank and<br>Gaza|Palestne Expenditure and Consumpton Survey|Consumpton|6.85|2023|
|Uganda<br>|Refugee and Host Communites Household Survey<br>|Consumpton|2.15|2018|

(*) these surveys do not comprise data on host communities.

(**) Lebanon became a lower-middle income country in 2022. Here the upper-middle income poverty line is preferred, mostly because the PPP conversion factor for Lebanon appears off leading to unrealistically optimistic selfreliance estimates. In addition, self-reliance estimates using the national poverty line (NPL) will be presented.

7
- FREY, C. B., AND M. A. OSBORNE (2013): “The future of employment: how susceptible are jobs to computerisation,” _Retrieved September_ , 7, 2013.

- HALL, B. H., F. LOTTI, AND J. MAIRESSE (2008): “Employment, innovation, and productivity: evidence from Italian microdata,” _Industrial and Corporate Change_ , 17(4), 813–839.

- HALTIWANGER, J., R. S. JARMIN, AND J. MIRANDA (2013): “Who Creates Jobs? Small versus Large versus Young,” _The Review of Economics and Statistics_ , 95(2), 347–361.

- HARRISON, R., J. JAUMANDREU, J. MAIRESSE, AND B. PETERS (2014): “Does innovation stimulate employment? A firm-level analysis using comparable micro-data from four European countries,” _International Journal of Industrial Organization_ , 35(C), 29–43.

- HOPENHAYN, H. A. (1992): “Entry, Exit, and Firm Dynamics in Long Run Equilibrium,” _Econometrica_ , 60(5), 1127–50.

- KLETTE, T. J., AND S. KORTUM (2004): “Innovating Firms and Aggregate Innovation,” _Journal of Political Economy_ , 112(5), 986–1018.

- RABE-HESKETH, S., AND A. SKRONDAL (2012): _Multilevel and Longitudinal Modeling Using Stata, 3rd Edition_ . StataCorp LP, 3rd edn.

- STOCK, J., AND M. YOGO (2005): _Testing for Weak Instruments in Linear IV Regression_ pp. 80–108. Cambridge University Press, New York.

- TETHER, B. (2003): “The sources and aims of innovation in services: Variety between and within sectors,” _Economics of Innovation and New Technology_ , 12(6), 481–505.

- VIVARELLI, M. (2012): “Innovation, Employment
Policy Research Working Paper 5999

# **Abstract**

Optimism about the use of laws, constitutions, and rights to achieve social change has never been higher among practitioners. But the academic literature is skeptical that courts can direct resources toward the poor. This paper develops a nuanced account in which not all courts are the same. Countries and policy areas characterized by judicial decisions with broader applicability tend to avoid the potential anti-poor bias of courts, whereas areas dominated by individual litigation and individualized effects are less likely to have pro-poor outcomes. Using data on social and economic rights cases in five countries, the authors estimate the potential distributive impact of litigation by examining whether the poor are over or under-represented among the beneficiaries of litigation, relative to their share of the population. They find that the impact of courts varies considerably across the cases, but is positive and pro-poor in two of the five countries (India and South Africa), distribution-neutral in two others (Indonesia and Brazil), and sharply anti-poor in Nigeria. Overall, the results of litigation are much more positive for the poor than conventional wisdom would suggest.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at vgauri@worldbank.org or danbrinks@austin.utexas.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
# **3.2 Survey of Trading Firms**

# _Design of the Study_

**In order to estimate the potential for trade expansion generally, and across Malebo Pool more specifically, we conducted a series of structured interviews with firms in Kinshasa and Brazzaville** in April-June 2010. The interviewed firms had to respond to a sole criterion: that they are, or were in the past, engaged in cross-border economic activities, be it in the form of trade in goods or in the form of passenger transport. Conditional on this criterion, we sought to cover as representative a sample of firms as possible, covering the full spectrum of relevant economic activities.

**We obtained interview answers from 57 firms** , 17 of which are manufacturers, 19 are based in Brazzaville, and 12 are informal.<sup>21</sup> The average sample firm has 238 workers. However, the size distribution is highly skewed, the median being 14 workers, and 16 sample firms being one-person organizations. Only four of the 57 firms are engaged in the transport of passengers, which means that our statistical analysis of the survey answers will have to be confined to analyzing trade in goods. Sample firms were presented with an identical 72-item questionnaire, containing questions on the existing structure of the business as well as hypothetical assessments of the impact of trade liberalizing measures on their activities. Interviewees were promised strict confidentiality, which is why we present no information in this report that could be traced to individual firms.

# _Results: Quantitative Analysis_

**Firms overwhelmingly expect to increase the volume of international trade in response to a fall in costs of importing and exporting.** We came to this insight by asking firms to estimate the medium-term percentage impact on the volume of their cross-border trade of four hypothetical scenarios:

- a 50-percent fall in transport costs on exports/imports to/from all foreign markets,

- a 50-percent fall in administrative trade costs on exports/imports to/from all foreign markets,

-
securing new accommodations, and experience a higher rate of evictions compared to the general population, highlighting the housing-related challenges. Moreover, Roma households are often affected by spatial marginalization: Roma communities frequently live in areas deprived of social services and far from job opportunities.<sup>10</sup>

# **Against this background, the aim of this working paper is threefold.**

**First, it constructs a measure of absolute multidimensional poverty for the EU to analyze recent temporal trends at regional, subregional, and country levels.** We construct a measure of anchored AROPE for the 27 EU countries (except Germany) to monitor changes over time, using data from the 2021 and 2022 EU-SILC cross-sectional surveys,<sup>11</sup> and following the at-riskof-poverty or social exclusion (AROPE) framework. The AROP indicator is a measure of income inequality, as the poverty threshold, set at 60% of the national equalized median disposable income (in national currency), can vary from year to year based on changes in the overall income level and its distribution. Therefore, to properly analyze changes in absolute multidimensional poverty over time, a measure of anchored AROP capturing changes in monetary living standards should be used as part of AROPE.

**Second, it quantifies the extent and composition of multidimensional poverty across four EU countries (Bulgaria, Romania, Croatia, and Poland)**<sup>**12**</sup> **; it also identifies factors associated with higher risks of poverty or social exclusion and vulnerable groups and extends the AROPE framework to incorporate additional dimensions of deprivations.** The multidimensional approach extends beyond a narrow focus on income to encompass broader dimensions of household and individual deprivation. This analysis aims to provide an updated picture of multidimensional poverty in four EU countries. The analysis is cross-sectional in nature and relies on the more recent 2022 EU-SILC cross-sectional wave available for these countries. The analysis includes estimates of the incidence of multidimensional poverty and the
across the sectors. Wage rates and capital prices (or user costs of capital) are different across the sectors. Similarly, the total supply of a good/service (imports plus domestic production) is equal to the total demand for that good/service (domestic consumption plus exports) – Walrasian condition. The total investment is equal to total savings, which is the sum of household, government, firms and foreign savings (macroeconomic balance).

The model is made dynamic through the population growth rate (i.e., labor supply growth rate) and investment. Total savings of the previous period (or year) is the investment of the current period (year). Demand for the total capital of the current period is determined by the previous period’s capital stock plus depreciation and interest payment and new-added investment (which is the previous period’s total savings). The total investment is allocated across sectors in proportion to each sector’s share in the aggregate capital account, and these proportions are adjusted by the ratio of each sector’s profit rate to the average profit rate for the whole economy. In addition, Autonomous Energy Efficiency Improvement (AEEI) in the CGE model is considered in this study, and is assumed to be 1% per year following the common assumptions in the CGE model. Since the available social accounting matrix (SAM) is for 2012, our base year is 2012. If the model has to adopt the projected growth rate of GDP (e.g., projected by the government), it is done through adjustments in total factor productivities (TFPs).

# **3. Scenario simulated**

We considered the following three scenarios:

**BAU Scenario** :  The BAU scenario assumes that the increasing trend of the wholesale price continues in the near future.

**Case 1** : The second scenario assumes that China follows market-based rules to allocate energy resources to produce electricity (e.g., priority building of power
00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Netherlands: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Spain: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 29
stand as the worst performing pillars: four of the seven developing East Asian countries covered by the GII data set present a deficit in these two domains.

**Table 1. How developing East Asian countries perform in terms of overall innovation enabling factors, 2017**

|**Country**|**Institution**<br>**s**|**Human**<br>**capital**|**Infrastructure**|**Market**<br>**sophistication**|**Business**<br>**sophistication**|
|---|---|---|---|---|---|
|Cambodia|1|1|1|0|1|
|China|1|0|0|0|0|
|Indonesia|1|1|1|0|1|
|Malaysia|0|0|0|0|0|
|Philippines|1|1|0|1|0|
|Thailand|1|1|1|0|1|
|Vietnam|1|0|0|0|1|

Source: Own elaboration based on GII dataset and WDI dataset.

Note: a value of 1 indicates a variable where the country is below the median country AND below the level predicted by its GDP per capita. The median value (for each variable) is calculated within the 127 countries covered under the GII survey.

To shed light on more granular aspects that might be driving these results, Table 2 focuses on the components under the _Institutions_ pillar: _Political environment_ , _Regulatory environment_ and _Business environment_ . Results suggest that _Regulatory environment_ – encompassing regulatory quality, rule of law, and cost of redundancy dismissal - is the key obstacle for innovation across developing East Asian countries covered in the GII data set; all of them have a deficit in this

> 9 The remaining 7.5 percent
state‐level value chains, which increases the competitiveness (reduces the price) of the final good. A more recent study using detailed GPS data for Indian trucks before and after the implementation of the GST shows that internal borders in India reduce the driving speed of trucks by around 3km per hour. The implementation of the GST is shown to have increased driving speed at the border by 1% (Krishna and Van Leemput, 2018a).

## Dispersion over time

Spatial price dispersion has fallen over time in wholesale markets, but not in retail markets. Coefficients of variation for retail prices across spatially separated locations have _not_ been decreasing over time, for the period analyzed.<sup>5</sup> On the contrary, when pooling all commodities, price dispersion has increased at a low rate of half a percent from 2006 to 2014. The coefficient of variation varies by commodity. Increases in dispersion are significant for bananas, coconut oil, split and whole grams and rice. It is only for mustard oil where price dispersion has fallen. For the rest, the trend is not significant (see Appendix, Figure 26).

At the wholesale level, price dispersion has been decreasing at a rate of a third of a percentage point per year. Dispersion in potato and wheat wholesale prices has been on the rise, while for all other products, dispersion has fallen – in the case of onions and paddy, dispersion has fallen at a rate of 1 percent per year (see Appendix, Figure 27).<sup>6</sup> To what extent are these patterns of price dispersion associated with the way price shocks are transmitted across Indian districts or cities? To what extent are they associated with the way international price shocks are transmitted? The following subsection focuses on these questions.

# Patterns of price transmission and market integration

Price differences across spatially separated locations are in part related to different patterns of price transmission. To examine patterns of price transmission we focus on three characteristics: the completeness of
||2019|2015*|
|---|---|---|
|Average annual exposure to ambient PM2.5|83.1 μg/m<sup>3</sup>||
|No. of deaths from PM2.5 exposure per 100,000 population|130||
|Deaths from PM2.5 exposure as a share of all deaths|20.0%||
|Annual cost of health damages from PM2.5 (% of GDP)|10.2%||
|Death from PM2.5 associated with ambient air pollution (AAP)|17,948 (21,603)|11,619|
|Annual cost of health damages from AAP PM2.5 exposure|$ 4,645 (5,723) million, PPP|$ 686 million|

_Notes_ : The figure in the parenthesis is for indoor household air pollution (HAP). Source: (IHME, 2020) * The World Bank (2019) estimated the social cost of PM2.5 per mortality at US$20,900–$74,200 for 2015 in PPP.

It is estimated that brick kilns are responsible for more than one-fourth of total PM10 concentrations and about 40% of total black carbon emissions in the winter season in Kathmandu Valley, the time most of the brick kilns are in operation (Eil et al., 2020). Most deaths related to air pollution are caused by human exposure to PM2.5 (World Bank, 2021).<sup>19</sup> Based on data from the Institute for Health Metrics and Evaluation’s (IHME) Global Burden of Disease (GBD), air pollution ranks as the second highest risk factor, after malnutrition, which drives the most deaths in Nepal. The IHME GDB study also estimated the number of deaths and cost of health damages from PM2.5 exposure in Nepal (Table 6). The number of deaths from PM2.5
therefore scores 50. However, from the patient point of view, the average quality of the system should be calculated accounting for each doctor’s share of total patients: If 1/4 of patients see Doctor A and 3/4 see Doctor B, the average interaction quality would be 35. If the doctors were to switch places, the average interaction quality would be 65 instead, with no change at all in the medical competence of either Doctor A or B, or the physical location of the patients.

Following this logic, we provide an estimate of how much the quality of the average patient consultation could improve in each country if providers were to be “optimally” reallocated. Specifically, we compute the competence of the provider at the average outpatient consultation in the observed data. Then, we calculate the same quality metric, but with the most knowledgeable health care providers hypothetically matched to the largest caseloads in their country and sector, holding constant the number of patients at each facility. We emphasize that this reallocation is not intended as a policy recommendation, but as a sufficient statistic to quantify the loss in quality due to the fact that some high-competence providers are underutilized, even while some low-competence providers manage very large patient loads.

For the average patient interaction, our hypothetical provider reallocations increased quality (defined as having the competence to correctly manage a basic case) by 4% to 30% relative to current performance. Two observations are noteworthy. First, even with the near-zero correlations between caseloads and competence in current systems, improved allocation offered substantial gains in only half of the countries. Second, in those countries where gains from reallocation were low, improvements were just as low in simulations of an alternate policy that “upskilled” every non-doctor health care provider to the level of a doctor from the same country, a major investment in training resources. In these countries, even though the health care provider population still has ample excess capacity, there is an insufficient number of high-competence providers to reach patients in any
_Sustainability._ It is difficult to compare arrears and default rates for the programs studied because few provide complete information on the aging of bad debts. Furthermore, programs write off bad debts sporadically or not at all so annual arrears figures include bad debts incurred over a lengthy previous period. Also, the programs report arrears information in different formats that are often not comparable. Finally, arrears data for some programs contained obvious errors. Given these shortcomings, arrears information is useful primarily as an indication of likely trends. This section provides information on the percent of outstanding loan volume in arrears for BKK, LKP, and BKD, and the percent of groups with outstanding loans that are experiencing arrears for PHBK and P4K.<sup>4</sup> BKK experienced a serious arrears problem in 1993. However, the volume of loans in arrears in 1994 and 1995 were the lowest for the five programs reviewed - 6 percent. The LKP program had about 20 percent of its portfolio volume in arrears in 1995. It experienced an increasing arrears problem from 1992 to 1995. PHBK experienced early problems with arrears. However, groups with arrears declined from a high of 29 percent of groups with outstanding loans in fiscal 1992/93 to about 13 percent in fiscal 1995/96. P4K experienced very low arrears through 1993, but the program's arrears have grown in recent years. By 1995, groups with arrears accounted for 19 percent of groups with outstanding loans. Preliminary data for 1996 indicate that in that year arrears were likely higher. The volume of BKD loans in arrears was 23 percent in 1992, and declined to 16 - 18 percent over the next three years (Table 6).

# The Committee of

Donor Agencies for Small Enterprise Development set as an acceptable standard for microenterprise lending that 10 percent or less of total loans should have late payments of 30 days or more. The programs do not separate out loans in arrears by 30 days or more. However, the above data, which measure the volume orr
the age of politicians might play a role in the privatization process. If the age-cutoff effect only reflects local politicians’ incentives, it should appear only in the privatizations of SOEs under the control of the local politicians. In other words, the privatization of SOEs under the oversight of the _central_ or _provincial_ governments, although located in the same cities, was not determined by local politicians, and these SOEs’ privatization should not be affected by the age and promotion incentives of city-level politicians.

We thus conduct a placebo test by exploiting the central and provincial SOEs sample to rule out the effect of any unobservable city-level factors. Among the sample of 9,813 central and provincial SOEs, 8,695 of them had been privatized in the sample period. We re-conduct the RDD regressions with the new sample and expect that the relationship between the age of the _local_ politician and the privatization of these non-locally-governed SOEs does not change abruptly. The results are shown in Table 5. The coefficients of the local party secretary’s age being 58 and older are insignificant in both the parametric and the non-parametric RDD. The magnitude of all point estimates is much smaller than those reported in Table 4. The placebo test thus suggests that unobservable local factors do not explain our previous results on the age threshold effect of local politicians on privatization of local SOEs.

# **Robustness Test: Other Definitions of Privatization**

In our baseline results, we use a broad definition of SOE privatization: an SOE is considered to be privatized when the firm’s state shares dropping below 50 percent, or when they exit the database. Since the ASIF data include all SOEs and all private firms with sales above 5 million yuan, when an SOE exits the database, it could only be three cases: (i) merging with private firms, which is clearly privatization; (ii) the SOE is privatized and the post-privatization sales drop below 5 million yuan, which again is privatization; (iii) merging with an SOE, in which case classifying