rafmacalaba/datause-encoder-data huggingface dataset:
# **_How Realistic Is the Compensation Scheme?_**

The simulation is useful to quantify the impacts of the liberalization, especially the income losses of the handloom weavers. It is also helpful to know the level of the government spending needed to compensate them. What needs to be examined next is whether such a compensatory scheme is administratively feasible, and whether such a costly scheme is politically desirable in the long-run. In answering these questions, there are at least four factors to consider.

First, it is very difficult to reach all the 6 million handloom weavers, who are scattered all over the country. In fact, there are many reports which reveal that the past handloom protection programs have been far from successful in India for this reason. The survey conducted by the Institute of Rural Management, Ahmedabad (IRMA, 1989), for example, revealed that only 29 per cent of the handlooms actually benefited from the state intervention. Secondly, if we take the high level of administrative costs, which are not included in the simulation analysis, into account, the financial cost of the government subsidies will become even larger. Thirdly, the problem of camouflaged handlooms by powerloom weavers would become an even more serious matter. Lastly, the more subsidies are provided, the more serious corruption will be. Direct subsidy that was proposed in the pre vious analysis can create new vested interests,<sup>and this will, in</sup> turn, make the future reform even more difficult. Are there any other politically desirable policy options, rather than the direct subsidy program? The next chapter will examine the issue.

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One exception is Santarelli (2011) who studied entrepreneurship in Vietnam using a panel of regional-level data for 61 provinces over the period 2000-2008; among other outcomes, the author found that an innovative climate (proxied by the share of technical/R&D personnel in the province) significantly and positively affects the regional net entry rate. Moving to post-entry performance, in the previously-cited work by Goedhuys and Sleuwaegen (2010), the innovative capability (proxied by a dummy for the introduction of new products) was found to increase a firm’s annual employment growth by 2% on average.

# **<u>4.2.6 Escape from unemployment</u>**

As far as unemployment (or the fear of becoming unemployed, see Section 3.1) is concerned, the literature points out two stylized facts: 1) those who have entered selfemployment from unemployment exit to a higher extent than those who have entered from paid employment (see Carrasco, 1999; Pfeiffer and Reize, 2000; for a slightly more optimistic evidence, Caliendo and Kritikos, 2010); 2) new founders who were formerly unemployed have on average lower economic outcomes and a lower propensity to contribute positively to job creation. For instance, in the previously-cited paper by Arrighetti and Vivarelli (1999), the authors found that defensive motivations such as concern about future career developments and the fear of becoming unemployed were predictors of a below-the-average post-entry evolution (ibidem, p. 936). By the same token, Andersson and Wadensjö, (2007), using a large sample of Swedish-born men who were self-employed in the period 1999-2002 and who were either wage-earners, unemployed or inactive in 1998, showed that those who were previously unemployed systematically had lower incomes compared to those who were previously wage earners; moreover, they also found that income from self-employment declines with the number of days spent in unemployment and that previously-unemployed entrepreneurs are significantly more likely to be ‘solo’ entrepreneurs, _i.e._ to have no employees.

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official time required to complete the process, and its official cost. Substantial empirical evidence suggests that more procedures and longer delays reflect bribe extraction and/or capture (make entry less attractive to potential competitors). Predictions regarding the cost indicator are ambiguous. A benevolent social planner may finance screening of market entrants from direct fees, thus rendering the latter as an imperfect measure of costs. On the other hand, higher fees are frequently used to deter entry. Indicators of the reforms in exit conditions would include, at least, the legal reform of bankruptcy procedures, a measure of efficiency of the bankruptcy agencies and officers, and its average cost (e.g., annual number of sentences as shares of filings, time required and official costs to sentence). The World Bank is assembling an index of the efficiency of the bankruptcy process, based on the procedures for filing, the petition hearing, the court's decision, the appointment of an insolvency practitioner, the assessment of claims and their ordering by priority, and the sale of assets.

Assessing the impact of reforms on the efficiency of the judiciary system for civil procedures is quite difficult. Djankov et al. (2002b) develop indicators based on the analysis by law firms of two specific legal procedures (the eviction of non-paying tenants and the collection of a bounced check). A measure of the reforms would be the change in the average time and the monetary cost involved in accomplishing each goal. In similar fashion, three indicators of contract enforcement are the number of independent procedural steps counted from the moment the lawsuit is filed in court until the contract is fulfilled; the associated time of the procedures; and their cost as a percentage of GNP.

**_3. Outcome indicators_** . Evaluating the impact of changes in legal and regulatory frameworks is difficult because of the microeconomic, specific nature of most reforms and also because changes occur slowly due to the presence of substantial adjustment costs. Hence, it is necessary to focus on intermediate measures of the impact of reforms. If new regulations are better, it should be first noticed in price-quality improvements, either as unit-cost reductions (and lower relative prices) for same-quality goods or, alternatively, quality improvements with constant unit costs. A second measure often used is the reduction in excess capacity of firms operating in a particular market, a result of improved management capabilities or better market access. A third suggested measure

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in MA shrinks to 1.13%-1.19% in all cases, implying that with a high trade elasticity, cross-border frictions become largely irrelevant. Evidence from inter-city migration (Zipf, 1946; Poot et al., 2016), international trade (Head & Mayer, 2014), and by Peng & Chen (2021), suggests that a lower elasticity is more plausible.

Cell-level MA losses due to border frictions range between -6.6% and -81.1% in GDP/min terms with _θ_ = 1. Figure 8 shows their spatial distribution. Large parts of central Africa incur 50-80% time- and 20-50% distance-denominated losses. Cells across the border from major markets also incur very large losses. Under _θ_ = 3 _._ 8, losses are large in some cells close to borders but negligible in most populated cells. Appendix Figure A6 shows the changes under _σ_ = 3 _._ 8.

<!-- Start of picture text --> Figure 8: Market Access Loss (%) from Border Frictions under θ = 1<br><!-- End of picture text --> <!-- Start of picture text --> Travel Time Road Distance<br>0<br>−10<br>−20<br>−30<br>−40<br>−50<br>−60<br>−70<br>−80<br><!-- End of picture text --> _Notes:_ Figure shows local MA %-loss from frictions (using Eq. 4 where _Lj_ is cell-GDP from Kummu et al. (2018)).

In summary, assuming _θ ≪_ 3 _._ 8, many regions could gain significant MA from lower border frictions. In the following sections, I show that these frictions also reduce the benefits of (transAfrican) road investments. Thus, their reduction should be a top policy priority. To simulate such investments, however, I first need to create suitable graph representations of Africa’s road network.

# **4 Optimal Network Representations and Extensions**

The central aim of this paper is to provide quantitative evidence on which roads should be enhanced or built. This requires accurate representations of the road network via graphs to simulate the effects of network investments. Most research creates these graphs in an ad-hoc fashion by either connecting adjacent cells in a gridded representation and parameterizing them by the connectivity along these links, or by choosing the largest roads and their intersections as nodes/edges of the graph. This section instead uses a routing engine (OSRM) to create graphs that optimally represent the road network as interpreted by the engine, implying that the optimal network graph produces road distance/travel time estimates that closely approximate those of the routing engine. I then extend this graph with high-value new links connecting previously unconnected nodes.

## **4.1 An Optimal Graph of Africa’s Transport Network**

Previous research such as Graff (2024) has created a graph representation of Africa’s road network by dividing the continent into cells and computing routes connecting adjacent cells. However, cell centroids may not be of economic interest or well-connected at all. I thus generate a more realistic network representation by sampling important cities and ports and letting OSRM compute routes between them, which I then process into an accurate graph. To sample cities, I use the updated 13
**How Does Violence Force Displacement during Active Conflict? Evidence from the Republic of Yemen**<sup>_∗_</sup>

By Anna D’Souza<sup>_†_</sup> , Eliana Favari<sup>_‡_</sup> , Siddharth Krishnaswamy<sup>_§_</sup> , and Sharad Tandon<sup>_∗∗_</sup> Keywords: Conflict; Displacement; Yemen JEL Classification: D12; I31; I38; O10; O53

> _∗_ The authors would like to sincerely thank Mr. Arif Husain and the VAM team of the World Food Programme. The views expressed here are those of the authors and may not be attributed to either the World Food Programme or the World Bank.

> _†_ Baruch College, City University of New York, 55 Lexington Ave, New York NY 10010, USA, anna.dsouza@baruch.cuny.edu.

> _‡_ World Food Programme, Regional Bureau- Middle East, North Africa, and Eastern Europe, 72 El-Nahda, Maadi as Sarayat Al Gharbeyah, Maadi, Cairo, Egypt, eliana.favari@wrp.org.

> _§_ World Food Programme, Regional Bureau- Middle East, North Africa, and Eastern Europe, 72 El-Nahda, Maadi as Sarayat Al Gharbeyah, Maadi, Cairo, Egypt, siddharth.krishnaswamy@wfp.org.

> _∗∗_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, standon3@worldbank.org.
health infrastructure; (2) modernization; (3) economic structure; (4) food prices; (5) ecological characteristics; and (6) availability of health care facilities and providers.

Covariates related to public health infrastructure in the community include indicators of the prevalence of water connections, sanitation facilities and the presence of infrastructure for garbage disposal. With respect to the first two, we consider as covariates the proportion of households in the community with piped water connections and flush toilets. Most studies have treated water and toilet facilities as household variables. Since we do not have information to test whether these inputs are simultaneously determined with choices about the health of children in the household, we decided to include them first as community-level variables and therefore as exogenous to the attainment of child height.1<sup>6</sup> With respect to the third, a dummy variable indicates whether there is a public dumpsite in the community.'<sup>7</sup> Communities in Suchitepequ&z and Totonicapan are the best and the worst equipped respectively (in the latter department only one per thousand of the surveyed households have tap water, only one percent have flush toilets, and only one of the 15 surveyed communities provides a public dumpsite for garbage disposal).

Measures of modernization include the percentage of households in the community with a television and the proportion of women in the community who have had either formal schooling or adult literacy classes. These variables aim to capture ideational effects on norms and behavior about modern feeding and child care practices.

The economic structure of the community is represented by a set of five variables that capture the degree of accessibility to the local economy, of regular employment opportunities in the agricultural sector, and of accessibility to credit institutions. Overall, communities in Chimaltenango and Totonicapan are the least equipped and communities in Suchitep6quez the best equipped. Another indicator of the economic structure of the community is represented by the total number of people living there as of the 1994 Census. The average community has 2587 inhabitants, the range being between 507 and 9928. Communities in Totonicapan tend to be bigger and communities in Jalapa tend to be smaller than the remaining communities.

Evidence related to the effects of market prices on child height-for-age is ambiguous. Barrera (1990) found no effect of the price of rice, cooking oil, kerosene or milk on child height in the Philippines. Foster (1990) finds that child height is negatively correlated with changes in rice prices in Bangladesh. Thomas and Strauss (1992) found that, taken together, prices have a significant effect on

> 16 In the analysis of the proximate determinants, we will add the availability of piped water and flush toilets as household-level covariates.

> **'7** The presence of a public dumpsite in the community is meant to capture a conscious public health effort by the local authorities.

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# **B Description of Variable’s Construction**

This section describes how we constructed each of the variables in our main analysis.

# **B.A Outcome Variables**

Outcome variables were constructed using the data from the Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021.

- Integration: we measured the integration dimension with three different types of questions. The first included the individual reported answer from a 1 to 5 scale, where 1 means strongly disagree, and 5 strongly agree of the following questions: (i) ”Would you feel comfortable if your child or grandchild were to socialize or be friends with children of Refugee community people?”, (ii) ”Do you feel welcomed in this village/town/city?”, (iii) ”Do you think that everyone living in this village/ town/ city feels like they are a part of this village?”. The second, included the answer to the following question ”On a scale of 1 to 10, with 1 being not very much to 10 very much, how much do you feel that Tajikistan is your home?”, and the third, the total number of refugee, and Tajik friends among all of their friends, respectively.

The Integration Index is constructed using the methodology of Kling, Liebman and Katz (2007) with the variables explained above. It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again.

- Educational Attainment: we measured the educational attainment dimension using the following questions: (i) ”Can ¡name¿ read and write in any language?”, and (ii) ”What is the highest level of formal school ¡name¿ completed?”. With the answer to the question (ii), we generate an indicator variable corresponding to each level of education: _Some Education [=1]_ if the refugee has completed any level under the primary education level, _Primary Education [=1]_ if the refugee has completed basic primary education level, and _Secondary Education [=1]_ if the refugee has completed secondary education level.

The Education index is constructed by calculating the average of the four indicator variables explained above.

- Mental Health: we measured the mental health dimension by constructing an indicator variable, equal to 1 if the respondent answers ”More than half of the days or Nearly every day” or 0 if the answer is ”Several Days or Not at All”, for each of the following questions: (i) ”Over the last 2 weeks, how often have you felt little interest or pleasure in doing things?” (ii) ”Over the last 2 weeks, how often have

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**Table S30. Educational activity and food insecurity**

||Moderate or<br>Severe Food<br>Insecurity|Severe Food<br>Insecurity|
|---|---|---|
|Education|||
|Activity|-0.075***|-0.033|
||(0.024)|(0.023)|
|Ethiopia|-0.118***|-0.054**|
||(0.029)|(0.024)|
|Nigeria|0.112***|0.102***|
||(0.029)|(0.027)|
|Uganda|-0.195***|-0.147***|
||(0.028)|(0.021)|
|Ethiopia-Nigeria|0.000|0.000|
|Ethiopia-Uganda|0.005|0.000|
|Nigeria-Uganda|0.000|0.000|
|Observations|5,978|5,978|
|R-squared|0.089|0.074|

_Note_ : Each column reports results from a single regression of a continuous variable that measures the prevalence of moderate or severe food security, or just severe food insecurity among adult household members. The variable is regressed on indicators for each country, with Malawi as the base case, and a binary for educational activity. Educational activity is a binary variable equal to 1 if the household has a school-aged child that has been engaged in some educational activity since schools closed and 0 otherwise. The question is asked conditional on the household containing a school-aged child. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 2 in Ethiopia, Malawi, and Nigeria, and round 1 in Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

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earnings for workers who were previously employed in informal sectors. The estimated coefficients range from (-0.054) to (-0.077). The coefficients are slightly higher for agriculture compared to manufacturing and services in the specifications with control variables, reflecting large inefficiencies in the sector. Finally, these coefficients are lower than those reported by Maloney (2001) and Loayza and Rigolini (2011) for Latin American countries.

Table 2. Regressions results of informality and labor productivity.

||(1)<br>Agriculture|(2)<br>Manufacturing|(3)<br>Services|(4)<br>Agriculture|(5)<br>Manufacturing|(6)<br>Services|
|---|---|---|---|---|---|---|
|Labor|-0.07***|-0.077***|-0.055***|-0.064***|-0.054**|-|
|Productivity|(0.017)|(0.022)|(0.019)|(0.018)|(0.029)|0.056***<br>(0.021)|
|Controls|No|No|No|Yes|Yes|Yes|
|Country fixed|||||||
|effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|545|543|544|532|530|531|
|R-squared|0.37|0.34|0.55|0.04|0.104|0.25|
|# Countries|87|87|87|79|79|79|

Note: The first three columns show panel data regression results for each sector. The last three columns include control variables such as export and import as shares of GDP, human capital, the dependency ratio, the real interest rate and the log of population. Standard errors in brackets; * shows significance at 10% level, ** at 5% and *** at 1%.

# **6. Conclusion**

This paper provides a theoretical framework and empirical evidence to understand and estimate the relationship between economic growth and employment growth. The results show that employment growth resulting from economic growth varies substantially across developing countries and across sectors, reflecting considerable differences in production technologies and informality. The paper 23
|Is Female Business Manager|0.543|0.498|||||
|---|---|---|---|---|---|---|
|**Number of Observations**|**4,646**||**2,077**||**2,569**||
|_Variables used in Agric. Output Regression Decomposition_|||||||
|Asinh(Harvest Value)|11.79|1.601|11.98|1.638|11.05|1.189|
|Individual Age|41.58|11.71|42.34|11.47|38.70|12.16|
|Individual Age Squared|1,866|989.8|1,924|982.1|1,646|988.1|
|Number of Adults in HH|3.484|2.044|3.633|2.077|2.913|1.804|
|Number of Children Aged 0-5 in HH|1.461|1.346|1.529|1.327|1.203|1.386|
|Individual owns a Financial Account|0.323|0.468|0.378|0.485|0.113|0.317|
|Individual is Married|0.919|0.272|0.956|0.205|0.780|0.414|
|Individual is Divorced/Married/Separated|0.054|0.225|0.0122|0.110|0.211|0.408|
|Individual Completed Secondary Schooling|0.007|0.0846|0.008|0.091|0.003|0.052|
|Individual owns a Vehicle|0.818|0.386|0.899|0.301|0.510|0.500|
|Household Owns Mechanized Farming Equipment|0.373|0.484|0.410|0.492|0.233|0.423|
|Area Of Parcel of Land|2.129|15.79|2.248|16.21|1.674|14.04|
|Area of Parcel of Land Squared|253.7|7,320|267.9|7,721|199.8|5,531|
|Distance Of Land from Home|24.21|118.4|24.64|132.2|22.57|29.96|
|Crop Grown for Export|0.043|0.202|0.053|0.224|0.004|0.059|
|Farm Manager is the Owner|0.613|0.487|0.684|0.465|0.341|0.474|
|Manager Controls the Sale of Crops|0.323|0.468|0.334|0.472|0.280|0.449|
|Number Of Days HH Female Member Worked|67.69|90.43|69.60|92.45|60.42|81.89|
|Number Of Days HH Male Member Worked|64.67|80.25|72.72|84.91|34.02|48.21|
|Total Days Worked by Non-HH Men|2.219|8.240|2.390|8.749|1.568|5.865|
|Total Days Worked by Non-HH Women|1.293|4.837|1.309|5.025|1.234|4.044|
|All Harvest from Parcel Sold|0.099|0.298|0.107|0.309|0.0668|0.250|
|Parcel Of Land uses Fertilizer|0.323|0.468|0.349|0.477|0.227|0.419|
|Parcel Of Land uses Pesticides|0.309|0.462|0.338|0.473|0.199|0.399|
|Is Female Farmer|0.208|0.406|||||
|**Number of Observations**|**13,577**||**10,693**||**2,884**||
|_Variables used in Agric. Output Sold Regression Decomposition_|||||||
|Asinh(Output Sold)|11.59|1.397|11.78|1.378|10.82|1.197|
|Individual Age|41.01|11.56|41.63|11.44|38.43|11.71|
|Individual Age Squared|1,816|974.5|1,864|973.8|1,614|951.4|
|Number of Adults in HH|3.579|2.173|3.664|2.164|3.217|2.176|
|Number of Children Aged 0-5 in HH|1.509|1.329|1.548|1.302|1.343|1.426|
|Individual owns a Financial Account|0.342|0.474|0.402|0.490|0.0891|0.285|
|Individual is Married|0.928|0.258|0.955|0.207|0.816|0.387|
|Individual is Divorced/Married/Separated|0.043|0.203|0.0125|0.111|0.172|0.377|
|Individual Completed Secondary Schooling|0.005|0.071|0.006|0.077|0.001|0.036|
|Individual owns a Vehicle|0.848|0.359|0.925|0.263|0.521|0.500|
|Household Owns Mechanized Farming Equipment|0.536|0.499|0.585|0.493|0.332|0.471|
|Area Of Plot of Land|2.021|14.78|2.158|14.10|1.444|17.36|

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