rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix Table A: Plot-Level Characteristics by Region**

||**Total**|**Central**|**Eastern**|**Northern**|**Western**|
|---|---|---|---|---|---|
|**Yield**||||||
|Value of Output per Acre (USD)|144|159|108|122|192|
|**Plot Characteristics**||||||
|Area of Plot in Acres|0.69|0.85|0.68|0.63|0.67|
|Good Land Quality|61.4%|72.5%|53.1%|62.3%|60.9%|
|Fair Land Quality|32.3%|20.6%|39.8%|31.3%|34.3%|
|Poor Land Quality|6.2%|7.0%|7.1%|6.3%|4.8%|
|Irrigated|1.9%|4.0%|2.7%|1.2%|0.7%|
|Rain-Fed|95.9%|92.4%|94.9%|97.0%|97.7%|
|Swamp/Wetland|2.2%|3.7%|2.4%|1.7%|1.6%|
|Hilly|9.9%|8.9%|1.1%|3.0%|26.6%|
|Flat|45.4%|29.2%|57.9%|55.7%|32.7%|
|Gently sloped|38.2%|53.6%|36.4%|38.3%|29.8%|
|Steep|3.2%|2.5%|3.6%|1.2%|5.7%|
|Valley|3.1%|5.8%|0.9%|1.7%|5.1%|
|**Distance from Homestead to Plot**||||||
|Less than 15 Min|65.0%|64.5%|66.3%|68.6%|59.7%|
|15 Min - 30 Min|16.3%|19.2%|13.9%|15.0%|18.0%|
|30 Min - 60 Min|11.9%|12.4%|13.7%|8.4%|14.1%|
|1 Hour - 2 Hours|5.7%|2.7%|5.5%|6.3%|7.1%|
|Over 2 Hours|1.2%|1.2%|0.5%|1.7%|1.1%|
|**Tenure & Investment**||||||
|Land Owned by the Household|82.2%|82.5%|79.0%|86.9%|79.3%|
|Erosion Control/Water Harvesting Facility is|31.7%|46.2%|28.3%|24.8%|33.2%|
|Located on the Plot||||||
|**Labor Inputs**||||||
|Any Family Labor Used on Plot|96.1%|95.5%|96.4%|95.3%|97.0%|
|Family Labor Days per Acre|130|144|122|94|169|
|Any Hired Labor Used on Plot|26.9%|20.4%|26.5%|31.0%|26.9%|
|Hired Labor Days per Acre|8.2|4.7|7.6|9.9|9.0|
|**Non-Labor Inputs**||||||
|Improved Seed Applied|6.7%|7.9%|8.1%|9.9%|1.0%|
|Purchased Seed Applied|25.3%|15.4%|23.3%|38.0%|18.6%|
|Value of Seed per Acre (USD)|3.98|2.06|3.15|4.36|5.57|
|Any Manure Applied|4.1%|9.5%|1.5%|0.2%|7.4%|
|Kilograms of Manure per Acre|40|98|10|0|75|

23
# **Obstacles to Growth for Small and Medium**

# **Enterprises in Turkey**

## **By**

## **Murat Şeker**<sup>*****</sup> **And Paulo Guilherme Correa**<sup>**†**</sup>

> * Enterprise Analysis Unit, Finance and Private Sector Development, World Bank; 1818 H Street NW. Washington DC MSN F4P‐400, 20433 Email: mseker@worldbank.org

> † Europe and Central Asia Region, Finance and Private Sector Development, World Bank, 1818 H Street NW. Washington DC MSN H 4‐410, 20433 Email: pcorrea@worldbank.org
Table 3: Phillips and Sul convergence results

|Club<br>1970-<br>2018<br>[mem-<br>bers]|Convergence<br>rate (_γ_)|Club<br>1970-<br>2000<br>[mem-<br>bers]|Convergence<br>rate (_γ_)|
|---|---|---|---|
|Club 1 [45]|0.18|Club 1 [26]|0.19|
|Club 2 [31]|0.16|Club 2 [21]|0.08|
|Club 3 [10]|0.09|Club 3 [18]|0.12|
|Club 4 [6]|0.06|Club 4 [25]|-0.03|
|Club 5 [5]|0.06|Club 5 [7]|0.74|

clubs 3-5 are considered to be a single group. The coefficients in **Table 4** are directionally informative around the probability of being a member of either Club 2 or clubs 3-5 relative to the fast-converging Club 1. A positive coefficient implies an increased probability of being in either club relative to Club 1. These are converted to odds-ratios in main text **Figure 7** — this converts the coefficients into the change in probability of membership of clubs 2-5 relative to Club 1 for a one-unit increase in the variable under consideration.

35
Policy Research Working Paper 8976

# **Abstract**

Dar es Salaam is frequently affected by severe flooding causing destruction and impeding daily life of its 4.5 million inhabitants. The focus of this paper is on the role of poverty in the impact of floods on households, focusing on both direct (damage to or loss of assets or property) and indirect (losses involving health, infrastructure, labor, and education) impacts using household survey data. Poorer households are more likely to be affected by floods; directly affected households are more likely female-headed and have more insecure tenure arrangements; and indirectly affected households tend to have access to poorer quality infrastructure. Focusing on the floods of April 2018, affected households suffered losses of 23 percent of annual income on average. Surprisingly, poorer households are not over-represented among the households that lost the most - even in relation to their income, possibly because 77 percent of total losses were due to asset losses, with richer households having more valuable assets. Although indirect losses were relatively small, they had significant well-being effects for the affected households. It is estimated that households’ losses due to the April 2018 flood reached more than US$100 million, representing between 2–4 percent of the gross domestic product of Dar es Salaam. Furthermore, poorer households were less likely to recover from flood exposure. The report finds that access to finance play an important role in recovery for households.

This paper is a product of the Global Facility of Disaster Reduction and Recovery. 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 aerman@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
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 7: Regional log Working-Age Population

|Change in log working-age population:||OL|S|||IV|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|1980 pop.<br>(5)|70-80 Δpop.<br>(6)|
|Panel A: Census-1991-2000|||||||
|Regional tariff change|-0.176|-0.835***|-0.331|0.055|-0.022|-0.085|
||(0.276)|(0.159)|(0.243)|(0.330)|(0.092)|(0.166)|
|Population pre-trend (80-91)|||0.407**||0.656***|0.605***|
||||(0.164)||(0.038)|(0.106)|
|Population pre-trend (70-80)||||0.295***<br>(0.073)|||
|State fixed effects (26)||✓|✓|✓|✓|✓|
|R-squared|0.009|0.399|0.657|0.557|||
|First stage partial F-Statistic|||||212.6|32.31|
|Panel B: Census-1991-2010|||||||
|Regional tariff change|-0.174|-1.175***|-0.389|0.162|0.078|-0.030|
||(0.432)|(0.285)|(0.320)|(0.474)|(0.163)|(0.233)|
|Population pre-trend (80-91)|||0.634***||1.010***|0.923***|
||||(0.225)||(0.059)|(0.147)|
|Population pre-trend (70-80)||||0.441***<br>(0.087)|||
|State fixed effects (26)||✓|✓|✓|✓|✓|
|R-squared|0.003|0.422|0.669|0.554|||
|First stage partial F-Statistic|||||197.9|31.54|

Decennial Census data. Positive (negative) coefficient estimates for the regional tariff change imply larger decreases (increases) in working-age population in regions facing larger tariff declines. 411 microregion observations. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Weighted by the inverse of the squared standard error of the estimated change in the relevant employment share. Pre-trends computed for 1980-1991 and 1970-1980 periods. To address potential endogeneity of 1980-1991 pre-trend due to overlap with dependent variable in 1991, 1980-1991 pre-trends are instrumented using the 1980 population level in column (5) and the 1970-1980 trend in column (6). First-stage partial F-statistics reported for instrumental variables specifications. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

69
- 1.70 -

Figure I.C.22 COCOA ACREAGE ADJUSTMENT, POPULATION AND INCOME GROWTH WITH ZERO FOOD IMPORTS <!-- Start of picture text --> 6 -<br>,/' '\\<br>4 -<br>2 J<br>=7 l 200t 2017 2027 2037<br>PERIOD MDP>TS<br>O 1X ~~+ 140 oI8^SO 20<br><!-- End of picture text -->
69 With respect to emigration from Italy, Simon (1987) notes a growing distaste among Italians to emigrate as the country develops, but still expects that there will be considerable emigration in the near future. The lack of solid information makes projections difficult but I would recommend adopting the United Nations assumptions of zero net international migration in 1985-2000. This would assume that the emigration of nearly 4,000 Italians per year to the United States and Canada would be approximately offset by a similar level of net immigration from other parts of the world.

# Netherlands

The Netherlands wants to increase emigration because of its very high population density, but emigration of the foreign population has decreased steadily since 1982 (SOPEMI,1988). The annual inflow of foreigners also decreased substantially until 1983 but it has increased by 45 percent since that time (SOPEMI, 1988; Entzinger, 1985). The stock of the foreign population is estimated to have increased from 520,900 in 1980 to 552,500 in 1985 (a jump of 31,600). No information is available on the net immigration of native-born persons or on illegal immigration, so it is difficult to arrive at a reliable overall figure for net immigration. However, the United Nations figures seem more reasonable than the World Bank figures. I would recommend net immigration of 75,000 persons in 1980-1985, 50,000 in 1985-1990, 40,000 in 1990-1995, and 30,000 in 1995-2000.

# Norway

Norway experienced actual net immigration of 4,346 persons per year during 1973-1984 and 4,727 persons per year in 1981-1984 (Statistisk Sentralbyra, 1986). Based on this fairly constant level of net immigration, Statistisk Sentralbyra has used a constant annual figure of 4,000 for the net migration assumption of their population projections for 1986-2000. An alternative projections shows the pattern of population growth in the absence of any net international migration. Given these scenarios, either the World Bank or the United Nations figures appear reasonable for 1985-2000. I would recommend increasing the net immigration figure for 1980-1985 to 20,000 and then accepting the United Nations assumptions for the other periods.

# Poland

Continuing political and economic uncertainties in Poland have precipitated substantial emigration in the 1980s. From 1980 to 1985, there were more than 50,000 immigrants from Poland to the United States and Canada alone, and return migration must have been negligible. At the current rate of migration, there will be 60,000 immigrants to the United States and Canada in 1985-1990. Therefore, I would recommend net emigration of 45,000 in 1980-1985 and 65,000 in 1985-1990, dropping slowly to 60,000 in 1990-1995 and to 55,000 in 1995-2000.
attainment, higher maternal mortality, and high levels of child malnutrition (Caldwell et al 1983). Using the generation II sample of men and women allows us to apply the same strategy as in equation (1).

A potential concern with our specification is that fathers who know that their daughters will inherit greater amounts of physical wealth at their death may reduce the amount of education they provide to daughters as compared to sons. This is relevant because human capital, in the form of education, is a key way to transfer wealth across generations. If girls have a comparative advantage in human capital rather than land-intensive economic pursuits (Quisumbing et al 2001), compliance with the letter but not the spirit of the HSAA, through a commensurate reduction of the amount of education transferred to girls, could potentially make women worse off. To account for this possibility, analysis of intergenerational transfers of human capital, in addition to those of land, will be useful. It has the added advantage of being able to include households where the generation I male is still alive.

We do so by analyzing the level of educational attainment by generation III individuals and compare relative changes in the level of female and male educational attainment between cohorts who completed educational decisions before and after the HSAA. As the educational decisions of generation II individuals had mostly been completed by the time the HSA had been amended, the only viable way of exploring potential human capital effects is to focus on elementary education of generation III individuals whose (expected) inheritance is not (yet) observed. We focus on elementary education of girls and boys who were less than 6 years old in 1994 so that their decisions would have been affected by the HSAA as compared to those who were 13 years or older in 1994 implying that, children who are 13 years or older would have completed their elementary education by then, and their schooling decision would not be affected by the Act. Children who were 6-12 years old in 1994 are excluded to avoid contamination of results as grade repetition and delayed school entry could lead to a few older girls to potentially benefit from the legal change (leading to a downward bias in the estimation if they are mistakenly considered credible dowry data that would enable us to measure the dowry response to this legal amendment, but this is certainly an area for future work.

17
**12** **<u>│</u>**

|**Pillar**|**Area**|**Indicator**<br>**type**|**Strategic question**|**Indicator’s description**|
|---|---|---|---|---|
|||Micro-<br>Based|Do foreign-owned firms source<br>inputs more intensively from<br>abroad?|Wedge between foreign-owned and<br>domestically owned firms in their<br>intensity of sourcing domestic<br>inputs|

Source: Authors’ elaboration of an earlier version prepared jointly with the OECD for the Investment Committee in October 2016.

The indicators presented in this study are all compiled at the level of production establishments. They adopt the first approach of measuring the differences between foreign multinational and domestic firms by employing two thresholds of foreign ownership: 10 percent and 50 percent.

# 3.1 The data: Foreign-owned firms in World Bank Enterprise Surveys

The World Bank Enterprise Survey (WBES) is a unique source of economic data, which has a pool of 131,000 firms from 139 countries, mostly developing nations. The data have been collected since 2006 using a harmonized methodology to ensure cross-country comparability.

The surveys only represent a country's non-agricultural economy. Thus, the sectoral scope of the surveys is not comprehensive. Manufacturing and services are the primary sectors of focus, with services including construction, retail, wholesale, hotels, restaurants, transport, storage, and telecommunications. Mining and financial services, which attract significant foreign investment, are excluded. This also excludes firms that employ fewer than five workers, as well as wholly state-owned enterprises. All these factors affect the benchmark for evaluating the differences between foreign multinational and domestic firms.

But how accurate is the WBES when it comes to foreign multinationals? The sampling follows frames provided by national statistical offices or other official sources over three dimensions: firm size, business sector, and geographic region, but does not consider domestic or foreign ownership. However, to the extent that variation in foreign ownership is driven by characteristics the survey is stratified on - such as sector of activity, scale of production, and location, all of which are established drivers of foreign multinational activity – then the surveys should exhibit high alignment in terms of representativeness, subject to a sufficient number of observations on both pools. We restrict the sample to surveys conducted after 2010 where at least 30 foreign multinationals with over 50 percent ownership are present, keeping only 63 surveys of the 139 available (see Table 4 for a description of the sample).
Policy ReseaRch WoRking PaPeR 4728

# **Abstract**

Internal migration plays an important role in moderating regional differences in well-being. This paper analyzes migrants' choice of destination, using Census and Living Standard Surveys data from Nepal. The paper examines how the choice of a migration destination is influenced by income differentials, distance, population density, social proximity, and amenities. The study finds population density and social proximity to have a strong significant effect: migrants move primarily to high population density areas where many people share their language and ethnic background. Better access to amenities is significant as well. Differentials in expected income and consumption expenditures across districts are found to be relatively less important in determining migration destination choice as their effects are smaller in magnitude than those of other determinants. The results of the study suggest that an improvement in amenities (such as the availability of paved roads) at the origin could slow down out-migration substantially.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the determinants of migration. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fshilpi@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
|In (Unskilled Workers)||-0.037<br>(0.160)<br>)|0244<br>(0.174)||0.111<br>(0.159)<br>j}|02S1<br> (0.160)|
|---|---|---|---|---|---|---|
|In (Skilled Workers)||6.BE-4<br>(1.E3)|||-6.9E-4<br>(1_2Y-3)_||
|In (Middle Managenent)||42B-4<br>(3.1E-4)|||45<br>(32A4)||
|In (Upper Management)||.E4|(4.7E4)||2.3E-4<br>~~~~~~~~|(4.6E-4;)|
|In (Wage BiD of<br>Temporary Workers)<br>InK|-53EB4<br>(4.7E-4)<br>0.655*<br>(0250)|-2.4E4<br>(3.OEB4)<br>0.748*<br>(0.145)|-12E-4<br> <br>(3.0E-4)<br>0545*<br>(0.161)|53E-4<br>(3.SE4)<br>0.706*<br>(0.202)|2.7E4<br>(2.6E4)<br>0.787*<br>(0.136)|2.7134<br>(2.3E-4)<br>0.659<br>(0.150)|
|rime|-0.023<br>(0.055)|-0.031<br>(0.038)|-0.004<br>(0.038)|-0.054<br>(0.043)|.0.036<br>(0.038)|-0.025<br>(0. 03)|
||_OBOS_|0.867|0.857|0.891|0885|089|
||92|92|92|87|87|87|

Signifcznt at the 5 % statistical test<sup>level</sup> - Sinifcant at the 10 % stadstical<sup>test level.</sup> Our sample in these industries consists<sup>of 18 finns</sup>
Despite the increasing attention in recent years to the spatial dimensions of economic development, the regard for the importance of space and the recognition that macro, _aspatial_ perspectives can prove to be misleading are not new. Beveridge (1944) was among the first to introduce a spatial dimension by examining the role of full employment in a society with a special focus on the spatial distribution of employment and unemployment. Readers were startled to find that during the Great Depression (1929-1937), unemployment rates varied by a factor of two to two and a half times between the Southeast of the UK (London-centered region) and Wales, Scotland, and the North of England. The impact of the Great Depression turned out to have been very uneven over space; the Beveridge book sought to understand the causes and the outcomes. Eighty years later, during a period in which a variety of initiatives had been undertaken, the regional disparities have persisted notwithstanding the introduction of significant initiatives by the national government to address these disparities. For the three months ending in July 2017, the highest unemployment rate was still in the Northeast (6.0%) with the lowest recorded in the Southeast of the UK (3.2%).<sup>4</sup> Many other places around the world experience similar deep regional inequalities, with disparities between rural and urban areas playing a central role in such inequalities (World Bank 2015; World Bank and Development Research Center of the State Council, People’s Republic of China 2014). In China for example, the rural-urban divide is estimated to account for 45 percent of overall inequality (Kanbur, Rhee, and Zhuang 2014).

Around the world, much of the disappointment with the outcomes from spatial interventions may be traced to a lack of understanding of how regional economies work. In retrospect, it would appear that many of the initiatives aimed at reducing regional disparities have been proposed with an incomplete understanding of how they might work; in many cases, there was little or no attempt to measure the efficacy of the initiatives. In other cases, the lack of consideration of spatial/regional disparities may have compromised the efficacy of broader development policies. Too often, there is a naïve assumption that investment in region _A_ can only generate benefits or costs in that region; spatial spillovers are more often than not ignored. A distinguishing feature of regional economies is their openness, which as we have seen previously, leads to complex webs of interregional

> 4https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/regionallabourmar <u>ket/september2017.</u> Accessed 12/04/2018.

1