rafmacalaba/datause-encoder-data huggingface dataset:
19

**Figure 10. The evolution of development indicators since the global crisis**

<!-- Start of picture text --> (Malnutrition, based on data from 34 countries)<br>(Gender education, based on<br>data from 152 countries)<br>0 10 20 30 40 50<br>post-crisis malnutrition<br>60 70 80 90 100 110<br>East Asia and Pacific Central-Eastern Europe Africa post-crisis ratio of girls to boys primary and secondary education<br>Latin America/Carribean Middle East and North Africa South Asia East Asia and Pacific Central-Eastern Europe Western Europe Latin America/Caribbean<br>Middle East and North Africa South Asia Africa North America<br>(Infant mortality, based on data<br>from 193 countries) (Maternal mortality, based<br>on data from 216 countries)<br>0 50 100 150 200 0 500 1000<br>post-crisis mortnality rate post-crisis maternal mortality ratio per 100,000 live births<br>East Asia and Pacific Central-Eastern Europe Western Europe Latin America/Caribbean East Asia and Pacific Central-Eastern Europe Western Europe Latin America/Caribbean<br>Middle East and North Africa South Asia Africa North America Middle East and North Africa South Asia Africa North America<br>50 110<br>40 100<br>30 90<br>20<br>80<br>precris-malnutrition<br>10<br>70<br>0<br>60<br>pre-crisis ratio of girls to boys primary and seconday education<br>200<br>1000<br>150<br>100<br>500<br>pre-crisis mortlaity rate<br>50<br>0 0<br>pre-crisis maternal mortality ratio per 100,000 live births<br><!-- End of picture text --> _Source_ : Authors’ computations, based on World Bank WDI Database.

_Note_ : Countries below the 45 degree line had higher prevalence of malnutrition, better gender education parity, and higher postcrisis under-5 and maternal mortality rates.
17

# Chart 3-2- FDI Flows in FLvy Asian Countries (in US$ billions)

<!-- Start of picture text --> Ij Nore ,<br>UN MD an. nN a<br>1"86 90 1 lmd 1978 3M18 78 17<br><!-- End of picture text --> Reflecting the record of other African countries, Kenya and Nigeria were experiencing lower levels of FDI inflows (in constant dollars) at the end of the 1980s than at the beginning; the basic trend for Nigeria has been one of decline for the two-decade perlod, while Kenya experienced a :'ositive though moderate inflow throughout the perlod. Whereas Kenya's inflows reveal no volatility, Nigeria experienced a large but brief negative deviation in 1980.15
Gyourko, J., Linneman, P., 1989. Equity and Efficiency Aspects of Rent Control: An Empirical Study of New York city, _Journal of Urban Economics_ , 26.

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Hoek-Smit, Marja C. and D. Diamond. 2003. “The design and Implementation of Subsidies for Housing Finance.” Paper prepared for the World Bank Seminar on Housing Finance, March 1013, 2003. Available at http://www.worldbank.org/wbi/banking/capmarkets/housing Jappelli, T., L. Pistaferri (2002), “tax incentives for Households Saving and Borrowing”, _CEPR Discussion Paper_ , 3881.

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53
_Table 1: Sectoral Trade Elasticities_

|Sector|Elasticity|<br>Sector|Elasticity|
|---|---|---|---|
|Beverages and tobacco products|2.55|Machinery and equipment nec|1.52|
|Communication|7.07|Manufactures nec|5|
|Construction|4.55|Minerals nec|2.76|
|Dwellings|4.55|Meat products nec|2.55|
|Electronic equipment|10.6|Other Agriculture|8.11|
|Metal products|4.3|Other Services|4.55|
|Forestry|8.11|Transport equipment nec|4.55|
|Fishing|8.11|Paddy rice|2.55|
|Gas manufacture, distribution|5|Petroleum products, plastics and Chemicals|19.16|
|Leather and wood products|10.83|Paper products, publishing|9.07|
|Metals|7.99|Textiles|5.56|
|Dairy products|2.55|Transport|4.55|
|Motor vehicles and parts|4.55|Trade|4.55|
|Mineral products nec|2.76|Wearing apparel|5.56|
|Food products nec|2.55|Water and Electricity|4.55|
|Oil, Gas and Coal|4.0|||

# **Estimated changes in trade costs**

We briefly review the methodology used to estimate the impact of the BRI transport infrastructure on trade times and trade costs in de Soyres et al. (2018). First, we identify the longitude and latitude of the two most populous cities in each country in addition to all the cities in the world with population greater than 500,000. Second, we compute the shipment times between each city-pairs using the shortest path algorithm applied to the existing network of railways and the location of major ports using data from Atlas of the Earth (DAE), 2015 release, and Global Shipping Routes (CIA, ESRI, 2012) respectively. Finally, we identify the effect of BRI infrastructure projects on shipment times at city-pair level by applying the shortest path algorithm to an improved network that includes manually digitized information on BRI projects based on Reed and Trubetskoy (2018).

The BRI transport infrastructure investments considered in this analysis are those related to new or improved rail and port projects.<sup>14</sup> To obtain more accurate time estimates, we complement the georeferenced data with proxies for port quality using data from Slack, Comtois, Wiegmans and Witte (2018) on the amount of time spent in port by vessels. Additional data on

> 14 See de Soyres et al (2018) for the full list of projects.

15
<!-- Start of picture text --> Job density<br>-51.1<br>———————E<br>17.2<br>—$———<br>-6.4<br>SS -46.4<br>-80.0 -60.0 -40.0 -20.0 0.0 20.0<br>© Accra © Antananarivo<br>} DES } Addis<br><!-- End of picture text -->
- 16 -

33percent have access to piped water. Flush toilets are less available. Most households have at least one mosquito net. However, only 45 percent of households own mosquito nets used for kids. These characteristics are considered to be important to determine if people are likely to be affected by disease, having a fever.

**<u>Table 2. Summary statistics</u>**

||Obs<br>|Mean|Std. Dev.|Min<br>|Max|
|---|---|---|---|---|---|
|Dependent variable:||||||
|Y: Visit a health facility|5,778|0.60|<br>0.49|<br>0|1|
|Transport connectivity variable:||||||
|Travel time to HF (hours)|36,134|0.14|<br>0.22|0.00002|2.67|
|Transport cost to go to HF ($)|36,397|0.38|<br>0.56|0.00011|6.34|
|Distance to HF (km)|36,570|5.46|<br>7.86|0.00239|66.11|
|Bike/Motorcycle owned|38,115|0.38|<br>0.49|<br>0|1|
|Car owned|38,115|0.07|<br>0.25|<br>0|1|
|Health facility characteristics:||||||
|Rural health center|38,092|0.64|<br>0.48|<br>0|1|
|Urban health center|38,092|0.17|<br>0.38|<br>0|1|
|Hospital|38,092|0.09|<br>0.29|<br>0|1|
|Health post|38,092|0.09|<br>0.28|<br>0|1|
|Remoteness of HF (hours to the nearest<br>city with 100,000 population or more)|37,533|1.81|<br>2.08|0.0010|16.35|
|Individual characteristics:||||||
|Age|38,115|16.74|<br>18.42|<br>0|95|
|Female|38,115|0.51|<br>0.50|<br>0|1|
|Household characteristics:||||||
|HH size|38,115|6.14|<br>3.00|<br>1|33|
|Female HH head|38,115|0.31|<br>0.46|<br>0|1|
|Age of HH head|38,115|42.68|<br>14.47|<br>14|97|
|No of bedrooms|38,115|2.35|<br>1.30|<br>0|22|
|Wealth index|38,115|3.25|<br>1.41|<br>1|5|
|Access to power|38,115|0.35|<br>0.48|<br>0|1|
|Piped water|38,115|0.33|<br>0.47|<br>0|1|
|Flush toilet|38,115|0.12|<br>0.33|<br>0|1|
|Solid roof|38,115|0.51|<br>0.50|<br>0|1|
|Mosquito net|38,115|0.84|<br>0.36|<br>0|1|
|Mosquito net for kids|38,115|0.45|<br>0.50|<br>0|1|
|Time fixed effects:||||||
|t=2011|38,115|0.25|<br>0.43|<br>0|1|
|t=2018|38,115|0.75|0.43|0|1|
and Segev, 2000; Chen et al., 2011). In 1980 the central government designated four cities, Shenzhen, Zhuhai, Shantou and Xiamen, to be the special economic zones. A few years later, another 14 cities were designated as „open coastal cities‟. These cities were granted special policy advantages in attracting FDI, which meant fewer restrictions on FDI and some tax concessions from the central government. During our sample period, the policy differentials across Chinese cities became much smaller. However, there may still be path-dependence effects or some policy advantages enjoyed by these open cities.

The second institutional variable is custom efficiency, as proxied by the log of the district average number of days for export to pass customs plus the district average number of days for import to pass customs.<sup>7</sup> As a measure of the efficiency of bureaucracy, it should be negatively correlated with FDI inflows. The third variable, the share of domestic private firms in a district with access to bank loans, reflects the maturity of a locality‟s financial institutions. If domestic private firms have good access to finance, particularly bank loans, the need for foreign direct investment might be lower (Huang and Di, 2004; Huang, 2007). Moreover, due to diminishing returns to capital, the return to foreign direct investment should be lower in a locality with relatively more abundant capital.

The fourth institutional factor we consider is the cost of maintaining _guanxi_ (relationship) with the relevant government officials and departments. In China, maintaining _guanxi_ means dining and wining, and it is costly. We therefore expect this cost to have a negative impact on FDI. The ratio of entertainment and travel costs (ETC) to sales is used as a proxy for the cost of _guanxi_ . ETC is an expenditure item in standard accounting books of firms in China, and it is a large sum, amounting to about 20% of the total wage bills in the sample firms of the investment climate survey of the World Bank. Cai, Fang and Xu (forthcoming) find strong evidence that

> 7 The two components are closely correlated.  We bundle them together to avoid multicollinearity.

13
is the highest in manufacturing sector. The number of applications per job post is 90% and 63% fewer in finance and real estate sector and ICT sector, respectively.

- _Finding 5: Higher salaries advertised in job postings are negatively correlated with the number of applications received._ Job postings that advertise double salaries receive 9% less applications.<sup>20</sup> This negative correlation is interesting. This may be because higher salaries may inform jobseekers that job requirements are higher or because jobseekers may expect higher-salary jobs to be more competitive, so they do not apply for them.

**Figure 23. What type of job postings receives more applications**

<!-- Start of picture text --> Log posted salary -0.09<br>Gender preference<br>No preference 0.00<br>Male -0.06<br>Female -0.75<br>Education<br>Base category: SS or less 0.00<br>HSS 0.62<br>Diplo/Bache 1.02<br>Graduate 1.20<br>Industries<br>Base category: Manufacturing 0.00<br>Construction -0 .19<br>Wholesale, retail, hotel, restaurant -0.20<br>Transport, storage, postal -0.05<br>Information, communication -0.63<br>Finance, real estate -0.90<br>Education, health -0.49<br>Professional, scienti�ic, technical services -0.26<br>Admin, social, personal services -0.52<br>Others 0 .16<br>-1.0 -0.5 0.0 0.5 1.0 1.5<br><!-- End of picture text --> _Source_ : Authors.

_Note_ : The dependent variable is the log of the number of applications a job post received. The other controls are dummies for experience requirements, occupation levels, company size, employment types, time shift, subscriptions of add-on services and year-months.

# **_Q. What are the behavioral patterns in jobseekers’ activities?_**

**_A. Jobseekers who are male, young, and with higher current salaries and educational degrees submit more applications. Many jobseekers apply for jobs that offer less than their desired salary level._**

> 20 The salary range is not always made public to job seekers. Out of the job postings where employers enter salary information in Rozee.pk’s website, approximately 50% keep the salary information confidential from job seekers. The regression result, however, is similar whether salary information is disclosed or not is taken into account.

27
**Similarly, the number of emigrants over the population in the state of origin (emigration rates) ranges from 1.7 to 6.4 percent.** Mexico City, Campeche, Guerrero, Tabasco, and Quintana Roo show the highest internal emigration rates (6.4, 6.0, 5.7, 5.3, and 5.3, respectively). The lowest emigration rates are observed in Guanajuato (1.7 percent), Nuevo León (1.9 percent), and Yucatán (1.9 percent). Furthermore, there are significant gender gaps in internal emigration rates in some states, especially Mexico City, Colima, and Quintana Roo (Figure 6).

Figure 6. Mexico City, Campeche, Guerrero, Quintana Roo, and Tabasco are the states that account for the highest emigration rates _Percentage of internal migrants by state of origin (emigration rates), 2020_ <!-- Start of picture text --> 8.0 Men Women All<br>7.0<br>6.0 6.46.05.75.3 5.3<br>5.0<br>4.0<br>3.0<br>1.9 1.8 1.7<br>2.0<br>1.0<br>0.0<br>States of origin<br>Percentage<br>CDMX CAM GRO TAB QROO VER BCS COL SIN NAY MOR OAX ZAC DGO TAM CHS MEX HGO QRO BC MICH PUE SLP TLA SON COA JAL AGC CHH YUC NL GTO<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

**There is a high correlation between net internal migration flows and labor market outcomes.** Figure 7 displays the net internal migration balance (i.e., immigration minus emigration flows) in 2020. It is observed that 17 out of 32 states have a positive net migration balance. Colima, Veracruz, Guanajuato, and Chiapas show the highest negative net migration balances. On the other hand, Nuevo León, Baja California, Querétaro, and Quintana Roo are the states with the highest positive net migration balances. The states showing the highest positive (negative) migration balances are among those displaying the highest (lowest) average monthly labor income, which reflects the high correlation between migration flows and labor market outcomes in the origin and destination (Figure 8).

11
scores for the IDA resource allocation index, a rating that reflects the CPIA as well as other considerations, is now publicly available for IDA borrowers. The African Development Bank's CPIA ratings are also publicly available by quintile only since 2004, and are fully public since 2005, and the Asian Development Bank's scores have been fully public for its concessional borrowers since 2005.

# **2.2  Revisions to Underlying Data Sources**

We have made a number of revisions to the data sources underlying the current update of the WGI. These revisions affect the data for all past years of the WGI, and so the current release of the data 1996-2008 supersedes previous releases. As we describe below, however, the revisions to the data for 1996-2007 are minor.

Beginning in 2006, we have included the new Index of Democracy produced by the Economist Intelligence Unit. This index ranks 167 countries on 60 dimensions of democracy, grouped into five clusters. Country scores are produced and reviewed by the EIU’s international network of correspondents, and details of the questionnaire and methodology can be found at www. eiu.com. This index was first produced in 2006, and was updated in 2008, with plans for biannual subsequent updates. We average this measure together with several other variables also produced by the Economist Intelligence Unit as a component of our Voice and Accountability measure. Details can be found in Appendix Table A11.

A second data revision applies to two sources produced by Business Environment Risk Intelligence, a commercial business information provider. In the past, we used data from a number of their products with differing country coverage: the Political Risk Index and Operational Risk Index for 50 countries, and the Quantitative Risk Measure for 115 countries. Given the small country coverage of the data we had previously obtained from the former two, we treated the first two as a non-representative source, and the third as a representative source (see Appendix D for details on why this matters for the aggregation procedure that we use). This year we have acquired data from all three sources for 120 countries going back to 1996. We average the together the various variables from all three products and treat it as a single representative source.

10
# **Appendix B: Characterizing Attrition**

## Table B.1. Determinants of Attrition

||(1)<br>Attrited HH[=1]|
|---|---|
|Household Venezuela: parents or siblings [=1]|-0.033<br>(0.024)|
|Household Venezuela: partner/spouse [=1]|-0.068***<br>(0.025)|
|Household Venezuela: others [=1]|0.003<br>|
||(0.034)|
|Knew of job opportunity before migrating [=1]|-0.022<br>(0.022)|
|Ever worked [=1]|-0.004|
||(0.082)|
|Employed at private frm [=1]|-0.042<br>(0.054)|
|Employed with Government [=1]|-0.050<br>|
||(0.060)|
|Self-employed or employer [=1]|-0.048<br>|
||(0.057)|
|Written contract [=1]|0.005<br>|
||(0.025)|
|Gap between last job and migration (months)|-0.002<br>(0.002)|
|Years of education before migration|-0.010***<br>(0.004)|
|Migrated for health reasons|0.038<br>(0.034)|
|Friends/family in Colombia|-0.037|
||(0.023)|
|Had smartphone [=1]|0.007|
||(0.021)|
|Owner of dwelling in Venezuela [=1]|0.003<br>|
||(0.031)|
|Electricity in Venezuela [=1]|-0.077<br>|
||(0.129)|
|Running water in Venezuela [=1]|0.046<br>|
||(0.032)|
|Sewage in Venezuela [=1]|-0.022<br>|
||(0.045)|
|Female [=1]|-0.051**<br>(0.023)|
|Age (years)|-0.004***|
||(0.001)|
|Number of children|0.000<br>(0.008)|
|Time in Colombia (months)|-0.002**|
||(0.001)|
|Observations|2,200|

_Notes:_ The table presents the correlation between pre-migration control variables and the likelihood of attrition at the head-of-household level. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

29
Policy Research Working Paper 7802

# **Abstract**

Trees on farms are often overlooked in agricultural and natural resource research and policy in Sub-Saharan Africa. This paper addresses this gap using data from the Living Standards Measurement Study–Integrated Surveys on Agriculture in five countries: Ethiopia, Malawi, Nigeria, Tanzania, and Uganda. Trees on farms are widespread. On average, one third of rural smallholders grow trees. They account for an average of 17 percent of total annual gross income for tree-growing households and 6 percent for all rural households. Gender, land and labor endowments, and especially forest proximity and national context are key determinants of on-farm tree adoption and management. These new, national-scale insights on the prevalence, economic contribution and determinants of trees on farms in Africa lay the basis for exploring the interaction of agriculture, on-farm tree cultivation, and forestry. This will improve our understanding of rural livelihood dynamics.

This paper is a product of the Program on Forests (PROFOR) and the “Agriculture in Africa—Telling Facts from Myths” project managed by the Jobs Group and the Office of the Chief Economist, Africa Region of the World Bank. 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 lchristiaensen@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
Table 7: The link between financial and real misallocation

||(1)|(2)|
|---|---|---|
|sd(log<br>TFPR<br>~~f~~n)|0.2737<sup>_∗∗∗_</sup><br>(0.0101)||
|sd(log<br>TFPR<br>~~f~~n) _×_ 1_{EFDs,USA}_|0.0336<sup>_∗∗_</sup><br>(0.0134)||
|sd(log<br>TFPR<br>~~f~~n)_t−_1||0.1674<sup>_∗∗∗_</sup><br>(0.0101)|
|sd(log<br>TFPR<br>~~f~~n)_t−_1 _×_ 1_{EFDs,USA}_||0.0405<sup>_∗∗∗_</sup><br>(0.0138)|
|Observations|50,064|42,100|
|Country fxed efects|Y|Y|
|Time fxed efects|Y|Y|
|Industry fxed efects|Y|Y|

Note: Standard errors in parentheses. This table presents the results obtained using equation 23. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 the misallocation of real resources that are created by the misallocation of finance is about 4 percentage points higher than the average.

Our goal next is to provide an economic interpretation of the regression coefficients reported in table 7. What are the _TFP_ consequences of the real misallocation that results from financial frictions? The question cannot be answered directly from the estimates in the table. The missing piece is a conversion factor that translates changes in the standard deviation of real marginal returns into changes in aggregate productivity. Such a conversion factor can be gauged by running the following regression: which projects a given industry-country’s _TFP_ gain from reversing misallocation, _TFPgains,c,t_ , against the given industry-country’s degree of real-input misallocation as captured by the standard deviation of the marginal return to real factors, _SDTFPRreal,s,c,t_ .

30