rafmacalaba/datause-encoder-data huggingface dataset:
In terms of gender wage equality, though Bangladesh fares much better than other South Asian countries, Rahman and Islam (2013) show that in Bangladesh, in 2006 women’s wage was only 66 percent of men’s wage, whereas it increased to 84 percent in 2010. As shown in Table 2, in terms of the gender wage gap, i.e., female to male wage ratio, wholesale and retail trade; arts, entertainment and recreation; and administrative and support service activities were the only 3 sectors where wages for women were slightly higher than those for men. The gender wage gap was 83.7 percent in the manufacturing sector and 88.5 percent in agriculture; forestry and fishing. It is the services sectors where women are earning at par with men in Bangladesh.

**Table 2: Gender Wage Gap by Economic Activity in Bangladesh in 2016**

|**No.**|**Economic activity**|**Men**|**Women**|**Wage Gap (%)**|
|---|---|---|---|---|
|||**Nomi**<br>**earnin**<br>|**nal annual**<br>**gs (in US$)**<br>||
|1|Activities of extraterritorial organizations and bodies|3840|2088|54.4|
|2|Activities of households as employers|1704|1248|73.2|
|3|Electricity;gas, steam and air conditioningsupply|3000|2304|76.8|
|4|Water collection, treatment and supply|2124|1680|79.1|
|5|Financial and insurance activities|4380|3624|82.7|
|6|Manufacturing|1992|1668|83.7|
|7|Information and communication|3576|3084|86.2|
|8|Construction|1776|1560|87.8|
|9|Agriculture; forestryand fishing|1464|1296|88.5|
|10|Accommodation and food service activities|1896|1680|88.6|
|11|Miningandquarrying|1488|1356|91.1|
|12|Transportation and storage|1908|1800|94.3|
|13|Human health and social work activities|3180|3012|94.7|
|14|Education|3024|2892|95.6|
|15|Other service activities|2088|2016|96.6|
|16|Public administration and defense|2808|2712|96.6|
|17|Professional, scientific and technical activities|3240|3132|96.7|
|18|Wholesale and retail trade; repair of motor vehicles and<br>motorcycles|1980|1992|100.6|
|19|Arts, entertainment and recreation|2004|2148|107.2|
|20|Administrative and support service activities|2232|2520|112.9|
|21|Real estate activities|4044|n/a|n/a|

Source: ILOSTAT database, International Labor Organization Overall, the participation of women in the Bangladesh economy may have increased, but it is mostly in low-paid and low-skilled jobs. The readymade garment industry is the main employment option for women outside agriculture. However, not only women earn much 13
- 4 -

The current paper examines the locational choice of agrobusinesses in Madagascar, one of the least developed countries in Africa. Of particular note, although growth in Africa has recently been accelerated, little empirical evidence is available to show firms’ locational behavior. Methodologically, the paper applies the spatial autocorrelation Tobit (SPTobit) model (Shehata and Mickaiel, 2013) to address two potential empirical problems in our spatial data: (i) spillover effects across geographic locations and (ii) data skewness with a number of zeros. The paper measures a wide variety of transport accessibility, including the Market Access Index and Farm Access Index, to answer the question of where agrobusinesses should be located, and why. Detailed road network data were collected and used to generate various accessibility measurements.

The remaining sections are organized as follows: Section II provides an overview of crop production, agrobusinesses and transport infrastructure in Madagascar. Section III develops an empirical methodology and describes our data. Section IV presents main estimation results and discusses policy implications. Then, Section V concludes.

# **II. AGRICULTURE, AGROBUSINESS AND ROAD NETWORK IN MADAGASCAR**

Madagascar is one of the poorest countries in the world, with a total population of about 25 million. The nation’s GDP per capita was about US$400 in 2016. Many people live below the poverty line. The latest available poverty rate is 71 percent for 2012 ( **Figure 1** ). There is significant urban-rural inequality: The poverty rate was estimated at 36 percent in urban areas and 78 percent in rural areas (World Bank, 2016). The vast majority of the rural poor engage in agricultural production. Agriculture remains an important sector, employing about 75 percent of the labor force in the country, generating 25 percent of GDP and contributing to 30 percent of the country’s total goods exports. Madagascar produces not only food crops, such as rice, cassava and maize, but also vanilla, coffee and lychee. In general, however, agricultural productivity remains minimal. Along with the distorted domestic crop prices, transportation is one of the most critical constraints in the country (World Bank, 2016).
- **Ibanez, Marcela and Stephan Klasen** , “Is the War on Drugs Working? Examining the Colombian Case Using Micro Data,” _The Journal of Development Studies_ , 2017, _53_ (10), 1650–1662.

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26
The gender gap in business ownership precludes the economies of the Middle East and North Africa from benefiting from the gains associated with female entrepreneurship, including job creation and economic _—_ development. A “full-potential” scenario in which women participate in the economy at the same rate as men _—_ would add an estimated $2.7 trillion, or 47 percent, to annual regional GDP by 2025 (Woetzel et al. – 2015, 34 35). This underscores the importance of developing policies and programs that support female entrepreneurship.

Of the 44 economies that provided related sex-disaggregated data, 24 provided data on new female LLC owners between 2012 and 2016 and 28 reported the data on new female sole proprietors for the same period. These data show that _—_ in some economies _—_ there was a significant increase in the percentage of new female LLC owners and sole proprietors over this five-year period. Figure 5 shows that the gender gap in business ownership narrowed in Azerbaijan, Belarus, Georgia, Kosovo, Nepal, Oman, and Romania between 2012 and 2016. The largest increase in the proportion of female LLC ownership is reported in Nepal, where new female LLC ownership rose from 4 percent in 2012 to 11 percent in 2016. Conversely, a decreasing number of new female LLC owners is reported in economies including Afghanistan, Saudi Arabia and Turkey. In Saudi Arabia and Turkey, a decrease in the number of new female LLC owners was accompanied by an increase in the number of newly-registered LLCs compared to 2012. An increasing proportion of new female sole proprietors is observed in economies including Azerbaijan, Georgia, Kosovo, Nepal and Oman, where the gender gap narrowed compared to 2012. The largest overall increase is observed in Kosovo where the proportion of new female business owners rose from 9 percent in 2012 to 25 percent in 2016.

**Figure 5: Economies with the most significant growth in the proportion of new female business owners**

<!-- Start of picture text --> 45 35<br>3,886<br>33,708 38,050 3,492<br>40 32,267 31,795 29,108 3,151<br>30<br>35 2,077 2,476 19,456 36,234<br>19,373<br>3,497 7,102 25 14,705 3,351 5,401<br>30 14,004<br>5,839<br>25 4,197 4,957 20 3,053 2,857 1,574<br>3,774 6,109 3,186 1,609<br>20 3,938 4,415 740 15 2,340<br>381 1,265 1,220<br>130 257 279<br>15<br>10<br>720 719<br>10 582 937<br>3,640 4,834<br>3,217 5<br>5 1,181 249<br>0 1,221 0 150<br>2012 2013 2014 2015 2016 2012 2013 2014 2015 2016<br>Kosovo Nepal Georgia Nepal Kosovo Oman<br>Belarus Romania Georgia Azerbaijan<br>New female LLC owners (%)<br>New female sole proprietors (%)<br><!-- End of picture text --> _Source:_ Entrepreneurship Database. _Note:_ The labels show the absolute numbers of new female LLC owners and sole proprietors.

10
# **Appendix B - Results referred to in Sections 4 and 5**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Increased<br>use of<br>digital<br>platforms|New<br>investment<br>in digital<br>solutions|More<br>work from<br>home|More<br>online<br>sales|Increased<br>use of<br>digital<br>tech|
|Tech Sophistication|0.14***|0.08***|0.10***|0.04|0.17***|
||(0.04)|(0.03)|(0.03)|(0.03)|(0.04)|
|Medium|0.03|0.10***|0.04*|0.02|0.04|
||(0.04)|(0.03)|(0.02)|(0.03)|(0.04)|
|Large|0.10**|0.12***|0.06**|0.03|0.08*|
||(0.05)|(0.04)|(0.03)|(0.04)|(0.05)|
|Age 6 to 10|-0.03|0.01|-0.00|-0.05|0.06|
||(0.05)|(0.03)|(0.03)|(0.03)|(0.05)|
|Age 11 to 15|-0.03|-0.03|-0.03|-0.05|-0.00|
||(0.05)|(0.03)|(0.03)|(0.04)|(0.05)|
|Age 16+|-0.01|0.00|-0.03|0.02|-0.02|
||(0.05)|(0.04)|(0.03)|(0.04)|(0.05)|
|Export|0.00|0.01|0.04|0.04|0.06|
||(0.04)|(0.03)|(0.03)|(0.04)|(0.05)|
|Foreign owned|-0.02|-0.05|-0.02|0.04|-0.05|
||(0.07)|(0.06)|(0.05)|(0.06)|(0.07)|
|Change in mobility|-0.03|-0.02|-0.03*|-0.01|-0.02|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Work experience|-0.00|-0.00|-0.00|-0.00**|-0.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Post-graduate degree|0.16***|0.07|0.03|-0.01|0.15***|
||(0.06)|(0.05)|(0.04)|(0.04)|(0.05)|
|Studying abroad|0.09|0.06|0.02|0.06|0.06|
||(0.05)|(0.05)|(0.04)|(0.05)|(0.05)|
|Experience in large business|0.04|0.02|0.04|0.00|0.05|
||(0.04)|(0.03)|(0.03)|(0.03)|(0.04)|
|Management Z-score|0.02|0.00|0.01|-0.01|0.00|
||(0.03)|(0.02)|(0.02)|(0.02)|(0.03)|
|Country-sector fxed effects|YES|YES|YES|YES|YES|
|Observations|885|891|853|605|892|
|R-squared|0.16|0.12|0.19|0.19|0.12|

Table B1: Regressions of digitalization on technology sophistication. Note: _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses. The average marginal effects of independent variables are reported.

30
intra-household sharing rule, they also give conflicting estimates of labor supply elasticities.

Market goods may also be inputs to domestic production, as in Becker (1965). If, for example, we assume the production of a composite domestic good, y, by time inputs of all family members and domestic capital, K, then the production function takes the form y = h(l1, ..., ln, K) (27) The implicit price of the domestic good, p, is then given more generally as the marginal cost of the domestic good p = wi/(∂h/∂li) = π/(∂h/∂K) (28) where π is the price of capital K. Thus, the higher are marginal productivities, the lower is the implicit price of the domestic good.

An advantage of time use data is that they provide information on leisure demands and on time inputs to domestic production required for the empirical implementation of this kind of modeling approach. Many surveys collect detailed information on different types of production and leisure activities and often include information on “social context” in terms of the presence of other persons and their relationship to respondent. These data allow the specification of a diverse range of systems for leisure activities<sup>33</sup> as well as for the domestic production of goods and services. Following the work of Hamermesh (1999, 2001), they also provide a database for analyzing the welfare implications of the timing of work and for modeling the household’s time coordination problem.

If a time use survey is included as a module of a household income and expenditure survey, systems can be estimated on a single dataset containing detailed information on wage rates and incomes, as well as on time allocations and timing. Few economists have, until recently, seen the relevance of time use survey data for estimating behavioral models. As Juster and Stafford (1991) observe in their review of the time allocation literature: “While the importance of time allocation as an analytic construct is close to

> 33 Note that the price of leisure will not necessarily be given by the net wage, but will depend on the assumptions implied by the system chosen for estimation.

27
# **Figure 1. The Gorkha Earthquakes hit areas that were not among the poorest in Nepal**

- _a. Dwellings destroyed (percent of the total)_

<!-- Start of picture text --> (50,100]<br>(10,50]<br>(5,10]<br>(1,5]<br>[0,1]<br><!-- End of picture text -->

- _b. Poverty rate (percent of the population)_

<!-- Start of picture text --> (50,100]<br>(20,50]<br>(10,20]<br>(5,10]<br>[0,5]<br>No data<br><!-- End of picture text --> _Note:_ The triangles in panel a represent the earthquakes’ epicenters.

- _Source:_ Central Bureau of Statistics of Nepal (2012b) and Government of Nepal (2015) for panel a and Central Bureau of Statistics of Nepal (2012a) for panel b.

Reportedly, 776,895 private houses – 12 percent of the total housing stock recorded by the 2011 population census – had been destroyed (Government of Nepal 2015). Given that before the earthquakes imputed rent for housing accounted for about 12 percent of overall household consumption expenditure, the likely impact on household wellbeing was massive. The PDNA evaluated the average loss per capita at USD 171, large enough to have pushed between 700,000 and 980,000 additional people into poverty (Tiwari and Uematsu 2015). Not surprisingly, the destruction of dwellings accounted for 25 percent of the total estimated losses from the Gorkha earthquakes, and for 60 percent of the total damages (National Planning Commission 2015).

# **5. Data description**

The dataset used to illustrate the proposed approach to estimating the cost of disasters combines information from three sources. These are: a household survey conducted in 2010/11 (Central Bureau of Statistics of Nepal 2012a), from the 2011 population and housing census (Central Bureau of Statistics of Nepal 2012b), and from official figures on assets/housing destructions covered by the Disaster Recovery and Reconstruction Information Platform established following the Gorkha earthquakes (Government of Nepal 2015).

The first of these three sources, the Nepal Living Standards Survey III (NLSS III), reports data on multiple welfare dimensions for 5,998 households in 2010/11. Its sampling frame is representative at the level of districts or Village Development Committees (VDCs).

Households are not geocoded in the NLSS III dataset but the districts or VDCs they live in are. The latter are taken as the reference geographic areas in what follows. It should be noted that in September 2015, as Nepal embraced a federalist government structure, its more than 3,100 districts and VDCs were consolidated under 753 local governments in seven provinces. However, this regrouping does not affect the analysis.

In addition to welfare-related indicators, the NLSS III contains household-level data on dwelling configuration, occupancy, endowments, and location. Imputed housing prices come from self9
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# **Toward a Theory of Optimal Financial Structure**<sup>**1**</sup>

Justin Yifu Lin<sup>2</sup> (World Bank) Xifang Sun (Seoul National University) Ye Jiang (Industrial and Commercial Bank of China ) .

> 1 This paper was made possible by the research project “The Financial Structure in Economic Development” (Grant Number: 04JJD790001) funded by the Ministry of Education of the People’s Republic of China. Comments by Asli Demirguc-Kunt and participants of Development Workshop at the China Center for Economic Research, Peking University were most appreciated. The views expressed in this paper are solely the responsibility of the authors.

> 2 Any suggestions and comments are welcomed. Please contact us at justinlin@worldbank.org..
Table 3: Percent distribution of industries by establishments (A) and by jobs (B): Tokyo’s Central City, Tokyo, Greater Tokyo, National Capital Region, and Japan in 2001

||Central City (23<br>|Wards)<br>|Tokyo Metro<br>|polis<br>|Japan<br>|<br>|
|---|---|---|---|---|---|---|
||A|B|A|B|A|B|
|Construction|5.6|5.8|6.5|5.9|9.6|8.2|
|Manufacturing|11.8|12.2|10.9|12.8|10.3|18.5|
|Utilities|0.1|0.5|0.1|0.5|0.2|0.5|
|Transport & telecom.|4.2|6.7|3.9|6.5|3.0|6.2|
|Wholesale, retail trade, restaurants &<br>water trades|40.9|30.9|40.8|30.8|41.0|29.3|
|Finance & insurance|1.7|5.0|1.7|4.6|1.6|2.8|
|Real estate|6.7|2.7|6.7|2.6|4.6|1.5|
|Service|28.6|33.3|29.1|33.6|28.8|29.3|
|Agriculture, forest & fishery|0.0|0.0|0.0|0.0|0.3|0.4|
|Government & Others|0.4|2.9|0.3|2.7|0.6|4.5|
|Total %|100.0|100.0|100.0|100.0|100.0|100.0|
|TotalNo. (in 1,000)|587|7,135|725|8,609|6,350|60,158|

Source: Statistics Bureau, _Jigyosho.kigyo tokei chosa houkoku_ (Survey on Business Establishments and Enterprises in 2001). Tokyo. 2002.

Table 4: Industrial Districts of Tokyo: Ota and Sumida

||Ota|Sumida|Tokyo|Japan|
|---|---|---|---|---|
|Total no. of establishments|35,367|19,342|724,769|6,350,101|
|Total no. of jobs|327,584|167,811|8,608,794|60,158,044|
|Manufacturing establishments|7,117|5,658|79,374|650,950|
|(Percent share in the total establishments)|(20.1%)|(29.3%)|(10.9%)|(10.3%)|
|Manufacturing jobs|73,676|43,480|1,097,984|11,126,145|
|(Percent share in the total jobs)|(22.5%)|(25.9%)|(12.8%)|(18.5%)|
|Manufacturing establishments with|||||
|fewer than 4 employees|4,145|3,651|44,158|319,277|
|(Percent share in manufacturing)|(58.2%)|(64.5%)|(55.6%)|(49.0%)|
|Manufacturing jobs in establishments<br>with fewer than 4 employees|9,953|8,584|106,193|765,822|
|(Percentsharein manufacturing)|(13.5%)|(19.7%)|(9.7%)|(6.9%)|

_Source_ : Statistics Bureau, Jigyosho.kigyo tokei chosa houkoku (Survey on Business Establishments and Enterprises in 2001. Tokyo. 2002.

78
**Figure 1. Foreign aid distribution to Developing Countries by Region**

<!-- Start of picture text --> 0.50 140<br>0.45<br>120<br>0.40<br>0.35 100<br>0.30<br>80<br>0.25<br>60<br>0.20<br>0.15 40<br>0.10<br>20<br>0.05<br>0.00 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Africa South America<br>Asia Middle East<br>Central & South Asia Oceania<br>All Developing Countries(recipients)<br>Share of total aid<br>aid received by developing countries(Bil UD$)<br>Total<br><!-- End of picture text --> Source: Our calculation based on OECD.STAT data base.

The literature on AfT is really quite new (e.g. Helble _et al._ , 2009; Hoekman _et al._ , 2010; Calì _et al.,_ 2011). In part this is because of data limitation; frequently, because it will be difficult to disentangle the impacts of AfT projects on trade, income and welfare. However, the literature on AfT can be traced back to that on foreign aid, whose role of foreign aid in the growth process and to reduce international inequalities in developing and least developed countries has been a topic of intense debate for several decades. This issue has been analyzed by researchers for decades due to its complexity in nature. It links with political relationship between donor countries and recipient countries, governance of public sector in recipient country, and how much the foreign aid is necessary and how long it should last.

The measurement of effectiveness of AfT, which is the core principle in the Paris Declaration on Aid for Trade, is extremely vital for the recipient countries, because the evaluation of the AfT

**4**
In the fixed effects model, an increase of the lagged overall determinant index by 1 percent is associated with an increase of the annual TFP growth rate by 0.05 percent, after controlling for the lagged TFP level and country and time effects. Suggesting convergence, an increase of lagged TFP by 1 percent is associated with a decrease of annual TFP growth rate by 0.10 percent, holding other variables constant. This implies that countries with a higher level of TFP need to increase the determinant index more than those with a lower level of TFP to achieve the same amount of increase in TFP growth. These results are robust in terms of signs and significance when we use different lags of three and seven years (see appendix D). They are also robust when we use the WDI-based data in the construction of TFP levels and growth rates (see appendix E).

# **5. Simulations and Scenario Analysis**

For illustration purposes, in this section we simulate the change in TFP growth rate for 78 low- and middleincome developing countries (that is, countries with GDP per capita in 2014 lower than $12,056, constant USD 2010). We present the simulation results in averages by region or income group. More generally, the Long-Term Growth Model (LTGM) toolkit can be used to generate country-specific projections for TFP growth for a much larger set of countries. This allows of the LTGM users to replace the assumption of an exogeneous path for TFP growth by one that is based on improvements in innovation, education, market efficiency, infrastructure, and institutions, feeding into the overall determinant index.

We provide four scenarios below. They present different ways and extents of improving the TFP determinant index, to regional or world benchmarks (or leaders). We use the fixed-effect regression results to relate changes in TFP growth to changes in the overall determinant index. The corresponding increase in TFP growth depends directly on the speed of progress in the country’s TFP determinants and inversely on the extent of previous TFP improvement. Thus, countries with a larger gap on their TFP determinant index with respect to the benchmark could experience a larger increase in TFP growth if they made reforms to approach the leader. In turn, countries with a high increase in TFP growth would slow down their subsequent growth in TFP. The positive impact of improvements in the TFP determinant index and the negative impact of previous TFP growth create an interesting, nonlinear path of projected TFP growth: In most cases, TFP growth follows a convex path that 27