rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Discussion and Conclusion**

The present study uses data from the 2003 and 2007 TLSS surveys to evaluate the long-term impacts of the 1992-1998 armed conflict in Tajikistan on education and labor market outcomes for men and women. The analysis of education focuses on those born in 1966-1973 and 1976-1985 (war-cohort). These cohorts should have completed at least secondary school by 2007. The analysis of labor market outcomes focuses on those aged 22-49 in 2007. The results of difference in difference regressions suggest that residence in the region more affected by conflict during an individual’s youth had a significant negative impact on the educational attainment of women. These women are also more likely to participate in the labor market, but their wages are not statistically different from the wages earned by the rest of the sample. No significant relationship between residence in the more affected region during one’s secondary school and early labor market years and employment or total earnings in 2007 is found for men.

The results are robust to alternative specifications and the inclusion of additional household and community controls that purport to have a significant impact on employment, such as migration and remittances and the employment status of household heads and spouses. Men’s labor supply is more responsive to changes in non-labor income than female labor supply.

The higher employment among young women in the more affected areas could be attributed to a “persistence” factor. It is possible that entry into the labor force is rather costly. The main share of the cost is not necessarily in terms of money or investment in acquiring education (as younger women received lower education in the conflict affected areas) but the intrinsic cost of adjusting to employment. Anecdotal evidence suggests that there is relatively little socialization among young women in Tajikistan once they stop attending schooling. Thus, employment may provide an avenue for such socialization, an extra income and a break from the drudgery of housework. An employment during the time of war may also have provided women with a valuable labor market experience and connections that help women to keep their job (although not with a higher income). While the maintaining employment is good for a female autonomy, lower wages traditionally received by women (on average female wages are 2.77 times lower in Tajikistan than average male wages), and lower education indicate that women are not 31
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 21 Côte d’Ivoire’s utilities are inefficient relative to peers**

- a. Uncollected bills and unaccounted losses in the power sector, as a percentage of GDP

<!-- Start of picture text --> unaccounted losses collection inefficiencies<br>b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP<br>unaccounted losses collection inefficiencies<br>% of GDP<br>% of GDP<br><!-- End of picture text -->

- b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP

_Source_ : Derived from Briceño-Garmendia and others 2009.

# Annual funding gap

Excluding the cost of the inefficiencies described, Côte d’Ivoire’s infrastructure funding gap amounts to $1,048 million per year, or about 6 percent of GDP. The funding gap in water amounts to around $543 million per year and accounts for almost 60 percent of the total funding gap (table 21). Most of the rest of the gap is found in the transport and irrigation sectors, where an additional $193 and $165 million is needed, respectively, to meet the country’s development goals. The reason the power sector funding gap is relatively small is thatthere is substantial scope to increase cost recovery through tariffs and to reallocate operating expenditures towards investment.

40
**Figure 2: Export volume by county**

<!-- Start of picture text --> Zagreb<br>Varaždin<br>Istria<br>Sisak-Moslavina<br>Osijek-Baranja<br>Međimurje<br>Split-Dalmatia<br>Primorje-Gorski kotar<br>Krapina-Zagorje<br>Koprivnica-Križevci<br>Karlovac<br>Šibenik-Knin<br>Slavonski Brod Posavina<br>Vukovar-Sirmium<br>Virovitica-Podravina<br>Zadar<br>Požega-Slavonia<br>Bjelovar-Bilogora<br>Dubrovnik-Neretva<br>Lika-Senj<br>0.0 5.0 10.0 15.0 20.0 25.0<br>2012 2007<br><!-- End of picture text --> _Source: Staff elaborations based on data from the CBS. All numbers are in billion HRK_ The _export product diversity index_ by county is shown in Figure 3 and Table 7. In 2012 Lika-Senj, Sibenik-Knin, Virovitica-Podravina, and Pozega-Slavonia were the most specialized counties, displaying only a small level of diversity in export products, whereas Zagreb, Istria and Varadzin emerged as the most diverse counties. A number of origin locations, including Karlovac, Osijek, Primorje, and Zagreb appear to have become more specialized over the preceding six years. In regard to destination diversity, Table 8 and Figure 4 present the _export destination diversity index_ by county. Istria and Zagreb are the most diverse counties, exporting to the widest number of destinations, while Lika-Senj and Pozega are currently the least diverse counties.

10
# **3. SECTOR-LEVEL ANALYSIS**

In order to shed light on the sectoral dimension of productivity growth, the following equation is estimated: where: i, j and t denote countries, sectors (Nace 1.1)<sup>22</sup> and years, respectively. LP stands for labor productivity and is defined as sectoral value added per employee, based on Eurostat data. Reflecting data availability, the model is estimated **at sector level over the period of 2003-08** for the same countries of Eq(1), except for Switzerland and Malta, given the lack of data on sectoral productivity for these countries.

All the variables are defined as in Eq(1), except Reg for which an additional proxy for the quality of the regulatory environment is used.<sup>23</sup> In addition to the regulatory quality indicator - from the WGI database - a set of alternative indicators constructed through a principal component analysis (PCA) based on Doing Business (DB) data is used. The first (All DB) is a comprehensive index including all DB indicators in all topics. The second (Business Start-up_DB) measures the complexity of a number of procedural aspects related to the entry and exit process; it encompasses the indicators for _starting a business, registering property_ and _closing a business_ . The third (Business Operations_DB) captures the burden of regulations faced by an enterprise in managing regular operations; it covers the topics of _dealing with construction permits, paying taxes, trading across borders_ and _employing workers_ . The fourth and last index (Institutional Environment_DB) measures the quality of the legal and institutional framework and aggregates the indicators for _protecting investors, getting credit_ and _enforcing contracts_ . All PCA indices based on DB data are coded such that higher values indicate simpler regulation.<sup>24</sup> It is worth mentioning that none of the regulatory proxies are available at the sector level, only at the country level. The same happens with Open_trade and humanK. Table VI (Annex) provides summary statistics for all proxies of regulatory environment used for the estimation at sector level while Figure 4 shows the evolution of the comprehensive PCA indicator on DB data (Table VII, Annex, shows the values for all PCA indicators for all comparator countries).

> 22 See Table IV in the annex.

> 23 The productivity leader is allowed to change over time and across sectors.

> 24 In this sense, the PCA indices – based on DB data - are consistent with the regulatory quality indicator (based on WGI data); even though the PCA indices are scaled differently, they are also defined in such a way that higher values mean better regulatory environment.

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**Figure 9:**

<!-- Start of picture text --> Return to schooling for males along time by<br>quantile<br><!-- End of picture text --> <!-- Start of picture text --> 0.140<br>0.130<br>0.120<br>0.110<br>0.100<br>0.090<br>0.080<br>0.070<br>0.060<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>q10 q25 q50 q75 q90<br><!-- End of picture text -->

# **Return to schooling for females along time by quantile**

<!-- Start of picture text --> 0.150<br>0.140<br>0.130<br>0.120<br>0.110<br>0.100<br>0.090<br>0.080<br>0.070<br>0.060<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>q10 q25 q50 q75 q90<br><!-- End of picture text --> 24
Our gravity regressions also include trade policy measures that enter as control variables: a) the U.S. statutory most favored nation (MFN) tariff rate taken from USITC (2018), and b) dummy variables indicating countries that are members of a preferential trade agreement (PTA) with the U.S. Finally, we include in the structural regressions product-level elasticities of substitution estimated by Fontagné et al. (2022).

## **3.3 Input-output relationships in PR**

Calculation of the welfare costs of the JA requires information that is more comprehensive than what is available in the trade flow data. A critical element for such calculations is data on PR’s purchases of its own output, activity that is not included in the trade data. We employ a Puerto Rican input-output (IO) table that separates expenditures on local output from expenditures on imports (including imports from U.S. sources). The IO table was produced by the Junta de Planificación de Puerto Rico for the years 2006-2007.<sup>17</sup> The table reports final demand expenditures (consumption, investment, etc.) for every 4-digit NAICS sector in the PR economy. The table also reports - for each purchasing NAICS code and final demand category - expenditures on local production and on imports, respectively. We match the trade data to the NAICS codes, and calculate U.S. and rest of world (ROW) shares of imports for each cell of the table, including purchases by final demand.

# **4 Fleet composition in the Caribbean**

The LLI data on vessel characteristics contains information on vessels’ place-ofbuild, flag-of-registry and ownership. We combine the vessel characteristics and the 17The Puerto Rican government has been under severe financial stress in recent decades, limiting its ability to produce economic statistics in a timely and credible manner. The 2006-2007 table was the most recent table available when we did the calculations. A 2011-2012 table has finally been produced, but it is imputed and contains an uncomfortably large number of negative values. We use the 2006-2007 figures (JPP, 2022). The dated nature of the IO table is a limitation.

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**Table 7. Decompositions of changes in survival, based on eqn** (5)

|||_Poorestquartil_|_e_|_Ri_|_chestthreequar_|_tiles_|
|---|---|---|---|---|---|---|
||_Percent_<br>_changein_<br>_hazard_<br>A[-In?]|_Changein_<br>_determinants_<br>AX393|_Changein_<br>_coefficients_<br>APX9 8|_Percent_<br>_changein_<br>_hazard_<br>A[-lnX]|_Changein_<br>_determinants_<br>AXP93|_Changein_<br>_coefficients_<br>APX9 8|
|Region|-0.768|0.630|-1.398|-1.124|0.053|-1.177|
|Childcharacteristics|-0.758|0.011|-0.768|-0.846|-0.045|-0.801|
|Household<br>consumption|7.774|-0.245|8.018|8.576|-0.302|8.878|
|Mother'seducation|-0.746|-0.113|-0.633|-0.704|0.133|-0.837|
|Sanitation|-0.004|-0.034|0.030|0.335|0.256|0.079|
|Water|-0.122|-0.079|-0.043|-0.451|0.041|-0.493|
|Healthservices|-0.445|-0.564|0.119|0.945|0.626|0.319|
|Constant|-4.968|0.000|-4.968|-4.968|0.000|-4.968|
|_Total_|-0.037|-0.394|0.357|1.763|0.762|1.000|
|ImpliednewIMR|34.60|37.59|31.57|22.76|29.49|27.90|
|Implied new U5MR|50.19|54.48|45.83|33.10|42.83|40.54|

**Table 8. Decompositions of changes in survival, based on eqn (6)**

||_DI_|_D2_|_D4_|_Nonpoor-_|
|---|---|---|---|---|
||_Differentchangesinx 's_<br>**_(INP_**<br>_-_<br>**_XP)fivP_**<br>_(AXNP_ tJCP )pNP_|_Differentlevelsof/3's_<br>**_(x/_**<br>-)yP<br>**_/3P)P_**<br>_p98_<br>_98_<br>98|_Differentlevelsofx 's_<br>**_xNP_**<br>_-__<br>p<br>_,_<br>_(X93_<br>_X93_>4|_poor_<br>_difference_<br>inA[-In]|
|Region|0.038|-0.386|-0.008|-0.356|
|Child|||||
|characteristics|0.028|-0.074|-0.043|-0.088|
|Household<br>consumption|-0.002|-0.017|0.822|0.803|
|Mother's|||||
|education|0.151|-0.007|-0.103|0.041|
|Water|0.181|0.127|0.030|0.338|
|Sanitation|-0.164|0.067|-0.232|-0.329|
|Healthservices|0.320|0.964|0.106|1.390|
|Constant|0.000|0.000|0.000|0.000|
|_Total_|0.552|0.674|0.573|1.800|

# **_Key points_**

There is no single factor explaining the faster decline in mortality among betteroff children in Vietnam over the period 1993-98. Rather it was due to three sets of factors: (i) the poor experiencing less advantageous changes in the determinants of child survival; (ii) these changes mattering less for the poor, since they faced a worse set of coefficients linking survival determinants to survival outcomes; and (iii) changes in coefficients mattering less for the poor since they started with worse determinants. These 24
- 39 -

# 1. Developing a Research Agenda Targeting Women Farmers

# a. Identifying the technology needs of women farmers

4.06 The first step in developing a research agenda for women farmers requires identifying their needs based on the following information:

- A description of the farming syetem (including crop associations and crop/livestock integration) and its component enterprises (main and minor crops, livestock, gathered crops, and processing enterprises).

- An analysis of the objectives, activities and constraints of women farmers in the home, farm, and community.

4.07 Planning relevant extension programs requires knowledge of the specific activicies and contributions of rural women to income and productivity. The socioeconomic and household level analysis that can provide such information is too often omitted from the research design. Socioeconomic research can also help determine whether technologies will be adopted by women or will result in lost income, lost control of income, or additional demands on women's labor.

4.08 Examining the current farming system to learn how problems are presently being solved may point the way to solutions which can be tested immediately. Researchers should .'Cse21 _rhe Wl:_ i _LW-CtJST. I»J-IIItT_ review women's current agricultural - **'IA: IoCOSJN** PO«ssw _IN_ practices as well as how to combine them with modern technologies, staggered ptantifn of woments horticutturaL knowing that women are more likely crops in a pilot project in the western Oivision led to significantty higher economie returms: 7.4% than men ta be partial adopters of for tomatoes, 2.8% for on ns, and 1.4% for recommendations because of cost and cabbage. The researcher considered the crop time constraints. Technologies demanda for Labor before recomending staggered offered to women and to small farmers ptanting. <Daniets 1989) in general should also emphasize the use of internal resources (such as labor and plant and animal wastes) as opposed to external, purchased resources. Finally, involving the end-users in the initial stages of technology development will increase the likelihood of their accepting the technology.

4.09 Information on women's agricultural enterprises should be gathered from three main sources:

- Baseline socioeconomic and agricultural data, disaggregated by gender to reflect the true magnitude of women's agricultural enterprises.

- Farming Systems Research (FSR) or similar diagnoses. FSR, which has been integrated into some extension projects, can improve
# **Figure 25: Perceived advantages of registration and likelihood for registration**

<!-- Start of picture text --> Helping the poor / community / country 66<br>Tax refunds 69<br>Being legal 73<br>Better access to government services 74<br>Better opportunities / situation for my business /<br>72<br>growth<br>Access to and price of financing 73<br>Total 66<br>Other 56<br>Don't know 54<br>No advantages 51<br>40 45 50 55 60 65 70 75 80<br>Percentage of informal businesses likely to register<br>Advantages of registering for taxes<br><!-- End of picture text --> Perceived disadvantages of registering for tax are presented in figure 26. As expected, the largest category of respondents cited the financial burden of taxation.<sup>35</sup>

**Figure 26: Perceived disadvantages of registration for tax**

<!-- Start of picture text --> 0 10 20 30 40 50<br>Tax too high / higher financial burden 32<br>Worse business situation / less opportunities 27<br>Gov't not giving services / mishandling money /<br>27<br>corruption<br>Fear of prosecution 19<br>Complicated / expensive / risky tax compliance 14<br>Other 13<br>No disadvantages 9<br>Don't know 11<br>Percentage of respondents<br>Perceived disadvantages of registering<br><!-- End of picture text --> 35 Analysis of the size of tax payments was outside the scope of the survey, but could be undertaken on the basis of SARS’ data for formal micro-enterprises. However, international survey data shows that complaints about tax burdens are nearly universal across countries and are not necessarily significantly correlated with actual tax burdens (World Bank, 2004, pg. 13).

21
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008b. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- Fall, Matar, Philipped Marin, Alain Locussol, and Richard Verspyck. 2009. ―Reforming Urban Water Utilities in Western and Central Africa: Experiences with Public-Private Partnerships.‖ Volume 2: Case Studies, Water Sector Board Discussion Paper Series, Paper 13, Africa Region, World Bank, Washington, DC. June.

- Gulyani, Sumila, Debabrata Talukdar, and Darby Jack. 2009. ―Poverty, Living Conditions, and Infrastructure Access: A Comparison of Slums in Dakar, Johannesburg, and Nairobi.‖ AICD Working Paper 10, Africa Region, World Bank, Washington, DC.

- IBNET (The International Benchmarking Network for Water and Sanitation Utilities). 2009. http://www.ib-net.org/.

- WHO (World Health Organization), Joint Monitoring Program (JMP). 2010a. ―Niger Estimates for the use of Improved Drinking-Water Sources.‖ Updated March 2010. http://www.wssinfo.org/resources/documents.html.

- ———. 2010b. ―Niger Estimates for the useof Improved Sanitation Facilities.‖ Updated March 2010. http://www.wssinfo.org/resources/documents.html.

- Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, Africa Region, World Bank, Washington, DC.

- Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

- World Bank. 2010c. _Implementation Completion and Results Report (IDA-3505 IDA-H259) on a Credit in the Amount of SDR 37.3 Million ($48 Million Equivalent) and a Grant in the Amount of SDR 7.0 Million ($10 Million Equivalent) to the Government of Niger for the Water Sector Project_ . Report ICR00001231, World Bank, Washington, DC. June 18.

- ———. 2010d. ―International LPI ranking.‖

<u>http://www1.worldbank.org/PREM/LPI/tradesurvey/mode1b.asp#ranking</u> 48
# **C Is the Date of Arrival in Colombia Related to Baseline Socioeconomic Characteristics?**

Table C.1. Socioeconomic Characteristics by Date of Arrival

||Normalized Date of Arrival (Months)|
|---|---|
||(1)|
|_A. Labor Outcomes_||
|Ever worked in Venezuela [=1]|3.578|
||(4.964)|
|Employed at private frm in Venezuela [=1]|-5.021*<br>(2.779)|
|Employed with Government in Venezuela [=1]|-2.985<br>(3.167)|
|Self-employed or employee in Venezuela [=1]|-5.280*<br>(2.981)|
|Had a written contract in Venezuela [=1]|0.397<br>(1.382)|
|_B. Education_||
|Years of education before migration|-0.044<br>(0.217)|
|_C. Assets_||
|Had smartphone in Venezuela [=1]|-0.867<br>(1.239)|
|Had a dwelling in Venezuela [=1]|-4.566***<br>(1.754)|
|Had electricity in Venezuela [=1]|4.178<br>(7.667)|
|Had running water in Venezuela [=1]|-2.523|
||(1.917)|
|Had sewage in Venezuela [=1]|-0.023|
||(2.665)|
|R-squared|0.004|
|Observations|3,416|
|Join F-Test|1.34|

_Notes:_ Dependent Variable: Normalized date of arrival is the date of arrival in Colombia reported by each migrant, normalized to zero with the first date of arrival in Colombia reported. Standard errors are reported in parentheses.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

55
**Table 14:  Incumbent SOE outcomes of city upgrading (using an alternative measure of capacity)**

|**_Incumbent_**<br>**_Firms_**|Log main<br>operating<br>revenue|Log main<br>operating<br>cost|Log main<br>operating<br>profit|Log # of<br>employees|Log labor<br>vocational|Log wage<br>per<br>employee|Log<br>export|
|---|---|---|---|---|---|---|---|
|**City**|-0.298|-0.251|-0.302|-0.512**|-0.418|-0.338**|-0.515|
||(0.340)|(0.339)|(0.412)|(0.236)|(0.284)|(0.147)|(0.687)|
|**Capacity**|0.00895**|0.00875*|0.0241***|0.00706***|0.00505|0.00402|-0.0114|
||(0.00401)|(0.00446)|(0.00579)|(0.00252)|(0.00508)|(0.00701)|(0.00845)|
|**SOE**|-0.708***|-0.706***|-0.394***|0.00481|0.0168|-0.230***|-0.501***|
||(0.0689)|(0.0709)|(0.0699)|(0.0535)|(0.0709)|(0.0261)|(0.156)|
|**City ×**<br>**Caacit**|4.205|2.606|4.621|6.125|4.677|10.16**|10.44|
|**py**|(11.15)|(11.06)|(13.31)|(6.679)|(8.124)|(4.378)|(21.20)|
|**Capacity ×**<br>**SOE**|-0.0148***|-0.0137***|-0.0215***|-0.00400|-0.0105|-0.00508|-0.0159***|
||(0.00379)|(0.00343)|(0.00309)|(0.00243)|(0.0107)|(0.00826)|(0.00380)|
|**City × SOE**|-0.340|-0.339|-0.448|0.222|-0.0435|0.00244|-0.0611|
||(0.410)|(0.403)|(0.555)|(0.317)|(0.413)|(0.174)|(1.158)|
|**City ×**||||||||
|**Capacity ×**|**16.10**|**16.16**|**16.56**|**4.236**|**13.08**|**-1.310**|**5.910**|
|**SOE**||||||||
||(12.48)|(12.15)|(16.50)|(9.334)|(12.06)|(4.969)|(31.68)|
|**Constant**|11.74***|11.30***|9.851***|6.061***|2.650***|8.602***|9.209***|
||(0.666)|(0.643)|(0.766)|(0.206)|(0.195)|(0.513)|(0.944)|
|Observations|32,517|32,428|21,283|18,193|3,892|17,496|4,759|
|R-squared|<br>0.346|<br>0.350|<br>0.231|<br>0.243|<br>0.221|<br>0.264|<br>0.189|
|<br>Block FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry**×**Year<br>FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Standard errors are in parentheses and clustered at county-level.

3. Analysis is restricted to firms opened before 1997.

4. Vocational labor data is only available in 2004.

Tax collection represents only a subset of total revenues that are available to a local government in China, where land sales and transfers from central government are also major sources of revenues. As a robustness test, we use total government spending as a proportion of city GDP as a proxy to measure the extent to which public services are being delivered using public funding. We obtain similar results - firms in high-capacity cities tend to create more jobs, especially skilled labor jobs, and SOEs are less likely to outperform private firms.

19