rafmacalaba/datause-encoder-data huggingface dataset:
the degree of substitutability across varieties of a given origin country is stronger than that between origin countries—see Crowley et al. (2022). In these settings, DTAs may have very different consequences on the export performance of large and small firms at destination, with large firms unambiguously benefiting the most from the trade agreement.

The impact of DTAs on small and less productive exporters also depends on whether the new agreement has an impact on the market structure of the origin country. If the number of exporters is unaffected by the entry into force of a deep trade agreement, small incumbent exporters may benefit through an increase in exports even if these gains are milder relative to large exporters. Conversely, if the DTA has an impact on the market structure in the origin country by increasing the number of exporters, small exporters may also experience a reduction in their exports (and market share) at destination, and eventually may be induced to exit from the export market due to enhanced competition.

All in all, while DTAs are expected to have an unambiguous positive effect on the export value of large and high-productivity firms, the effect on small and less productive firms is an empirical question. The within-origin competition effect of DTAs can be so strong to offset the reduction of fixed and variable costs and induce the exit of small firms from the destination market. This effect has relevant welfare implications. If small and less productive firms exit from a specific destination market and large and high-productivity firms survive and thrive, resources are reallocated from low- to high-productivity firms. This selection effect implies adjustment costs, but also an increase in the average productivity of firms. The heterogeneous trade effects of DTAs on low- _versus_ high-productivity firms, and the consequent selection effect, is at the core of the empirical investigation in the rest of this paper.

# **3 Data and descriptive evidence**

Our empirical analysis is based on two main World Bank data sources: (i) the Exporter Dynamics Database (Fernandes et al., 2016) providing firm-level exports for 55 developing countries in the period 1996-2020, and (ii) the Content of Deep Trade Agreements (Hofmann et al., 2017) on the content of RTAs in force and notified to the WTO for the period 2000-2020. Moreover, we complete our dataset by including gravity-related variables (such as distance, common border, language, etc.) from the CEPII gravity database, and data on the effectively applied tariffs faced by each exporter at the destination from the MacMap (CEPII) database.

We build our final dataset in several steps. First, we reorganize the original DTA database into a country-pair-year-specific dataset. Indeed, since a given pair of countries (origin and destination) may have two (or more) RTAs contemporaneously in force, we collapse trade agreement 5
# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Description of federal credit programs|4|
|III.|Methodological issues|12|
|IV.|Empirical evidence|18|
||Hounsing<br>Agriculture<br>Education<br>Business|19<br>20<br>22<br>23|
||Tax-exempt|24|
||Export Financing|24|
|V.|Conclusion|26|
|Selec|eted Bibliography|29|
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE 10 percent per year over the last ten years and is not expected to slow down. This is putting an enormous pressure on the country’s generation and transmission capacity, which is already stretched too thin.

Going forward, the high (and increasing) cost of power in Mali is one of the country’s most salient challenges. The cost of power in Mali between $0.33 and $0.39 per kilowatt-hour, depending on the referential year, is significantly higher than in similar countries (figure 13). This is because Mali’s power costs are pegged to international oil prices given its predominantly thermal-based generation capacity, and are prone of additional markups related to transport. Mali has no known reserves of hydrocarbons (oil, gas, or coal) and relies on distant coastal ports (more than 1,000 km away) for fuel imports to meet both its transport and power generation needs, which are currently in excess of half a million tons annually. This poses a long-term challenge, as the way to respond to high power prices is to change the generation mix. However, gaining access to Mali’s full hydropower potential will require the consolidation of international agreements on the use of the relevant transboundary river basins (Senegal and Niger) and the materialization of investments in and by neighboring Senegal and Guinea.

**Figure 13. Mali’s power costs are among the highest in Africa**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>capital costs operating costs<br>US cents per kWh<br>Mali Burkina Faso Niger Congo Benin Cape Verde Cameroon Rwanda Madagascar Kenya Tanzania Botswana Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Uganda Nigeria Malawi Mozambique Ethiopia Congo, Dem. Rep. of Zambia South Africa<br><!-- End of picture text --> Source: Eberhard and others 2009.

Given the constraints on access to hydropower, diversifying the Mali’s generation mix will not be easy. Mali has limited hydropower potential inside its national border (about 1,000 megawatts in total). Furthermore, relying on imports requires significant investments. Mali’s electricity grid is not yet connected to neighboring countries, preventing access to cheaper power available on the coast. Long-term investments in interconnectors should help the country to secure access to more cost-effective power sources. The cost of power supply could gradually fall from over $34 cents to $25 cents per kilowatt-hour (Rosnes and Vennemo 2009), which will nevertheless remain one of the highest costs in Sub-Saharan 28
ratio of benefits over cash pay.<sup>9</sup> These measures do not reflect all aspects of job quality. Unfortunately, we could not create a more comprehensive index of working conditions due to the data availability.

To empirically analyze working conditions, dummy variables equal to one for each good working condition are averaged across all conditions for each worker. The empirical model is presented in equation (7) where the dependent variable—the average working conditions ( ) of individual _k_ in period _t_ —is a function of (i) years of education ( ); gender (using the dummy variable that equals the value of one if the worker is female); ; _j_ industry ( ), and _i_ occupational ( ) dummies;and _p_ marital status dummies ( ); (ii) _year_ dummy equal to the value of one for the year 2005 and beyond, the _TG_ dummy that is equal to the value of one for the textile and garment sector, and their interactions with female dummy; and (iii) a remaining match-specific component that is captured in the residual term .

(7) The coefficient of interest in this regression is which shows the change in working conditions for women in the post-MFA textile and garment industry relative to men and compared to other industries.

# _3.2.Data Description_

The paper used household and labor force surveys to carry out the analysis. The Cambodian data come from the Cambodia Socio-Economic Survey (CSES) which was collected by the National Institute of Statistics. The data in this paper include data from the 1996, 1999, 2004, 2007, 2008, and 2009 surveys, which contain roughly 12,000 households each. These are crosssectional household surveys that contain detailed household and individual information including wage, education, age, marital status, gender, location, industry, occupation, some working conditions, and the hours worked.

The Sri Lankan data come from household and labor force surveys. The Sri Lanka Household Income and Expenditure Surveys (HIES) were carried out by the Sri Lankan Department of Census and Statistics. The paper used the 2002 and 2006 HIES data, which cover about 22,000 households each and contain similar information as CSES. The Sri Lankan Labor Force Survey Sri Lanka (LFS) is conducted quarterly by the Department of Census and Statistics. We used annual data for 2002 and 2008 covering approximately 60,000 individuals each. These surveys contain information about work-related activities (for example, employment status, occupation, industry, and wages), household characteristics (for example, size and location), and individual demographic characteristics such as age, gender, and education, among others.

> 9 For exact definitions please refer to empirical section.

8
_Brazilian spatial labor dynamics Page 38_

## Government accountability in computers. 1999

<!-- Start of picture text --> Government accountability in computers<br>0<br>1<br>N<br>W E<br>1000 0 1000 2000 Miles<br>S<br><!-- End of picture text -->

**Map 11**

# Market Potential.  Brazil.  2000.

<!-- Start of picture text --> Market Potential - 2000<br>0 - 100,000<br>100,000 - 1,000,000<br>1,000,000 - 50,000,000<br>50,000,000- 994,320,704<br>N<br>W E<br>0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Kilometers<br>S<br><!-- End of picture text --> <!-- Start of picture text --> Map 12<br><!-- End of picture text -->
Re srarrh Ccxixonent,. of World Bank Education Projects, since 1982.

|rT<br>I<br>Area<br>I<br>i|<br>-T<br>Couintry and Loan/Credit|<br> <br>hNumber<br>j|Approved!<br> <br>j|T...<br> <br>Research Components<br> <br>||Current Status of Studies,<br>including<br>Completed Documents/Data<br>j|
|---|---|---|---|---|
|AFRICA|jl<br>a CAR<br>CR1359-CA|<br> <br>1983||lNo research component.<br>Completion Report.<br>Second Education<br>()<br>developandevaluate<br>|<br>achievement tests;|<br>Studies compteted by Luc Demba of RCA and Clement<br>Thiebert<br>of Societa CEGIR|
||||(ii)<br>future project<br>preparation<br>to include<br>feasibility<br>of extension of rural<br>vocational<br>trairing<br>centers,<br>textbook production,<br>teacher training;|IDET-CFGOS S.A. (1982) "Etude Formation Emploi"<br>Tome 1 & 2; Botogne; Billancourt<br>(doc. # 128.443 (A),<br>(B1) and (92))|
||||(iii)<br>evaluation<br>to relationships<br>between;<br>| better<br>housing and teacher retention;<br>better<br>school envirornents<br>and improved school<br>lcommunity<br>interaction;<br>improved school<br>environments and changes in cmmwnity behavior<br>(paras.<br>2.21-4 p.16).||
|AFRICA||CARCR18u3-CA|1988<br>|Education Rehabititation<br>and Development.<br>Studies on:<br>(i)<br>scholarships<br>in higher education;<br>(ii)<br>employment/education<br>linkage<br>at upper-<br>secondary and post-secondary<br>levels;<br>(iii)<br>evaluation<br>studies<br>linked<br>to textbook use<br>| and wider evaluation<br>of quality<br>of planned<br>education;<br>(iv)<br>teacher training|Studies started:<br>no resutts<br>as of January 1990|

26
22

|Girls in<br>Variables<br>girls '<br>schools|Girlsin<br>coed<br>sc40ols|Boysin<br>boys'<br>_schols_|Boysin<br>coed<br>schools|
|---|---|---|---|
|Teachers arxd teachirg:||||
|Years experience***<br>8.69<br>|4.38<br>|11.94<br>|4.69<br>|
|(6.20)|(4.66)|(11.04)|(5.08)|
|Teacher preparation,***<br>2 '5<br> <br>|2.64<br>|4.18<br>|3.38<br>|
|#sem. sec. math ed.<br>(0.48)|(1.44)|(2.32)|(1.25)|
|Timn, in mirute/wek, spent**on:**||||
|Aministration***<br>92.54<br>|65.11<br>|78.55<br>|57.98<br>|
|(85.62)|(48.95)|(68.57)|(51.99)|
|Instructiora**<br>138.14|97.46|171.85|90.88|
|(24.60)|(100.23)|(137.25)|(92.41)|
|Evaluatiornf*<br>233.91<br>|116.28<br>|189.86<br>|125.60|
|(23.75)|(76.99)|(130.51)|(79.81.)|
|IJole-clcs<br>60.06<br> <br>|50.56|33.29|45.57|
|lecturingr*<br>(33.86)|(37.47)|(33.03)|(31.93)|
|Use of materials index:||||
|Ceurcial<br>texts***<br>8.35|-9.36|8.68|9.27|
|(0.48)|(2.06)|(1.62)|(1.95)|
|Personally nade***<br>6.00|5.53|5.36|5.64|
|(0.00)|(0.62)|(1.00)|(0.58)|
|Opportunity to lear***<br>4.83<br>|10.96<br>|11.70|11.62|
|(6.32)|(11.95)|(10.47)|(11.83)|
|Outccau**Measures:**||||
|Score, math test***<br>16.12|12.76|14.51|13.87|
|(5.35)|(4.99)|(5.88)|(6.18)|
|Stereotypic view***<br>3.09|3.54|4.84|4.61|
|of math<br>(1.42)|(1.63)|(1.53)|(1.72)|

Note: (a) Asterisks indicate significarce levels (*p<.0S; **-p<.Ol; *k**p<.001) for the F-statistic fran a one-way analysis of varianDe of four sex-by-school grouping and each variable.

(b) Strdard deviations in parentheses.

(c) Variable coded so that a higi score irdicates a low self-perception of - ability in mathematics.
### **Account penetration in urban and rural areas**

Adults with an account at a formal financial institution (%) <!-- Start of picture text --> RURAL 88 89<br>URBAN<br>69<br>URBAN<br>60<br>53<br>50<br>RURAL<br>43<br>37 38 35 39 44<br>31<br>21<br>19<br>9<br>MIDDLE EAST SOUTH SUB-SAHARAN LATIN AMERICA EUROPE & EAST ASIA HIGH-INCOME<br>& NORTH AFRICA ASIA AFRICA & CARIBBEAN CENTRAL ASIA & PACIFIC ECONOMIES<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012.

The urban-rural divide also figures prominently in the use of formal accounts in the developing world (figure 1.11).<sup>11</sup> In all regions adults living in cities are significantly more likely than those living in rural areas to have a formal account—in the Middle East and North Africa, more than twice as likely. This relationship persists even after controlling for income and other individual characteristics.

# **What are the barriers to the use of accounts?**

Income levels and individual characteristics clearly help explain differences in the use of accounts around the world. But what are the conditions in the economy and in people’s lives that may put up barriers to the use of accounts? Does the relative supply of credit in an economy—its financial depth—play a part? What do people themselves say when asked why they do not have an account? And what do the answers suggest about the potential for policy interventions to expand financial inclusion?

## FINANCIAL DEPTH A FACTOR?

Large amounts of credit in a financial system—both commercial and consumer— do not always correspond to broad use of financial services, because the credit can be concentrated among the largest firms and wealthiest individuals. Indeed, the use of formal accounts is imperfectly correlated with a common measure of financial depth—domestic credit to the private sector as a percentage of GDP— particularly in the bottom half of the distribution of economies (figure 1.12). Country examples bear this out. Vietnam has domestic credit to the private sector amounting to 125 percent of GDP, but only 21 percent of adults in the country report having a formal account. Conversely, the Czech Republic, with relatively modest financial depth (with domestic credit to the private sector at 56 percent of GDP), has relatively high account penetration (81 percent).

**18**

MEASURING FINANCIAL INCLUSION
much more likely to move out poverty, 44 percent of HE start-ups moved out of poverty compared to 18 percent among Never HEs. This very large difference strongly indicates that HE served as a vehicle providing upward mobility, moving rural households out of poverty. Further, among rural HE start-ups only 12 percent fell into poverty compared to 23 percent among Never HEs<sup>11</sup> . Rural HE Survivors were also more likely to move out of poverty than Closers and Never HEs.

**Figure 7 Movement in poverty and HE ownership**

**Panel A HE as primary employment**

<!-- Start of picture text --> Urban Rural<br>100 100<br>80 28 13 6 8 6 13 10 80 36 14 15 27 19<br>25 26 15 6 17<br>60 16 Always poor 60 25 44 37 19 Always poor<br>40 24 40<br>56 60 62 Into poverty 20 16 45 Into poverty<br>20 31 23 26 31<br>0 Out of poverty 0 Out of poverty<br>Never poor Never poor<br>Panel B HE as primary and secondary employment<br>Urban Rural<br>100 100<br>80 24 30 11 10 13 4 80 37 28 13 8 23<br>60 20 21 6 23 23 Always poor 60 25 21 42 27 Always poor<br>40 24 40 25 14<br>20 31 44 56 60 Into poverty 20 21 17 26 37 37 Into poverty<br>0 Out of poverty 0 Out of poverty<br>Never poor Never poor<br><!-- End of picture text --> _Source:_ NPS panel and authors calculations.

In urban areas there are more opportunities for wage employment, so HE start-up has less of a mobility effect compared to other opportunities. Those households that never had an HE are the most mobile – into poverty and out, but they are also twice as likely to be poor in both periods compared to the other groups. Of the start-ups, survivors, and closers, an equal share was never poor in both periods, reflecting the fact that urban households in general are less likely to be poor, so in any case the scope for poverty reduction is smaller in urban than in rural areas. Comparing urban Never HEs and HE startups, an equally large group moved out of poverty, which does indicate some mobility effect of starting an HE since the start-up households were less likely to be poor in the first period. Six percent of urban HE start-up households fell into poverty compared to 16 percent among Never HEs, another signal of the mobility potential.

> 11 A chi test reject same distributions and a t-test for same share of households moving out of poverty or into poverty for Never NFEs and NFE Start-ups is rejected at the 1 % level.

18 | P a g e
Where SFD interventions reduce mortality, it is necessary to use a value of a statistical life. To the best of our knowledge, this has not been estimated in the context of Egypt. Based on their review of studies undertaken in the United States, Boardman et al. (2006) believe that in the United States a statistical life may be valued at US$4m ± 2m. We adjust this number to reflect the lower levels of income in Egypt than in the United States, giving a value of LE 796,000 in 2004 prices. Sanitation projects also reduce the incidence of disease, and these too need to be valued. One approach is to estimate the Disability-Adjusted Life Years (DALYs); for each medical condition, the DALY measures the estimated reduction in life expectancy plus a measure of the handicap faced by sufferers. For an incident of bilharzia, the DALY is 0.006 (Mathers et al. 2001), which means that each case of bilharzia that is avoided leads to a saving equivalent to 0.006 of the value of a life. The DALY for renal disease is 0.0945.

The combined impact of these effects is shown in Table 17. Consider, for example, the regression estimates of the effects of SFD road projects, comparing areas with SFD projects (A) with areas that do not have SFD projects (B). The total benefit of these projects come from savings in transport costs (LE 90m), job creation (LE 334m), higher farm income (LE 193m), adjusted for lower non-farm income (-LE 70m), for a net total benefit of LE 548 m. This exceeds the cost of the road projects (LE 500m), indicating that these projects appear to be economically worthwhile. Since some of these results are sensitive to the valuation of a statistical life, we also report the totals under the assumption that a statistical life has onefifth of the valuation discussed above.

The most consistent results are for SFD road projects, where the measured benefits are generally positive, and in the case of the A vs. B comparison, are arguably higher than the costs. The net benefits are not as large in the D vs. F and C vs. E comparisons, where they typically do not exceed half of the cost of the program. For SFD potable water projects, the benefits are also generally positive, but in only one case (the regression estimate based on C vs. E, assuming a high value of a statistical life) do the benefits exceed the cost. The SFD health projects generate modest benefits in raising people out of poverty and reducing private spending on health care, but by some of the econometric measures they lower child mortality. If the value that we use for a statistical life is appropriate, then the main benefit of these health projects occurs by reducing child deaths.

The benefits of the education projects are rather modest, relative to the cost; and the effects of the sanitation projects cannot be determined with much reliability; they are driven by the effects on renal disease, which in turn are highly sensitive to the value put on a statistical life; in this case some of the _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 27 of 36_
tariffs. Once we focus on within-firm variations (columns 2-4), we do not find any significant effect of changes in output tariffs on firm productivity. Hence, our estimates suggest that the input channel is a larger force in driving productivity changes, compared to the competitive channel in line with the findings of Topalova and Khandelwal (2011) for India and Amiti and Konings (2007) for Indonesia. Our results persist when controlling for the presence of FDI in upstream manufacturing sectors (Table 8, columns 1 and 3) which allows us to account for other integration-enhancing reforms that could potentially confound the effects of input tariffs.

Firms operating downstream also use inputs from the services sector. We focus on those that are traded (supplied) through commercial presence in the country and examine how increased FDI penetration in these upstream services affects the productivity of firms downstream. Results are presented in Table 8. Controlling for input tariffs, our results show that increases in FDI in upstream services sectors are associated with increased productivity of firms operating in downstream sectors that use these services inputs more intensively. Indeed, a 1 standard deviation increase in FDI presence in upstream services sectors (1.5 percent) is associated with a 0.2 percent increase in productivity (30 percent of a standard deviation in TFP). This suggests vertical spillovers from FDI in services upstream. The same does not hold for FDI in manufacturing sectors upstream. These results are in line with those presented in Arnold et al. (2012) for India, Fernandes and Paunov (2013) for Chile, and Duggan et al. (2013) for Indonesia.

Table 8 – The effect of upstream FDI

||Whole|period|2014|-2017|
|---|---|---|---|---|
|Dep. Var.: TFP (log)|(1)|(2)|(3)|(4)|
|Output tariffs (lagged)|0.007|-0.001|-0.002|-0.011|
||(0.009)|(0.008)|(0.010)|(0.007)|
|Upstream tariffs (lagged)|-0.081***|-0.055**|-0.071*|-0.044*|
||(0.025)|(0.023)|(0.033)|(0.024)|
|FDI in upstream manufacturing sectors|||||
|(lagged)|0.006|0.008|0.001|0.003|
||(0.004)|(0.006)|(0.004)|(0.003)|
|FDI in upstream service sectors (lagged)||0.123***<br>(0.020)||0.102***<br>(0.010)|
|Sector-by-year trend|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|1477|1477|1170|1170|
|Firms|322|322|316|316|

Note: The table reports the estimates of linear regressions. In all specification we control for foreign ownership, sector-level FDI and share of exports. Standard errors clustered at the sector level are reported in parenthesis. We obtain similar results when using an alternative measure of TFP in table A2 of the Appendix. Significant at *10%, **5%, ***1%.

# **_Deciphering the mechanisms_**

So far, we have established that tariffs on goods inputs and FDI presence in services sectors upstream affect the productivity of firms operating downstream. In this section, we provide some suggestive evidence to disentangle possible underlying mechanisms.

17