rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 16 Upstream and output tariffs<br>7 BASELINE SCENARIO SIMULATED SCENARIO CHANGE IN P.P.<br>19 Upstream tariff as % of gross output 3.1% 1.6% -1.6%<br>21 Upstream tariff as % of total inputs cost 4.2% 2.1% -2.1%<br>23 | Upstream tariff as % of total goods inputs cost 9.3% 47% -47%<br>24<br>25<br>26 | Output tariff (average tariff on WAP - Wearing apparel) 19.0% 19.0% 0.0%<br>27 Rate of Effective Protection 647% 71.1% 6.4%<br>28<br>29 Composition of upstream tariff by largest contributing sectors Reset<br>30<br>Baseline Share in input<br>contribution to cost structure Simulated<br>31 # Sector in NAICS upstream tariff Baseline Tariff (US) Tariff Change in p.p.<br>32 1 CRP - Chemical, rubber, plastic products 46.6% 8.3% 23.4% 4% 4%<br>33 2 TEX - Textiles 43.1% 10.8% 16.6% 5% -5%<br>34 3 NMM - Mineral products n.e.c. 8.3% 13.5% 2.6% 7% 7%<br>35 4 FMP - Metal products 1.6% 95% 07% 5% -5%<br>36 5 WAP - Wearing apparel 0.3% 19.0% 0.1% 19% 0%<br>37| 6 LEA - Leather products 0.1% 11.9% 0.0% 6% 16%<br>38 7 COA - Coal 0.1% 5.0% 0.0% 3% -3%<br>39 8 P_C - Petroleum, coal products 0.1% 8.9% 0.0% 4% -A%<br>16 Upstream and output tariffs<br>7 BASELINE SCENARIO SIMULATED SCENARIO CHANGE IN P.P.<br>19 Upstream tariff as % of gross output 4.2% 2.9% -1.3%<br>21 Upstream tariff as % of total inputs cost 4.8% 3.3% -1.5%<br>23 Upstream tariff as % of total goods inputs cost 6.6% 4.5% -2.1%<br>24<br>25<br>26 Output tariff (average tariff on TEX - Textiles) 10.8% 10.8% 0.0%<br>27 Rate of Effective Protection 51.7% 61.9% 10.2%<br>28<br>29 Composition of upstream tariff by largest contributing sectors Reset<br>30 P<br>Baseline Share in input<br>contribution to cost structure Simulated<br>3132 #1 TEXSector- Textilesin NAICS upstream37.2% tariff Baseline10.8% Tariff 16.4%(US) Tariff11% Change0% in p.p.<br>33 | 2 NMM - Mineral products n.e.c. 18.8% 13.5% 67% 7% 7%<br>34 2 FMP - Metal products 17.0% 9.5% 8.5% 5% -5%<br>35 4 CRP - Chemical, rubber, plastic products 12.6% 8.3% 7.2% 4% -4%<br>36, 5 PFB - Plant-based fibers 10.5% 43% 117% 2% -2%<br>37 6 MVH - Motor vehicules and parts 27% 24.8% 0.5% 12% -12%<br>38 a LS - Ferrous metals 0.9% 10.7% 0.4% 5% -5%<br>39 8 COA - Coal 0.2% 5.0% 0.2% 3% -3%<br>40 9 P_C - Petroleum, coal products 0.1% 8.9% 0.0% 4% 4%<br><!-- End of picture text -->
Potentially effective interventions that are in need of evaluation data include job information for young women, firm certification programs for wage workers, and non-traditionally female skills training for women wage workers and entrepreneurs. Further evaluation is also needed for business associations and networks for entrepreneurs as well as mentors and role models for young women. Both of the latter programs are well known and frequently implemented, but we found little rigorous information on their impact on women’s productivity and earnings in developing countries. There is only one rigorous study of SEWA bank clients in India, which suggests that the presence of peers positively influences women’s business behavior and changes aspirations, especially among those exposed to more restrictive social norms (Field et al. 2014). These results suggest that business networks and mentors are useful and potentially transformative, but more data are needed about their workings and their labor market impacts. Finally, as this review has shown, there is a growing body of rigorous evidence on what works to empower women economically, but there is very little evidence on the financial and economic costs and benefits and even less evidence on the sustainability of interventions, both in terms of the lasting impacts of programs and the institutional and political dimensions of sustainability. The gaps in knowledge are large and cut across the reviewed interventions.

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SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE largest share of the gap can be traced to the power sector, followed by the ICT sector. Senegal has the potential to close this gap by bringing in more private sector investment and emphasizing projects in infrastructure with a regional dimension, taking advantage of its unique geographical location in the region.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure across almost all African countries, including Senegal. The results have been presented in reports covering various areas of infrastructure—ICT, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Senegal, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Two sets of African benchmarks will be used to evaluate Senegal’s situation: nonfragile, low-income countries, and middle-income countries. Detailed comparisons will also be made with immediate regional neighbors in West Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of the data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2008. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In common with the rest of the continent, Senegal’s growth performance improved markedly between the 1990s and 2000s. The overall improvement in per capita growth rates has been estimated at 1.03 percentage points, mainly from the ICT revolution (1.26 percentage points), while deficient power infrastructure held growth back (by -0.23 percentage points, figure 1a).

2
Policy Research Working Paper 10159

# **Abstract**

This paper studies the relationship between distortions, plant size, and bribery possibilities. In a distorted economy, bribery is a transfer from a private party to government officials to ‘get things done’. Enterprise Surveys data shows that small plants spend a higher fraction of their output on bribery than big plants. In this paper, a one-sector growth model is developed in which size-dependent distortions, bribery opportunities, and different plant sizes coexist. In the model, bribery is endogenous in the sense that managers decide to use it as a way to deal with distortions. Two sets of exercises are conducted to quantify the interplay of size-dependent distortions and bribery. First, the model parameters are calibrated to generate the plant size distribution of the U.S., by assuming the U.S. is free of distortions. Then, size-dependent distortions are introduced to the undistorted economy, and their effects with and without bribery opportunities are compared. Counterfactual exercises show that size-dependent distortions become less distortionary in the presence of bribery opportunities since plants are able to avoid distortions by paying larger bribes. Second, the model is calibrated with distortions and bribery opportunities using Turkish data. The choice of this country for analysis does not imply that bribery or size-dependent distortions are particularly large in Türkiye relative to countries of comparable development. The choice is driven by the availability of data on both the plant size distribution and spending on bribery in the country. The results indicate that the inferred level of distortions is sizable for all plants. The removal of distortions, which would eliminate the incentive for paying bribes, can have a substantial effect on both the output and the mean plant size which could increase by 63.6 and 82.5 percent, respectively.

This paper is a product of the Global Indicators Group, Development Economics. 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 author may be contacted at mtamkoc@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 6: Investment in mutual fund Y (Decreasing returns to socially responsible fund)**

||Dependent V|ariable: Inves|tment in alte|rnate (Social)|fund|||
|---|---|---|---|---|---|---|---|
||I<br>Baseline|II<br>Controls|III<br>Decision<br>interaction|IV<br>Risk<br>preference<br>interaction|V<br>Social<br>preference<br>interaction|VI<br>All<br>interactions|VII<br>Triple<br>interaction|
|Treatment (Social fund = 1)|118.8|140.3|-57.3|-259.1|92.1|-500.8|-587.0|
||(105.6)|(110.3)|(137.0)|(376.2)|(160.1)|(401.7)|(411.2)|
|Decision number (1 - 12)|-195.0***|-195.0***|-210.2***|-195.0***|-195.0***|-210.2***|-222.1***|
||(6.3)|(6.3)|(8.8)|(6.3)|(6.3)|(8.8)|(13.4)|
|Risk preferences||-90.6|-90.6|-163.4*|-93.3*|-165.7*|-165.7*|
|7 = Risk-seeking||(56.08)|(56.08)|(86.29)|(56.68)|(86.80)|(86.80)|
|Social preferences||0.448|0.448|0.393|0.264|0.217|-0.363|
|<br>Donation to charity||(0.49)|(0.49)|(0.50)|(0.66)|(0.66)|(0.83)|
|Treatment X|||30.40**|||30.40**|43.65**|
|Decision number|||(12.49)|||(12.49)|(18.42)|
|Treatment X||||130.8||130.1|130.1|
|Risk preference||||(117.8)||(118.3)|(118.3)|
|<br>Treatment X|||||0.431|0.413|1.078|
|Socialpreference|||||(1.03)|(1.03)|(1.27)|
|Social preference X|||||||0.089|
|Decision number|||||||(0.08)|
|Treatment X|||||||-0.102|
|Socialpreference X Decisi|on number||||||(0.12)|
|Constant|3042.4***|1469.7|1568.5|1440.0|1413.0|1484.5|1561.7|
||(85.03)|(1079.00)|(1079.80)|(1078.20)|(1091.60)|(1091.60)|(1093.50)|
|Additional Controls|NO|YES|YES|YES|YES|YES|YES|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level. Additional controls include gender, age, financial literacy, investing experience, confidence that the charity was paid in line with instructions, and clarity of instructions (see table A.3 in the appendix B). Social preferences are measured in tokens, with a maximum of 400 tokens. Risk preferences are measured based on the number of times a subject selected the gamble in the risk preference measure. Financial literacy is measured by the number of correct answers the subject provided in the financial literacy questionnaire. Appendix A defines the main variables.
40 percent of households engaged in animal husbandry and more than 25 percent in 2014/15 were poor.<sup>8</sup> These characteristics further illustrate the vulnerability of agricultural households to shocks. High poverty rates among these households is likely to significantly affect their capacity to fully cushion the impact of shocks, threatening their ability to escape poverty. Furthermore, for rain-dependent farmers, for instance, exposure to the risk of variations in rain, resulting in floods/droughts, crop diseases/failure, and loss of income/livestock, may have large negative welfare effects, particularly among the poor who may lack the capacity to cope with the shocks. Crop farmers are particularly likely to be less resilient to these shocks since (unlike households involved in animal husbandry) they may not have sufficient assets such as livestock, some of which they can sell in the event of a shock. As a result, they may resort to using emergency coping strategies such as selling the next year’s seeds, tools, or farm land, which potentially traps them in poverty.

Another category of households highly exposed to shocks is those that rely on pensions and remittances. Inactive labor market participation possibly due to old age and fluctuations in remittances is likely to drive vulnerability among these households. It is interesting to note that more than 65 percent of households that rely on remittances as a source of livelihood live in rural areas whereas more than 50 percent of pensioners live in urban areas. In addition, poverty rates among these households are relatively low—less than 30 percent in both years.

Apart from differences in the prevalence of shocks across household likelihoods, differences in the types of shocks faced by households is another important aspect in the analysis of the vulnerability of households. Overall, it can be observed that poor rural agricultural households are highly exposed to shocks in Sudan. Furthermore, in the absence of social protection programs, low asset levels of these households and risk inherent in rain-fed agriculture potentially lower their resilience to shocks. Distinguishing the extent to which the vulnerability of agricultural households to shocks is driven by the nature of their livelihood (such as the risk of rain variation) or by the fact that they are likely to be less resilient (due to low asset levels, fluctuations in agricultural income, and so on.), though an interesting empirical question is beyond the scope of this paper.

> 8 Based on authors’ calculation using 2009 NBHS and 2014/15 NHBPS.

15
literature that this may be due to poor indicators of school price (Grootaert and Patrinos, 1998).

We follow a different route. We examine how parents' choices between sending their kids to school versus work in rural Bangladesh are affected by the Food-for-Education (FFE) program. The program aims to keep the children of poor rural families in school. In 1995-96, 2.2 million children participated (13% of total enrolment). Participating households receive monthly food rations as long as they send their children to primary school. Targeting is done in two stages. First economically backward areas are chosen by the center. Second, community groups -exploiting idiosyncratic local information - select participants within those areas. From the 1995-96 Household Expenditure Survey (HES) done by the Bangladesh Bureau of Statistics (BBS), the mean amount of rice received under the FFE program was 114 kg per year per participating household. Based on the same survey, we estimate that the average price of rice paid by the poor in 1996 was 12.5 Tk per kilo in rural areas. That translates in an average monetary value for the FFE stipend of 119 Tk per month. A separate BBS survey in 1996 found that the average monthly income of boys in paid work was 464 Tk while it was 291 Tk for girls (BBS, 1996, Table 5.11, p.53). Given that there are on average about two children of primaryschool age in participating households, the value of the FFE stipend is about 13% of the average monthly earnings of boys and 20% of that for girls.' To receive the stipend, children must attend at least 85 percent of all classes each month. The headmaster of the school monitors school attendance and the food distribution is made within the school each week. The schools submit estimates of their grain needs to the local district headquarters, which then takes charge of transport, distribution, and handling.

7 These are probably underestimates, since there is anecdotal evidence that FFE rations are sold to buy cheaper grain, suggesting that FFE rice has a higher price that we have assumed.

6
noted earlier, theory predicts that the domestic value-added content of foreign goods plays a different role inside versus outside reciprocal agreements, and accordingly we examine this prediction in the data. Together, these strategies constitute a new approach to examining bilateral trade policy data, which can be used to address many trade policy questions beyond this paper.

Summarizing our results, we first find that higher domestic value added in foreign final goods results in lower applied bilateral tariffs. This result holds across alternative specifications that control for confounding factors using both observable proxies and fixed effects. Consistent with the theory, this liberalizing effect of domestic value added holds for tariffs set under non-reciprocal preference programs, but not for reciprocal tariff preferences. Moreover, the estimated influence of domestic value added on tariffs becomes stronger when we instrument for domestic value-added content and correct for censoring. Second, we find that higher foreign value added in domestic final goods results in lower applied bilateral tariffs. This effect again strengthens when we correct for censoring and holds most strongly inside reciprocal trade agreements, where reciprocity does not neutralize the domestic-price externality.

Finally, we show that bilateral TTB coverage ratios respond to value-added content in much the same way as bilateral applied tariffs. These results both corroborate our findings for tariffs and extend our analysis to include these increasingly important discretionary trade policy instruments. Furthermore, we find the role of domestic value added in foreign production to be strongest for TTB-use against China, where antidumping and other TTBs were most actively deployed during the 1995-2009 period.

In addition to highlighting the role of global supply chains, our empirical results contribute to the existing trade policy literature in several other ways. Our evidence linking the domestic value-added content in foreign production to bilateral tariffs fits into an important literature documenting that terms-of-trade concerns matter for trade policy formulation [Broda, Lim ̃ao and Weinstein (2008), Bagwell and Staiger (2011), Ludema and Mayda (2013), Bown and Crowley (2013)]. To our knowledge, we are the first to demonstrate the relevance of the theory for _bilateral_ tariff policy.<sup>3</sup> Along the way, we take care to distinguish the predictions of the theory inside versus outside RTAs. We are also the first (to our knowledge) to document that tariffs set via reciprocal bilateral trade agreements behave in a manner consistent with the neutralization of terms-of-trade motives.

This paper also contributes to a recent literature that applies input-output methods to

> 3In this, our work complements Bown and Crowley (2013), who document the importance of terms-oftrade influences in US application of bilateral antidumping and safeguard measures, and Blanchard and Matschke (2015), who show that the United States is more likely to offer preferential market access to destinations that host US multinational affiliates that sell goods back to the US.

4
# Table 4: Robustness to spatial controls

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dep. Var.:Growth inper capita|income(1996-20|11)||||
|Homeland (=1)|0.660|0.728|1.189**|0.691|0.660|
||[0.454]|[0.462]|[0.504]|[0.449]|[0.448]|
|Share Employed mining1996|-0.447*|-0.443*|-0.216|-0.513**|-0.544**|
||[0.258]|[0.257]|[0.207]|[0.260]|[0.262]|
|Homeland*Share Employed|0.840***|0.848***|0.655**|0.915***|0.957***|
|mining1996|[0.284]|[0.282]|[0.286]|[0.285]|[0.288]|
|Observations|9,583|9,583|9,583|9,583|9,583|
|R-squared|0.634|0.635|0.593|0.636|0.637|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|f(Distance to homeland<br>border,in km)|Linear Inter|Cubic Inter|Cubic<br>BSpline|X,Y,XY|Cubic<br>X,Y,XY|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996. Columns 1 to 3 include different functions of the linear distance between communities and the homeland borders. Columns 4 and 5 instead include different functions of the geographic coordinates (lat./lon. – X/Y). All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1