rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4492

# **Abstract**

When seeking a public service, users may be required to pay in bribes more than the official price. Consequently, some users may be discouraged and choose not to seek a service due to the higher price imposed by the bribery “tax.” This paper explores the price and quantity components of the relationship between governance and service delivery using micro-level survey data.

The authors construct new measures of governance using data from users of public services from 13 government agencies in Peru. For some basic services, low-income users pay a larger share of their income than wealthier ones do; that is, the bribery tax is regressive. Where there are substitute private providers, low-income users appear to be discouraged more often and not to seek basic services. Thus, bribery may penalize poorer users twice – acting as a regressive tax and discouraging access to basic services.

The paper explores the characteristics of households seeking public services. Higher education and age are associated with higher probability of being discouraged. Trust in state institutions decreases the probability of being discouraged, while knowledge of mechanisms to report corruption and extent of social network increase it, suggesting that households may rely on substitutes through networks. The study complements the household analysis with supply-side analysis based on data from public officials, and constructs agency-level measures for access to public services and institutional factors. Econometric results suggest that corruption reduces the supply of services, while voice mechanisms and clarity of the public agency’s mission increase it.

This paper—a product of the Global Programs Group, at the World Bank Institute—is part of a larger effort in the department to understand the link between governance and development outcomes.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at frecanatini@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
Banking on the Poor?

Branch Placement and Non-Farm Rural Development in Bangladesh

# Martin Ravallion and Quentin Wodon'

# _World Bank_

Keywords: Non-farm rural development, Grameen Bank, poverty, Bangladesh JEL classification: I38, L31, 018 ' Ravallion is with the Development Research Group, World Bank, Washington DC. Wodon is with the Development Research Group, and the University of Namur (FUNDP), Belgium. This study was made possible through a long term collaborative effort between the Bangladesh Bureau of Statistics and the Country Operations Division of the World Bank's South Asia Country Department 1. Comments were received from Hans Binswanger, Karla Hoff, Shekhar Shah, and seminar participants at the World Bank Resident Mission, Dhaka. The support of the World Bank's Research Committee (under RPO 68139) is gratefully acknowledged.
# **A**

- **A.A** **_Guerrilla territories_**

- **Territories under control by 1991:** Following Casta ̃neda (2016), this study uses the maps that document FMLN-held areas as submitted to the United Nations and approved by the different political parties in El Salvador during the cease-fire process. Since the map originally had an image format, we used ArcMap to digitize it by hand and convert it to a shapefile format. Thus, this is the only part of the spatial analysis that is not coded.

- **A.B** **_Geospatial variables_**

- **Night light luminosity:** Data on night light luminosity comes from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS). This data was obtained from the US National Oceanic and Atmospheric Administration (NOAA) at https: //ngdc.noaa.gov/eog/download.html. This data has a resolution of 30 arc seconds (1 _km_<sup>2</sup> ) and spans 1992 to 2013. The challenge with night light luminosity data is the significant fraction of observations that take the value of zero and the existence of extreme values in the right tail of the distribution (Michalopoulos and Papaioannou, 2013; Pinkovskiy and Sala-i Martin, 2016). To account for this potential concern, we adjust the outcome of interest using the logarithm and the inverse hyperbolic sine transformation.<sup>38</sup>

- **Elevation:** Elevation was obtained from the Google Earth Engine Data Catalog and is available at https://developers.google.com/earth-engine/datasets/catalog/USGS_ SRTMGL1_003. This data provides elevation information in meters at the 3 arc-seconds spatial resolution (90 _mts_<sup>2</sup> ). The digital elevation model (DEM) was created based on the images of the Shuttle Radar Topography Mission (SRTM) of NASA. In this study, we calculated the average elevation for each census tract.

- **Slope:** this study uses the terrain() function in R to compute the slope from the elevation data accordingly with Ritter (1987).<sup>39</sup> The algorithm uses four neighboring pixels to compute each pixel’s slope in degrees. Thus, higher values represent steeper terrain. Our study uses the average of the slope at the census tract level.

> 38 2 The inverse hyperbolic sine transformation is defined as _log_ ( _yi_ +( _yi_<sup>+1)1</sup><sup>_/_2) and can be interpreted as a logarithmic</sup> dependent variable (Pence, 2006).

> 39Documentation of the R tool can be found at https://www.rdocumentation.org/packages/raster/ versions/3.4-10/topics/terrain

53
Annex 2: DRC - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
||||**Democratic R**|**epublic of Congo**|**National**|||
|Rice|3.2|57.3|**71.3**|71.8|72.2|71.7|71.9|
|Cassava|10.2|91.9||71.8|72.3|71.6|71.8|
|Maize|5.9|50.7||72.0|72.6|71.5|71.6|
|Palm oil|4.0|96.2||71.9|72.4|71.6|71.8|
|Plantain|1.1|30.0||71.4|71.4|71.3|71.3|
|Wheat|1.8|35.1||71.6|71.7|71.6|71.7|
|Sugar|1.4|57.4||71.6|71.7|71.6|71.7|
|Milk|0.7|23.0||71.4|71.5|71.4|71.5|
|All|28.3|100.0||73.9|76.2|72.6|73.7|
||||**Democratic**|**Republic of Cong**|**o Urban**|||
|Rice|3.3|77.2|**61.5**|62.2|62.9|62.2|62.9|
|Cassava|5.5|89.4||62.3|63.4|62.2|63.2|
|Maize|6.3|78.7||62.2|63.0|62.1|62.9|
|Palm oil|2.6|96.1||62.0|62.4|62.0|62.4|
|Plantain|0.3|14.4||61.5|61.5|61.5|61.5|
|Wheat|3.0|70.2||62.0|62.3|62.0|62.3|
|Sugar|1.4|79.5||61.9|62.0|61.9|62.0|
|Milk|1.0|50.0||61.6|61.7|61.6|61.7|
|All|23.5|100.0||65.1|68.4|64.9|68.1|
||||**Democratic**|**Republic of Cong**|**o Rural**|||
|Rice|3.0|49.7|**75.7**|76.0|76.2|75.8|75.9|
|Cassava|14.5|92.8||76.0|76.3|75.7|75.6|
|Maize|5.6|39.9||76.3|76.8|75.7|75.4|
|Palm oil|5.3|96.2||76.3|76.8|75.9|76.0|
|Plantain|1.9|36.1||75.7|75.8|75.6|75.6|
|Wheat|0.7|21.5||75.8|75.9|75.8|75.9|
|Sugar|1.4|48.9||75.9|76.0|75.9|76.0|
|Milk|0.4|12.5||75.8|75.8|75.8|75.8|
|All|32.7|100.0||77.8|79.7|76.0|76.2|

Source: Authors’ estimation using country household survey data 21
operated in three districts—Bagamoyo (70 km from Dar es Salaam), Chamwino (500 km), and Kibaha (35 km)—where 80 eligible study villages were randomized into treatment and control groups of 40 villages each, stratified on village size and district (median village size at baseline was 560 households). Randomization was carried out after identification of potential beneficiary households in all 80 villages. At village meetings held prior to randomization, TASAF communicated that control villages would receive the program in late 2012, and the program would continue in treatment villages. Median village size was quite small (560 households at baseline, in 2009), and every village had both a primary school and a public dispensary or health center, facilitating fulfillment of program conditions.

Treatment households received transfers every two months. Transfer amounts ranged from US $12 to US $36, depending on household size and composition. The CCT provided US $3 per month for orphans and vulnerable children up to age 15 (approximately 50 percent of the food poverty line) and US $6 per month for vulnerable individuals age 60 or older. In our follow-up surveys, the median size of the last transfer is US $14.12; assuming six annual payments of this size, this is about 13 percent of annual household expenditures.

While CCT payments were made at the household level, conditions applied at the individual level.<sup>5</sup> Children aged 0–5 had to visit a health clinic at least six times per year (the condition was relaxed for children aged 2–5 to two visits per year starting in 2012),<sup>6</sup> those age 60 or over had to visit at least once per year, and no health conditions applied to others. Both preventive and curative visits fulfilled the health clinic visit conditions of the program, though visits had to be to a public facility (either a dispensary, health center, or hospital). There were no further restrictions on the timing of visits, nor on the services to be received. Children aged 7–15 had to enroll in school and maintain an 80 percent attendance record.

> 5We lack administrative data on compliance with conditions. However, in each follow-up survey, we asked: “[For your last transfer payment,] did you receive less money than you usually get?” and “What do you think was the reason?” While one may hesitate to admit to non-compliance (for fear of sanction) and while this cannot tell us how many households had at least one payment reduced (it only tells us about the last), this gives some indication of compliance levels. At midline and endline respectively, 1.9 and 3.0 percent of treatment households reported receiving less than usual for a reason related to not meeting conditions.

> 6As our endline survey was carried out during August–October 2012, we define compliance with clinic visit conditions at endline for 2–5 year olds as having two or more clinic visits in the last year.

7
**Figure 18**

<!-- Start of picture text --> Weighted Average of Distance by Productivity Level, 2004<br>Top 50 and "New" Exports<br>2.0<br>1.72<br>1.62<br>1.6 1.47<br>1.31<br>1.2<br>0.8<br>0.4<br>0.0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br><!-- End of picture text --> Regarding the relationship between RCA and product income, observe in Table 4 that Argentina does not have revealed comparative advantage for many of the high-productivity goods. For products where Argentina has a RCA the product income is generally low. However, notice that it has a considerable RCA for wheat, aluminum, fish, tubes and pipes all of which are of high productivity.

Furthermore, in Figure 19, we present the weighted average of RCA separately for the top ranked 50 and for the top ranked “new” exports, both grouped according to high and low productivity. As can be observed, Argentina has on average five times more revealed comparative advantage for the low productivity goods within its top 50 exports. It is important to notice though, that for the top 50 high productivity exports the weighted average RCA is greater than unity which means that Argentina is not without some comparative advantage in these products too.

In contrast, when looking at the “new” exports, the high productivity goods have a level of RCA twice that of the low productivity products. Actually, the level is very close to that of the top 50 high productivity goods. Conversely, the RCA level of the low productivity “new” exports is one-tenth of that of the low productivity top 50 exports.

**Figure 19**

<!-- Start of picture text --> Weighted Average of RCA by Productivity Level, 2004<br>Top 50 and "New" Exports<br>35 31.59<br>30<br>25<br>20<br>15<br>10<br>5.95 6.25<br>5 3.06<br>0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br><!-- End of picture text --> 23
In this paper, we study the relationship between school enrollment, selection, and test scores. For this purpose, we use data from Cambodia. Specifically, we analyze the effects of a program, known as the CESSP Scholarship Program (CSP), which gives scholarships to poor children for the three years of the lower secondary school cycle.

We first show that the CSP program had a large impact on school enrollment—children offered scholarships were approximately 25 percentage points more likely to be enrolled than they would have been in the absence of the program. Data on time use shows that these children spent more hours in school; their parents paid school fees and made other education expenditures; finally, because they were promoted on time, children who were offered scholarships attained more schooling than did comparable children in the control group. Despite these increases in school enrollment and attainment, however, children who had received scholarships did no better on tests of mathematics and vocabulary than those in the control group 18 months after the beginning of the program. Moreover, we find no impacts on learning even in schools where scholarship recipients were only a small fraction of the total number of students, suggesting that over-crowding is not the main reason for the absence of program effects on test scores.

We next consider the possible role of self-selection into schooling. Our analysis is based both on tests taken by children at home, regardless of whether they were enrolled in school, and school-based tests. School enrollment data are available at numerous points in time. By combining these different sources of data we show that children who stayed in school as a result of the scholarships were drawn primarily from the left-hand side of the distribution of ability. If lower-ability children, like those whose schooling choices were affected by the CSP program, do not learn much in school, it is not surprising that there are no overall program effects on learning. We also discuss program effects on knowledge of health practices, expectations about the future (including desired age of marriage and birth of the first child, and the socioeconomic status children expect to attain in adulthood), and adolescent mental health. We find that most of these outcomes were unaffected by the scholarship program.

This paper is related to a literature on the determinants of learning outcomes and skills acquisition 3
Table 3: Baseline Results - Average and size heterogeneity in effects of SPS and TBT provisions

||Ex|port value of ag|ricultural p|roducts||Export value o|f any prod|uct|
|---|---|---|---|---|---|---|---|---|
||All SPS|Essential SPS|All SPS|Essential SPS|All TBT|Essential TBT|All TBT|Essential TBT|
|ln(1 +_τ_)|-1.622<sup>_∗∗∗_</sup>|-1.616<sup>_∗∗∗_</sup>|-1.630<sup>_∗∗∗_</sup>|-1.620<sup>_∗∗∗_</sup>|-1.814<sup>_∗∗∗_</sup>|-1.823<sup>_∗∗∗_</sup>|-1.818<sup>_∗∗∗_</sup>|-1.834<sup>_∗∗∗_</sup>|
||(0.412)|(0.412)|(0.411)|(0.412)|(0.420)|(0.420)|(0.422)|(0.422)|
|ln(_GDP_)|1.332<sup>_∗∗∗_</sup>|1.346<sup>_∗∗∗_</sup>|1.315<sup>_∗∗∗_</sup>|1.329<sup>_∗∗∗_</sup>|2.170<sup>_∗∗∗_</sup>|2.164<sup>_∗∗∗_</sup>|2.163<sup>_∗∗∗_</sup>|2.159<sup>_∗∗∗_</sup>|
||(0.191)|(0.192)|(0.189)|(0.190)|(0.177)|(0.177)|(0.177)|(0.175)|
|Depth|-0.363<sup>_∗∗∗_</sup>|-0.262<sup>_∗_</sup>|-0.382<sup>_∗∗∗_</sup>|-0.279<sup>_∗_</sup>|0.163|0.133|0.166|0.159|
||(0.133)|(0.147)|(0.134)|(0.147)|(0.231)|(0.262)|(0.234)|(0.265)|
|Prov|0.0932|0.203<sup>_∗∗_</sup>|||0.145|0.278|||
||(0.0596)|(0.0945)|||(0.133)|(0.423)|||
|1<sup>_st_ </sup>tercile _×_ Prov|||0.370<sup>_∗∗∗_</sup>|0.657<sup>_∗∗∗_</sup>|||0.387<sup>_∗∗∗_</sup>|0.842<sup>_∗_</sup>|
||||(0.0779)|(0.122)|||(0.150)|(0.443)|
|2<sup>_nd_ </sup>tercile _×_ Prov|||-0.0123|0.0461|||0.131|0.397|
||||(0.0698)|(0.104)|||(0.176)|(0.512)|
|3<sup>_rd_ </sup>tercile _×_ Prov|||0.0488|0.146|||0.0871|0.0250|
||||(0.126)|(0.177)|||(0.176)|(0.504)|
|fxed-efects<br>cluster|fkj fkt<br>ij|fkj fkt<br>ij|fkj fkt<br>ij|fkj fkt<br>ij|fkj fkt<br>ij|fkj fkt<br>ij|fkj fkt<br>ij|fkj fkt<br>ij|
|N|1532134|1532134|1532134|1532134|6283454|6283454|6283454|6283454|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

33
formalize, and from Brazil, where Andrade et al. (2013) used tax inspectors to force formalization. In neither case were firms able to benefit from many of the purported advantages of formal status, including access to business banking, participation in government training programs, receiving government contracts, or increased certainty over taxes. De Mel et al. (2013) find some impact of formalization on firm profitability, but this impact appears to be driven by a handful of firms for which profit increased substantially, with most firms experiencing no change. This evidence shows the link from formalization to the benefits of formalizing is not automatic, and suggests the need for supplementary services to enhance formalization assistance. There is also little evidence from Africa, where development levels are lower, and the informal sector even larger, than in the Latin American and Asian contexts where most of the existing studies have been done.

In this paper, we test the effectiveness of offering supplementary services to enhance the takeup and returns to formalization in the context of a randomized experiment in Benin. We do this in the context of the launch of the _entreprenant_ legal status, a simplified regime being offered to small informal businesses in 17 African states with the goal of making it easier for them to enter the formal economy. This status includes tax registration, and in principle offers the key benefits of formalization in terms of access to bank accounts, government programs and contracts, and tax certainty. In the pilot phase of launching this regime, we worked with the Government of Benin to test experimentally three different programs with a sample of 3,600 informal businesses.

Firms were randomized into a control group and three treatment groups. The first treatment group received in-person visits in which the new status was explained, the potential benefits of formalization verbally described, and advisors helped firms with the paperwork as needed. The second treatment aimed to enhance the benefits of formalization by also offering business training and support opening a business bank account if they formalized. The third treatment built on the second by offering tax mediation services, with the goal of providing protection and assurance against fear of the tax administration. A supplementary treatment provided information in the form of leaflets and a verbal explanation to see whether information alone had an impact.

We use administrative data on formalization coupled with two rounds of follow-up surveys to measure the impact of these treatments. Only 2 percent of the control group formalized over a 3
finance ministries. Third, we classify each revenue source following the OECD’s tax classification (see OECD, 2020). Table B1 details the data sources used.

When available, OECD tax revenue data is our preferred source, because it covers and classifies all types of tax revenues, usually back to 1965 for OECD countries. OECD data accounts for 41% of the country-year observations in our dataset. Its drawback is its limited coverage of non-OECD countries: in total it covers 93 countries, and only over the past two decades.

To increase coverage, we augment the OECD data with the tax revenue data from the ICTD/UNU-WIDER (2020) (17% of observations). This dataset achieves near worldwide coverage but, for our purposes, faces limitations: it only starts in the 1980s; it does not follow the tax classification of the OECD; it sometimes mixes personal and corporate income taxes; and it often lacks payroll taxes and decentralized taxes. To address these shortcomings, we use historical public finance data from government reports, primarily from the Harvard Library archives (30% of country-year observations) and from the IMF GFS (2005) offline historical database (10% of observations).14 To stitch together country-by-country time series of tax revenues, we follow three principles. First, we aim to only rely on a maximum of two data sources by country: the OECD when it exists, and the alternative source with the best coverage over time and by tax type. Archival data is our second in priority since it often dis-aggregates revenue by source, and goes back to the 1960s. Our data hierarchy choice also depends on which source best matches the OECD data over their shared time frame. Second, we interpolate series with gaps, but only up to four years between two data points. Finally, we check country-specific policy reports and scholarly studies to triangulate across data sources and to identify events which may explain discordance across sources.

Tax revenues are disaggregated as finely as possible by source, according to the OECD tax classification (OECD, 2020). To allocate taxes to capital and labor, we pay attention to three dimensions. First, we systematically separate income taxes into personal and

> 14The ICTD/UNU-WIDER data draws principally from the IMF Government Finance Statistics online data, which covers the past few decades well. Our use of the IMF data is restricted to the offline historical dataset, which covers 1972-89 and fills gaps from the OECD and historical archives data. The ICTD does not report pre-1980 data.

13
start a new business, and both cost (5% of income per capita) and paid-in minimum capital (15 % of income per capita) are insignificant (See Figure 9 and figure 10).

**Figure 9. Starting a new business**

<!-- Start of picture text --> Rank<br>Procedures (number)<br>140<br>120 107 114 110 119 12 10<br>97 98 10<br>8 8 8 8 8 8<br>100<br>78<br>8<br>80 6<br>56<br>6<br>60<br>40 4<br>20 2<br>0 0<br>Paid-in Min. Capital (%<br>Cost (% of income per<br>of income per capita)<br>Time (days) capita)<br>160 149<br>5045 40 43 44 41 39 120 108 140 122121 123<br>100<br>40 120 104<br>35<br>80 100<br>30 65<br>58<br>80<br>25 20 60<br>20 15 14 40 41 38 32 60 42<br>15<br>40 25<br>10 20 15<br>5 5 5 20<br>0 0 0<br>Source: Doing Business Report 2011<br>OIC GCC OPEC OECD OIC‐GCC Low income MENA developing countries OIC GCC OPEC OECD OIC‐GCC Low income MENA developing countries OIC GCC OPEC OECD OIC‐GCC Low income MENA developing countries<br><!-- End of picture text --> 34