rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLE OF CONTENTS**

|I.|Introduction.................................................................................3|
|---|---|
|II.|Provision of Finance...................................................................... 3|
||A. Credit Lines (non-agricultural)......................................................3|
||B. Micro-finance.......................................................................... 6|
||C. Rural Finance.......................................................................... 9|
|III.|Technical Assistance, Extension, Training and Technology Upgrading.......... 10|
||A. Technical Assistance to Private Firms.............................................10|
||B. Interventions to Increase Private Sector Training................................ 16|
||C. Support to Technology Development............................................. 17|
|IV.|Conclusions.............................................................................. 19|
|Bibliog|raphy....................................................................................... 21|
|Annex||
|1.|World Bank projects with credit line components<br>24|

2
Policy Research Working Paper 6844

# **Abstract**

In Central America, like many other developing regions, high transport costs are cited as an impediment to trade and economic growth. Prices for road freight transport —a key mode of transport comprising a significant share of total transport costs for intra- and extra-regional trade, are particularly high. Averaging 17 cents per tonkilometer on main trading routes, these rates stand out even relative to other inefficient developing country markets (e.g., central and west Africa). However, the policy and other factors associated with increased prices have not been well understood. This paper uses data from a survey of trucking companies operating on the region’s main trade corridors to analyze the determinants of firms’ costs of providing service, as well as the effect of market structure and competition on prices. The analysis finds that whereas improved cost efficiencies could reduce prices by 3 cents per ton‐kilometer, increased competition on national routes—those entirely within a nation’s borders—would reduce prices by significantly more. Although there are many trucking companies, including small and somewhat informal operators, the degree of competition varies by route because of domestic restraints on competition and the prohibition on international competition on national routes. The paper shows empirically that imperfect competition accounts for at least 35 percent of mean prices on national routes. In addition, a lack of competition is likely to explain the persistence of an inefficient market structure, as well as a lack of innovation to reduce costs and enhance the quality of service.

This paper is a product of the Sustainable Development Department, Latin America and the Caribbean Region. 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://econ.worldbank.org. The authors may be contacted at tosborne1@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
the probability of receiving the books in one month by 0.198. School financial resources measured by PTA funds per student was correlated negatively with the probability of receiving textbooks. One possible explanation could be a reverse causality: schools that had not received the books tried to raise more PTA funds for collecting the books.

**Table 6. Factors related to textbook availability at baseline—Grade 2 textbooks**

||Pr(Receiving<br>textbooks)|Textbook-pupil<br>ratio|Pr(Receiving in a<br>month)|
|---|---|---|---|
|Communication on availability of<br>textbook, dummy|0.284***|0.190***|0.198***|
||(0.051)|(0.046)|(0.048)|
|School budget per student, log(amount)|-0.015|0.024|-0.028|
||(0.020)|(0.018)|(0.019)|
|PTA fund per student, log(amount)|-0.050***|-0.005|-0.006|
||(0.017)|(0.015)|(0.016)|
|Located 21–44 km away, dummy|0.040|0.084|-0.012|
||(0.066)|(0.060)|(0.062)|
|Located 45–80 km away, dummy|0.057|0.011|0.041|
||(0.065)|(0.059)|(0.061)|
|Located more than 80 km away, dummy|0.074|-0.002|0.005|
||(0.065)|(0.059)|(0.062)|
|Constant|-0.065|0.003|-0.026|
||(0.086)|(0.078)|(0.081)|
|N|243|243|243|
|R-squared|0.370|0.103|0.200|

_Note:_ Regression is estimated using ordinary least square (OLS). Availability of textbooks in DEBS and the dummy variables for missing observations in school resources are controlled in the regression but not shown. The coefficient is significant at the1% level (***), the 5% level (**), and the 10% level (*). Standard errors are in parentheses.

# **Cost estimates for delivery or collection of textbooks**

The survey asked the sampled schools how much they spent or would spend if they collected the books from DEBS. The school would, on average, spend about K 308 (about US$22). The estimated cost is different depending on where the school is located: the average estimated cost is K 142 (about US$10) for schools located within 20 km from DEBS; K 202 (about US$14) for schools located 21–44 km away; K 323 (about US$23) for schools located 45–80 km away; and K 573 (about US$40) for schools located more than 80 km away. The source of funds for textbook collection varies: some schools raise funds through the PTA or use the education budget received from the MoGE; others use school staff’s personal money. Thirtyfour percent of schools responded that funds were raised through the PTA, 19 percent using education budget, and the majority of the rest mentioned using their staff’s personal money to collect the books.

DEBS, on average, spent K 347 (about US$24) per school for the distribution of textbooks at baseline, and the cost estimate per school is K 506 (about US$35) if DEBS were to deliver the books to all schools in their districts. This is slightly lower than the per-school cost for the DEBS incentive (Treatment 2), which is K 455 including financial incentives (K 280) and the up-front grant (K 174).

13
||_Men should have one_<br>_partner only_|(5)<br>(6)|-0.037<br>-0.011|(0.033)<br>(0.047)<br>0.110***<br>0.129***|(0.020)<br>(0.031)<br>0.001<br>-0.005|(0.036)<br>(0.038)<br>0.073<br>0.085|(0.054)<br>(0.055)<br>-0.038<br>(0.041)|3,394<br>3,394|0.044<br>0.045<br>Yes<br>Yes|0.903<br>0.903|denote signifcance at the 1, 5|live with the family, household|<br>ary education, muslim, speaks||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|uncing social norm (T2)|_Not ok date sugardaddy to f-_<br>_nance education_|(3)<br>(4)|0.011<br>0.069|(0.032)<br>(0.050)<br>0.215***<br>0.246***|(0.020)<br>(0.033)<br>-0.130***<br>-0.117**|(0.048)<br>(0.048)<br>-0.038<br>-0.065|(0.064)<br>(0.065)<br>-0.060<br>(0.042)|3,402<br>3,402|0.080<br>0.081<br>Yes<br>Yes|0.751<br>0.751|ning centre level (54 clusters). ***, ** and *|(T1+T2).<br>tion, English spoken at home, single, does not|<br>ucation, mother obtained higher than second||
|Table 6: Efect of anno|_Dep. Var. (Yt−_1_):_<br>_Would reveal HIV status_|(1)<br>(2)|T2<br>0.016<br>0.019|(0.027)<br>(0.038)<br>Y_t−_1<br>0.231***<br>0.235***|(0.018)<br>(0.021)<br>Prior on community (r_t−_1)<br>0.125**<br>0.123**|(0.051)<br>(0.050)<br>T2 * Prior on community (r_t−_1)<br>-0.073<br>-0.071|(0.067)<br>(0.067)<br>T2*Y_t−_1<br>-0.006<br>(0.037)|Observations<br>3,402<br>3,402|R-squared<br>0.091<br>0.091<br>Controls (_a_)<br>Yes<br>Yes|Mean Dep. Var. (Control)<br>0.738<br>0.738|Notes. Standard errors in parentheses adjusted for clustering at the scree|and 10 percent level, respectively. Sample includes treated individuals only<br>(a) Controls include: female, age, currently attending school, years of educa|<br>size, wealth index, home owner, father obtained higher than secondary ed|Yoruba as a native language.|

41
# **2 Gender indicators for the SDGs**

Although nearly all countries have agreed to report on the SDG indicators, major gaps exist in indicator availability since the SDG agenda’s inception in 2015 (Dang and Serajuddin 2020). Gender-related SDG indicators are no exception. There are 231 unique SDG indicators. Many of the indicators, even if not obviously related to gender, nonetheless have sub-indicators, such as, indicators by sex, age, or disability status. The UN global SDG indicators database provides access to the data compiled for tracking progress toward fulfilling the SDGs. We use this data source, rather than individual NSO websites, because data submitted to the UN Global SDG monitoring database goes through a standardized process including a certain level of quality control and documentation review.

We explore the coverage of the 50 gender-related SDG indicators out of the 231 unique indicators.<sup>1</sup> As noted earlier, gender-related SDG indicators are not limited to SDG 5 on gender equality, but rather span indicators across 10 out of 17 of the SDG goals. All 50 SDG-gender indicators are Tier 1 or 2 SDG indicators.<sup>2</sup> While most are related to sex disaggregation of data 1 These 50 indicators closely match the UN Women minimum set of 52 quantitative gender indicators from the SDGs (United Nations Economic and Social Council 2012), subsequently revised to be 51 quantitative indicators, with a few exceptions. These exceptions are: (i) indicators 4.7.1, 4.a.1, and 13.3.1 are in the UN Women minimum set but not here as in our view they are not gender-related or sex-disaggregated measures. (ii) indicator 1.1.1 includes the "working poor" (employed population below international poverty line) by sex component which is in Table 1 but not in the UN Women list. Relatedly, Open Data Watch (2019) refers to 32 SDG gender indicators and another 36 “additional” SDG gender indicators. The difference between their 68 and our 50 SDG gender indicators is that some of theirs are, in our assessment, gender neutral in terms of the present drafting of the indicator (such as 1.5.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population).

2 Tier 1 indicators, according to the UN, are indicators that are conceptually clear, have an internationally established methodology and standards available, and data are regularly produced by countries for at least 50% of countries and of the population in every region where relevant. Tier 2 indicators are conceptually clear and have an internationally established methodology and set of standards, but are not regularly produced by countries. There are no gender equality indicators in the third category, Tier 3, which is defined as an indicator with no internationally established methodology or standards established, and, thus, these indicators are likely to have the lowest rate of coverage. (UNSD, 2022).

4
Table 3. Impacts of the PEP Program on Crime Reports by Migrants

||Homicides<br>(1)|Threats<br>(2)|Domestic Viol.<br>(3)|Thefts<br>(4)|Sex Crimes<br>(5)|Total Crimes<br>(6)|
|---|---|---|---|---|---|---|
|_Panel A. OLS - Crime Reports b_|_y Venezuelans_|_(ratesper_|_100,000)_||||
|_PEPc × I_[_PostAugust_2018]|0.040***|0.018|0.034|0.043|0.021**|0.087***|
||(0.014)|(0.015)|(0.020)|(0.024)|(0.008)|(0.024)|
|FDR q-values|[0.016]|[0.141]|[0.068]|[0.066]|[0.019]|[0.007]|
|bootstrap p-val.|[0.004]|[0.423]|[0.131]|[0.133]|[0.015]|[0.000]|
|Crime Reports Tot|0.071***|0.042**|0.069***|0.189***|0.058|0.442***|
||(0.024)|(0.018)|(0.023)|(0.057)|(0.038)|(0.084)|
|AdjR-squared|0.319|0.244|0.423|0.575|0.370|0.653|
|_Panel B. 2SLS - Crime Reports b_|_y Venezuelans_|_(ratesper_|_100,000)_||||
|_PEPc × I_[_PostAugust_2018]|0.033|0.024|0.078**|0.036|0.051**|0.105**|
||(0.017)|(0.016)|(0.033)|(0.035)|(0.017)|(0.041)|
|FDR q-values|[0.040]|[0.078]|[0.031]|[0.113]|[0.019]|[0.026]|
|bootstrap p-val.|[0.134]|[0.264]|[0.019]|[0.418]|[0.001]|[0.032]|
|Crime Reports Tot|0.072***|0.042**|0.075***|0.186***|0.059|0.450***|
||(0.023)|(0.017)|(0.024)|(0.052)|(0.037)|(0.082)|
|Adj R-squared|0.319|0.244|0.418|0.575|0.366|0.653|
|KP F Stat|20.29|21.11|21.27|21.88|21.16|21.46|
|_Panel C. OLS - Crime Reports b_|_y Venezuelans_|_(ratesper 1_|_00,000) - Excluding_|_Controls_|_for Total Crime_||
|_PEPc × I_[_PostAugust_2018]|0.052***|0.018|0.028|0.035|0.020***|0.073***|
||(0.015)|(0.016)|(0.020)|(0.023)|(0.007)|(0.026)|
|FDR q-values|[0.007]|[0.151]|[0.115]|[0.115]|[0.017]|[0.017]|
|bootstrap p-val.|[0.000]|[0.421]|[0.213]|[0.185]|[0.004]|[0.012]|
|AdjR-squared|0.298|0.231|0.416|0.572|0.362|0.646|
|_Panel D. 2SLS - Crime Reports_|_by Venezuelans_|_(ratesper_|_100,000) - Excludin_|_g Controls_|_for Total Crime_||
|_PEPc × I_[_PostAugust_2018]|0.049***|0.025|0.075**|0.032|0.050***|0.094**|
||(0.016)|(0.016)|(0.034)|(0.034)|(0.016)|(0.044)|
|FDR q-values|[0.007]|[0.049]|[0.032]|[0.128]|[0.007]|[0.032]|
|bootstrap p-val.|[0.018]|[0.229]|[0.040]|[0.472]|[0.004]|[0.065]|
|Adj R-squared|0.298|0.231|0.411|0.572|0.358|0.646|
|KP F Stat|21.21|21.21|21.21|21.21|21.21|21.21|
|Observations (All Panels)|1,080|1,080|1,080|1,080|1,080|1,080|

_Notes:_ The table presents the OLS and 2SLS estimations of specification (1). All columns include city and month-year fixed effects as well as the set of controls described in section IV. Standard errors are clustered at the city level. Cluster-robust wild-bootstrap p-values, and FDR q-values are presented in square brackets. To ensure statistical rigor, we consider a coefficient statistically significant only if it remains valid after adjusting for multiple hypothesis testing and accounting for the small number of clusters.

First of all, regardless of the specification choice the program resulted in higher reports in total 21
# **Table 2: Comparing the age/education distributions in the 2000 DHS and 2001 sentinel (ANC) survey data for various groups of women**

|||All women|(2000 DHS)|ANC|attendees<br>D|in last ye<br>HS)|ar (2000|ANC atte<br>blo|ndees i<br>od samp|n last year<br>le(2000|who gave<br>DHS)|Cu|rrently pr|egnant|women(|2000 DHS|)|**ANC attendees**<br>**(2001 Sentinel**<br>**Survey)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||a|ll districts|only districts<br>with sentinel<br>sites||all districts|on<br>wi<br>si<br>|ly districts<br>th sentinel<br>tes|all|districts|o<br>w<br>si<br>|nly districts<br>ith sentinel<br>tes|a|ll district|s||only distric<br>sentin|ts with<br>el sites|<br>|
|||95% con|fidence||95% con|fidence|||95% con|fidence|||95% con|fidence||95% conf|idence||
||mean|inter|mean <br>val|<sup>mean</sup>|inter|val|mean|mean|inter|val|mean|mean|inter|val|mean|inter|val|mean|
|**Age**|||||||||||||||||||
|15-19|21.69|_20.98_|_22.39_<br>21.56|14.87|_13.51_|_16.22_|14.69|14.02|_11.89_|_16.14_|13.25|18.41|_16.48_|_20.34_|18.49|_16.26_|_20.72_|21.43|
|20-24|22.37|_21.66_|_23.08_<br>22.63|33.54|_31.75_|_35.34_|33.88|33.44|_30.55_|_36.33_|33.62|32.86|_30.52_|_35.19_|32.30|_29.61_|_34.99_|37.90|
|25-29|18.16|_17.50_|_18.82_<br>18.45|25.36|_23.70_|_27.01_|25.70|28.22|_25.46_|_30.97_|28.95|22.39|_20.32_|_24.46_|23.44|_21.00_|_25.87_|22.64|
|30-34|11.85|_11.29_|_12.40_<br>12.00|12.35|_11.10_|_13.60_|11.97|10.95|_9.04_|_12.87_|11.74|13.34|_11.65_|_15.03_|13.60|_11.63_|_15.57_|11.53|
|35-39|10.77|_10.24_|_11.30_<br>10.76|8.99|_7.90_|_10.08_|8.86|8.99|_7.24_|_10.74_|8.21|7.91|_6.56_|_9.25_|7.27|_5.78_|_8.76_|5.15|
|40-44|7.97|_7.51_|_8.43_<br>7.74|3.30|_2.62_|_3.98_|3.17|2.63|_1.65_|_3.60_|2.29|3.49|_2.58_|_4.41_|3.44|_2.40_|_4.49_|1.28|
|45-49|7.19|_6.75_|_7.63_<br>6.85|1.59|_1.12_|_2.07_|1.74|1.76|_0.96_|_2.57_|1.94|1.60|_0.98_|_2.23_|1.47|_0.78_|_2.16_|0.08|
|**Education**|||||||||||||||||||
|no education|27.04|_26.28_|_27.79_<br>25.30|28.44|_26.73_|_30.16_|27.28|24.12|_21.50_|_26.74_|22.67|27.14|_24.93_|_29.36_|26.37|_23.84_|_28.90_|18.44|
|primary|61.86|_61.03_|_62.69_<br>62.34|63.65|_61.82_|_65.48_|64.06|66.24|_63.35_|_69.14_|67.05|65.81|_63.46_|_68.17_|66.57|_63.86_|_69.28_|63.81|
|secondary+|11.11|_10.57_|_11.64_<br>12.37|7.91|_6.88_|_8.94_|8.65|9.64|_7.83_|_11.44_|10.28|7.04|_5.77_|_8.31_|7.06|_5.58_|_8.53_|17.75|
|_Number of_|||||||||||||||||||
|_observations_|13220|13220|13220<br>10239|2664|2664|2664|2073|1028|1028|1028|784|1557|1557|1557|1166|1166|1166|7285|

Note: all tabulations using DHS are weighed; ANC attenders (DHS) are women who had a birth in the last year and who had a prenatal care from a professional source (doctor/nurse/midwife); 23
We use the 90-day recall periods to construct quarterly estimates of each of these outcomes. For the intensive margin outcomes, we use the survey date and the variables indicating which months the indicated person worked to calculate the percentage of the month that is applicable for each individual. This is then used to calculate the months and hours worked during that 90-day period.<sup>10</sup> Using the 12-month recall data we create similar quarterly measures to match the dates of the shorter recall.<sup>11</sup> Figure 1 provides a timeline of the four rounds of the surveys, showing the survey dates, the months covered in each recall period, along with the agricultural calendar for maize, the main staple crop in Malawi. We note that there was a delay between the third and fourth (final) survey. This has the effect that some of the time covered in the 90-day recall asked in the Round 1 interview is not well covered by the 12-month recall questions asked at the endline. As such, we observe very low levels of work in Quarter 1 as measured in the 12-month recall data. In particular, we were concerned that including all four rounds of short recall from the four surveys would result in artificially low estimates of work in Quarter 1 for the long recall group. Additionally, these delays resulted in capturing labor data for May and June—the harvest months for the primary agricultural season, in both Round 1 and Round 4.

To address both of these issues, we exclude Quarter 1 from our main analysis and use quarters 2- 4 to improve the comparability of the short and long recall periods. As seen in Figure 1, these three quarters cover the main components of one maize growing season, although there is a small gap between the final two interviews and some seasonal activities just after harvest time may be undercounted and missed in our estimates. This would likely lead to an understatement of long recall losses. We show robustness (and strengthening) of our main results using all four interviews in Appendix Table 1.

To identify the number of jobs done by individuals over the full study period, we linked reported activities across survey rounds, so that if a respondent reported working the same job in all four

> 10 This is necessary because for a 90-day recall period, there are 4 calendar months in which the respondent may have worked. To estimate the months worked or hours worked, we must calculate the relevant fraction of the first and fourth month options.

> 11 We calculate these quarterly measures by using the question in the 12-month recall module that asks respondents to indicate which months they worked in each job, and then use the survey dates from the quarterly interviews to reflect the same time periods covered by the short recall questions. We use the day of survey for each household, so the period covered varies slightly by household based on the date of interview.
_‘Measurement error is an ever-present, generally significant, but usually neglected, feature of survey based income and expenditure data.’_ Chesher and Schluter (2002: 377)

# **I.  Introduction**

Household consumption surveys are at the heart of attempts to measure living standards, especially in developing countries where labor and income surveys cover neither the full population nor all economic activity. All surveys likely suffer from measurement error but the complexity of consumption surveys makes them especially prone to error. Yet surprisingly, validation studies to describe the nature and consequences of measurement error are mainly for labor market settings, using either administrative data (Bound and Krueger, 1991) or firm records (Pischke, 1995) as their measure of truth. Such validation studies also are limited to rich countries, including for the one type of consumption – out of pocket health expenditure – for which validation has been attempted (Cohen and Carlson, 1994).

This note reports on a consumption survey experiment in Tanzania that is potentially informative about the nature of measurement error in developing country consumption data. We randomly assigned eight alternative survey designs to 4,000 households (three households per design in each of 168 sampling units), covering urban and rural areas.<sup>1</sup> Validation from store data is neither feasible nor useful in this setting, but one resource intensive design – individually kept diaries with daily supervision for 14 days – represents a “gold standard” for consumption measurement in developing countries. The cost per household for this design was almost ten times the cost to survey consumption by recall interview (Beegle, _et al._ 2012), indicative of the resources needed for careful tracking of all commodity in-flows (harvests, purchases, gifts, stock reductions), out-flows (sales, gifts, stock increases, food fed to animals), daily attendance at meals, and acquisitions and disposals by children and other dependents the diary-keeper reported on. Survey diagnostics, such as the time profile for diary entries and mean daily consumption, show no diary fatigue for this “gold standard” module and just three households that started a diary did not complete their final interview.<sup>2</sup> We deem the data from the individually-kept diary a suitable benchmark for assessing measurement error in data from the other survey designs. Beyond random assignment, several steps were taken to ensure that differences in measured consumption are solely due to survey design. The experiment was conducted by small, intensively supervised teams of experienced

- 1 Full details on the design and implementation of the experiment are reported in Beegle _et al._ (2012).

> 2 Across the full experiment, of the original 4032 households assigned a module, only 13 were replaced due to refusal. The final achieved sample was 4025.

2