rafmacalaba/datause-encoder-data huggingface dataset:
exponential form: where _Z_<sup>_k_</sup> is a vector of trade cost and activity/size variables, and _β_<sup>_k_</sup> is the vector of corresponding trade elasticities with respect to the various components of _Z_<sup>_k_</sup> . Specification (4) leads directly to an estimable equation of the form Here, _χ_<sup>_k_</sup> denotes a constant term; _x_<sup>_k_</sup> _i_<sup>isanexporterfixedeffectforcountry</sup><sup>_i_,</sup><sup>_mk_</sup> _j_<sup>isan</sup> importer fixed effect for destination _j_ , and _τij_<sup>_k≤_1isatradecostfactorrepresentingthe</sup> effect of gravity forces that reduce bilateral trade between _i_ and _j_ in sector _k_ , _Xij_<sup>_k_.</sup><sup>_εk_</sup> _ij_<sup>isan</sup> error term explained below. An important issue is whether sufficient data are available to distinguish between internal and external trade, i.e. within and between countries. When such data are available, which is the case in this study, it is possible to include and identify _τii_<sup>_k_, the intra-country trade cost for each sector</sup><sup>_k_.Its relationship to</sup><sup>_τ k_</sup> _ij_<sup>_,i_=</sup><sup>_j_is a component</sup> reflecting the relative cost of crossing a border. An important contribution of our work is that we construct a multi-dimensional (country-sector-year) database of such relative border cost estimates and we study their determinants.

The final step in obtaining an econometric gravity specification is to model the unobservable bilateral trade frictions _τij_<sup>_k_fromequation(5).Followingthevastgravityliterature</sup> for goods trade, the volume effect of bilateral trade costs _τij_<sup>_k≡tk_</sup> _ij_ 1 _−σ_ for services are approximated by a set of observables: Here, ln _DISTij,m_ is the logarithm of bilateral distance between trading partners _i_ and _j_ .

6
materialize because of enumerator behavior affecting non-response (Couper and Grove, 1992; West and Olson, 2010; Randall et al., 2013) or due to enumerator characteristics affecting the answers of the respondent (Brunton-Smith et al., 2017).

Previous research has shown that survey responses are associated with enumerator characteristics such as gender (Huddy et al., 1997; Flores Macias and Lawson, 2008), religion (Blaydes and Gillum, 2013; Benstead, 2014), ethnicity (Adida et al., 2016), experience and personality traits (Jäckle et al., 2013), differences in social status with the respondent (Kane and Macaulay, 1993), or even physical attractiveness (Jæger, 2016). There is also evidence that the enumerator effect varies with the type of question, being more salient for questions concerning gender-related issues, religion, ethnicity, and politics (Schaeffer, 1980; Davis and Silver, 2003; Himelein, 2016; Laajaj and Macour, 2017).

One of the main challenges for this literature is to avoid confounding enumerator interviewer and respondent characteristics. The only way to exclude this possibility is to randomly assign interviewers to respondents. However, practical factors often make design unfeasible. Thus, much of the literature on the enumerator effect consists of either telephone surveys with small numbers of random interviewers or face-to-face surveys with non-random assignment of interviewers (West and Blom, 2017). Only a few studies have rigorously documented the causal impact of the enumerator on survey responses and all these studies have been conducted in the United States. A first group of studies looked at how the gender of the interviewer affected the response to gender and sex-related questions (Johnson and Delamater, 1976; Catania et al. 1996; Huddy et al., 1997). Another important set of studies has documented the presence of the enumerator effect in surveys on political opinions, showing that it depends on enumerator characteristics such as race, behavior, and 5
# **D. Endline results**

## **D.1 Outcome Family tables (raw, not z-scored)**

**Table D.1:** General Utilization

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**General utilization**(SUR)||0.112<br>(0.031)<sup>_∗∗∗_</sup><br>[0.003]<sup>_∗∗∗_</sup>|0.126<br>(0.034)<sup>_∗∗∗_</sup><br>[0.003]<sup>_∗∗∗_</sup>|0.099<br>(0.036)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|4496|
|(1) Number of health episodes in which sought|0.962|0.044|0.049|0.039|4496|
|western care||||||
||(0.393)|(0.012)<sup>_∗∗∗_</sup>|(0.013)<sup>_∗∗∗_</sup>|(0.014)<sup>_∗∗∗_</sup>||
|||[0.001]<sup>_∗∗∗_</sup>|[0.001]<sup>_∗∗∗_</sup>|[0.008]<sup>_∗∗∗_</sup>||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Questions about ante and post natal care, vaccinations and illness/injury episodes are asked for the past one month. Questions about child birth are asked about the past year. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.2:** Maternal Utilization

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Maternal utilization index**(SUR)||0.046|0.130|_−_0.031|888|
|||(0.045)<br>[0.322]|(0.054)<sup>_∗∗_</sup><br>[0.079]<sup>_∗_</sup>|(0.054)<br>[0.463]||
|(1) ANC/PNC visits index|0.000|_−_0.038|0.008|_−_0.079|887|
||(1.000)|(0.064)|(0.082)|(0.068)||
|||[0.386]|[0.861]|[0.968]||
|(2) Birth in western medicine facility|0.834|0.048|0.094|0.006|877|
||(0.373)|(0.025)<sup>_∗_</sup>|(0.028)<sup>_∗∗∗_</sup>|(0.030)||
|||[0.114]|[0.002]<sup>_∗∗∗_</sup>|[0.968]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A16
shows difference-in-difference results: PCRs in lower primary decreased significantly more rapidly in MESIP districts, by 14.8 pupils per classroom; overall PTRs decreased by 12.3 pupils per teacher; and PTRs in lower primary experienced a large reduction of 21.4 pupils per teacher.

# **Di** ff **erence-in-di** ff **erence analysis: Outcomes**

## **Repetition**

**Table 5** shows difference-in-difference analysis of the impact of MESIP on repetition rates. We find that repetition rates increased slightly between 2016 and 2020 in both MESIP and non-MESIP districts. However, we find that the increase in MESIP districts was significantly smaller in MESIP districts in lower primary by 0.7 percentage points.

In order to confirm the relationship between class size reduction and repetition, we explore the dynamics of change in PTR and lower primary repetition rates, exploiting the fact that both PTR and repetition rate are measured using EMIS data which is available on an annual basis. **Table 6** shows the year-by-year change in PTR in both MESIP and comparison districts. A particularly rapid reduction in PTRs in MESIP districts occurs between the 2016/17 and 2017/18 school years, reflecting the commencement of MESIP, the allocation of auxiliary teachers to schools, and the targeting of regular deployment of teachers to the eight disadvantaged districts.

To more clearly assess the relationship between PTR reduction and repetition rates, **Table 7** presents year-on-year, difference-in-difference analyses of repetition rates for a particular subset of treatment and control schools for three time periods: 2017-18 to 2018/19, 2018/19 to 2019/20, and 2019/20 to 2020/21. For treatment schools, for each year-on-year comparison, we limit the analysis to schools in the MESIP districts which had an overall PTR of above 90 in the first year of the comparison, received additional teachers, either auxiliary teachers or new regular government teachers, and as a result had a PTR below 90 in the second year of the comparison. For control schools, we limit to schools in the control districts which obtained one or more additional teachers but did not reduce PTR from above to below 90. This is a lagged analysis, with repetition measured one year following the observed change in PTR. For example, where we report changes in repetition in the period 2017/18-2018/19, the treatment schools are those whose PTR fell below 90 between 2016/17 and 2017/18. This is because repetition rates are established at the end of the academic year when promotion decisions are made, so the first set of promotion decisions reflecting the impact of this change in PTR would be made at the end of the 2017/18 year and reflected in the data for 2018/19.

We find that MESIP reduced repetition rates in lower primary by a large amount - 5.5 percent - between 2017/18 and 2018/19, and that this reduction is statistically significant in comparison to control (difference-in-difference). This suggests that the large reduction in PTR observed in 2017/18 had an impact on promotion decisions made at the end of that year, reflecting improvements in student performance.

18
CSDIF-50 (or CSDIF). However, in spite of the apparent poor performance of the RDD approach in the third round of the survey, in the fifth round of the survey the RDD estimates of program impact on the school attendance of boys and girls appear to be quite similar to those obtained by CSDIF. Specifically, in the fifth round the RDD estimates for boys are lower than the CSDIF50 estimates, while the estimates for girls are practically identical to the local CSDIF estimates.

All in all, if one were to put aside, for the moment, the discrepancies observed in round 3, the performance of the RDD appears to be remarkably good. The RDD estimates of program impact agree with the “local” experimental estimates (CSDIF-50) in 10 out of the 12 possible instances. However, the apparent failure of the RDD method to detect any impact in round 3 for either boys or girls raises some serious concerns. For this reason, it is necessary to investigate in more depth some of the possible explanations for these discrepancies between the RDD and the experimental estimates.

# _Choice of Bandwidth and Inter-Regional Differences in the Threshold_

One of the possible explanations may lie with the choice of bandwidth. The analysis so far presented estimates using a bandwidth of 50. Perhaps a different bandwidth may provide a more reliable estimate of the impact of the program. For this purpose we have also re-estimated the RDD estimates of program impact using a bandwidth of 75 and a bandwidth of 100 (see tables A1a, b and A2a, b in the appendix). However, increasing the bandwidth of the kernel functions appears to provide only a partial explanation of the weak performance of RDD. For example, with a bandwidth of 75 and 100 the third round estimates of program impact on the schooling attendance of boys continue to be insignificant. In contrast, the estimates for girls’ school attendance now become significantly different from zero, albeit somewhat lower than the local experimental estimates. Also, the same general patterns continued to hold when we 21
|**Table 1: GDP**|**Growth Rat**|**es for In**|**dividual Countries in Sub-Sahara**|**n Africa**<sup>**1**</sup>||
|---|---|---|---|---|---|
|**Countries with GDP Gro**<br>(70% of SSA P<br>(78% ofSSA|**wth > 4%, 2**<br>opulation)<br> GDP)|**000-06**|**Countries with GDP Growth**<br>(30% of SSA Popul<br>(22% ofSSAGD|**< 4%, 200**<br>ation)<br>P)|**0-06**|
||**2000-**<br>**2006**|**1995-**<br>**2006**||**2000-**<br>**2006**|**1995-**<br>**2006**|
|Equatorial Guinea|23.1|36.2|Mauritius|3.9|4.4|
|Sierra Leone|11.6|1.7|Cameroon|3.7|4.1|
|Chad|10.9|7.4|Kenya|3.6|3.3|
|Angola|10.6|9.8<br><sup>*</sup>|Niger|3.5|3.6|
|Liberia|8.9|12.1|Lesotho|3.4|3.2|
|Mozambique|7.6|7.9|Madagascar|3.2|3.2|
|Sudan|7.3|5.8|Guinea|2.8|3.6|
|Tanzania|6.3|5.2|Congo, Democratic Republic of|2.6|0.6|
|Ethiopia|6.2|5.7|<br>Malawi|2.6|4.4|
|Burkina Faso|6.1|6.6|Comoros|2.4|2.2|
|Cape Verde|5.7|6.9|Swaziland|2.4|2.8|
|Nigeria|5.6|4.4|Burundi|2.2|0.3|
|<br>Uganda|5.6|6.4|Togo|1.7|2.3|
|Rwanda|5.5|9.7|Eritrea|1.3|2.6|
|Botswana|5.3|6.3|Guinea-Bissau|1.2|0.3|
|Ghana|5.0|4.8|Gabon|1.1|1.4|
|São Tomé and Príncipe|5.0|3.7|Central African Republic|0.2|1.1|
|Mauritania|4.9|4.6|Seychelles|0.1|2.3|
|Gambia, The|4.9|4.6|Côte d'Ivoire|-0.3|2.0|
|Congo, Republic of|4.9|3.6|Zimbabwe|-5.5|-2.6|
|Mali|4.9|5.1||||
|Zambia|4.8|3.4|Average|1.8|2.3|
|Namibia|4.5|4.1||||
|Benin|4.2|4.6||||
|South Africa|4.1|3.5||||
|Senegal|4.1|4.3||||
|Average<br> Source:World Bank World Devel<br>1The cut-off point of 4 percent is bas<br>Bank, Djibouti is classified as part o<br>*Data for Liberia was available only|6.8<br>opment Indicator<br>ed primarily on t<br>f Middle East an<br>for 1999-2006.|6.9<br>s database.<br>he performa<br>d not part of|nce during 2000-06. Somalia is not included for l<br>Sub-Saharan Africa.|ack of data. At t|he World|

# **_B. Performance by different types of countries_**

Nonetheless, the range of growth among individual countries can vary widely, anywhere from −5.5 percent to a high of 23.1 percent. Alternative taxonomies of countries are often necessary to characterize the growth story better. Table 2 presents the growth performance for frequently used country groups for the 1980s, the 1990s, and the more recent periods. The numbers show that, unlike the past, the recent acceleration of growth is broadly registered across different types of countries.

4
Cochabamba than in La Paz.

Santa Cruz also accounts for the largest share of production of soybeans and sunflower (98% and 100% respectively), the main inputs in production of cooking oil. Figure (6) shows that in Santa Cruz, soybean output per capita reaches 502 kilograms. Despite this strong concentration of output in one department, cooking oil price differences across departments are not significant from a statistical and economical point of view (less than one per cent). With respect to La Paz, in Santa Cruz consumers pay about 0.6% more for cooking oil, and in Cochabamba only 0.1% more. As a processed product, the portion of the final price that is explained by transport costs may be relatively low. In addition to this, there is upward pressure in the price of cooking oil in Santa Cruz as an important portion of exports is canalized through that department.

Similar to the cooking oil case, price differences in wheat flour across cities are not significant. Although half of wheat output is concentrated in Santa Cruz, where output per capita is about 32.5 kilograms per annum (13% is produced in Cochabamba - output per capita is about 10.5 kilograms), it is still likely that all cities are net consumers, as Bolivia relies substantially on imported wheat and wheat flour (mainly from Argentina and from the US). This, together with nature of the product, with a low portion of the final price being explained by transport costs, may explain these low price differences.

Figure (8) shows that output of sugar cane is concentrated in Santa Cruz - about 1781 kg/per capita. In turn, the price of refined sugar there is 7.3% lower than in La Paz (see RHS map).<sup>18</sup> Figure (9) shows output and price differences for potatoes. The lowest price is found in Cochabamba, where output per capita is the highest (among the three regions considered in this analysis). The converse happens in Santa Cruz where output per capita is the lowest, and the price is the highest.

> 18 The 13.4%-higher price in Cochabamba relative to La Paz is explained by the packaging being different. The price information collected by the INE for Cochabamba is that of sugar bags of one-pound weight, whereas for La Paz and Santa Cruz, they collect information on sugar bags of 5 kilos.

15
2 disaggregated by wage/self-employed/unpaid family workers, and by sector of economic activity. These data are the focus of this paper, though tney clearly are not a representative sample of the world's population. The sectoral composition of employment, as well as the mix of firm and family based jobs, changes systematically with development.' This paper considers whether recent trends in women's labor force participation and the type of jobs held are explained by intersectoral shifts in the distribution of employment or by trends within these sectors.

The association between the distribution of employment by industry and job type and the rate of participation in the market labor forc3 by women probably embodies relationships operating in both directions. Changes in the final demands for goods and services, due to increases in per capita income for example, may alter the mix of employment by industry with consequences on the fraction of jobs that women are likely to hold. Conversely, an increase in the rate of participation by women will depress wages in those industries that are peculiarly intensive users of female labor, lowering unit costs and encouraging expansion of output and of female employment in these sectors. No attempt is made here to identify statistically and separate the consequences of a shift in the demand or the supply schedule by specifying the variables that operate only on the supply of women wanting work, or only on the demand for goods produced more efficiently by women. The decompositions of labor force outcomes by industrial sector can be strictly interpreted, only if it is assumed that changes in demand occurring with economic growth are responsible for part of the variation in women's labor force participation patterns but that the causal effect in the opposite direction is relatively minor.
-8 -

of remaining uninfected after one intercourse with a partner, taking into account the prevalence of STDs.

Under these assumptions, the dynamics of the HIV/AIDS prevalence rate is given by:

# _Calibrating models parameters_

The model parameters can be grouped into three categories: economic parameters (those influencing growth and the dynamics of labor markets); parameters determining the economic impact of the epidemic for a given prevalence rate; and parameters affecting the diffusion of the epidemic (see Appendix C for a summary of parameter values and a description of calibration methods). Key economic parameters were estimated in order to replicate targets in terms of economic growth and savings rates, on the basis of the World Bank Country Assistance Strategies for each of the countries included in the analysis (World Bank 1997, 1999b, 1999c, 2000, 2001a, 2001b, 2001c, 2001d, 2002a). For the parameters determining the diffusion of the epidemic and potential economic impacts (for instance the distribution of deaths among skilled and unskilled workers), given the lack of data, we defined uniform probability distributions. The supports of these distributions were calibrated on the basis of results from the literature, in particular the works of Haacker (2001), MacFarlan and Sgherri (2001), Bonnel (2000), and Ainsworth and Over (1994), for economic impacts; Floyd and Gilks (2001), United Nations (2001), and Cavallini et al. (2000), for estimates about health expenditures; Jenkins et al. (2002) and Rehle et al. (1998), for sexual and drug use behavior; and UNAIDS and Jenkins and Robalino (2002), for initial prevalence rates.

## 2. Simulating the costs of inaction

Given high levels of uncertainty in terms of model parameters traditional simulation methods, where outcome variables are analyzed for a handful of scenarios, are not appropriate. Here we adopt an exploratory modeling approach (see Bankes, 1995). Hence, for each country, we sample a large number of points in the parameters space from the uniform probability distributions described in the previous section (our priors). At each of these points we compute the value of five variables of interest: the present value of total GDP and its average growth rate during the period 2000-2025; the size of the population in year 2025; and the HIV/AIDS prevalence rate and HIV/AlDS related health expenditures in year 2015.

As an illustration, Figure 1 graphs losses in the present value of total GDP and the HIV/AIDS prevalence rate for 100 sampled points in the case of Tunisia. We observe that there is a wide range of plausible futures (see Appendix D for a sample of diffusion profiles). The HIV/AIDS in year 2015 could fluctuate anywhere between 0.18% and 15%, while losses in the present value of GDP for the period 2000-2025 could be equivalent to 2% to 300% of today's GDP. Clearly, not all the futures have the same likelihood. For instance, in 35% of the cases explored the HIV/AIDS prevalence rate in year 2015 would be below 2%. That being said, only in 16% of the cases the HIV/AIDS prevalence would be below 1%. In fifty percent of the cases, the prevalence rate would be above 3%.
24

# **7.  Conclusions**

In this paper we explore the relative efficiency of stock markets across countries using newly available data on transaction costs and the quality of the informational environment of stock markets.

We then develop a framework to understand the linkages between efficiency, liquidity and the factors that matter for the efficiency of markets. To give empirical content to the framework, we study the determinants of transactions costs and the quality of the informational environment.

We find that certain indicators of the physical and market infrastructure, along with certain institutional characteristics of stock markets is associated with lower transactions costs. We also find that certain institutional and legal underpinnings are more conducive to a better informational environment in stock markets.

This in turn serves as the basis for the policy framework and recommendations for the East Asian region. In particular, the region needs to continue to strengthen the implementation and enforcement of corporate governance, to further enhance the market and institutional infrastructure, and focus on policy measures to foster a larger and more diversified investor base, to continue to see gains in the efficiency of their stock markets.
16

# Table 2 Normal Response to the External Environment Various Performance and Policy Measures

# Dependent variable: **YGR**

- F value of regression: 13.11

Probability given the null (all coefficients insignificant): 0.0001

|Explanatory<br>Variable|<br>Coefficient|Standard<br>Error|T value|
|---|---|---|---|
|**..**...<br>.........|......................|.................................|.......................<br>............**...**.........|
|YASHR|0,019|0.045|0.426|
|TOTB|1.069|0.200|5.348|
|RR|-0.224|0.037|-6.061|
|LTDPC|-2.590|1.387|-1.867|
|STDPC|-7.952|2.231|-3.564|

# Dependent variable: DINF

- F value of regression: 6.50

Probability given the null (all coefficients insignificant): 0.0001

|Explanatory<br>Variable|<br>Coefficient|Standard<br>Error|T value|
|---|---|---|---|
|USINF|-0.037|0.288|-0.128|
|YASHR|0.602|0.311|1.936|
|TOTB|1.166|0.834|1.398|
|RR|-0.569|0.271|-2.099|
|LTDPC|12.346|5.598|2.205|
|STDPC|2.507|7.512|0.334|

# Dependent variable: CAR

- F value of regression: 15.22

- Probability given the null (all coefficients insignificant): 0.0001

|Explanatory||Standard||
|---|---|---|---|
|Variable|Coefficient|Error|T value|
|.......................................................|...........|||
|YASHR|0.077|0.061|1.270|
|TOTB|1.797|0.228|7.896|
|RR|0.067|0.076|0.875|
|LTDPC|8.333|1.716|4.855|
|STDPC|1.820|2.728|0.667|