rafmacalaba/datause-encoder-data huggingface dataset:
(teachers, principals and supervisors background, performance, attitudes, assiduity, pay, etc., as well characteristics of the school infrastructure and availability of textbooks, supplies, etc.) were captured more precisely, the impact of the program could be larger. The data allows us to do better than the simple model of Table 6; and to make use of the available information without introducing damaging multicollinearity in the results we constructed two sets of principal (orthogonal) components measuring respectively the characteristics of the demand and supply of schooling.

Table 7 shows the results of the model built on this more complex structure captured through the two principal components. _The results are very similar to those of Table 6. In fact, the impact of the program is greater and more significant. The factor capturing the conditions of supply is also significant and large, especially in the case of schools serving native communities._

**Table** 7. Student's change in performance, 1994

||**Nativ**|**e**|Rural||
|---|---|---|---|---|
||Beta coefficient|<br>t-score|Beta<br>coefficient|t-score|
|Control|0.273609|<br>6.210a|<br>0.12721|4 3.000a|
|Factor - Characteristics<br>ofcommunity<br>& family|-0.009075|<br>-0.205|<br>-0.16103|3 -3.815a|
|Factor - Characteristics<br>ofschool & system|0.201875|<br>4.664a|<br>0.07444|9 1,754c|
|R2-adjusted||0.12376||0.035|
|F||23.599a||7.713a|
|N|480||548||
|Memorandum<br>item:|||||
|Maximum total contribution<br>of PARE program|<br>0.475484||0.20166|3|
|a - Significant at the 1% level or more|||||
|b -Significant at the 5% level or more<br>c - Significant<br>at the 10%/o<br>level or more|||||
|d - Significant<br>at the 20% level or more|||||

Dependent variable: Difference in normalized test scores between 6th and 4th grade. Source: Own calculations based on PARE's database.

An objection may be raised, nonetheless, about the measure of performance. What if small differences in test score are very imperfect measures of relative capabilities and/or achievements? To try to get around this issue, we perform a final experiment on the test scores. We stratify the samples in two sub-samples each: those of students with performance above and below their respective medians. These results are shown in Table 8. Once again the estimates are consistent. The program has a positive and significant impact, and especially so for the native population.

10
# **5 Conclusion**

Measuring willingness to pay for goods and services is an important part of many research studies. Among other things, information on WTP can provide insights about welfare (e.g., Allcott and Kessler, 2019), allow cost-benefit comparisons (e.g., Cole and Fernando, 2020), and help explain treatment effects (e.g., Guiteras and Jack, 2018). Multiple price list elicitation is attractive for many reasons. It offers a compromise between the precise information revealed by BDM and the simplicity of TIOLI, and data from repeated choices can in principle be used to learn about error from inconsistent and dominated choices. As we show in this paper, in combination with standard MPL design features this error can introduce bias in WTP estimates.

We propose a straightforward, two-part approach to revealing both idiosyncratic and systematic choice error and incorporating it into WTP estimation. First, random variation in the order of choices within MPL and within binary choice reveals bias and minimizes its net impact on the estimated WTP. Second, latent utility estimation using random effects probit accommodates multiple switching, never switching and interval data, and can be used to estimate bias terms and both individual choice error and subject-level variance in WTP. To support other researchers interested in adopting these innovations, we offer a SurveyCTO package and a Stata ado file, along with the necessary instructions.

We focus on a single case study to demonstrate both the challenges with alternative approaches and the strengths of our approach. This case is intended to be illustrative and we are well aware of the limited general conclusions we can draw. For example, WTP estimation in populations that exhibit less error and bias will benefit less from both the randomized choice implementation and the estimation approach. Note, however, that only by randomizing the order of choices in the MPL and options within each binary choice researchers can test for any framing or anchoring effects in new samples.

A second limitation of our approach is that the correction of order bias can be applied to the study sample, or to large enough subgroups within the study sample, but likely not – to individual-level data. The implication is that MPL data carefully implemented and – analyzed is appropriate for measuring sample average WTP, but not individual-specific WTP. It should also be noted that inattention, incomprehension, and framing effects can lead to choice error in other elicitation methods too. Given the prevalence of these errors in WTP estimation, the method we propose in this paper offers a way forward: an estimation approach that correctly accounts for individual error and a measurement approach that reveals bias.

34
The preceding discussion suggests that estimates of GDP growth based on nightlights could, at least for countries in SSA, be improved by controlling for the importance of agriculture in the economy. However, specifying a control variable for which national accounts or survey data is needed (such as the share of agriculture in GDP or the share of the population engaged in agriculture) would run counter to the purpose of this study, which is to see whether proxies such as nightlights can be used to estimate growth in areas (sub-national or supra-national) for which there are no national accounts or survey data. One option is to use rainfall or vegetation data, which share many of the desirable features of nightlights data, to proxy the performance of the agricultural sector. This is something to explore in future work.

# **4. Application to Kenya and Rwanda: GDP Growth**

In this section we use the results from the long-differenced specification to estimate subnational GDP growth in Kenya and Rwanda based on the evolution of night lights.

## **4.1 Kenya**

Kenya’s 47 county governments came into existence at the March 2013 election. Transfer of staff and functions from around 280 de-concentrated district administrations and 175 local authorities was largely completed by January 2014 when counties took over management of payroll for all their staff. Funding for devolved functions is provided for through an unconditional equitable share, allocated among counties on the basis of a formula proposed by the Commission on Revenue Allocation and approved by the National Assembly in November 2012. The formula is due for revision after three years, and thereafter every five years. The formula incorporates four proxy measures of expenditure needs: population, equal shares (reflecting diseconomies of scale in smaller counties), poverty and land area. At present, the formula ignores the very different fiscal capacity between counties, particularly those in rural and urban areas. An important question for the future is whether to also include a measure of revenue-raising capacity, so as to achieve more effective equalization. To generate the right incentives for maximum fiscal effort by the county governments, such a component in the formula should be based on an objective measure of each county's potential to raise revenue, not how much is actually raised. In the absence of detailed information on the size of the tax base in each county, one approach might be to use county GDP as a proxy measure.

Focusing first on Kenya as a whole, how do night lights perform in predicting aggregate GDP growth? The short answer is: not too bad. For Kenya, GDP as predicted by night lights closely tracks GDP as measured by national accounts, in particular since the start of the 2000s (Figure **5** ). Based on the growth of night lights and using the long differenced specification (last column of Table 3), we estimate an annual GDP growth of 4.0 percent between 2000 and 2012, slightly lower than the actual national-accounts based growth rate of 4.2 percent.

Figure 5: Estimated and measured GDP in Kenya 12
Zhejiang is a coastal province that ranks fifth in terms of GDP per capita (NBS 2012). After selecting the two provinces, we sampled the most populous prefectures within each province (three in Shaanxi and four in Zhejiang) and all the counties in those prefectures.<sup>2</sup> In sum, we sampled two provinces, seven prefectures in those provinces, and the seventy-five counties in those prefectures.<sup>3</sup> We next sampled vocational high schools from the seven prefectures. According to administrative records, there were 204 and 285 vocational schools in the sample prefectures in Shaanxi and Zhejiang, respectively. Using administrative records, we included all vocational high schools that offered a computer major in our sample. We focused on the computer major for two reasons. First, computing is studied in academic high schools (albeit to a lesser degree), which allows us to compare learning gains in specific skills (i.e., computers) across vocational and academic high schools. Second, the computer major is the major with the largest number of enrollments in the two provinces. Over half of all vocational high schools had computing majors, and we only had to exclude 101 schools in Shaanxi and 133 schools in Zhejiang due to the fact that they did not offer computer majors.

After selecting vocational high schools with computing majors, we called these schools to ask how many new (grade ten) students enrolled in autumn 2011. Schools that reported fewer than fifty grade ten students enrolled in the computer major were excluded from our sampling

> 2 In contemporary China each province is split into multiple prefectures, which are in turn split into counties. 3 Note that the seven prefectures in our sample contain seventy-five counties. Since not every one of these counties had a vocational school or a vocational school with the computing major, the schools in our sample were distributed in thirty-five of the seventy-five total counties.
# **Annex 3. Robustness: Enlarging the Sample**

## **Table A3.1. Use of Labor, Extended Model**

Dependent variable: employment ratio (ln)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|
|**Pillar 1**|-0.001|-0.001|0.003|0.004|-0.001|0.004|0.005|0.003|
||(-0.04)|(-0.06)|(0.87)|(0.92)|(-0.07)|(0.98)|(1.02)|(0.60)|
|**Pillar 2 and 3**<br>Infra index<sup>a</sup>||||||||-0.001<br>|
|||||||||(-0.24)|
|Infra index (WEF)|-0.001|-0.001|-0.002|-0.003|-0.002|-0.002|-0.003||
||(-0.11)|(-0.11)|(-1.09)|(-1.17)|(-0.41)|(-1.03)|(-1.13)||
|**Pillar 4**|||||||||
|Output gap|0.006***|0.006***|0.002***|0.001***|0.006***|0.002***|0.001***|0.001***|
||(13.90)|(13.91)|(5.01)|(4.61)|(13.63)|(4.98)|(4.60)|(4.54)|
|Change in terms of trade|0.007|0.007|0.004|0.004|0.009|0.005|0.004|0.005|
||(0.43)|(0.43)|(0.33)|(0.29)|(0.58)|(0.39)|(0.35)|(0.36)|
|**Pillar 5 and 6**|0.089***|0.088***|0.001|-0.001|0.095***|-0.001|-0.001|-0.001|
||(3.08)|(3.05)|(0.01)|(-0.03)|(3.41)|(-0.15)|(-0.17)|(-0.17)|
|**Pillar 7**|||||||||
|Tax rate|-0.098***|-0.098***|-0.018**|-0.020**|-0.093***|-0.017**|-0.018**|-0.018**|
||(-3.49)|(-3.49)|(-1.92)|(-1.95)|(-3.36)|(-1.76)|(-1.79)|(-1.74)|
|Regulatory quality|0.068***|0.068***|0.003|0.002|0.059***|0.001|0.001|0.002|
||(5.43)|(5.42)|(0.69)|(0.65)|(4.81)|(0.30)|(0.29)|(0.58)|
|Regulatory trade barriers<sup>b</sup>|-0.006**|-0.006**|-0.001|-0.001|-0.005**|-0.001|-0.001|-0.001|
||(-1.86)|(-1.86)|(-0.44)|(-0.32)|(-1.67)|(-0.39)|(-0.27)|(-0.39)|
|**Pillar 8**|||||||||
|Women|-0.041***|-0.041***|0.001|0.001|-0.043***|0.001|0.001|0.007|
||(-2.87)|(-2.84)|(0.46)|(0.71)|(-3.07)|(0.50)|(0.73)|(0.63)|
|CBR|0.031|0.031|0.001|0.002|0.033|-0.000|0.001|-0.001|
||(0.43)|(0.42)|(0.16)|(0.24)|(0.46)|(-0.05)|(0.06)|(-0.10)|
|**Pillar 9**|||||||||
|Private credit|0.004|0.004|-0.009***|-0.015*||-0.009***|-0.016*|-0.017*|
||(0.45)|(0.45)|(-3.09)|(-1.46)||(-3.13)|(-1.55)|(-1.61)|
|Private credit (t-1)||||0.006|||0.007|0.007|
|||||(0.60)|||(0.69)|(0.63)|
|Credit market regulations<sup>c</sup>|||||0.004***|0.001|0.001|0.001|
||||||(2.07)|(1.14)|(1.02)|(0.97)|
|**Pillar 12**|-0.004|-0.004|0.003*|0.003|-0.003|0.003*|0.003|0.002|
||(-0.81)|(-0.81)|(1.36)|(1.26)|(-0.59)|(1.39)|(1.27)|(0.98)|
|Time effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Emerging market dummy|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Employment ratio (Ln, t-1)|||1.004***|1.004***||1.004***|1.003***|1.003***|
||||(427.37)|(410.68)||(420.74)|(403.75)|(393.50)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|496|496|496|494|507|496|494|494|
|Wald Test|417.9|418.1|242038.2|223432.0|439.1|235559.3|216851.0|214819.6|
|WaldTest p-value|0|0|0|0|0|0|0|0|

_Note t_ statistics in parentheses * p<0.2, ** p<0.1, *** p<0.05

> a Weighted average of energy use pc (ln) and number of individuals using internet (ln), with equal weights.

> b The index measures the extent to which non-tariff regulatory measures exist in the country. The higher, the lower the amount of NTMs. Fraser 2016.

> c Fraser 2016.

33
To address the question of the “hollowing out of the middle” in Ghana, we use _relative distribution_ methods. Developed by Handcock and Morris (1998, 1999), these techniques based on the relative distribution powerfully assist in the description of distributional change and enable counterfactual comparison of location-adjusted distributions.

Basically, relative distribution methods can be applied whenever the distribution of some quantity across two populations is to be compared, either cross-sectionally or over time.<sup>6</sup> For our purposes, the relative distribution is defined as the ratio of the density in the comparison year to the density in the reference year evaluated at each decile of the consumption distribution, and can be interpreted as the fraction of households in the comparison year’s population that fall into each tenth of the reference year’s distribution.<sup>7</sup> This allows us to identify and locate changes that have occurred along the entire Ghanaian household consumption distribution.

To formalize, let: be the proportion of households in year _t_ ’s comparison sample falling into each decile and: be the proportion of households in year 0’s reference sample falling into the same deciles, where _m_ and _n_ reflect the comparison and reference sample sizes and: denotes the indicator function. The cut points _c_<sup>0</sup> [ _i_ -1] and _c_<sup>0</sup> [ _i_ ] for each interval are estimated as deciles of the reference sample, hence the proportion of the sample from the reference distribution falling into each decile is exactly 1/10 _._ The relative distribution is given by the proportion of year _t_ ’s households

> 6 Here we limit ourselves to illustrating the basic concepts behind the use of relative distribution methods. Interested readers are referred to Handcock and Morris (1998, 1999) – but see also Hao and Naiman (2010, ch. 5) – for a more detailed explication and a discussion of the relationship to alternative econometric methods for measuring distributional differences. A method very similar in spirit to the relative distribution one has recently been developed by Silber et al. (2014).

> 7 To keep the notation simple and the graphical displays informative, we will focus throughout the paper on _group-level_ data from underlying continuous distributions. We will also assume that discretization is based on decile ranges with respect to the reference year’s distribution. By extending the fundamental concepts of the relative distribution approach to the grouped data context, Handcock and Morris (1999, ch. 11) allow the analysis of discretized distributions to retain the tractability and interpretability of its continuous counterpart based on ungrouped data. For an application of relative distribution methods to ungrouped samples, see e.g. the Brazilian and Nigerian case studies by, respectively, Clementi and Schettino (2015) and Clementi et al. (2014, 2015).

6
disadvantageous tax rates on secondary incomes. Finally, the completed FIA is then used to analyze whether, as executed, the policies support an equitable distribution between different household members via a mapping of household roles and functions across individual households.<sup>27</sup> When additional microdata beyond the standard household budget or income or expenditure survey is available, it can be incorporated to more precisely identify individuals who fulfill the roles or most often carry out the activities supported by public expenditures or made costlier via the application of taxes. Time-use data, for example, which records how individuals spend their waking hours, are used to capture who most often carries out care activities or other aspects of social reproduction. Then, each household type’s share of taxes and transfers can be further allocated to roles or activities and the individuals that most often perform those roles or carry out those activities.

# 5 Applying the Method to Jordan and Armenia

This section discusses the application of this methodology in Armenia and Jordan. Both countries have recently completed CEQ assessments and are using the methodology described here to dig deeper into potential impacts of fiscal policies on gender inequality. The fiscal and social structures of the two countries provide useful contrasts and illuminate how country specificity may be important in the identification and operationalization of household categories relevant for gender-specific incidence analysis.<sup>28</sup>

## 5.1 Armenia

Armenian women have low labor force participation rates. Less than 60 percent of working-age women participate in the labor force, compared with just over 70 percent of men (World Bank 2023a). Women are also more likely to work part-time positions than men. Women are underrepresented in science, technology, engineering, and math fields, accounting for only 38 percent of graduates in these fields (World Bank 2023b). In 2019, one-quarter of women workers were employed in agriculture, while women were virtually excluded from industrial activities, such as construction and mining. Men consistently earn higher wages than women, regardless of age and educational attainment; even tertiary-educated women employees earn only 72 percent of the average wage earnings of tertiary-educated men. That evidence suggests that women are segregated both across and within sectors of employment, facing exclusion from higher-paying sectors and higher-paying positions.

> 27. This likely requires a consultation with in-country experts or primary or secondary data sources to determine _who_ most often fills the roles that are either supported by or made costlier by fiscal policy.

> 28. Armenia is a transition economy with an aging population wherein much of the redistributive impact of fiscal policy is due to pensions. Jordan, on the other hand, is a country with strong social norms against female participation in the labor market.

17
industries and regions that are experiencing positive shocks to the demand for labor. In that case, the OLS estimate of the effect of immigration on native employment and wages will be upward biased. It is also possible that declining industries make a special effort to attract immigrant labor to lower their wage bill. For example, immigrants require a work permit to legally work in Malaysia; one way that declining industries may respond is by exerting political pressure that more work permits be issued for their industry. In that instance there is a negative correlation between the inflow of immigrants and shocks to the wages and employment of native labor, and the OLS estimates would be downward biased.<sup>24</sup> The likelihood of biased OLS estimates makes it important to instrument for the inflow of immigrants to an industry-region.

A valid instrument for immigration flows needs to be uncorrelated with any demand shocks, caused by changes in technology or output prices, that may affect the demand for native or immigrant labor. To construct such an instrument we use changes in the population and age structure of immigrant source countries over time. These source countries are Bangladesh, Cambodia, India, Lao PDR, Myanmar, Sri Lanka, Thailand, Vietnam, and most importantly Indonesia and Philippines. Using the data from the United Nations Population Division, we calculate the number of individuals in each of 7 age-groups in each of these source countries in every year during 1990-2010.<sup>25</sup> These population numbers form the potential pool of immigrants to Malaysia, where the likelihood of migration would vary by age group, country of origin and year. This is our measure of the supply of immigrants St<sup>ac</sup> to Malaysia from source country c in age-group a and year t.<sup>26</sup> Since

> 24This is what Friedberg (2001) finds when examining the distribution of Russian arrivals in Israel after the end of the Cold War.

> 25The age groups are 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, and 45 and above.

> 26We multiply these population numbers by the average propensity of people from each country to

26
# **Appendix Table A.6**

# **Descriptive Statistics for Children Graduating from Middle School Selected Variables, for Selected Years**

||All|||Ye|ar|||
|---|---|---|---|---|---|---|---|
||Years|1987|1990|1993|1996|1999|2002|
|**Individual Level Variables**||||||||
|Enrolled in High School? (1=yes)|0.43|0.32|0.44|0.41|0.42|0.47|0.47|
||(0.50)|(0.47)|(0.50)|(0.49)|(0.49)|(0.50)|(0.50)|
|Gender (1=male)|0.57|0.58|0.60|0.58|0.53|0.53|0.48|
||(0.50)|(0.49)|(0.49)|(0.49)|(0.50)|(0.50)|(0.50)|
|First Born (1=yes)|0.45|0.47|0.49|0.46|0.52|0.50|0.37|
||(0.50)|(0.50)|(0.50)|(0.50)|(0.50)|(0.50)|(0.48)|
|Birth Order|1.87|1.92|1.94|1.92|1.76|1.71|1.94|
||(1.04)|(1.12)|(1.18)|(1.14)|(1.02)|(0.85)|(0.93)|
|**Household Level Variables**||||||||
|First Born in Household|0.40|0.28|0.44|0.43|0.38|0.42|0.43|
|was Male (1=yes)|(0.50)|(0.45)|(0.50)|(0.50)|(0.49)|(0.50)|(0.50)|
|Father's Years of Schooling|6.39|5.44|5.52|6.25|6.47|7.27|6.79|
||(3.21)|(3.28)|(3.28)|(3.32)|(3.16)|(3.06)|(3.04)|
|Mother's Years of Schooling|4.22|3.30|3.07|3.67|4.23|5.02|5.14|
||(3.30)|(3.03)|(2.99)|(3.22)|(3.36)|(3.17)|(3.32)|
|Number of Potential Migrants,|0.46|0.30|0.47|0.45|0.44|0.45|0.54|
|Household, Male|(0.62)|(0.48)|(0.58)|(0.61)|(0.59)|(0.62)|(0.62)|
|Number of Potential Migrants,|0.49|0.25|0.51|0.55|0.52|0.47|0.59|
|Household, Female|(0.71)|(0.48)|(0.69)|(0.81)|(0.81)|(0.66)|(0.79)|
|**Village Level Variables**||||||||
|Share of Migrants, Village Workforce|0.11|0.03|0.02|0.09|0.13|0.14|0.21|
||(0.12)|(0.04)|(0.03)|(0.09)|(0.12)|(0.10)|(0.12)|
|ln(Village Mean Income Per Capita)|6.42|6.20|6.21|6.22|6.52|6.52|6.66|
||(0.39)|(0.32)|(0.35)|(0.31)|(0.32)|(0.38)|(0.31)|
|ln(Village Mean Wealth Per Capita)|8.81|8.62|8.51|8.69|8.89|8.93|9.07|
||(0.55)|(0.48)|(0.57)|(0.48)|(0.50)|(0.50)|(0.45)|

**Appendix Table A.6 Continued on Next Page**

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