rafmacalaba/data-use-mentions-extended huggingface dataset:
for public use. We hope this database is a public good that will enable tracking of human capital formation and deepen understanding of the factors driving human capital formation and economic development. A large-scale effort using this database to track and understand human capital formation is the World Bank’s new Human Capital Index.<sup>6</sup>

# II. THE NEW DATABASE

The database includes 164 countries from 2000 to 2017 and was produced through a large-scale effort by the World Bank to identify, collect and collate student assessment data worldwide. We include seven assessment regimes total, including three international tests and three regional standardized achievement tests. We also include the Early Grade Reading Assessment (EGRA), which adds 48 countries to the database with at least one data point in the past 10 years, including large developing economies such as Bangladesh, Nigeria and Pakistan. Each test covers between 10 and 72 countries. By combining these assessments and making them comparable we include countries which represent 98 percent of the global population. The supplement includes a detailed description of the methodology we use to develop harmonized learning measures and all data included in the database.

The final database includes mean scores as well as standard errors for each measure to quantify uncertainty. Scores are disaggregated by schooling level (primary and secondary), subject (reading, math and science) and gender (male and female). We include year-by-year data. The database will be made publicly available by the World Bank and will be updated regularly. We do not extend the time series prior to 2000 since data quality is low and this does not significantly affect country coverage, with an addition of just two territories.

Several statistics demonstrate the coverage and detail of the database. Table I presents coverage for country-year observations by region. The database includes 2134 observations across all countries from 2000-2017. Disaggregation by gender is available for nearly all the data, with 2,105 country-year observations. Most data come from math scores, with 768 country-year observations, followed by science scores,
- 81 -

# CHAPTER VI

# THE **CONPARATIVE MITS** OF **CENSUS** UNIT VALUES AND SPECIFICATION PRICING

It is sometimes suggested that unit values derived from census informat_on in the industry of origin approach are inherently inferior to specification pricing as practiced by the ICP expenditure approach, but we do not believe this to be the case.

# The Unit Value Problem

Specification pricing involves meticulous characterisation of the representative products. For consumer goods items, the ICP III exercise for 1975 provided a 462 page manual for the guidance of national statistical offices which was designed to ensure that the prices submitted should be for comparable products. This was supplemented by extensive research by the international secretariat on prices of capital goods. In our approach, by contrast. we do not solicit new information by questionnaire but use existing national censuses whose classification of products sometimes varies significantly. In some important cases the census breakdown of production is not disaggregated finely enough. A "product" for which we derive a unit value, may in practice be a mix of items, rather than a single item. This would not matter if the degree of disaggregation were uniform across countries, and if the mix and quality variaton for a "product" were similarly structured, but we know that such variations do exist.

The practical importance of the "unit value" problem in industry of origin comparisons, which is in fact a problem of matching heterogeneous items of different qualities, varies between industries. In the case of cement we come closest to the optimal situation of comparing more or less identical products across countries. Sugar, beer, tobacco products, tyres and grain mill products also pose no great problems. However, with textiles, radio and TV receivers and motor vehicles we clearly enter a different domain. To use the terminology of Gilbert and Kravis (1954, p. 79) we are dealing here with "common" products which have a similar function across the countries, but which vary in quality.

Our unit value specification was particularly poor
increase in access to public secondary schools and a small reduction in access to private schools in subsequent years compared to baseline. This suggests that while in the past the private sector stepped in to provide schooling in underserviced peripheral areas, the significant increase in access to primary and secondary public schools that followed the mass influx of refugees gradually hollowed out private schooling in areas with greater exposure to refugee settlements.

The bottom row reports the effects for health access (proximity to health facilities, normalized by the population served, and weighted across five facility types), health utilization (averaged across utilization measures for children and their families sampled by the DHS), and road density (density within each parish-year weighted by road quality type). In 2006 (the baseline year for health outcomes), households and parishes with a higher level of refugee presence had worse health utilization and somewhat worse access. However, as with other service delivery domains, we find that compared to baseline, health access improved for more exposed parishes starting in 2011 (0.08 sd), aand continued to improve in 2016 (0.05 sd) and 2020 (0.05 sd). Health utilization also improved for more exposed parishes starting in 2011 (0.09 sd) and continued in 2016 (0.04 sd).

The bottom-right graph displays the results for roads: compared to the baseline year of 2011, when refugee presence was already positively associated with road density, the changes in the effect in 2016 (0.05 sd) and 2020 (0.11 sd) are positive and significant. SI Section S4.1 confirms that these results are robust to different specifications. Since we are using a DiD specification, SI Section S2 shows the lags and leads for the development outcomes.

# **5.2 Refugee Presence Does Not Lead to Backlash**

Our second set of outcomes proxy for a possible backlash. Figure 6 examines how refugees’ presence affects public opinion and insecurity. Recall that all Afrobarometer outcomes are
enterprises and households.<sup>2</sup> Second, this paper considers access to and use of all financial services, not just credit as done in large parts of the literature. In addition, it also looks beyond formal financial services to informal financial services. Third, this paper contributes to the literature on gender differences in Sub-Saharan Africa. As rigorous analysis for SubSaharan African is often impeded by the lack of appropriate data, the data compilations used in this paper offer a unique opportunity to explore gender differences in the participation in formal and informal economies in Sub-Saharan Africa.

The remainder of this paper is organized as follows. The next section focuses on the gender gap in enterprise finance using a large cross-section of firm-level surveys. Section 3 presents results on the gender gap in the use of financial services by individuals and section 4 concludes.

# **2. Gender and Enterprise Access to Credit**

This section assesses whether there is a gender gap in enterprises‟ access to and use of bank finance. Specifically, using firm-level survey data, we will assess whether businesses with female ownership participation (i) are less likely to use a formal financing channel (e.g. overdraft or loan), (ii) have a lower share of investment financed by financial institutions, and (iii) have a lower share of working capital financed by financial institutions. The section first describes the data and then turns to multivariate regressions.

# **2.1. Data**

To explore the relationship between gender and enterprise access to credit, we use the World Bank-IFC Enterprise Surveys. The Enterprise Surveys have been conducted over the

> 2 See World Bank (2007) for an overview of studies on access to finance.

5
programming algorithm to predict an adjusted house price index for arbitrary spatial units from repeated cross-sections of geocoded micro data has been used to generate a panel of German property prices and rents that is unprecedented in its spatial coverage and detail. It provides price and rent indices for residential and commercial properties while also raising questions for follow-up research (Ahlfeldt _et al._ 2023).

Based on such experiences, the methodology for property valuation has been standardized (Diewert et al. 2020), allowing quick transfers to other contexts. In Lithuania, assessed property values more than quadrupled as a result of adopting and gradually refining the mass valuation system (Almy 2015) that is now at the core of a private sector–driven registry system that provides many ancillary benefits (Grover et al. 2017). Most African countries still require valuation of all property by a licensed valuer (Franzsen and McCluskey 2017), despite the high cost and potential arbitrariness. Using CAMA for residential properties and focusing scarce valuation capacity on commercial property has proven to be a more viable way to ensure buoyancy by allowing frequent revaluation.

_Credible enforcement and minimal exemptions._ Reducing compliance costs, for example, via e-billing and mobile payment options (McCluskey et al. 2018), can help increase property tax collection.<sup>9</sup> To improve enforcement, treating property tax arrears as a high priority lien on the property or requiring tax clearance before any transfer, mortgage, or other encumbrance can be registered on a property seems to be a low-cost but feasible option. By implicitly deferring tax payment until the point when the asset is liquidated, this can also address concerns about negative liquidity effects of property taxes, especially on seniors who, if they are unwilling to move, might have to reduce consumption to pay increasing property taxes levied on the assessed value of their home (Brockmeyer et al. 2020).<sup>10</sup> Tax codes often contain ample exemptions, including for owner-occupied properties or pensioners that are difficult to
innovation, the two survey instruments are fully comparable as the same questions are asked in both surveys. The long questionnaire, however, includes some additional follow‐up questions designed with the purpose of collecting additional evidence on reported innovation while systematically probing the answers to the standard yes/no questions. Follow‐up questions include, a question on the year in which the innovation was introduced as well as open questions where respondents are asked to explain the innovation introduced and how this is different from previous products or processes in the firm. The description variables are critical in our analysis since they allows us to verify the innovation implemented. As a result, for product and process innovation our dataset contains three measurements: response in short questionnaire, response in the long questionnaire and response in the long questionnaire verified based on the description provided.

For organization and marketing innovation, the short questionnaire includes the standard questions based on the Oslo manual,<sup>8</sup> while the in‐depth innovation survey disentangles different aspects of organizational and management innovation and different marketing strategies in which firms may innovate. Answers to these more detailed questions are mapped to the more general yes/no question included in the short questionnaire. Finally, four questions about R&D are asked in the long questionnaire, covering firms’ engagement in internal and external R&D and the associated costs. Those questions are mapped to the yes/no question on R&D included in the short questionnaire. The reference period is the same in both survey instruments.

The following analysis focuses mainly on product and process innovations as these can be verified using the additional information in the long questionnaire to corroborate the reliability of the answers provided. We also provide some information on marketing innovation and R&D.<sup>9</sup> Data cover about 22,000 firms operating in the manufacturing and services sectors in the short section in the ES and 11,150 firms in the innovation follow‐up survey (see Table A2 in
from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperforming LMICs and dotted red shows them for non-overperforming UMICs. The “spider” package is taken from Naqvi (2024a).

Spiderweb charts illustrate that China and Malaysia excel in all AIPI pillars relative to the average of non-overperformers within their income group. Notably, China demonstrates exceptional proficiency in digital infrastructure (Figure 8a), whereas Malaysia shows significant strengths in regulation and ethics, as well as human capital and labor market policies (Figure 8b).

As already underlined, China's AI readiness strategy heavily emphasizes the development of robust digital infrastructure as a cornerstone for its technological ambitions (de Seta, 2023). Central to this effort is the “New Generation Artificial Intelligence Development Plan” or AIDP (China State Council, 2017), which outlines a roadmap to position China as a global AI leader by 2030, underscoring the 17
***|‐0.00570***|‐0.00609***|‐0.00609***|‐0.00866***|‐0.00758***|‐0.00758***|
||(0.000276)|(0.000199)|(0.000199)|(0.000500)|(0.000326)|(0.000326)|(0.000929)|(0.000543)|‐0.000543|
|Year Fixed Effects|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Gridcell Fixed Effects|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Geographic Time Trends|State|State|State|State|State|State|State|State|State|
|Distance to nearest station|50km|100km|150km|50km|100km|150km|50km|100km|150km|
|Cropland Threshold|>30%|>30%|>30%|>75%|>75%|>75%|>90%|>90%|>90%|
|N|33236|68984|68984|16550|35070|35070|9110|20002|20002|
|R-sq|0.243|0.237|0.237|0.281|0.275|0.275|0.325|0.314|0.314|

Notes: Table shows results from estimating equation 1 for the global sample using the GEMStat database via ordinary least squares. Each column is from a separate regression. Specifications across regressions remain constant, with only the sample of gridcells included differing. Columns 1‐3 include gridcells where land area is at least 30 percent cropland. Columns 4‐6 restrict the sample to gridcells to those with at least 75 percent cropland, and columns 7‐9
|0.1|145|
|Other agriculture|3.4|6.3|0.3|89|
|Other mining|5.1|20.4|1.2|145|
|Other manufacturing|8.2|8.6|0.3|89|
|Export agriculture|9.5|15.6|0.2|211|
|Export manufacturing|12.7|23.2|0.7|145|

Source: GTAP and ROSSTAT data, and CGE simulation results Sectors use different inputs in their production, and specifically employ skilled and unskilled<sup>11</sup> workers in different proportions; these reallocations produce imbalances in labor markets. In particular, as on average skilled workers are employed more intensively in shrinking sectors – such as Business Services and Public Services, Oil, and even in Construction (where for every 100 unskilled workers, 182 skilled workers are employed, see rightmost column in Table _2_ ) – job losses affect more severely skilled workers than unskilled ones. Similar imbalances influence the urban premium, defined here as the ratio of non‐ agriculture activities (essentially urban) wages over agriculture (mainly rural<sup>12</sup> ) wages. These imbalances in the labor markets put downward pressure on the skill and urban premia which, as shown in Table 1, decrease by a few percentage points.

Another relevant aggregate result estimated by the CGE model is the change in relative prices of goods and services. The oil price reduction and the real exchange rate depreciation make energy cheaper,

> 11 The partition of the population into skilled and skilled workers is based on the highest level of education attained; specifically, unskilled are all those who have completed, at most, secondary education while all the others are considered skilled (even if they did not completed tertiary education). Based on this definition, unskilled workers account for 40 percent of the population and skilled for 60 percent. Among
30 and has the expected sign, which is consistent with our expectations. Market deposit growth rate (MDGR), turned out to be insignificant with a positive sign suggesting that MDGR does not impact profits . significantly. The coefficient for the ownership variable is significant suggesting that privately owned banks are more profitable than the public banks.

# VI.2. Competition Model Equations

The competition model was estimated by tobit regression on the pooled cross section - time series data set. Table 9 presents regression results. Two equations are estimated. The first one uses mobility among the top 10 banks while the second uses mobility among all banks in the market as the dependent variable. In both equations, most variables have the expected sign and are significant at the 1 percent level, except the entry variable. The entry variable, in its lagged and unlagged form, enters with a negative sign in the first equation and is not significant. This is inconsistent with our expectations on the basis of theory although similar results were obtained by Rhoades (1980). When mobility among all firms is used the sign of entry variable changes but it still remains insignificant. While these results would imply that the entry of some 30 banks during the last decade or so did not have a significant procompetitiv **e** ffect on rivalry and have not been sufficien **t** o alter the interfirm relationships among banks in the market, they may be due to a number of reasons.

First, it may be that more time is needed before the new entrants can have an impact on market relationships and 5 year lag allowed in the model might not be enough to capture the expected adjustments in interfirm relationships. Second, the entry of new banks have been on a small scale which reduces the likelihood that they will change the nature of established relationships. In other words, a minimum entry size may be needed in order to penetrate into the market and compete with the established banks effectively.
Medical consumption|-34.793|-1.35|0.7917||
|Nonmedical consumption|229.767|3.52|0.5973||
|Food consumption|9.972|0.40|0.0000|Bottom|
|Total nonfood consumption|175.541|3.64|0.4876||
|Garbage collection expenditures|0.247|0.97|0.0001|Top|
|Water expenditures|2.103|1.48|0.0000|Top|
|Electricity expenditures|2.599|0.74|0.0000|Top|
|Education expenditures|55.872|3.27|0.0000|4<sup>th</sup>, Top|
|Imputed rent expenditures|11.866|1.28|0.5324||
|Other non-food expenditures|78.532|2.55|0.0412|Bottom|
|Use value of durables|36.057|1.65|0.8604||

# IX. **CONCLUSIONS**

The results in this paper lend support to recent research that argues that health insurance can improve health outcomes and can expand household consumption through the reduction of financial risk. The use of panel data collected before and after the insurance program’s introduction allows us to eliminate any confounding effects due to selection on time-invariant unobservables, and our coupling of propensity score matching with a double-difference estimator allows us to reduce the risk of biases due to inappropriate specification of the outcome regression model. The fact remains, however, that insurance was not assigned randomly, and there remains the possibility that people may have been selected into the program in part on the basis of timevarying unobservables. It is possible, in other words, that those in the program did not share the 19
18

# **Fig. 1.3. Types of households living in privately owned** (i) **and non-privatized (ii) housing.**

# **Vladimir,** 1996

<!-- Start of picture text --> 38.A4% 69.3%<br>M Pensioners<br>14.0%<br>& _O. % ( 14.0% E Aduilts without<br>* \ __ ~~~~~~~~~~~children<br>_40 w.1% fl - CFarmnlies 0 with<br>* 21.5% ~ 16.7% children<br>39.3% 67.0%<br>P* Pensioners<br>10.4%/<br>MAdults without<br>children<br>1-. 43.3 1/9 22.6% ....... 3.. ClFain llles w ith<br>17.4% _ _ 43.3% 22.6% ............... children<br><!-- End of picture text --> Gorodets, 1996 Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

The impact of the household's demographic category on privatization proved so significant that it practically invalidates the other factors. For instance, apartments in the low-quality Khrushchev-era buildings were privatized quite eagerly because a large proportions of their tenants are older persons (this is true of all old buildings). The concentration of pensioner households in the middle-income groups resulted in quite high rates of privatization of the apartments occupied by such households.

# SECTION 2. **The Development of the Housing Market** in **the Context of Public Priorities**

This section considers the social and economic aspects of the developrment of the housing market. The findings of sociological surveys of the
to the survey.

In the Online Appendix, we show that the franchise treatment increased the likelihood of working in the salon or beauty sector at endline; otherwise, neither the franchise treatment nor the grant treatment had a significant impact on occupational sector at endline. We also find no evidence of impacts on labor market churning: women assigned to treatment are not more likely to have either started or closed a business between midline and endline, nor are they more likely to have left a job or started a new job.

# **4.4 Impacts of Treatment on Firm Structure**

In Table 5, we examine the impacts of the two labor market interventions on the characteristics of microenterprises. As always, we estimate Equation 5 in the full sample of women who completed the endline survey, but we also report the results of analogous specifications in a restricted sample of self-employed women. These latter specifications help to test the hypothesis that the interventions led to the creation of enterprises that differed in structure from those started by women in the control group.<sup>19</sup> As one would expect, we see that the franchise treatment increased the likelihood that a woman operates an enterprise that is directly linked to vocational training that she has received.<sup>20</sup><sup>_,_21</sup> The grant treatment leads to significant increases in the amount invested to start a business and the likelihood that a business was started with NGO funding; moreover, the businesses launched by women assigned to the grant treatment are significantly larger (in terms of the amount invested in them when they were launched) than the business operated by women assigned to either the control group or the franchise treatment. More interestingly, businesses launched by women in the grant treatment are also significantly more likely to employ others. The point estimate suggests that women assigned to the grant treatment are 5.8 percentage points more likely to run a business that employs anyone than women assigned to the control group (
09)|(9.18)|(5.56)|
|City size (log population)|0.81|1.80<sup>**</sup>|2.13|0.78|2.96|0.99|
||(0.68)|(0.74)|(1.76)|(1.14)|(2.25)|(0.79)|
|Constant|-42.33<sup>***</sup>|-51.54<sup>***</sup>|-55.28<sup>*</sup>|-36.78<sup>**</sup>|-74.10<sup>**</sup>|-35.91<sup>***</sup>|
||(9.83)|(10.41)|(28.59)|(16.94)|(30.45)|(10.36)|
|Field FE||X|X|X|X|X|
|Mean of Dependent Var.|0.00|0.00|2.02|-4.98|-5.77|1.52|
|Adjusted R-squared|0.17|0.25|0.27|0.23|0.24|0.23|
|Observations|851|851|529|322|223|628|

_Source_ : Authors’ calculations using administrative data. See Appendix 1 for the definitions of the variables. _Notes_ : This table reports the associations between program-level contribution to formal employment (between 0 and 100), quality determinants and local market conditions. See Figure A4, Panel B for an illustration of the coefficients’ relative size. A program is the unit of observation. All variables