rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7518

# **Abstract**

This paper seeks to understand what drove the trends in learning outcomes in Argentina between 2000 and 2012, using data from four rounds of the Program for International Student Assessment. A year-specific education production function is estimated and its results used to decompose the changes in learning outcomes into changes in inputs, parameters, and residuals via microsimulations. Estimates of the production function show the importance of socioeconomic status, gender, school autonomy, and teacher qualifications to determine learning outcomes. Despite an important increase in the level of resources invested in public education, learning outcomes in public schools decreased vis-à-vis private schools. According to the results presented here, the increase in the number of teachers in the system, pushing the pupil-teacher ratio in Argentina to 11, had no effect on learning outcomes. The microsimulation further confirms that changes in the system’s ability to transform inputs into outcomes accounted for most of the changes in test scores. Overall, the study shows the ineffectiveness of input-based education policies to improve learning outcomes in Argentina.

This paper is a product of the Education Global Practice Group. 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 rdehoyos@ 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
# **_Annex C:  Projecting out‐of‐pocket expenditures_**

Future values of OOP spending per capita are needed as input parameters into the demand model projections to predict future values of physicians per 1,000 population. Based on historical data for total health expenditures per capita (PPP constant 2011 international $) and the percentage of total health expenditures spent OOP, we calculate the OOP health expenditures per capita for each country from 1995 to 2013 based on the following formula for each country _c_ in year _t_ :

-

- (Eq 8) _OOPPCct_ = _% total health expenditures spent OOPct Total health expenditure per capitact_

For 27 countries where health expenditure information was completely missing, year‐specific mean OOP spending per capita by region and income group were substituted. These countries were as follows: American Samoa, Aruba, Bermuda, Cayman Islands, Channel Islands, Curacao, Faeroe Islands, French Polynesia, Greenland, Guam, Hong Kong SAR, China, Isle of Man, Democratic People’s Republic of Korea, Kosovo, Liechtenstein, Macao, New Caledonia, Northern Mariana, Puerto Rico, Sint Maarten, Somalia, St. Martin, Taiwan, China, Turks and Caicos, Virgin Islands, West Bank and Gaza, and Zimbabwe.

To select the appropriate model, the data was split into two parts:

1. Initialization data set included data for years 1995 – 2008.

2. Testing data set included data for years 2009 – 2013 (except for OOPPC variable, which was set to missing).

To project future values of OOP spending per capita, we tested eight models with the initialization data set:

- 1) For each country _c_ , a moving average for 18 prior periods without weights was estimated:

(Eq 9) ������� � ��� ∑����� ������,��� A total of 18 previous periods was chosen to maximize the number of prior year observations possible to base projections off of (
> 7 The widely-cited work of Faccio (2006), for example, uses a data set of firms worldwide and finds no politically connected firms in Zimbabwe and Venezuela—two countries with an arguably weak record for the control of corruption. Even for the United States, where the author’s data set includes more than 7,000 firms, her approach only identifies 14 connected firms (p. 374), a number that other works have found to be much higher (Goldman, Rocholl and So, 2009).

> 8 Note that the differentiation between PCF1 and PCF2 is mutually exclusive. In the few cases in which we find connections to both circles of elites, we code the firm according to its superior connection (i.e., PCF1) as such firms would prefer invoking their direct connection to decision makers to influence the procurement process, rather than their connection to a third party. 9 Multiple imputation, however, requires that the mechanism that produces missing values is at least missing at random (MAR) in that the missing values are not completely random but that other observed variables can be used to predict the value of the missing ones. MAR moreover requires the ignorability assumption in that the probability of missing data does not depend on the value of the missing information itself. In our case, missing observations are distributed in a non-systematic way among both small and big firms winning both small and big contracts, as well as those that have other information reported.

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**Figure 2: SSEPS Timeline**

||_Year_|J F MA MJ J A S O N D J F MA MJ J A S O N D<br>2010<br>2011|
|---|---|---|
|**Project Component**|**Duration**||
|Project Planning/ Equipment Procurement<br>Pilot<br>Enumerator Training<br>Phone Delivery & Baseline Survey|5 months<br>1 month||
|Data collection(monthly)|4 months||

Households targeted for the SSEPS consisted of those that had been surveyed in round 4 of the Household Budget Survey (HBS), which were a representative sample drawn from the ten state capitals. These households were drawn from 10 enumeration areas in each of the state capitals, with 12 households per enumeration area for a total of approximately 1200 households. Of these households, 1007 were located and agreed to participate in the SSEPS. Given that the bulk of South Sudan’s urban population lives in the state capitals, results from the SSEPS are broadly representative of the conditions in urban areas. Selection of the individual respondent within each household to receive the mobile phone and answer calls was performed during an initial household visit made by enumerators from NBS.

To assess the effect of incentives on participation, sampled enumeration areas were randomly assigned to two incentive groups: one receiving pre-paid credit of 5 Sudanese Pounds (SDG)—approximately US$2.17—for each call leading to a completed survey and another receiving pre-paid credit of 10 SDG (US$4.35) for each call leading to a completed survey.<sup>4</sup> This credit was transferred on a weekly basis: the call center would send a list of participants that had completed the survey in the previous week to partners at the mobile phone operator (Zain), which would then transfer the credit to households. In the month of
In terms of geographical location, taxpayers are less concentrated than might be expected: less than 20% are registered as Nairobi taxpayers, and over 40% are outside the six largest cities/towns in the sample (Nairobi, Nyeri, Mombasa, Meru, Eldoret, and Nakuru). One possible reason for this is how tax officials’ incentives for registration vary across regions. For example, in the largest metropolitan areas of Nairobi and Mombasa, the relevant margin to increase revenues is improving taxes from large corporations. On the other hand, in smaller cities and rural regions, small businesses registered as TOT taxpayers might be a relatively more important source of revenue and, therefore, face stronger registration and enforcement efforts. In other words, the true distribution of small businesses is likely more concentrated in the largest cities, but the relative proportion registered for TOT is likely higher in smaller towns.<sup>13</sup> We also show that the total amount of tax declared and paid was close to Ksh. 90 million per year in 2016 - 2018, decreased to close to Ksh. 40 million in 2020, and reached 391 million by the end of the 2023/24 financial year. We note that the aggregates for tax due and tax payments are somewhat different since i) some taxpayers can file taxes but never pay, and ii) some taxpayers can pay back taxes and also pay their liabilities without filing, in case they use the M-service app.

# **3.3 Survey Data**

To complement the tax administrative data, we draw on specific questions from two surveys of registered small businesses in Kenya that include questions about respondents’ knowledge of the TOT regime. The first survey (hereafter the “in-person surcovered 766 small businesses across the urban areas in vey”) registered five largest Kenya (Nairobi, Mombasa, Eldoret, Kisumu, and Nakuru). It was carried out between June and September 2022. The sample frame was drawn from the list of businesses registered with the Kenya
Over a longer term perspective the ISSER calculation suggested that the proportion went up from 25 per cent in 1960 to its 1990 level of 45 per cent.

# **6.4.2 The Formal-Informal Earnings Difference in Urban Ghana**

The crucial factor here seems to be the formal-informal earnings divide which has already been analyzed in Sections 3 and 5 above. Real wage recovered quite strongly in the public sector after the ERP (and in fact there has been a further large increase in July 1992). Mean wages of these workers were above the earnings of private formal sector employees in large firms, and considerably above those finding employment in micro and small enterprises. The earnings of some of the self-employed in petty businesses could be as low as and, indeed below the low income of informal sector wage earners. It is clear that the self-employed sector must be characterized by a wide dispersion of earnings and it is this which has contributed to the increased inequality of income in the urban areas relatively to the rural.

# **6.4.3 Income Growth and Poverty Reduction in the Rural Economy**

The analysis of the GLSS surveys show that poverty measured by the headcount ratio fell sharply for both cocoa farmers and food producing farmers between 1987-8 and 1991-2-- from 42.7 percent to 32.0 percent for the former and from 42.8 percent to 37.2 percent for the latter (World Bank 1995a, Table 3.4,p.28). The work also suggests that the poverty reduction is dominated by the growth of income. Distributional changes by themselves would have, if anything, increased the poverty ratio in most regions of rural Ghana (Jones and Xiao, 1995, Table 2, p.6). However, it has been noted that this widespread reduction in poverty ( as measured by the proportions in the distribution of expenditure below the poverty line) has not been accompanied by matching increase in agricultural production or producer income. National accounts estimates suggest that agriculture performed
the total effect of childcare attendance on outcomes rather than the partial effect of the childcare attendance with other variables held constant (Duflo et al., 2008).

We limit the sample to women whose children were born in December and January of two consecutive years and therefore differ in age by only 1 month. In the terminology of regression discontinuity design, the bandwidth is 1 month. In the pooled sample of VHLSS, there are 3,869 children age 1-5 born in December and January. There are six twins, and we drop these twins from the sample. The final number of observations for analysis is 3,863.

The main reason that we focus on a 1-month bandwidth is that parents can choose seasons or even the month of birth for their children. A wider bandwidth is more likely to be associated with bias. However, we also try to extend the bandwidth to 2 and 3 months for sensitivity analysis. For example, a 2-month bandwidth means that we compare women with children born in November and December with women whose children were born in January and February of the following year. A wider bandwidth can improve efficiency since it allows for a larger number of observations. However, it can also increase estimation bias, since children born in October to December may differ significantly from those born in January to March in different aspect such as health and non-cognitive skills which can affect parental employment. Thus, we use the results from a 1-month bandwidth for interpretation.

In the second stage, we regress the employment variable of women on the childcare attendance of children as follows where Y�,� is the employment variable of interest. Equation (3) is estimated using instrumental variable regression, where the instrumental variable for childcare attendance is a dummy variable indicating whether the child is born in December.

<mark>Our dependent variables include both continuous and dummy variables. For continuous variables, such as log of wages and log of the number of working hours, we use 2SLS. When the dependent variable is a binary
statistical theory known as model‐based geostatistics (MBG)<sup>36,37</sup> . MBG models are a class of generalized linear mixed model, with an approximation of a multivariate Normal random field (i.e., a Gaussian process) used as a spatially autocorrelated random effect term. The proportion of children who were recorded as being stunted , _Y_  _xi_ , at each location in Tanzania was modeled as a transformation of a spatially structured field superimposed with additional random variation . <mark>The count of individuals stunted from the total sample of in each survey cluster was modeled as a conditionally independent binomial variate given the unobserved underlying</mark> _Y_  _xi_ value. The spatial component was represented by a stationary Gaussian process with mean and covariance . <mark>The unstructured component was represented as Gaussian with zero mean and variance . Both the inference and prediction stages were coded using the INLA framework,</mark><sup>38</sup> <mark>primarily in the R programming language.</mark><sup>39</sup> The mean component was modeled as a linear function of the _n_ geospatial covariates described above,<sup>,wherewas a vector consisting of a constant and the</sup> covariates indexed by spatial location , and was a corresponding vector of regression coefficients. Each covariate was converted to _z_ ‐scores before analysis. Covariance between spatial locations was modeled using a Matern covariance function: 4
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- Luttmer
up to a certain level of scarcity (and variability), after which formal cooperation is negatively affected as scarcity and variability continue to increase (Dinar, 2009; Dinar et al., 2010; Dinar et al., 2011). In light of the findings in the extant literature, our model tests for both linear and nonlinear effects ( **_Precipitation CV and Precipitation CV Squared_** ) between variability and cooperative/conflictive behavior.

> 7 Accessed Version TS3.23 on 9 February 2016.

> 8 Using run‐off data provides another way to measure variability but run‐off data is not consistently available on a yearly basis like the precipitation data, which is also available across a much longer time frame. See World Bank Water and Climate Change Project (World Bank, 2009) and CLIRUN‐II (Strzepek et al., in preparation).

8
also declines with rising transport costs and rising costs of financial services in furniture making, which is the industry that is most similar to the industries of Tables 6B and 10B in that regard. But according to Tables 11 and 11B productivity does not seem to respond to changes in the size of the local market even in the furniture industry among this group of industries.

# 4. Conclusion

Based on analyses of the panel data on plants in 11 four‐digit ISIC manufacturing industries in Ethiopia, this paper estimates the effects on productivity of local market size, transport costs and license fees. The first two of these variables are important demand‐side drivers of industrial productivity growth. The third is an important item of sunk costs of entry and consequently a supply‐side factor. Identification of the effects relies on the Melitz‐Ottaviano model of productivity and producer turnover under monopolistic competition, in which all three are joint determinants of the average cost of the marginal producer in long run equilibrium.

All three predictions of the model are borne out in the most homogenous of the 11 industries, namely, the production of cinder blocks (ISIC 2695). In that industry, expansion of the local market boosts revenue productivity while increases in transport costs and licensing fees reduce it. The picture is somewhat mixed in the other industries but nonetheless supportive of the model in as far as at least one and, more often, two of the predictions hold up. The most significant result is that local market size is a major factor in aggregate revenue productivity within each of 9 of the 11 industries, the exceptions being wood work (ISIC 2000) and the plastic products industry (ISIC 2520). Rising transport costs also reduce revenue productivity significantly within each of 8 of the 11, the industries where the result does not hold being producing footwear (ISIC 1920), furniture (ISIC 3610), or structural metal products (ISIC 2811). Increasing licensing fees reduce aggregate productivity within all industries excepting the manufacturing of textiles (ISIC 1710)
Weingast 1989, Acemoglu, et al. 2002, Clague, et al. 1996) more typically argues that secure property and contractual rights are the product of political checks and balances that create institutional road blocks to government efforts to expropriate.  Empirically, however, political checks and balances are weak predictors of the property rights measures used here.  The divergent incentives of politicians to pursue broad public interests, the core problem in the analysis here, explains why checks and balances may not always be a strong guarantee of the security of property rights.

> 5 This definition of rule of law is not the only one.  However, the specifics of the definition fit squarely in any definition, and are tracked by the Political Risk Services rule of law indicator.
for an average of 8.3% of the total industrial value added. Such a small proportion precludes the NBS survey teams from effective checks on the rest of the statistics from the local statistical agencies.

Furthermore, data on industrial firms suffer from limited information disclosure, further undermining monitoring from the public. As of now, only the aggregate industrial value-added data are publicly available at the province and the prefecture level, and these data cannot even be decomposed into the one- or two-digit subsectors.<sup>17</sup> Indeed, the largest discrepancy between the total national value added and the sum of the corresponding provincial values is found in the industrial sector. Finally, neither the census nor other data of the NBS are capable of adequately adjusting these skewed statistics. For example, while the 2008 Benchmark Revisions following the census reduced the overall value-added discrepancy as well as discrepancies in the tertiary sector, the data adjustments only eliminated a small proportion of the discrepancy in the industrial sector.

# **2.2.2 Setting growth target**

Chinese governments at all levels make their economic plans in advance, setting key economic targets, including GDP growth rates, industry value added, fiscal revenues and expenditures. The drafting of plans involves a top-down process: In December, the Central Economic Working Conference, jointly held by the Central Committee of Communist Party and the State Council, reviews economic performance in the current year, and then sets the national growth target for the next year. Provincial and prefecture governments then set their own targets on the basis of the national targets, taking into account the local conditions (Lyu et al. 2018).<sup>18</sup> These annual plans are released in the government work reports, and presented in the local People’s Congress at the beginning of each year.

# **2.3 Evidence of manipulation at the subnational level**

The quality and credibility of China’s officially released economic data have been scrutinized despite the lack