rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1.  Introduction**

Empirically identifying fiscal multipliers requires a strategy to isolate changes in public spending and/or taxes that are plausibly uncorrelated with contemporaneous economic shocks. In this paper I propose a novel method of identifying such fluctuations in public spending that relies on two features unique to many low‐income countries: (1) borrowing from the World Bank finances a substantial fraction of public spending, and (2) actual spending on World Bank‐financed projects is typically spread out over several years following the original approval of the loan. The first fact means that fluctuations in spending on World Bank‐financed projects are a significant source of fluctuations in overall public spending in these countries. The second fact means that fluctuations in World Bank‐financed spending in a given year are largely determined by fluctuations in project approval decisions made in previous years, and thus are unlikely to be correlated with shocks to output in the current year. I use World Bank project‐level disbursement data to isolate this component of public spending associated with past project approval decisions, and use it to estimate government spending multipliers in a sample of 29 mostly low‐income countries where this source of fluctuations in public spending is large relative to the size of the economy.

The recent financial crisis has renewed interest in the long‐standing question of the size of fiscal multipliers. Knowledge of the size of the multiplier is crucial to informing policy discussions about the appropriate scale and duration of fiscal stimulus packages in response to macroeconomic crises. Years of intensive and creative research have, however, yielded a bewildering array of estimates of the multiplier, ranging from zero and even negative to well above one. Nearly all of this evidence comes from a handful of developed economies, and is based on one of three primary identification strategies. The first are VAR‐based identification schemes, of which Blanchard and Perotti (2002) is a leading example. These studies rely on the availability of quarterly data, together with the assumption that discretionary changes in fiscal policy
differ on what percentage of the top incomes the RLMS can capture, as well as the methods and auxiliary data sources that can be employed to correct for these missing values.

We employ a modelling approach to measure inequality that addresses this under-coverage issue (see Jenkins (2017) for more discussion on this approach). In particular, we obtain an inequality estimate for the poorer _p_ percent in the RLMS data using non-parametric methods, and then derive an inequality estimate for the richest ( _1 - p_ ) percent by fitting a Pareto Type I distribution to the top income observations from the same source. The adjusted Gini coefficient can then be obtained by adding together three inequality measures: one for the top incomes (i.e., the richest ( _1 - p_ ) percent), another for the non-top incomes (i.e., the poorest _p_ percent), and another between these two population groups. Using three different values of p that include 90 percent (Panel A), 95 percent (Panel B), and 97.5 percent (Panel C), we plot the results in Figure 1.8 (Appendix 1), which shows larger values for the adjusted Gini coefficients, but a reassuringly similar downward trend over time.

As a further check on whether (and how much) the RLMS missed out on the top incomes, we examine another high-quality household survey that is commonly used for (cross sectional) income and poverty monitoring purposes in Russia, the Household Budget Survey (HBS). The HBS has been conducted quarterly on an annual basis since 1987, covering 47,800 households across the country, but the micro data are only made publicly available since 2003. However, while the HBS collects detailed data on household expenditures, it does not collect data on household income. Consequently, we construct an income variable for the HBS on the basis of indirect accounting of household consumption items, where household money income is the total of household cash expenditures and financial assets (savings).
33 percent of a standard deviation, equivalent to more than a year of schooling. Losses are greater for disadvantaged students, those struggling before the pandemic, and by duration. However, until now, there has only been one international student assessment. It covered only fourth grade students in just 55 countries.

In this paper we examine the impact of schooling disruptions on student reading, mathematics, and science scores using large-scale, internationally comparable student achievement tests from the OECD’s Programme for International Student Assessment (PISA) study. These assessments are conducted every three years since 2000, with the latest implementation in 2022 collecting student results after the school closures caused by the COVID-19 pandemic. Overall, the data explored in this study represent 175 million 15-yearold students in 72 countries. By combining data from previous rounds and the post-COVID round for countries that have over-time data, we can see the evolution of mathematics and science scores before and after COVID-19. We model the effect of closures on achievement by predicting the deviation of the most recent results from a linear trend in reading, mathematics, and science achievement.

Our analysis reveals that school closures led to student learning losses when de-trending the losses to account for school closures. Math scores declined due to pandemic-era school closures from 2018 to 2022 by an average of 12 points, or 14 percent of a standard deviation (SD), roughly equivalent to seven months of learning. Larger declines are recorded for students in schools that faced relatively longer closures and for lower-achieving students. Countries with the shortest closures experienced relatively small losses, 10 percent of a SD, or equivalent to 5 months of learning. Countries with average length of school closures experienced losses of around 13 percent of a SD, or 7 months of learning. Countries with the longest closures experienced losses of around 20 percent of a SD, or up to 12 months of learning.

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discrimination by gender or ethnicity cannot be established correctly. Many studies of wage determination report positive coefficient estimates on the age of an employee, conditional on a variety of covariates. These estimates neither imply that older workers are more productive than younger ones, nor that wages rise faster with productivity because no bridge has been made between productivity and wages (see Hellerstein, Neumark, and Troshe 1996). These problems may be overcome by estimating the wage and productivity equations jointly and, thus, comparing wages and productivity for various groups of workers.

Section two describes the methodology and data used in this study. Section three outlines the wage determination model used. Section four shows descriptive statistics and presents regression results. Section five presents conclusions.

# 2. **Data and Methodology**

I carry out this study with the so-called RPED dataset from the 1994 survey in Ghana. The survey included 215 firms and interviewed about 1,20C' of their employees. This matched employee-employer dataset has many advantages compared with datasets of just employees. An employee dataset may contain information about the sector in which a worker is employed but little information about the firm. An employer dataset has information about the firms but limited information about the individuals actually employed in the firms, apart from aggregate wage costs and, in some cases, information _5_
Table 4. The <u>effects of COVID-19 exposure on educational outcomes by year</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_A. Dropout_|||||
|Warning index x 2020|0.009|0.009|0.009|0.009|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|0.005|0.005|0.005|0.005|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2020=2021)|0.006|0.005|0.007|0.009|
|Mean outcome|0.086|0.086|0.086|0.086|
|Adjusted R<sup>2</sup>|0.007|0.079|0.090|0.128|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|_B. Effective promotion_|||||
|Warning index x 2020|-0.029|-0.028|-0.027|-0.027|
||(0.003)**
<br>Direct transfers Education Health<br>% of GDP % of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers. For Kenya, the source is World Bank (2018a), and the value reported for direct transfers corresponds to social protection expenditure of the national government._ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, for Kenya 2015/16._

# 2.4.1. Public Pension System

Uganda’s public pension system was established in 1946 to provide retirement benefits for public sector servants and includes one scheme for the Armed Forces and one for the Civil Service. The first covers all military officials and the second includes all civil servants: central government officials, police and prison officers, members of the judiciary, doctors, primary and secondary school teachers. There are 45,200 beneficiaries out of a total of 274,200 enrolled individuals. Beneficiaries receive payments (part lump sum on retirement and an annual pension) based on length of service and salary during service. Survivors receive benefits in case of death before retirement. On average, the annual pension is about 3,460,400 UGX, or 1.34 times the GDP per capita (World Bank, 2019b). There were no public pension contributions in fiscal year 2016/17; pension transfers are mainly funded by general government revenues.

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|168.29|6.4|14.6||
|||Botswana<br>Mid-2000s<br>Late|<br>p<br>2000s|Countries with<br>redominantly<br>thermal<br>generation|Other<br>developing<br>regions|
|Power tariff (residential at 75 kWh)|U.S. cents|5.2|5.2|15.71||
|Power tariff (commercial at 900 kWh)|U.S. cents|||18.83|5.0–10.0|
|Power tariff (industrial at 50,000 kWh)|U.S. cents|||14.24||

_Source:_ Eberhard and others 2008. Derived from AICD electricity database (www.infrastructureafrica.org/aicd/tools/data **).** _Note:_ kWh = kilowatt-hour; MW = megawatts. — **=** Not available.

The Botswana Power Corporation (BPC) has achieved relatively high efficiency in the power distribution sector. Botswana was able to secure for many years relatively inexpensive power purchase prices in the past importing most of its power from South Africa. While South Africa was able to provide power, Botswana’s good governance practices allowed it to build up a national power utility with full operational autonomy and the country established sound power regulation policies and incentives. This scheme of buying power to South Africa and focusing in increasing access and quality of distribution, worked well for years until the generation crisis hit South Africa and the power supply from Eskom was reduced significantly. This external shock made it evident that Botswana needed to secure its own generation capacity and could no longer postpone the needed generation and transmission investments.

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other economic conditions are associated with households being more likely to move, one would expect greater geographic mobility to be associated with more income mobility: households experiencing large positive shocks may move to better housing while households experienced large negative shocks may migrate or move to cheaper housing. As a result, the attrition bias will lead panel studies to understate mobility.

This paper shows how dynamic pseudo-panel methods can be used to consistently estimate the degree of income mobility when earnings contain nonclassical measurement error. A pseudo-panel tracks cohorts of individuals over repeated cross-sectional surveys (Deaton, 1985). Construction of a pseudo-panel involves taking cohort means within each time period, and this averaging process eliminates individual-level measurement error. Since each household is only observed once, non-random attrition becomes much less of an issue. A further advantage is that repeated cross-sectional surveys are available in more countries and typically over longer time-periods than genuine panels. This allows one to estimate mobility measures over many more time periods than typically used in the panel literature. Gottschalk (1997) notes that many movements in income are transitory, so that individuals who experience an increase in earnings in one year will tend to have a fall in income a few years later. Therefore mobility over several periods may be different from what one would predict based on extrapolating measures based on a one year interval.

This paper uses 58 quarters of household earnings data in Mexico over the period 1987 to 2001 to examine earnings mobility. Mexico’s income distribution displays a high degree of cross-sectional inequality, and therefore a high degree of income mobility is of importance in lowering inequality in lifetime distributions of income. However, our pseudo-panel results find very low levels of absolute mobility in Mexico. While OLS estimation would suggest that 33 percent of the gap in income between two randomly selected households would close within one quarter, pseudo-panel analysis finds only 1.2 percent of this gap would be eliminated within a quarter, and only five to seven percent of income differences disappear after five years. The OLS bias appears almost entirely 4
<mark>suggest that statistical adjustments can reduce, if not eliminate, coverage bias in the situation we study. Schonlau et al. (2009) study a survey of respondents that have Internet access in the Health and Retirement Study. They analyze whether propensity score and Genetic Matching algorithm can correct for imbalances between Internet and non-Internet samples for variables used for adjustment and for variables not used in the adjustment. Using these methods, they find that the estimated bias is almost always reduced, but significant differences in many cases remain.</mark> <mark>Mercer et al (2018) study public opinion polls and find that the choice of adjustment variables is more important than any reweighting method for the accuracy of estimates. R</mark> aking performs nearly as well as more elaborate techniques based on matching. In their application, b <mark>oth the demographic and political variables resulted in lower average bias than adjusting on demographics alone. V</mark> ery large sample sizes do not fix the shortcomings of online opt-in samples. While an online opt-in survey with 8,000 interviews may sound more impressive than one with 2,000, the study finds virtually no difference in accuracy. Ferri-Garcia (2020) uses Machine Learning methods as an <mark>alternative to propensity score adjustment (PSA) for reducing or eliminating selection bias in online surveys. The results indicate that the ML algorithms are more effective in removing selection bias than logistic regression when used for PSA, but this difference is not important when the dimensionality of the data is low, and the covariates are not very discriminant.</mark> <mark>In sum, the existing literature broadly suggests that</mark> various weighting schemes do not adequately reduce the difference between weighted Internet survey data and the reference survey. Propensity score adjustment seems to work better than post-stratification in settings where they have been compared directly, but other <mark>studies show that the choice of adjustment variables is more important than any reweighting method for
top earnings bracket due to the calibration, the corrections make a substantial difference to trends over time in the top tail. Without imputation and reweighting the earnings for the top 10 percentiles supposedly shrank by between 25% and 30%, whereas with these corrections they rose by between 1 and 8 percent.

# Replacement

Replacement using household and administrative data means that some of the incomes in the household surveys are replaced with incomes in the administrative data. There are various methods that have been used in this broad approach. One distinction is between those that use parametric and non-parametric replacement (Lustig 2020). Parametric replacement involves estimating the Gini coefficient or some other inequality measure for the top x% of the income distribution from tax or other administrative data by assuming the top incomes follow a Pareto distribution, estimating the Pareto parameter and using this to obtain the implied Gini coefficient of the top x%. This is very similar to the methods described above using survey data only. One can then estimate the Gini coefficient for the bottom (1-x) % of the population using survey data and combine them to obtain an overall measure of inequality, using the results of Alvaredo (2011) that the Gini can be decomposed using shares of the top (say) 1% and the bottom (say) 99%. Alvaredo (2011) used tax data and the Current Population Survey for the US to show that the increase over time in the Gini coefficient doubled when adjusting for missing top earners. This method was also used by Alvaredo and Londono Velez (2013) for Colombia, who showed that when using tax data to replace the top 1% of incomes in the surveys, inequality levels were substantially higher, and the trend of declining inequality was much smaller than when using uncorrected household surveys.

Non-parametric replacement with admin data involves either replacing higher income individuals in the household surveys with individuals from admin data or imputation of incomes in surveys using incomes in admin data observably similar individuals. Bach et al. (2009) undertook this this for
# **IV.2.3 Impacts on healthcare utilization among the poor**

Did higher exit rates among low-quality (and low-priced) providers, combined with higher prices at least in some facilities, hurt the poor even though prices for the average patient did not increase? To test for this possibility, we assess the impact on the distribution of patients by socioeconomic status. We construct a wealth index using exit surveys of 11,098 outpatients based on asset ownership following the Demographic and Health Survey (DHS) in Kenya (see variable construction in Section 6 in the Supplemental Material). If care seeking had declined among the poor, we should have seen a mean increase in wealth among those visiting facilities in treated areas and lower densities at lower wealth levels. In fact, as Figure 2 shows, we cannot reject the hypothesis that the distribution of the wealth index is identical among patients in treatment and control markets (Kolmogorov–Smirnov test p-value = 0.325). Table A6 in the Appendix presents further robustness checks confirming that there is no treatment effect, either for the mean or for different quantiles of the wealth index. We can thus confirm that access to health care among poorer patients was not reduced by the intervention, suggesting an overall improvement in their quality of care.

Figure 2: Distribution of Patients by Wealth Index and Treatment Status <!-- Start of picture text --> 16%<br>14%<br>12%<br>10%<br>Treatment<br>8%<br>Control<br>6%<br>4%<br>2%<br>0%<br>-3.5 -1.5 0.5 2.5 4.5 6.5 8.5 10.5 12.5 14.5<br>Wealth Index<br>% of Patients in Arm<br><!-- End of picture text --> _Notes._
Policy Research Working Paper 8875

# **Abstract**

This paper uses plant-level, panel data from the Ethiopian manufacturing census to estimate the effects of demand-side and supply-side factors on industrywide aggregate productivity. The paper focuses on the effects of three factors: (1) local market size, (2) the value of transportation costs that firms incur in selling to customers outside their market, and (3) licensing fees needed to enter the market. Identification is based on a model of production under monopolistic competition, which enables interpreting the estimated coefficients of a reduced form, dynamic productivity equation.

The paper analyzes 11 industries in Ethiopia over 2000 to 2010. Several interesting results emerge. In the most parsimonious specification, the estimated coefficients are consistent with all three predictions of the model—but only for one industry: cinder blocks. In this industry, the expansion of the local market boosts industrywide total factor revenue productivity, while increases in transport costs and licensing fees reduce it. The picture is somewhat mixed in the other 10 industries but broadly consistent with the predictions of the model.

This paper is a product of the Office of the Chief Economist, Africa Region and the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at jonesp@newpaltz.edu; elartey@worldbank.org; and azeufack@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
- (c) The household-specific errors are simulated as

in the homoscedastic case. When there is heteroscedasticity, they are simulated as Unlike the original parametric bootstrap procedure in MR (2010) described in Section 3, here the sample errors _e_<sup>_∗_</sup> _ch,s_<sup>are not a subset of the census errors simulated in step 2, although they</sup> are generated from exactly the same model (with the same variance).

5. Fit the model (1) to the newly simulated survey data from step 4. This yields a bootstrap vector of estimated model parameters: 6. Obtain the bootstrap extended CEB estimator _τ_ ˆ _c,b_<sup>_CEB∗_</sup> through MC simulation using the bootstrap parameter estimates _θ_<sup>ˆ</sup><sup>_∗_</sup> from step 5, by the approach described in the previous section.

7. Repeat steps 2 to 6 a sufficiently large number of times, _B_ . Let _τc_<sup>_∗_(</sup><sup>_b_)</sup> be the true value and _τ_ ˆ _c,b_<sup>_CEB∗_(</sup><sup>_b_)</sup> be the CEB estimator obtained in the bth replicate of the bootstrap procedure. A parametric bootstrap estimator of the MSE is given by:

# **7 Simulation experiments for comparison of methods**

## **7.1 Simulation experiment with poor model**

Here we compare the procedures described in the previous sections for the estimation of poverty rates and gaps (FGT indicators with _α_ = 0 _,_ 1 respectively) and their corresponding error measures. To this aim, we perform simulation experiments under the same setup as in MR (2010), but extended to consider also more realistic scenarios.

Thus, like in MR (2010), a census data set of _N_
47|1.72|2.23|2.90|2.57|Y|
|Sanaa|1.86|3.23|2.54|1.70|2.20|1.95|Y|
|Sanaa_City|3.33|3.89|3.61|2.45|3.26|2.86|N|
|Shabwah|2.82|3.69|3.25|2.34|3.10|2.72|Y|
|Taiz|2.32|2.78|2.55|1.85|2.24|2.04|N|

Notes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone. The 2014 HBS is the last known population estimates of these variables for Yemen.

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