rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 6** ). Public consumption represents the largest portion, constituting 13.5 percent of GDP (with the wage bill accounting for nearly 11 percent of GDP).<sup>12</sup> Social security spending reached 10 percent of GDP, primarily driven by the various pension sub-systems. Meanwhile, public investment comprised 3.8 percent of GDP in 2022. The remaining 9.5 percent of GDP in spending is largely attributed to various types of transfers to households, private and public enterprises, including energy and transport subsidies. Remarkably, the unemployment insurance program is insignificant in budgetary matters and is equivalent to only 0.02 percent of GDP in 2022, pushing other -typically more rigid- items to replace the role of unemployment insurance.

> 11 For instance, Lamo, Perez and Schuknecht (2007) show evidence that public consumption items, namely the wage bill, co-moves with the business cycle in a procyclical way with a 1–2-year lag, for most of the euro area countries. Moreover, the authors prove that wage bill procyclicality appears to derive mainly from wage-setting behavior as opposed to employment decisions. In a similar vein, Holm-Hadulla et al (2010) build on previous works by showing evidence of strong co-movements between public and private wages, suggesting that governments should be cautious so that wage-setting and employment measures do not put macroeconomic balances at risk. Moreover, opting for a different methodology, Eckardt and Mills (2014) show that public sector wage bill spending across Europe and Central Asia tends to behave pro-cyclically, especially in transition economies. At last, according to Ilzetzki and Vegh (2008), public consumption is mildly procyclical in high income countries, though far less procyclical than in developing countries.

> 12 The data for overall public consumption is an estimate since official figures exclude municipal spending. However, they do include the transfers made by the provinces to the municipalities. It is assumed that 70% of these transfers to municipalities are spent on consumption spending (given that consumption
Table 8 looks at attrition of firms in the sample. It shows both the number of firms that dropped out that year, as well as the mean non‐compliance rate of those firms that dropped out. It also shows the total number of firms, and the dropout rate. It is interesting to note that in 2015, the year of disclosure program announcement, there was a high incidence of poorly performing firms dropping out. The higher number reported for 2017 results from data only being available for half of 2018.

**Table 8: Summary statistics of firm attrition**

|**Year**|**Number of firms**|**Number of firms dropping out**|**Dropout rate**|**Mean noncompliance rate**|
|---|---|---|---|---|
|2010|65|4|0.062|0.162|
|2011|116|11|0.095|0.094|
|2012|136|15|0.110|0.102|
|2013|139|19|0.137|0.110|
|2014|177|29|0.164|0.144|
|2015|219|25|0.114|0.179|
|2016|253|31|0.123|0.132|
|2017|286|211|0.738|0.084|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Note: Attrition is defined as dropping out of the ILO‐IFC Better Work Program.

Table 9 provides the summary statistics of the other firm characteristics available in the data set. The average age of firms that participate in the Better Work Vietnam program is about 13 years, though with substantial variation across firms, ranging from only 1 year to more than 50 years in operation. There is also substantial
>are assigned<br>to Tier 1, the<br>rest to Tier 2|Newly<br>connected<br>households<br>are assigned<br>to Tier 3|50 percent of<br>newly<br>connected<br>households are<br>assigned to Tier<br>4, the rest to<br>Tier 5|Newly<br>connected<br>households<br>are assigned<br>the current<br>Tier<br>breakdown|Newly<br>connected<br>households to<br>keep up with<br>demographic<br>growth, Tier<br>breakdown is<br>BAU.|

Source: Authors. Note: industrial demand varies independently of the access scenarios.

These five policy choices were assessed under dozens of scenarios for each of the 48 countries, by varying five parameters: population growth, urbanization rate, industrial demand growth, technology cost evolution, and fuel price.

The fossil fuel price was defined for two levels (low and high) based on the World Bank Commodities Price forecast (World Bank, 2016, July) for the short term and World Energy Outlook 2015 (IEA (2015)) low oil price scenario and current policies scenario the long term. The renewable technology cost and performance outlooks were defined in three levers (low, reference and high) which are based on the projected learning rates from (NREL, 2016).

# **TABLE 2 INPUT PARAMETERS**

||Values|
|---|---|
|Populationgrowth|5 values: 5 SSPs|
|Urbanization|5 values: 5 SSPs|
|Techno costs|3 values (NREL low,<br>medium and high ref)|
|Fuelprices|2 values: low & high|
|Industrial demand<br>growth|5 values: same as GDP<br>growth of the 5 SSPs|

6
# **2. Data and methods**

# **2.1 Phone survey**

In 2020, as in-person data collection activities of many statistical offices came to a halt, the World Bank started the collection of multiple waves of phone survey data in numerous countries around the world. The aim was to assess the evolution of household wellbeing during the COVID-19 pandemic. The survey comprised modules on different wellbeing dimensions including employment, income, coping mechanisms, access to health, school participation, gender inequalities, and food insecurity. The surveys were designed to be representative at the national level for households and individuals (over 18) who have an active cellphone or a landline at home.<sup>1</sup> Sample sizes typically ranged from 800 to 1,000 interviews in smaller countries, and to over 2,000 interviews in larger countries (including Haiti). World Bank (2021) gives a detailed description of the sampling design and calculation of survey weights. The surveys were administered over the phone to a random sample of individuals aged 18 or older, identified through so-called random digit dialing. As discussed in World Bank (2021), this “methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.” For the countries in our sample, it is either known or reasonable to assume that the vast majority of households has access to a phone. According to the International Telecommunication Union’s (ITU) 2019 core household indicators, 93.9 percent of the households in Belize had a mobile phone and 7.7 percent had a landline. In Suriname, 94.4 and 39.4 percent of households, respectively, had a mobile phone and a landline.<sup>2</sup> For Saint Lucia, our own calculations based on the 2016 Survey of Living Conditions and Household Budgetary Survey indicate that 95.4
in addition to household size, the number of adults in the household, the education level of the household head and asset ownership levels can induce systematic differences between different measures of household consumption.

To test whether any potentially important heterogeneities are overlooked by our preferred specification, we allow the linear relationship between CPHS and NSS consumption to vary by these household characteristics. We consider six binary household level indicators: households with more than three adults, households with at least one member with a high level of education, household head with over primary levels of education, households with agriculture as the primary industry, Hindu households, and households that belong to schedule caste, schedule tribe or other backward classes. Each of these will be combined with the rural-urban indicator, such that four different linear relationship are estimated for each of these six cases.

Figure 19 plots the headcount poverty rate at the $1.90 line for each of the six specifications -- each accounting for a different choice of heterogeneity (labeled as the “heterogeneous” series). The “homogenous” series refers to our main specification that only accounts for heterogeneity between rural and urban India. All six specifications, each accounting for a different form of heterogeneity, produces similar levels and trends in headcount poverty than the estimates obtained with our preferred specification. The one outlier is the headcount estimate obtained for 2018 that accounts for heterogeneity in household head literacy.

We can further check the robustness of our imputation model of approach 1 by estimating poverty in 2004 and comparing it to the actual estimates for the year. This “back casting” exercise generates poverty figures for 2004 based on the estimated coefficients in Table 2 and imputing consumption for 2004 based on the NSS consumption round for the year. The back casted estimates can also help compare our approach to those of Newhouse and Vyas (2019) and Edochie, et al. (2022). As all three papers use the same training and validation dataset (NSS-2011 and 2004 respectively), these comparisons can reveal the accuracy of prediction
rationalization and other activities. The central commission sets the bulk tariffs for all central generation and transmission utility companies and decides on issues relating to interstate exchange of electricity. The state commissions have the authority to set tariffs for all types of electricity customers in their respective states; however, state governments are entitled to set policies on the subsidies allowed for electricity supply to any consumer class and are authorized to cross‐subsidize. With this administrative setup in place, the government outlined an ambitious plan for achieving 100 percent electrification of villages by the end of 2007 and universal coverage of households by 2012 (Cust, Singh, and Neuhoff 2007).

In 2005 the government launched the Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY) program, the national program aimed at electrifying all 100,000 unelectrified villages of more than 100 people. The program was also to provide free electricity connections to more than 23 million rural households living below the poverty line. The program has led to substantial progress: according to a recent World Bank report (Pargal and Banerjee 2014), India’s official electrification rate rose by 15 percentage points in 10 years, from 59 percent in 2000 to 74 percent in 2010. Most of the new customers were located in rural areas. The new targeted timeline for 100 percent connectivity is during the 12th five‐year plan (2013– 17).

While the electrification rate has risen, the quality and reliability of electricity service vary considerably. For example, using night lights data, Min and Gaba (2016) show that many villages in India that were officially classified as electrified under the RGGVY program remained in the dark for years after the completion of electrification projects. In addition, India continues to face widespread power outages. In July 2012, the country experienced the largest blackout in world history, with more than 600 million people unexpectedly losing power for two days. The scale of this blackout highlights the challenges that India faces in keeping the lights on. The government has made achieving universal and fully reliable electricity supply
and Xu (2004) discuss supply and demand of corruption by bribe takers and payers, using WBES data but do not separate the two sources of bribery. More recently, Freund et al. (2015), similarly make use of WBES data, examining how delays in services and time spent with officials relate to bribe solicitation, concentrating on bribe requests by public officials rather than the actual payment of bribes by firms and without differentiating between the demand and supply sides. We examine the extent to which both sides emerge distinctly and are present in certain types of transactions with public officials, particularly taxation and public procurement.

Our findings are qualitatively robust to various methodologies, specifications, and

> 7In Uganda, Gauthier and Goyette (2014) show that negotiation takes place over bribes and tax payments, where the amount of bribe offered is positively associated with a tax rebate. Using a sample of 32 mainly developed and transition economies, Alm et al. (2016) find that rent extraction by tax drives tax evasion.

> 8The literature on graft activities at the micro-level mainly stems from studies conducted in the early 1990s using firm- and individual-level survey data. Using the World Bank’s Regional Program on Enterprise Development (RPED) 1995 Cameroon survey, Gauthier and Gersovitz (1997) report that business owners are eager to discuss issues related to tax evasion, fiscal privileges, and bribes, with 58% of firms reporting bribe payments made to the tax administration. Using the 1998 Uganda survey, Svensson (2003) shows that a large majority of businesses are bribe payers, and that firms that interact more frequently with state officials are more likely to be requested to pay bribes, while those that are more profitable are extracted larger bribes.

4
Inpres schools in section (6) below.

# (5.3) Robustness Checks and Other Concerns

We check the robustness of our estimates above in a number of ways. First, we use mother's schooling instead of father's schooling as an indicator of children's socioeconomic status. This is motivated by substantial evidence that mother's inuence is stronger on daughters (Torche (2019), Emran and Shilpi (2011)). The evidence, however, suggests that the results and the conclusions do not change in any signicant manner. Please see subsection (OB.1.1) in the online appendix.

The second robustness check uses alternative comparison groups, by excluding the oldest birth cohorts who might be less comparable. Again, the conclusions are robust. Please see online appendix subsection (OB.1.2) for the details.

The third robustness check addresses the issue of potential biases in the estimates from nonrandom sample truncation owing to coresidency in the census data. We provide evidence on this issue from two approaches, and the evidence taken together suggests that our conclusions are unlikely to be driven by coresidency bias. First, we use rich data from Indonesia Family Life Survey (IFLS) which includes information on nonresident parents and children, and thus do not suer from cooresidency bias. A comparison of the census estimates of the mobility equations with those from IFLS shows that the estimates are in general close, and the degree of downward bias in the census estimates is small. Second, we implement correction for possible truncation bias (even if small) by using inverse probability weighting (IPW). Nicoletti and Francesconi (2006) provide evidence that IPW performs better than Heckman selection correction when dealing with such coresidency bias in intergenerational mobility analysis. They suggest house rental cost for identication, as house rental is the largest cost for children planning to leave parental home. Since house rental rates are not available for our study period, we use the recently available data on
, A4 in the Appendix.

The third set of variables, related to teachers is presented in column (4) and results in a reduction of the Vietnam dummy by only 3 points to a level of 95.13. This does not mean that teachers are unimportant as a reason for Vietnam’s superior performance in mathematics, only that the observed teacher related variables in the PISA dataset do not collectively help as an explanation. In the regression itself, one can see that PROPCERT, proportion of certified teachers affects mathematics scores positively. The same goes for a few of student assessment related variables, including TCM ~~S~~ TUASS and STU ~~F~~ EEDB, that relate to the use of student assessment and student written feedback to assess teachers.

22
# (b) <u>Descriptive statistics</u>

In **Table 3** we report school characteristics by geographical sample. The information corresponds to the 2014 School Census (Panel A to D) and ECE 2015 (Panel E). Differences between both samples ( _metropolitan_ versus _regional_ ) are substantial. Schools in the _metropolitan_ sample are larger –with more classrooms, and more students per classroom– more likely to have an afternoon shift, and in most cases have better physical infrastructure (access to sewage, existence of a science laboratory) than those in the _regional_ sample; they are also less likely to have bilingual schools and to be in districts where the main Peruvian social programs operate. Concerning students’ achievement in these schools, attainment is higher in the _metropolitan_ sample, but grade attainment is low in general; in _Mathematics_ and _Reading Comprehension_ , only 6% and 11% attain a satisfactory level in each subject, respectively.<sup>18</sup> Based on this information, we also report results from the balancing tests (see **Table A1** in the Web Appendix). Treatment and control schools appear to be similar in most observed dimensions. Some differences are observed (by day shift and sex), however, these differences are not substantial in magnitude. Unfortunately, no ECE was available before treatment to confirm balancing in the outcome variables, as 2015 was the first year in which ECE was administered in schools at the secondary level.

**Figure 2** presents kernel distributions for _Mathematics_ and _Reading Comprehension_ standardized test scores in 2015, separately for treatment and control schools, **Figure 3** presents analogous information for 2016, including also scores from _History, Geography, and Economics_ . A clear pattern arises whereby improvements are observed in all areas and both years in the _regional_ sample. In contrast, no such improvements are observed in the _metropolitan_ sample. This graphical analysis strongly mirrors the parametric analysis presented later in the paper.

> 18
14% of coefficients being negative. By contrast, ARC2, ERA5, and TAMSAT all report mostly negative coefficients, with 60 _−_ 71% of coefficients being negative. While it is unclear what is driving these results, what is clear is that counts or shares of days in which it rained is a poor metric for use in Tanzania.

The results regarding remote sensing temperature products are more ambiguous than those for rainfall. Already we have observed that while mean and median daily temperature produce the most significant coefficients, the signs on these coefficients are nearly evenly split, which violates our assumption about the consistency of sign. As a consequence of this, we also include variance of daily rainfall, which while producing fewer significant coefficients, those coefficients are consistently negative. Figure 36 presents results for mean daily temperature and Figure 37 presents results for median daily temperature. Results for both metrics are similar, which is expected as the correlation between these two metrics is 0 _._ 99. Results are also similar across country. In general, about half of coefficients are significant. Among significant coefficients, about 60% of coefficients using MERRA2 data are negative, while between 45 to 55% of coefficients from ERA5 and CPC are negative. The one exception to this is Niger, where coefficients tend to be either negative or not significant, with very few positive coefficients.

Examining the specification charts reveals one reason for this near equal division between pos40
or individuals, all LSMS-ISA surveys use distant tracking to track and interview movers out of the original EA, as long as they still live within the country.

In the Malawi, Tanzania, and Uganda panel surveys, once the individual forms a new household, all household members residing with the individual enter the sample and become tracking targets. Integrating the joint household into the sample accounts for new populations who may be otherwise underrepresented and captures the evolving dynamics of the population at large. However, including the mover’s new household increases data collection costs. An alternative and less costly way to enhance representativeness in longitudinal studies are the adoption of rotating panels or sample refreshes, which have been used by some LSMS-ISA surveys. For instance, the Uganda UNPS 2013/14 has rotated out and replaced one-third of the EAs from its initial sample. The Tanzania NPS sample was refreshed in 2014/15, but the subsequent 2020/21 round of NPS data collection only follows up with this refresh sample, marking a discontinuity in the longitudinal representativeness of the population from initial rounds. In 2022, the Ethiopia ESS sample was fully refreshed and the Burkina Faso Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) followed up with its 2018/19 households, complementing the sample with a set of new EAs selected from a more recent sampling frame. Finally, Nigeria also partially refreshed its survey sample in 2018/19.

# 6. EMPIRICAL EVALUATION OF THE UGANDA DATA

As a case study, we apply the estimator methods discussed above to the Uganda National Panel Survey (UNPS). The UNPS is a multi-purpose household panel survey implemented by the Uganda National Bureau of Statistics (UBOS) with the technical support of the World Bank LSMS-ISA project. Started in 2009/10 as a follow up of the Uganda National Household Survey 2005/6, its primary aim is to inform policy makers in budgetary decisions and policy interventions and to monitor major national policies and programs. Implemented on an annual basis, the UNPS also provides representative information
Sivadasan (2009) uses a dataset that is similar to ours for the early years of the reforms (1986-1994) and finds that the reduction in final goods tariffs and the FDI liberalization increased productivity. He also documents that these reforms were linked with average productivity increases, but not reallocation, in the early 1990’s. Nataraj (2010) compares the reactions of the organized and unorganized manufacturing sectors to trade liberalization, and finds that while the reduction in final goods tariffs increased productivity significantly in the unorganized sector, the reduction in input tariffs was more important for productivity growth in the organized sector. Aghion, Burgess, Redding and Zilibotti (2008) find that following the removal of licensing requirements, the number of factories and output in the organized sector increased, particularly in states with relatively less restrictive labor regulations.

Our study contributes to these two strands of literature in several ways. First, we document that market-share reallocations were important to overall productivity growth immediately following the start of the 1991 reforms, while average productivity gains were more important during the periods from 1985-1990 and 1998-2004. Second, we show that the trade and FDI liberalizations explain a substantial amount of overall productivity growth. Third, we construct a panel that represents a large share of the organized manufacturing sector and spans the period from 1985 to 2004. We use the panel to explore a new decomposition method that disentangles the contributions of surviving, entering and exiting firms to overall productivity growth. We illustrate that this method can provide a richer set of results, but that the results are very sensitive to the correct treatment of exit. Our findings indicate that researchers working with panel datasets in which entry and exit are not well-identified should be cautious when applying this type of decomposition. Finally, we also use the panel to confirm that the trade and FDI reforms contributed substantially to average, within-firm productivity growth.

The rest of this paper is organized as follows. Section 2 provides a brief background on the Indian reforms; Section 3 describes the data and outlines the construction of