rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Fiscal Incidence in Moldova: A Commitment to Equity Analysis**

_Alexandru Cojocaru, Mikhail Matytsin and Valeriu Prohnitchi_ *,** _JEL Codes_ : H22, H5, D31, I3 _Keywords_ : Fiscal Incidence, Fiscal Policy and Inequality, Income Inequality, Poverty, Social Spending, Social Assistance, Taxation, Moldova.

> * Alexandru Cojocaru is a Senior Economist with the Poverty and Equity Global Practice of the World Bank (acoocaru@worldbank.org), Mikhail Matytsin as a Research Analyst with the Poverty and Equity Global Practice of the World Bank (mmatytsin@worldbank.org), Valeriu Prohnitchi is a consultant with the World Bank (prohnitchi@gmail.com).

> ** The authors would like to thank Irina Capita for contributing the analysis of in-kind transfers based on the BOOST database. The work benefited from the overall guidance of Carlos Silva-Jauregui. All remaining errors are ours alone. The findings, interpretations, and conclusions in this research note are entirely those of the authors.
measurement. The conclusion of that study, which controls for errors in land measures indirectly by comparing fixed and random effect estimates, is that when errors in self-reported farm size are accounted for the IR can be completely explained by factor market imperfections and differences in land quality.

In this paper, we revisit the role of land measurement error in the IR controversy by drawing on data from a nationally representative household survey from Uganda, in which self-reported land size information were complemented by plot measurements collected using Global Position System (GPS) devices. This allows us to systematically analyze the differences in land area data using both measurements, and discuss the impact of such differences on estimates of agricultural productivity. In particular, we explore: (i) what are the determinants of the bias in land measurement, (ii) whether this bias varies systematically with plot size and landholding, and (iii) whether the data support the IR hypothesis, and the extent to which land measurement error affects the relative advantage of smallholders implied by the IR. Preliminary empirical evidence (Goldstein and Udry, 1999) suggests that the differences between GPS and self-reporting may be substantial, and that such difference varies by farm size. If this intuition is correct, using GPS data can have considerable implications for the much debated and contentious relationship between farm size and productivity.

2
perceived intensity of the earthquake, a subjective measure. It is assumed that damage starts at an MMI level of V and a PGA of 3.9 percent of gravity ( _g_ ). These damage levels are found for California in (Wald, et al. 1999), but the relationship has been found for other areas in the United States in Atkinson and Kaka (2006) and Atkinson and Kaka (2007) and for places such as Costa Rica (Linkimer 2007) and Japan, Southern Europe and the Western United States (Murphy and OBrien 1977). It is worth noting that the numerical relationship differs from region to region.

The different measures are largely interchangeable, and in the International and Regional Development 2001 report, they use PGA to measure damage, pointing to the fact that PGA, unlike MMI is an objective measure, implying that MMI is not easy to obtain reliably across the globe. Also, for large scale modeling, where it is unfeasible to model local conditions precisely, PGA serves as a good proxy for intensity of earthquakes.

For the actual construction of the damage index, two types of data will be used; the intensity data ‐ expressed as PGA ‐ and building inventory data. The building type data stems from the USGS building inventory for earthquake assessment, which provides estimates of the proportions (based on total number of buildings) of building types observed by country; see 14
men for the same work. Other major sectors where majority of women are employed show similar trends (Table 10).

24 CBS. 2009. Report on the Nepal Labor force survey 2008. Kathmandu, National Planning Commission.

25 Published 18 December 2015. The Kathmandu Post at <u>https://ceslam.org/index.php?pageName=newsDetail&nid=7054.</u> 23
are intrinsically motivated they might need only to be provided autonomy, and the provision of explicit incentives might crowd out their intrinsic motivation. Importantly, the partial correlations of these two dimensions of management can still be separately estimated from each other and from the CS-other practices index.<sup>9</sup>

# **2.4 Management Practices Across Civil Service Organizations**

Figure 4 shows the across-organization variation in management practices, using our preferred aggregation of survey responses over the most senior divisional-bureaucrats. As with bureaucratic performance on projects, there is high variation in the management practices bureaucrats are subject to across organizations. For practices related to the provision of autonomy, the 75th percentile organization has a CS-autonomy score that is 145% higher than 25th percentile organization. On management practices related to incentives/monitoring, the 75th percentile organization has a CSincentives/monitoring score that is 97% higher than 25th percentile organization. To reiterate, this variation occurs despite the fact that all organizations share the same colonial and post-colonial structures, are governed by the same civil service laws and regulations, are overseen by the same supervising authorities, are assigned new hires from the same pool of incoming bureaucrats each year, and are located proximately to each other in Accra.<sup>10</sup> It is this variation in management practices along the dimensions of the autonomy given to bureaucrats, and the extent to which they are provided incentives or monitoring, that we now link to variation in project completion rates.

9 The CS-autonomy (CS-incentives/monitoring) index has a correlation of _._ 59 ( _._ 70) with CS-other.

> 10To provide practical detail on what drives this variation in management scores, we go back to consider the raw management scores in Table A1. In relation to the component of the autonomy score based on the question, “When senior sta↵in your division are given tasks in their daily work, how much discretion do they have to carry out their assignments? Can you give me
periods potentially understates participation in specific economic activities. For example, if an interview takes place in a month after the agriculture season, a person who primarily works on a family farm during the agriculture season could accurately report being inactive or a non-farm main activity if asked their main employment in the 7 day recall but would report the farm activity in the 12 month recall. Consequently the 7 day recall questions can underestimate overall participation (if the person reported no economic activity during the post harvest season) as well as producing a distorted picture of the categorization by activity type.

Do the differences between the answers to the 7 day and 12 month recall matter? In the UNHS 2009/10 respondents were asked if their primary activity in the past 12 months was the same as their 7 recall primary status, 7 day recall secondary activity or some other activities. As shown in Table 6 about 10 percent of the respondents reported a 12 month main employment status different from the 7 day recall main employment status. Below, we consider how in practice different recall periods actually affect the analysis of employment outcomes, at the aggregate level (e.g. structure of employment) and the individual level.

16
<!-- Start of picture text --> Balancing on the 1st order Balancing on the 1st order<br>.11<br>.1<br>.1 .09<br>.08<br>.09<br>.07<br>treated treated<br>.08 control .06 control<br>6 8 10 12 -4 -2 0 2 4<br>log(GDP per capita) log(Net ODA received in % of GNI)<br>Balancing on the 1st order Balancing on the 1st order<br>.1<br>.11<br>.08<br>.06<br>.105<br>.04<br>.02<br>.1 treated treated<br>control 0 control<br>1 2 3 4 5 0 20 40 60 80 100<br>log(Regulatory Quality) Foreign private participation<br>Balancing on the 1st order<br>.1112<br>.111<br>.1108<br>.1106<br>.1104<br>treated<br>.1102 control<br>0 .2 .4 .6 .8 1<br>Own website<br>Kernel density Kernel density<br>Kernel density Kernel density<br>Kernel density<br><!-- End of picture text --> Figure 5: Kernel densities of the covariates for the treatment and control group (Set 2) 18
more from the deregulation.

Our paper contributes to the growing literature on deregulation and investment. Using industrylevel data for 21 OECD countries, Alesina et al. (2005) show that regulatory reforms are associated with an

> 1 The allotment of input and import licenses were based on the output limit specified in the licenses. See Chamarbagwala and Sharma (2011) for additional details on the industrial licensing system in India.

> 2 While a major portion of delicensing was initiated following the 1991 crisis, some industries were delicensed in 1985 under Rajiv Gandhi, who unexpectedly became the Prime Minister upon the assassination of Prime Minister Indira Gandhi. Aghion et al. (2008) argue that both episodes of delicensing were exogenous shocks.

2
of marriage were converted in 1996 taka using national consumer price index. Bangladeshi wives had far less assets at the time of their marriage than their husbands primarily because their value of landholding, housing, and cattle were much less than those of their husbands (see Table 1). The assets at marriage may suffer from measurement errors due to recalling information, particularly for longer marriages. One option is to instrument spouses’ assets by their respective family background information, such as the wealth of their parents. However, those measures may also suffer from recall errors. Hence, I do not instrument these bargaining measures. To the extent the measurement errors are white noise, the evidence of bargaining (if any) will be an underestimation of the true bargaining effects. Education is measured by years of schooling. The mean years of schooling of husbands is almost double of that of wives.

While spouses’ assets at marriage are my key bargaining measures, following Quisumbing and Maluccio (2003), in the empirical analysis, I also include spouses’ age and age square to control for cohort effects, and for the possibility that their age difference could be another source of

> 151% of households have two wives, while 4.5% are female headed with no husband, and 3% have head without his wife. 91% households are intact with both head and his spouse. Based on data availability of assets at marriage, my analysis contains almost 98% of these intact households, or, roughly 89% of the all surveyed households.

17
incomes (Figure 6 (b)).<sup>17,18</sup> The impacts of labor income and transfers are higher in rural areas than in urban areas. In addition to these two factors, the contribution of the demographic variables in turn suggests the positive role played by a declining dependency ratio over the period.

**<u>Figure 6: Drivers of Poverty Reduction between 1995 and 2010</u>**

(a) By labor and other sources of income <!-- Start of picture text --> 100% 1<br>5 7<br>16<br>15<br>80%<br>36<br>60%<br>50<br>52<br>40% 33<br>20%<br>32<br>27<br>24<br>0%<br>National Urban Rural<br>Remittances Labor income Household demographics* Other factors**<br><!-- End of picture text -->

- Household demographics is a sum of share of adults and share of workers.

- **Other factors include <u>consumption-to-income ratio and capital income.</u>

> 17 An important caveat to this methodology is that it fails to satisfy the independence property. Dividing an income component into multiple components (e.g., labor income into farm and non-farm labor income) results in the total income effect different from the sum of the effects of the multiple income components (World Bank 2014).

> 18 The significant effect of labor income on poverty reduction is consistent with Inchauste et al. (2013), who conducted the same decomposition exercise using international poverty lines of $1.25 and $2.50 for 16 countries including Nepal.

13
# **3 Data**

## **3.1 Data Collection**

A baseline questionnaire was collected before performing the random allocation of the scholarship offer. This questionnaire included socio-demographic characteristics of the applicants, educational attainment, questions related to coding skills and previous coding knowledge, employment variables (status, industry where the applicant works, wages), and social skills as measured by the Big Five Inventory by Soto and John (2017). We gathered the tax identification numbers of respondents, which may be used in the future to analyze long term outcomes using administrative data. Given the scale of our experiment and power calculations, we applied the baseline questionnaire to all 803 eligible candidates.

Originally, a first follow-up face-to-face data collection was planned to take place after about one year of the program completion, with the objective of measuring short-term impacts on skill acquisition and employment dynamics. This would have been during September 2020 in Buenos Aires, December 2020 in Bogotá. However, these activities needed to be re-adjusted given the outbreak of the COVID-19 pandemic. In order to still capture the short-term impacts of the program, we designed and implemented an online survey during May and June 2020. The objective of this online survey was threefold: first, it was important to gather information about labor market outcomes of program participants right before the COVID-19 global crisis affected labor markets in both cities. For this, the period of reference of all employment questions in the survey dated back to February 2020. Second, information on educational outcomes and skills was collected in order to measure short-term impacts of the program on these indicators. Third, a specific module related to COVID-19 was administered in order to assess whether bootcamps participants were somehow in a better position than non-participants to tackle the crisis. This module included a question on resilience to a crisis in the job market generated by the COVID-19 outbreak, as well as employment information relative to May 2020, when the questionnaire was administered. Given the challenging times during which this online questionnaire was collected and
overcome the so-called collateral technology hurdle, which has hindered the development of credit markets in Africa (Ndung’u, Morales, and Ndirangu. 2016).

# **3. Empirical Strategy**

# **Data**

The data source used in this paper consists of firm-level surveys for Kenya, Tanzania, and Uganda conducted by the World Bank’s Enterprise Surveys with most of the information referencing fiscal year 2012.<sup>4</sup> The sample used for analysis covers a total of 1,228 manufacturing and services firms

> 4 The same set of questions on the use of mobile money was included in the Enterprise Survey conducted in Zambia as well. The data for Zambia, however, show a very low rate of adoption with only 25 firms out of a total sample of 720 using mobile money. Given the small sample of mobile money users, Zambia is not included in the analysis.

6
Yes|Yes|Yes|
|||P|anel B: Above|Median Wealth|at Baseline|||
|Goal-Setting|0.071|0.098**|0.056|0.052|0.056|0.089|0.048|
||(0.049)|(0.045)|(0.044)|(0.040)|(0.045)|(0.087)|(0.078)|
|Goal-Setting + Recognition|0.068|0.036|0.038|-0.030|-0.009|0.072|0.074|
||(0.062)|(0.052)|(0.047)|(0.056)|(0.053)|(0.090)|(0.072)|
|Observations|6,264|5,924|6,417|6,395|6,027|6,574|6,574|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by baseline wealth level of student’s household) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A reports the results for sample which is below the median level of wealth at baseline and Panel B reports the results for sample which is above the median level of wealth at baseline. Wealth is measured using the standardized index of the sum of all the self-reported assets owned by the household. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
In this section, we investigate ways to achieve the moderate and ambitious reform TFP paths using the LTGM-TFP extension of Kim and Loayza (2019). Based on an extensive literature review, Kim and Loayza (2019) identify the main determinants of economic productivity as innovation, education, market efficiency, infrastructure, and institutions (Box 2). Based on underlying proxies, that paper constructs indexes representing each of these main categories of productivity determinants and, combining them through principal component analysis, obtains an overall determinant index. TFP growth has a positive and significant relationship with the proposed TFP determinant index, and this relationship is then used to provide a set of simulations on the potential path of TFP growth if certain improvements on TFP determinants are achieved.

The Innovation and Infrastructure determinants of TFP perform worse in Peru than in its peers. Within the five components of TFP growth in Kim and Loayza (2019), Peru underperforms in Innovation and Infrastructure compared to aspirational and structural peers, lagging also behind the LAC, UMIC and HIC Median (Table 3). These are the two areas where the country possesses a wider gap with the rest. The stance regarding Market Efficiency and Institutions is lower than the aspirational peers, although it surpasses some of the structural peers and the regional median. Peru fares relatively well compared to peers on Education.

**Table 3. Main determinants of TFP in Peru and peer countries**

|||Asp|irational P|eers|S|tructural Pe|ers||||
|---|---|---|---|---|---|---|---|---|---|---|
||Peru|Chile|Malaysia|Romania|Ecuador|Colombia|South Africa|LAC Median|UMIC Median|HIC Median|
|Overall determinant index*|43.8|56.4|60.8|55.2|38.
Rather than sweep out variation in export supply with product-origin-year fixed effects, we include in the regression explicit measures of export supply. We do this because of our interest in _HOMEj_ , which would be co-linear with the usual full set of fixed effects. Instead, we fully parameterize export supply - using the total volume of exports of each product from each origin in each year - since these data are readily available in the trade data we have.<sup>30</sup> This approach allows us to estimate a coefficient on _HOMEj_ itself, not only on the associated interaction terms.

We do include year fixed effects in the regressions. These are useful as controls for aggregate shocks, but they also assist in moving between the general equilibrium theory and the constant elasticity of substitution import demand model we estimate. Fally (2015) shows that fixed effects in a PPML specification have a useful adding up property (in this context<sup>�</sup> _k M_<sup>�</sup> _j,t_<sup>_k|_</sup> _t_ = _T_<sup> ̄=�</sup> _k_<sup>_M k_</sup> _j,t_<sup>_|_</sup> _t_ = _T_<sup> ̄,where</sup><sup>_T_ ̄isaspecific</sup> year). The implication in this case is that the other parameters of the regression model determine the share of each product-country combination in PR’s annual import flows; they do not have independent effects on the level of trade.

# **5.3.1 Model specifications**

Our reduced-form is a Poisson Pseudo Maximum Likelihood (PPML) model: _Mj,t_<sup>_k_=</sup><sup>_e_[</sup><sup>_δ_(</sup><sup>_h−_1(</sup><sup>_X_</sup>
This allows us to verify the robustness of our findings to the variations in the sample. Namely, we can limit the sample to miscarriages late in the pregnancy, when the option of abortion is likely to have already been exercised by those who intended to do so.

# **3.2. How do women underreport abortions?**

As mentioned earlier, one of the major threats to the identification strategy is the possibility of abortions being reported as miscarriages. The present section looks at this issue in more depth. Substantial underreporting of abortions _is_ present in ENADID data. Juarez et al. (2008) construct an estimate of the number of induced abortions in Mexico in 2006, based on data on hospitalization for complications due to induced abortion, and a survey of medical personnel. The results show a low and a high estimate of the abortion rate: 28 and 39 abortions per 1000 women aged 15 to 44 (Table 2).

According to ENADID data, there were 8,438 induced abortions in Mexico in 2005<sup>9</sup> – slightly more than 1 percent of the low estimate of the number of induced abortions estimated in Juarez et al., 2008 (Table 2). Nevertheless, underreporting of abortions will impede identification only if women report abortions as miscarriages (as opposed to just not reporting pregnancies that terminated in abortion). Using epidemiological data on the likelihood of a miscarriage, we demonstrate that this scenario is highly unlikely.

Epidemiological studies show that the probability of miscarriage varies depending on the time elapsed since conception. The probability of very early miscarriage (within six weeks after the last menstrual period [LMP]) is quite high – 25 percent (Wilcox et al., 1999; Wang et al., 2003). This statistic however, is based on studies that use very sensitive pregnancy tests. Within normal settings, women are likely to mistake such early miscarriages for delayed menstrual periods, without realizing they were pregnant. Six weeks after the LMP, the
# **3.6 Regional Taxes**

The regional taxes taken into consideration in our model are the following:

- PIT at regional and municipal level aggregated by regions for the tax year 2016. The former is equal to €11.8 billion, the former €4.5 billion. Note that, given the regional structure of our SAM, we attribute municipal PIT to regions, although this is actually a tax collected by municipal governments and not by the regional ones.

- IRAP (BT) minus IRAP paid by public agents. It is regionalized and includes IRAP calculated on the regional-plant breakdown table for the tax year 2016. The sum over regions brings to a total IRAP collected by regions equal to €13 billion;

- IMU (property tax) from F24 revenue data, for the tax year 2016. The result is obtained from the sum of the government share and the municipalities share. In addition, the taxes were partitioned between natural persons and legal persons. The percentage of natural persons has been incorporated into the real estate sector. The ATECO sector already broke down the share relating to legal persons in the original data. As for municipal PIT, even IMU should be attributed to municipalities instead of regions. However, here we attribute the total amount (€20 billion ) to regional governments.

# **3.7 Savings**

The savings account is the sum of households, the Government, and the rest of the world’s savings.

In the specific, these components are calculated as follows:

- The households’ savings are equal to €70.3 billion. As for capital income in Equation 6, they are obtained by applying the share of households’ income from public debt interest ( _σhh_<sup>_interest_</sup> ) to the households’ savings from the national macro SAM. The calculation is the following

- _Sr,hh_ = _σhh_<sup>_interest_</sup> _Shh_<sup>_macroSAM_</sup> (7)