rafmacalaba/data-use-mentions-extended huggingface dataset:
2000-10 2011-20 2000-2021 2000-2010 2011-2021<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Educational, Scientific, and Cultural Organization (UNESCO) Institute of Statistics; UN Population Prospects; World Bank; World Development Indicators database. Note: EAP = East Asia and Pacific; TFP = total factor productivity. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates).

A.C.-F. Bars show period averages of annual GDP-weighted averages. Markers show median of GDP-weighted averages of the six EMDE regions. Orange whiskers show min-max ranges.

A.B. Estimates based on production function approach. Sample includes 53 EMDEs, of which six economies are from EAP (China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand).

C.D. Sample includes China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand (where potential growth estimates are available for both investment growth and TFP growth measures for the period 2000-21). E. Period averages of simple annual averages. Percentage of population ages 25 and above that completed at least lower secondary education. “EAP ex. China” includes Indonesia, Malaysia, Mongolia, the Philippines, and Thailand.

F. Working-age population refers to population ages 15-64. Sample includes six EAP economies.
changes represent major modifications in the fiscal incidence landscape. Third, the paper incorporates an analysis of Brazilian indirect taxes under the same cross-country and time comparable methodology.

Our results indicate that the Brazilian fiscal policies are typically poverty- and inequality-reducing, but with a large heterogeneity in the effectiveness of fiscal tools. We document that the fiscal system in 2019 reduces extreme poverty by 5.9 percentage points (p.p.) and moderate poverty by 0.6 percentage points. 1F<sup>2</sup> Our results also indicate that indirect taxes are responsible for a large increase (6.1 p.p.) in poverty, but the poverty reduction effects of rural pensions, BPC, Abono Salarial and Bolsa Familia more than compensate that. Despite the modest overall reduction in moderate poverty, we show a substantial decline in both the poverty gap (4.7 p.p.) and the squared poverty gap (5.9 p.p.) in terms of moderate poverty when we shift from market income plus pensions (MIPP) to consumable income. Considering the extreme poverty line, fiscal policies reduce the poverty gap by two-thirds (6.9 p.p.) and the squared poverty gap to a fifth of its original value (a 7.1 p.p. decrease). This means that even though the fiscal system does not reduce much the moderate poverty headcount ratio, it does alleviate the situation of the poor. Notably, out of 40 countries with poverty rates available in the CEQ database, only 11 were reducing moderate poverty from MIPP to consumable income. 2F<sup>3</sup> We also show that income inequality, measured by the Gini index, is reduced by 6.4 points when we compare the pre-fiscal income concept (MIPP) with consumable income. We additionally show that such

> 2 The threshold for moderate poverty is half of the 2019 minimum wage (BRL 499), while that for extreme poverty is the one that
sustainability if efficiency is not improved enough. Specifically, BRI investment is assumed to be fully realized during the period 2019-2023. For each BRI-recipient country, the annual level of BRI investment, assumed constant in U.S. dollar, is used to estimate annual levels of additional growth. The country specific estimates of additional growth from BRI investment are reported in Annex IV.

# _Empirical estimates_

In addition to model-based growth projections, this paper also estimates growth based on an empirical production function approach. BRI investment is expected to significantly increase the infrastructure stock of BRI-recipient countries. Following Calderon (2009), we built an unbalanced panel for a subset of 31 countries, created an index of different infrastructure assets and dealt with endogeneity and reverse causality issues of the infrastructure stock noted in the literature.<sup>28,29</sup> We also depart from Calderon (2009) as we use annual data and not 5-year averages, as we are interested in medium-term effects of installed capital on growth.<sup>30</sup> To measure the quantity of physical capital we built indices of infrastructure using Principal Components Analysis (PCA). This technique allows to avoid multicollinearity problems and measurement errors associated with single-infrastructure indicators. Using weights generated by PCA, we aggregate individual physical measures of infrastructure in telecommunications, electricity, and roads to compute an index of infrastructure stocks. Annex VII describes in more details the PCA analysis and data sources.

We run a regression of growth in real output per capita on a standard set of growth determinants, and on aggregate indices of infrastructure stock. Our estimation methodology is the generalized method of moments (GMM) for dynamic models of panel data (Arellano and Bond, 1991; Arellano and Bover 1995; Blundell and Bond, 1998), which is suited for dynamic panel data and accounts for some endogeneity in the explanatory variables through instrumentation. Our set of control variables comprises information on the level of trade openness, human capital (as proxied by the human capital indicator of Penn World
The results are consistent with those obtained earlier – i.e., in the GATT period, the estimated IRR is less than one (though not significant) indicating that periods of lower unemployment were associated with heightened import protection through TTBs. While the differential between these two estimated IRRs is not statistically significant in this sample of data, part of this is likely explained by the poorer quality of unemployment data during the early period, in terms of how accurately such measures captured the health of the domestic economy, given the role of the informal sector.<sup>27</sup> Furthermore, because the sample of countries for which the unemployment data is available at all is significantly

> 27 Even for emerging economies with available unemployment rate data included in the sample, the argument is that unemployment rate itself may be becoming a more accurate and representative indicator for the overall health of the domestic economy over time due to the role that the informal sector plays in many countries. I.e., unemployment rate data for these countries may be noisier earlier in the sample if there is a general upward trend in formality within a country over time.

<mark>21</mark>
sample. For example, our data do not reveal policy impacts on firm entry and exits. Also, because our data cover only manufacturing firms, our results do not apply to nonmanufacturing sectors. Finally, our sample does not contain what some view as the most productive sector in India—the software industry—which may bias our estimates and overstate China’s productivity advantage.

# **_Data_**

We draw our firm-level data from World Bank surveys on the two countries’ investment climate in 2003. The two surveys are similar in sample design and survey instruments. However, some differences remain. The India survey covered 1,860 manufacturing establishments, sampled 5
Using a combination of a manufacturing survey and data on expansion of preschools in Indonesia, we provide causal evidence of the impact of institutional childcare availability on manufacturing plants’ productivity. We do so by employing a triple diff-in-diff estimation comparing the outcomes of plants in sectors that rely on female labor at baseline to different degrees. This allows us to address several key threats to the identification by employing a large array of time varying fixed effects. Our preferred results suggest that an additional preschool per 1,000 children (approximately 1/3 standard deviation increase in preschool density) boosts TFPR by 11 percent for plants with average fraction of women among their employees. The increase is driven by changes in technical efficiency of the firms: we find zero impact on markup and impacts on TFPR and TFPQ are of similar magnitude.

Our results are robust to various changes in specification. In addition to our preferred OLS specification, we measure impacts of preschool expansion on plant productivity in an instrumental variables framework. We also confirm the robustness of the estimates to using different levels of sectoral aggregation, different methods of accounting for missing years in preschool data, and to including a rich set of time-variant control variables. We find suggestive evidence in support of two channels underlying the observed increase in productivity: better allocation of talent leading to improved matching between firms (à la Pissarides, 2000), and increase in job stability, which we measure through a reduction in turnover.

Our study offers the first evidence that provision of childcare may enable governments to reap short-term economic benefits through higher firm productivity. To what extent this effect could hold beyond the specific Indonesian context remains an open question. At the beginning of the study period only 49 percent of Indonesian women worked, and a large fraction of these women were highly educated. Moreover, Indonesian firms, more than their counterparts in the neighboring countries, suffered from low access to qualified personnel. Thus, removing childcare constraints to women’s labor force participation resulted in an influx of educated workers in the labor market
1|0.3|8.2|3.5|1.1|4.6|6.3|1.2|

_Notes:_<sup><u>a</u></sup> Results are based on round 4 of ESS. Results of round 4 were not available for Italy and Austria; the latest available results are presented instead.<sup>b</sup> ̳Core period‘ is the quarter during which _most_ of the field work has been performed; it is given for the reference only; by contrast, LFS-based unemployment rate has been calculated as weighted average of quarterly unemployment rates for quarters covering the whole field work period.<sup>c</sup> Due to data limitations (only year but not month of the last job is known for non-employed respondents), the shares of non-employed who were recently informally employed (see the last column) are not perfectly comparable across countries. Average 'recent' period varies as follows: 10-16 months in the South, 1118 months in the East, 11-15 months in the West, and 10-12 months in the North. Experiments with the data show, however, that changing this period by few months do not change the results significantly. _Source:_ Calculation with ESS data.

51
>India from 2001-2013.|<br>An increasing level of TFP is associated with the lower<br>level of EI or better EE. Better skills and capacity<br>utilization have a positive impact on EE performance of<br>the paper industry, while the heterogeneity within the<br>structure of the industry hurts EE performance.|
|Filippini et al. (2020)|The two-stage approach<br>of translog cost function<br>and DID approach are<br>applied to 5340 Chinee<br>iron & steel firms for<br>2003 and 2008|<br>The national EE program had a positive and statistically<br>significant effect on productivity in iron & steel firms. The<br>EE program increased annual productivity growth by 3.1%<br>points, with approximately equal contributions from<br>technical change and scale efficiency change.|
|Palčič & Prester<br>(2020)|.<br>A four-step OLS is<br>applied to survey data<br>collected from 232<br>Croatian and Slovenian<br>manufacturing firms<br>during 2018-2019.|Green innovation has an impact on firms’ competitive<br>advantage through their image, and not necessarily<br>through financial gains. Advanced manufacturing<br>technologies also contribute to both the firm's productivity<br>and green innovation.|
|Gong et al. (2020)|<br>An OLS method is<br>applied to high-pollution<br>firms (Shanghai and<br>Shenzhen) in China from<br>2009 to 2018.|The influence of rising labor costs on green technology<br>innovation has a threshold effect. The effects of the rise of<br>labor costs on the green technology innovation of high-<br>pollution firms illustrate an “inversely U-shaped” variation<br>trend with the increase of the degree of market
.4|62.4|68.5|66.2|59.9|54.1|
|Medium|41.4|49.2|70.1|81.7|84.3|81.6|68.1|
|Large|47.8|80.4|54.1|85.5|84.0|81.0|72.1|

Source: Author’s own elaboration from enterprise surveys _Kenya is by far the country with larger internet intensity use from the six African countries._ Looking at the internet index described above for all African countries, Kenya’s internet intensity is higher than other African countries (see Table 12). At the sector level, manufacturing firms uses more internet than firms in the service sector. Moreover, huge differences emerge while looking at the firms’ size. On average, the internet index is nearly three times higher in large firms than small firms.

_<u>Table 12 Internet Index by Country, Sector and Size</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|91.26|62.33|59.78|66.04|105.45|152.08|100.00|
|**Main Sectors**||||||||
|Manufacturing|94.07|70.50|63.69|59.66|138.19|149.85|104.63|
|Service|88.33|59.64|58.32|68.13|96.37|153.07|95.44|
|**Size**|||||||
.

<!-- Start of picture text -->
1.00<br>0.89<br> 0.90 0.84<br>0.78<br> 0.80 0.73  0.72  0.75<br> 0.70<br>0.5732<br> 0.60 0.53750.5207<br> 0.50<br> 0.40<br> 0.30<br> 0.20<br> 0.10<br> -<br>Rank correlation AUC R-squared<br>Full sample predictions Out of sample predictions Out-of-sample plus training  values<br><!-- End of picture text -->

The RWI, although the second-most accurate method for predicting the benchmark welfare, is far less accurate than the partial registry results, and only explains 4 percent of the variation in our measure of average village predicted welfare. This is probably because the RWI is based on a measure of household wealth instead of consumption or predictive consumption. Wealth may not be as accurate at distinguishing the welfare levels of villages within 10 poor districts. Moreover, the wealth index reflects the full distribution of households, whereas the benchmark welfare measure only pertains to the bottom half.

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status, are overwhelmingly large (with an average household size of almost eight), more likely to be female-headed, have much lower per capita consumption, and with children less likely to be enrolled in school. These characteristics can be used to increase the share of applicants assigned to beneficiary status. Alternatively, instead of using a list of demographic eligibility criteria, programs can take advantage of routinely collected administrative data on refugees to predict per capita household expenditures (Altındağ et al. 2020), which would help create a continuous score (like the commonly used proxy means test scorecards) that could be used to rank households, and expand/contract the program rolls over time as needed. This was not possible when ESSN was being designed in 2016. Finally, donors can also consider universal child support grants (and, perhaps, old age pensions) for refugee households, meaning that all refugee households would receive modest transfers to support children (and elderly), while cash transfers for able-bodied adults could then be targeted to the most vulnerable among refugee populations.<sup>34</sup>

> 34 For example, UNICEF is currently implementing a conditional cash transfer program for education in Turkey (CCTE), which provides monthly cash transfers to school-aged children of refugees. However, the transfers are not universal and are conditional on regular school attendance.

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schools and with the distribution of schools across regions being in proportion to their relative population size (using the 2002 census). The final sample consists of 62 schools in Lindi, 138 in Morogoro and 220 in Shinyanga.<sup>15,16</sup> Schools were initially randomly assigned to different treatment groups using a three-step process. First, for each region, a natural category (region, district, or combination of districts) with about 60 schools was constructed.<sup>17</sup> Second, within each natural category, we generated a ranking of school quality using Form II pass rates (derived from Inspectorate Data) and then we created three strata using terciles of the pass rate ranking.<sup>18</sup> Third, random assignment was undertaken as follows: (i) within each tercile, half of the schools were randomly assigned to receive teacher incentives with other half as control; (ii) within the teacher incentives arm, half of the schools were randomly assigned to teacher incentives only and the other half to teacher incentive plus student incentives; and (iii) within the control arm, half of the schools were randomly assigned to receive another treatment which turned out to be unfeasible and these schools were not visited again in Phase 1, with the other half assigned to have continual data collection during phase 1. This produces a sample with phase 1 assignments of roughly 25 percent to teacher incentives only, 25 percent to both teacher and student incentives, 25 percent to unfollowed control, and 25 percent to followed control.

In phase 2, half of the schools in which teacher incentives had been implemented (either with or without student incentives) were randomly selected to continue with the scheme, and half of the schools were selected to discontinue it. In addition, the control group from phase 1 (including the unfollowed arm) was randomly split into a pure control group and a student-incentives only group. This produces a sample with phase 2 assignments of roughly 25
increases monotonically with income. This presumed pattern of non-response to the household survey would also help explain this difference in capital‘s share, since the rich are likely to have more capital than the poor.

For our central model, we took total value added by sector from the National Accounts, but given our desire to preserve ―real households‖ and our focus on poverty, we did not want to alter the HBS factor shares. Thus, we did not alter the HBS data or value-added data by industry from the National Accounts. Rather we adjusted factor shares at the industry level to be consistent with the factor payments implied by the HBS. This reconciliation of the two sets of data significantly decreased the share of capital reported by the input-output table, especially in some of the more capital intensive sectors like ferrous and non-ferrous metals (see table 3.1).<sup>44</sup> We acknowledge that alternate methods of reconciliation are valid. For the purposes of sensitivity analysis, therefore, we have also run the model with the opposite reconciliation. That is, we adjusted factor shares at the household level to be consistent with the factor payments of the national accounts. We report these results in the sensitivity results.

# **4.4 Estimated Impacts of Russian WTO Accession**

# **Aggregate Results in the Full 55,098 Household Model**

In our general WTO scenario, we make the same assumptions regarding the counterfactual changes in parameters as in section 3. Aggregate results are summarized in table 4.1. Aggregate welfare results in table 4.1 are obtained by aggregating the equivalent variation gains (as a percent of consumption) of the 55 thousand consumers, using population weights. We report both the mean and

> 44 Of course as capital‘s share decreases, labor‘s share must increase, but reconciliation of data with the household data, also resulted in an increase in the relative share of skilled labor to unskilled labor. This is because, lacking more
# **1. Introduction**

Increasing tax collection in developing countries is challenging. The core issues encompass a broad spectrum, including inefficient tax systems, high levels of informality, and constrained tax bases, which lead to high levels of tax evasion and avoidance. In this paper, we present an evaluation of a tax enforcement program conducted in the City of Gorontalo, Indonesia, where the tax authorities sent officers to visit properties and engage directly with owners about their property tax obligations. By conducting visits to discuss tax liabilities and explaining the payment process, the program aimed to enhance tax compliance and augment revenue.

We had access to an administrative dataset encompassing all properties in the City of Gorontalo from 2014 until 2022. The data includes property characteristics, historical compliance, and some information on the ownership. This allowed us to use machine learning techniques to assess the program’s effectiveness and investigate heterogeneous effects. The techniques used allow us to assess the program’s effectiveness by contrasting the outcomes of visited households with comparable households that were not visited.

We are mainly interested in the program’s capacity to induce compliance in the absence of immediate penalties. More explicitly, our goals are to answer the following questions: (1) Are visited houses more likely to pay property taxes? (2) Does this effect persist over time? (3) What are the characteristics associated with higher/lower responsiveness to the visits?

We find a notable positive impact of the visits on tax compliance, underscoring the potential of direct engagement strategies in improving tax collection efforts. Specifically, these interventions increased the tax compliance on the extensive margin by 3.5% and on the intensive margin by 3.4%. Considering the baseline results for the visited properties, the program was responsible for a relative increase in the extensive margin of 4.3% and on the intensive margin of 5.1%.

Visited properties differ considerably from their counterparts, among many factors, they displayed much better historical tax compliance. The
is hardly a difference in the effect size whether we use the sample of all firms controlling for their status at baseline—already existing or new—or whether we use the sample of already existing firms at baseline only. The estimated impact on investment corresponds to a modest increase of about 16 percent. In terms of shares, in the group of cash beneficiaries, 35 percent reported some investment (on average XOF 0.882 million or US$1,600), whereas this share was only at 19 percent (on average XOF 0.154 million or US$280) in the control group. So, clearly, more firms in the group of cash beneficiaries invested and they invested on average much higher amounts. It is interesting to see that the average of US$1,600 is not far from the average cash withdrawn reported in the monitoring data for the same firms over the same period. As expected, we do not yet find any effects for beneficiaries of matching grants.

**Figure 4. Rollout of cash and matching grants**

(a) Share of beneficiaries of cash grants who received transfer into their account by month (b) Share of beneficiaries of cash grants who withdrew cash from their account by month <!-- Start of picture text --> 1,0 1<br>0,9 0,9<br>0,8 0,8<br>0,7 0,7<br>0,6 0,6<br>0,5 0,5<br>0,4 0,4<br>0,3 0,3<br>0,2 0,2<br>0,1 0,1<br>0,0 0<br>(c) Share of beneficiaries of matching grants who filed a (d) Share of beneficiaries of matching grants who<br>request to MEBF by month received approval for their request from MEBF by<br>