rafmacalaba/data-use-mentions-extended huggingface dataset:
to identify the causal impact of PAF program on outcomes of interest. Using two rounds of survey data and difference-in-difference combined with instrumental variable estimation method, we find that there is a positive and significant “PAF effect” on key welfare indicators. The estimated _net program impact_ on real per capita consumption growth is 19 percent for PAF participants. Other impacts include a 19 percentage points decline on incidence of food insecurity (as defined as self-reported food sufficiency for six months or less) and 15 percentage points increase in school enrollment rate among 6-15 year olds. While improvements in nutrition and social empowerment indicators are seen for all groups (treated/untreated), the impact of the PAF program on indicators associated with child malnutrition and indicators of social capital (trust, respect, relationships between different ethnic groups within the community) is yet to be detected. The policy implications of these results should be of interest to the government and to development partners in determining what may be effective instruments to deliver services to marginalized communities in what remains a fragile and difficult political environment.

This study contributes to the existing literature on impact evaluation of social fund in a few ways. First, we present results of a large-scale intervention using an experimental impact evaluation design. Second, we estimate the causal impact even when there is no perfect compliance with randomized assignment. Imperfections in randomized evaluations are quite reasonable in real world settings of large-scale programs and allowing for such in fact can maintain external validity of the impact estimates. Third, in addition to using per capita household consumption, we also report impact on food security and access to education which are important indicators of vulnerability in conflict settings.

# **2. Evaluation Design**

<u>PAF Setting: As mentioned earlier, PAF interventions are predominantly income generating</u> activities (IGAs), particularly in the initial rounds, since the social cohesion required to motivate investments in public goods often takes longer to form, particularly in communities affected by conflict. The interventions typically have the following sequencing
The main aim of the SPI exercise is a cross-classification of expenditures in which, for each infrastructure function or subsector (e.g. power generation), expenditures can be disentangled by their economic use (e.g. operations or investment). Standardized infrastructure expenditure data that are comparable across countries permit tracking over time while benchmarking trends and levels of infrastructure government outlays.

Template 10 consolidates the core of the budgetary data of the SPI exercise and follows definitions and classification of expenditures as presented in sections 2 and 3, largely based on the GFSM 2001. Reporting of spending is done using a cash-based approach.<sup>9</sup> Filling template 10 is a labor-intensive process that involves three steps (i) code mapping the budget, (ii) extraction of budget lines from budget documents, and (iii) actual consolidation of data in template 10 .

# **_Code mapping the budget_**

Reporting infrastructure expenditures using standardized functional and economic classifications involves a remapping of the country-specific budget classification to the GFSM 2001 format.

Code mapping has two components: functional code mapping and economic code mapping. In no other phase of the infrastructure data collection process do the data collector and the relevant technical personnel need to work so closely together as during the code mapping. The Ministry of Finance is usually the most relevant partner for the code-mapping exercise.

_Functional code mapping_ . The prevalent functional coding system needs to be identified as a starting point. Provided the country uses the GFSM 2001, the government expenditure data can be used directly; however, a functional code mapping exercise should be undertaken when the country is not utilizing GFSM 2001 (for example, when the country utilizes GFS86, United Nations 93, or any other system) or for reported years that precede the GFMS 2001 reporting. By way of example, table 3.5 presents the functional code mapping for Kenya, in which ―votes‖ of the Kenyan budget system were mapped to the GFSM 2001‘s COFOG.

> 9 In the GFSM 2001
sample of 14,000 small businesses in 12 developing countries. McCaig and Pavcnik (2021) use multiple rounds of the Vietnamese nationally representative household surveys–conducted once every two years—to examine the entry and exit dynamics of family-owned firms over a long horizon. The tax-census data allow us to document exit and entry of household businesses at a monthly interval, better capturing business cycle fluctuations.

Second, our study is among the first to document the extent of (temporary) suspensions of household businesses in a developing country context. Earlier literature has documented a significant dormancy rate of registered enterprises in developing countries. For example, Sengupta and Singh (2019) show that about 20 percent of all registered Indian firms are dormant. High exit barriers associated with governments’ costly and uncertain legal procedures—such as those related to labor retrenchment and bankruptcy—are often the major reasons behind formal enterprises’ prolonged inactive status. Household businesses in Vietnam, however, are not subject to significant exit barriers; most owner-operated firms do not employ any contracted workers, and the business closure does not usually involve lengthy legal procedures.<sup>4</sup> Their motivation to suspend operation, therefore, could be considered more purely as a copying mechanism to disruptions in business activities, such as those caused by the COVID-19 pandemic lockdowns.

Third, our analysis contributes to the literature that examines the impacts of COVID-19 on small and/or informal businesses, especially during the onset of the pandemic in early 2020. Several studies have explored the initial COVID-19 effects on small businesses in developed economies such as the United States (Fairlie, 2020; Crane et al., 2021), Canada (Beland et al., 2020), and Western European countries Kraus et al. (2020). In a developingcountry setting, Shafi et al. (2020) investigate the pandemic effects on micro, small, and medium-sized enterprises in Pakistan and find that most firms were severely affected and faced with financial and supply chain disruptions, and decrease in
the control group and the original treatment group were randomly selected according to the originally planned experiment. A balance test on these baseline study participants shows no systematic differences between the original control and the treatment groups (Table2). We find weak evidence that the control group was slightly less affected by the conflict. Our measure of conflict exposure is based on geo‐referenced data by UCDP (Sundberg and Melander 2013). It consists in an average of deaths by event within a 300 km radius weighted by geographic proximity to participants’ baseline location.<sup>7</sup>

> 6 In WEQ: Mvolo, Mundri East, and Mundri West ; in CEQ: Kajo Keji, Morobo, and Lainya ; in Lakes: Rumbek North (flooding during time of data collection).

> 7 300 km equals the maximum distance of any program participants to a KCB bank branch. We also tested 50 km, 100 km, 200 km radii, but the results remain extremely similar.

9
Beet Production – Results from a Farm Survey and Field Trials." _Frontiers in Plant Science_ , **_9_** (231). Krasnozhon, L. A. 2013. "Political Economy of Agricultural Market Reform in Ukraine: 'Good Bye Lenin'." _Journal of Private Enterprise_ , **_29_** (1), 119-140. Krupinsky, J. M., et al. 2006. "Crop Sequence Effects of 10 Crops in the Northern Great Plains." _Agricultural Systems_ , **_88_** , 227254. Lark, T. J., R. M. Mueller, D. M. Johnson and H. K. Gibbs. 2017. "Measuring Land-Use and Land-Cover Change Using the U.S. Department of Agriculture’s Cropland Data Layer: Cautions and Recommendations." _International Journal of Applied Earth Observation and Geoinformation_ , **_62_** , 224-235. Livingston, M., M. J. Roberts and Y. Zhang. 2015. "Optimal Sequential Plantings of Corn and Soybeans under Price Uncertainty." _American Journal of Agricultural Economics_ , **_97_** (3), 855-878. Meisner, M. H. and J. A. Rosenheim. 2014. "Ecoinformatics Reveals Effects of Crop Rotational Histories on Cotton Yield." _PLOS ONE_ , **_9_** (1), e85710-e85710. Mitchell, P. D. 2011. "Economic Assessment of the Benefits of Chloro-S-Triazine Herbicides to U.S. Corn, Sorghum, and Sugarcane Producers." University of Wisconsin, Agricultural and Applied Economics, Staff Paper Series. National_Sunflower_Association. 2013. "The Sunflower Production Guide." Mandan, ND. Plourde, J. D., B. C. Pijanowski and B. K. Pekin. 2013. "Evidence for Increased Monoculture Cropping in the Central United States." _Agriculture, Ecosystems & Environment_ , **_165_** , 50-59. Porter, P. M., et al. 1997. "
endowment is constructed from the human capital index, employment and average hour worked variables in version 9.0 of Penn World Tables (PWT) (Feenstra et al. 2015). The product of persons employed and average hours worked per person is multiplied by the human capital index they constructed from years of schooling to give a measure of effective (or augmented) labor – human capital endowment. Data on endowment of (physical) capital, defined as the value of capital stock at 2011 constant national prices and converted to millions of US dollars, are also from PWT. The number of days, number of procedures, and monetary costs (as percent of income per capita) needed for business startup, used in the probit models, come from the Doing Business database (last updated 03/01/2017, and available at http://www.doingbusiness.org). For each of the three variables, we used the average of the entries for men and women. Distance between exporter-importer pairs refers to the geographical distance in kilometers between the most populated cities and the data are from CEPII’s GeoDist database (see Mayer & Zignago 2011).

**Industry-specific Measures** Data on the contract-intensity of industries are from Nunn (2007). His measure of institutional intensity has two definitions. Both definitions refer to the weighted average of the proportion of relationship-specific inputs used in the production of the product concerned, where the weights are the value of the input relative to the value of all inputs. The difference is that whereas the first definition counts only inputs that are neither sold on exchange nor reference priced as relationshipspecific, the second one additionally includes inputs that are reference priced. Each of these definitions has two estimates: liberal and conservative. We used the first definition because it displays more variation (in terms of both range and standard deviation) than the second. Also, the correlation between liberal and conservative estimates is stronger for the first definition than for the second (0.9799 versus 0
(ii) the condition for a positive optimal brain drain ( dm<sup><u>dI</u></sup> s<sup>> 0forms= 0)is</sup> (iii) and the condition for an interior optimal brain drain ( dm<sup><u>dI</u></sup> s<sup>< 0forms= 1)is</sup> In these conditions, variables zs, z0 and z1 are country-speci...c and their distribution can be computed using the Gaussian kernel density estimator. The ratio<sup><u>�2</u></sup> �1 results from our estimation. Using the point estimates from our baseline regressions both in an unbalanced and balanced sample (column (3) of tables 1, 2, 3, 4 for the unbalanced sample, and columns (1), (3), (5) of table 5 for the balanced sample), we can simulate these three conditions.<sup>25</sup> To save place and given that we are mostly interested in the ...rst of these conditions, we do not present simulation results for the last two.<sup>26</sup> Figures 2a to 2d show the kernel distribution of 11��mh<sup>�zsandverticallinesat</sup> values of<sup><u>�2</u>the4indicators(thicklinesfortheunbalancedsample;thinlines</sup> �1<sup>for</sup> for the balanced sample). As can be seen from the ...gures, the marginal e¤ect of the skilled emigration rate is positive for almost all of the countries (the kernel distribution is completely to the left of the thresholds, except for CL in a balanced sample).<sup>27</sup> However, when we calculate 90 percent con...dence intervals (dashed
be such an issue in their studies given that the tariffs were reduced uniformly or proportionally across sectors. This is not true at least for Colombia; the liberalization was not uniform. Edwards (2001) notes that the trade liberalization reform of Colombia (“La Apertura”) was “announced during the presidential campaign [of Cesar Gaviria] as a ‘gradual’ and ‘selective’ process.” As can be observed from Figures 2 through 4, there is quite some variation in the tariff reductions across sectors.<sup>24</sup> Moreover, the Spearman’s rank correlations of tariffs in Table 1 indicate that the correlations reduced over time, implying a selective process as opposed to a uniform one in liberalization. Otherwise, the ranking of sectors in terms of their protection rate would not change.

The average advalorem tariff rates in my sample of 4-digit ISIC industries declined from 43% in 1983 to about 14% in 1992 and stayed around that rate in the following years (Table 2). The dispersion of tariffs across sectors also declined (Table 2). If we just look at the standard deviations, the decline appears to be markedly higher. However, we need to take into account the differences in the magnitude of tariffs across periods and the coefficient of variation<sup>25</sup> is a better measure for that matter. The decline in the dispersion is notably lower when we use the coefficient of variation. However, this outcome does not indicate that political economy is no more a factor in tariffs after reform. The decrease in the dispersion is predicted by my models given the fact that the liberalization occurs through the elimination of some extra channels other than the political economy channel.

In my theoretical models and the main estimations, I use tariff rates as a measure of protection for import-competing sectors. I also have access to effective rate of protection (ERP) data which I use to augment my results with tariffs. The effective rates take into account the tariffs on inputs
workers becoming students. Women in the agriculture sector became jobless with the drought and higher fuel prices in the second half of the 2000s, and were unable to find other work, thus returning to domestic services in their homes. In the case of the increase in students, this was partly affected by the easing of entry into universities, and the development of the financial sector (entry of private banks and insurance companies) that required high‐skilled workers. It is not clear though, if the developments cited in relation to student enrollment were expected to have a long‐term impact on the LFPR. Early in the conflict, International Labour Organization (ILO)‐ modelled estimates and projections suggested a stabilizing participation rate after 2010 (Graph 4(b)), though some caution needs to be exercised in taking this at face value given uncertainty surrounding the underlying data.<sup>9</sup>

**<u>Graph 3: Pre‐Conflict Population Growth Rate and Projection</u>**

<!-- Start of picture text --> (a) 2017 Vintage (b) 2011 and 2013 Vintage<br>Average annual Average annual<br>growth growth<br>4.0% 4.0%<br>3.0%<br>3.0%<br>2.0%<br>2.0%<br>1.0%<br>1.0%<br>0.0%<br>0.0% 1991‐95 1996‐00 2001‐05 2006‐10 2011‐15 2016‐20 2021‐30 2031‐40<br>1991‐95 1996‐00 2001‐05 2006‐10 Total population (UN 2011)<br>Working‐age population/total population (UN 2011)<br>Total population (UN 2017) Total population (UN 2013)<br>Working‐age population/total population (UN
via the United Nations platform COMTRADE. Notice that, in view of the time lag in uploads to COMTRADE, by the national institutes of statistics, a wait period of a few months should be observed before downloading the most recent annual bilateral trade data.<sup>3</sup> Due to this time lag, the mirror analysis cannot identify new trends in customs fraud in Madagascar.

Based on mirror gaps, we are then able to identify some sectors/products for which noncompliance seems common. Estimates suggest that undervaluation and misclassification<sup>4</sup> in 2014 accounted for a loss of revenue of US$53.7 and US$42.4 million, respectively.<sup>5</sup> Total estimated losses (96 million of US$ or MGA 232 billion) represented 30 percent of total non-oil revenues collected by customs. The analysis indicates that clothing (textile, footwear and leather goods) and high tech products (telephones, digital cameras) seemed to be substantially undervalued in 2014. Undervaluation of clothing products and telephones (and digital cameras) are, respectively, estimated at US$25.4 million and US$13.6 million.

Regarding misclassification, the paper highlights that, in Madagascar, some customs tariff headings not subject to duties and value-added tax (VAT) were probably used, in 2014, for tax evasion purposes. Considering that abnormal statistics (e.g. inconsistent unit values) are indicative of fraud, there is reasonable evidence that some high-taxed goods were declared as zero-taxed products, notably fertilizers and rice. The declared unit value of imports of rice and fertilizers (products exempt from duties and VAT) largely exceeded corresponding world prices. Based on discrepancies in trade statistics, we estimate losses in VAT revenues at US$12 million.<sup>6</sup> Despite the fact that rice imports were almost systematically physically inspected, _i.e.,_ directed through the red channel, very few infractions had been reported
1

# The Impacts of Public Hospital Autonomization: Evidence from a Quasi-Natural Experiment

## by

## Adam Wagstaff<sup>a</sup> and Sarah Bales<sup>b,c</sup>

a Development Research Group, The World Bank, Washington DC, USA b Lee Kuan Yew School of Public Policy, National University Of Singapore, Singapore c Ministry of Health, Hanoi, Vietnam

**Corresponding author:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: awagstaff@worldbank.org.

**Keywords:** Hospital autonomization; hospital autonomy; hospital efficiency; out-of-pocket health spending; Vietnam.

JEL codes: I12; I18.

**Acknowledgements and disclaimer:** Our thanks to Ha Thai Son for advice on the hospital inventory data, Pham Thi Lan for extensive data-cleaning work, and Dao Thanh Huyen for her support. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
<!-- Start of picture text --> Figure 5 - How many countries have disclosure guidelines at account opening?<br>Local Language<br>Plain Language<br>General<br>Recourse<br>Standardized Format<br>APR & Interest<br>Compounding Method<br>Deposit Early Withdrawal<br>Fees & Penalties<br>Minimum Balance<br>APR<br>Computation Method<br>Credit<br>Fees<br>Required Insurance<br>0 20 40 60 80<br>% of Countries<br>Source: Financial Access Database<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6 - How many countries have periodic disclosure guidelines?<br>General Info on Dispute<br>APR<br>Account Balance<br>Deposit<br>Fees<br>Interest Earned<br>APR for Period<br>Date Due<br>Fees for Period<br>Credit Interest Charged<br>Minimum Amount Due<br>Outstanding Balance<br>Transactions<br>0 10 20 30 40 50<br>% of Countries<br>Source: Financial Access Database<br><!-- End of picture text --> 23
a commonly-used proxy for income, Nighttime Lights (NTL) per capita.<sup>5</sup> We process DMSP-OLS data for three years (1992, 2000, and 2012) for the areas matching with the GHS-UCDB urban areas used in the rest of the paper. Column (5) reports the results using Nighttime Lights per capita instead of the previous income measure. Our preferred specification is given by the between and within estimator in Column (2). Estimates are similar when using instrumental variables and Nighttime Lights as a proxy for income. Income and population elasticities can be inferred from the results of the regression.<sup>6</sup> As a city’s population doubles, its built-up surface increases by 34.7% when keeping income constant and controlling for unobserved city and year characteristics. This number is lower than previous estimates reported in the literature (Angel, et al., 2016) and shows that cities have not extended their built-up as much as their population growth would have suggested. As a city’s income doubles, the built-up surface increases by 10.4% when keeping population constant and controlling for unobserved city and year characteristics.

As a final robustness check, we run the same regression as in Table 4 using population, income and builtup from the Functional Urban Area (FUA) database developed by the OECD and the European Commission. The spatial entities represent the commuting areas of the urban centers in 2015, which are geographically larger than our urban extents as they often contain multiple urban centers. Annex 2 presents a similar table as in Table 4. The new coefficients are similar to the ones estimated using our urban centers.

**Table 5 : Determinants of built-up surface across income groups**

|**Dependent variable:**<br>**built-up**|**All**<br>**countries**|**Low income**|**Lower**<br>**middle
and thus more or less likely to be also later acquired by foreign investors.<sup>5</sup> The impact of these spillovers and externalities is presumably substantial, given that the measured effects of financial liberalization often differ depending on the aggregation level of the used data. For example, foreign investors seem to have strong impact on the productivity measured both at the firm level (Varela 2018) and at the country level (Bonfiglioli 2008; Bekaert, Harvey and Lundblad 2011; Chari, Henry and Sasson 2012), while at the industry level there might be no productivity effects at all (Levchenko, Ranci`ere, and Thoenig 2009).<sup>6</sup> The effects of financial liberalization on investment and economic activity also seem to be stronger when measured at a more disaggregated level (Henry 2007; Bekaert et al 2011). It is thus important to distinguish between the impact of financial liberalization on firms attracting foreign investors and the overall net effect on the recipient economy (Mitton 2006, p. 646).

In our paper, we sidestep the issue of economic externalities and examine the full structural impact of financial liberalization with the help of product-level data covering countries’ complete export portfolios. Furthermore, the Heckscher-Ohlin framework of international trade allows us to look beyond the usual focus on the misallocation of physical capital and consider also other factors of production like labor, land, and human capital.

Second, we contribute to the literature examining the disciplining impact of foreign investors. Foreign investors might act more forcefully as external monitors because they lack business relations with local firms and thus do not feel loyal to the local management (e.g., Gillan and Starks 2003; Ferreira and Matos 2008; Aggarwal et al. 2011; Bena et al. 2017; Luong et al. 2017). This disciplining impact can also extend beyond the firms where foreign investors ultimately acquire equity stakes. For instance, foreign-owned firms can generate substantial spillovers in terms of improved corporate governance to

> 5