rafmacalaba/data-use-mentions-extended huggingface dataset:
insignificant impact of spending on aggregate health status.<sup>23</sup> But their findings highlight the importance of considering the incidence of the health benefits: they benefit (at best) only the poor so cuts without reallocation would fall on the poor. As explored below the impact on the poor versus the non-poor is not a constant or immutable parameter, but depends on the composition and efficacy of public spending. Jamison et al (1996) use an unusual econometric procedure and find that public spending on health lowers mortality in their sample of Latin American countries.24 When we attempt to replicate their finding using our regressions and limiting them to only Latin America, we find a positive and significant impact of public spending on health if only income is included. However, adding our non-income control variables reduces the magnitude of the estimate by half (and it is becomes statistically insignificant)." 23 For infant mortality they find that the elasticity at the population weighted means is - 0.213 for the poor and -0.056 for the non-poor, which is roughly comparable to our -0.078 in the aggregate.

24 Jamison et al (1996) use a panel of data from 1960 to 1990 to first estimate (the natural log of) under 5 mortality as a function of income and year (and interactions) and whether the country was Latin American for a broad cross section of countries. They then use the anti-log of the predicted values to create a percentage deviation of the level of the actual and predicted for each country and then regress those percentage deviations on the public and private share of health in GDP for the sub-sample of Latin American countries.

25 The regression with just income has an R-squared of .6474 and estimates (t-statistics) for the sample of 21 countries: In(uS) = -.70S In(GNPpc) - .464 InPHS (5.3) **(3.7)** The regression which includes our non-income control variables has an R-squared
.265)|(1.254)|(1.151)|(0.579)|(1.106)|(1.907)|
|Years in office|0.088|0.137**|0.075|-0.069|0.030|-0.200|
||(0.067)|(0.065)|(0.057)|(0.057)|(0.056)|(0.129)|
|Years to election|0.095|0.096|0.050|0.061|0.060|-0.025|
||(0.071)|(0.085)|(0.086)|(0.086)|(0.123)|(0.177)|
|Country FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|560|561|561|561|480|1,017|
|R-squared (within)|0.213|0.237|0.212|0.152|0.447|0.151|
|Countries|40|40|40|40|40|40|

Notes: Standard errors clustered by country in parentheses. Dependent variables are from the September 2018 update of the Worldwide Governance Indicators (estimates of GE = Government Effectiveness; RQ = Regulatory Quality; RL = Rule of Law; CC = Control of Corruption), various editions of the Corruption Perceptions Index (CPI), and the July 2018 version of the V-Dem dataset (ECI = Executive Corruption Index, reversed). See data appendix for full variable definitions and sources. *** p < 0.01, ** p < 0.05, * p < 0.1.

11
Data on financial inclusion mandates and reforms are introduced and analyzed in section 4. Section 5 concludes with a discussion.

# **2 Counting the unbanked**

We follow Beck _et al._ (2007) to predict the extent of access to formal deposit services offered by regulated financial institutions by households according to the following model: where _HH sharei_ is the percentage of adults with a bank account in country _i_ based on various household surveys collected on or after 2006. This data are compiled from various sources: recent Living Standard Measurement Surveys (World Bank, various years) where available, as well as regional sources: for the European Union, the European Commission’s Eurobarometer, Special Barometer 260 (2007); for Africa, FinMark Trust’s FinScope; for Latin America, Tejerina and Westley (2007), the MECOVI database, and Barr _et al_ . (2007); and Nenova _et al_ . (2007). These data are referenced and expanded upon in Claessens (2006), Honohan (2008), Gasparini _et al_ . (2005) and Beck _et al._ (2007). See Table 1 for further details. Note that although some of these surveys are at the household level and some are at the individual level, Honohan (2008) argues that they can be used interchangeably.

Beck _et al._ (2007) point out that the logarithmic specification on the right-hand-side of (1) is due to outreach indicators having fat tails. Note also that the dependent variable takes values between zero and one only, and to avoid the predicted values from falling outside this range, it is possible to estimate equation (1) using a Tobit specification. Beck _et al._ also state that the coefficients and significance levels are similar under Tobit and OLS, which is also confirmed by our estimation results. Nevertheless, for the predicted values to lie within zero and one as well, Tobit specification is preferred. Table 2 provides the results from estimating equation (1)
in the billing data and comparing it to the IBT structure publicly issued by the utility. Appendix 2 contains an example of how this exercise is carried out.

In general, JVVNL generates a new bill once every two months. In some cases, however, bills are delivered before or after the two-month interval based on the route taken by the utility’s roving meter readers to visit respective households. Observations for which the billing interval is more than 180 days are excluded from the analysis. If the meter reader is unable to establish contact with the household or if the meter appears to be malfunctioning at the time of reading, the consumption figures for the household are not captured in the billing data set. In such cases, JVVNL makes an imputation based on the average consumption of the household over the past six months. Such observations are dropped from the sample resulting in exclusion of an additional 1,250 observations (13% of the data).

To provide an overview of prices and consumption variables in the data set, Table 1 shows the summary statistics for all households over 2014-2017 period. The average monthly per capita consumption of electricity in the sample is 39.7 kWhs – more than double the per capita residential usage of 15 kWh in Rajasthan in 2012.<sup>11</sup> The increase in the per capita electricity consumption reflects robust economic growth and rapid rates of electrification in the region.

Policy induced changes in prices are key to recovering model estimates. Marginal prices on the increasing block tariff for residential consumption were revised twice within the sample period. Figure 6 shows the distribution of average daily household consumption by the month of bill issuances,<sup>12</sup> with red labels in the horizontal axis indicating the periods in which these price changes occurred. There is considerable heterogeneity in consumption across households within a month which provides the household-level variation required to estimate the parameters of the model. Expectedly, there is seasonality in the consumption levels – with peak and lows during the summer
Appendix Table A.11 reports robustness checks on bureaucrat–citizen alignment outcomes: removing controls, adding region fixed effects, including a third-degree polynomial in the running variable, varying the RD bandwidth, estimating the fuzzy RD, and removing villages with Suharto-era elites.

**Bureaucrat-Citizen Information Flows: Application to Roads.** Bureaucrats may invest in the services prioritized by citizens for at least two different, complementary reasons: because they have collected accurate information about what citizens want, and because they have gained better knowledge of service quality through direct observation. To probe these channels, we focus on one dimension of service quality—the quality of roads in the village—and examine the full causal chain going from the policy diagnosis to the relevant policy action. We find that both channels likely matter: in villages with high road quality, turnovers increase accurate perceptions of road quality among bureaucrats, while in villages with poor road quality, turnovers increase the likelihood that bureaucrats have heard citizens’ complaints about roads, and that the village government has taken relevant action on roads.

To perform this test, we measure baseline road quality using the 2014 _Podes_ survey. Road quality is proxied by an index of transportation costs (per km) to the subdistrict office and the regent or mayor’s office, where lower transportation costs indicate better road quality.<sup>9</sup> Table 6 splits villages into those with higher road quality (transportation costs below the sample median; panel A) and lower road quality (transportation costs above the sample median; panel B). This baseline measure of road quality is balanced at the RD threshold (Table 6, column 1). This balance is critical, as transportation costs may reflect a range of factors beyond village-level investment, including geography, remoteness, and highertier infrastructure. The RD design therefore isolates the effect of turnovers on bureaucratic decisions shaping road provision within the village’s purview, net of these confounding sources of variation.

In villages with high road quality, bureaucrats
coffee prices (note that Zone is a higher level administrative unit than Kebele/Village).<sup>10</sup> An alternative way of capturing this is using zone-specific time trend, which gives very similar estimates. _C_ denotes consumption. ESS data reports household level adult-equivalent per capita consumption expenditures disaggregated into food and non-food. _LandShareCoffee_ is the share of household land allocated to coffee. _Price_ is the average international coffee price in the four quarters before the consumption survey month.<sup>11</sup> Identification comes from large variations in the fraction of farmland allocated to coffee across households within a zone and massive swings in the international coffee prices over time. Table A.2 shows that about 82% of variation in adult equivalent consumption and about 61% variation in fraction of land allocated to coffee are within-zone variation. The identification assumptions are: (i) households cannot endogenously adjust the fraction of their farmland allocated to coffee in response to coffee price (at least in the short run); (ii) international coffee price change is exogenous to Ethiopia; and (iii) coffee price movement does not have any significant effect on non-coffee producers.

To rule out any concern about households endogenously adjusting the fraction of land allocated to coffee in response to coffee price change, I use the share of land allocated to coffee at the beginning of the panel, i.e., the year 2011. The lumpy nature of coffee production by itself rules out any endogenous adjustment of coffee production in the short run. Coffee plants take about 5 years to grow into trees and bear fruit. Hence, it is unlikely that farmers would adjust the share of their land allocated to coffee following fluctuations in coffee prices. Moreover, variation in the fraction of farmland allocated to coffee is largely attributed to geographic variation in agro-climatic suitability to grow coffee (see figure 3 and table A.2). Table A.2 decomposes the variation in consumption, landholding, and the share of land allocated to coffee.
Other anecdotal evidence from Iran also suggests that bad living conditions intensify negative attitude towards refugees. Nasr Esfahani (2020) reports that among Tehran citizens, the opinion that “do you agree to prohibit the residence of Afghan refugees in Tehran?” is responded “yes” much more often in poor districts of the city and among people who stated having problem in their livelihood.

**5.5. The effect on inter-community and intra-community inequalities** As the last empirical exercise, we carry on examining how economic shocks affect inter-community and intra-community inequalities. For doing this, we measure inequality in total expenditure per capita using the Theil index, which allows us to decompose inequality into the parts due to inequality within communities and between communities. Specifically, the Theil index can be written as

where μ is average expenditure for the total population, and s� and x̅�, respectively, show expenditure share and average expenditure for Afghan and Iran nationality groups.

Using HEIS data, we compute the monthly Theil index and its between and within components. Panel A of Table 9 shows the summary statistics of the measures. As expected, the numbers suggest higher inequality among Iranians (9.85E-4) compared to Afghan refugees (4.95E-4). Moreover, since Afghan refugees comprise a small share of the sample, the between-component of the Theil index is much smaller than the within-component. For this reason, we also use the ratio of between to within components as a dependent variable in the regressions.

After computing monthly inequality measures, we estimate the following regression for between and within components and their ratio Panel B of Table 9 shows the estimation results using both measures of shocks which are year-on-year difference of exchange rate and its cyclical component. We find that both between and within components of inequality are positively associated with the economic shock variables. In addition, columns (5) and (6) suggest that bad economic conditions increase the between-component of inequality more than the withincomponent. Therefore, in regard to Afghan refugees in Iran
# **1 Introduction**

Recent trade theory stresses the importance of market-share reallocations in increasing aggregate productivity following a trade liberalization (Bernard, Eaton, Jensen and Kortum 2003, Melitz 2003). In contrast, the earlier literature emphasized the idea that trade could improve average productivity among existing firms (Corden 1974, Grossman and Helpman 1991, Helpman and Krugman 1985, for example). Yet there are few empirical studies that quantify the relative importance of average productivity gains versus gains from market-share reallocations in the wake of a major trade liberalization.

In this paper, we use a comprehensive, firm-level dataset that allows us to examine the role played by market-share reallocations in aggregate productivity growth in India’s organized manufacturing sector from 1985 to 2004.<sup>1</sup> In 1991, India embarked on a series of reforms, including a major trade liberalization. We confirm that for the organized manufacturing sector as a whole, market-share reallocations were an important source of productivity growth in the years immediately following the start of the 1991 reforms, but not during other periods.

We document three distinct phases in India’s manufacturing productivity during the period from 1985 to 2004. During this time, aggregate productivity (defined as output-weighted, mean firm productivity) grew by nearly 20%. From 1985 to 1990, the growth in aggregate productivity was driven by “learning” - that is, an increase in unweighted, average firm productivity. This measure of learning captures the change in productivity for the average firm, and therefore includes not only changes in productivity among surviving firms, but also changes in average productivity that can be attributed to firm entry and exit. In the period immediately following the start of the reforms (1991-1994), the “stealing” of market share - that is, the reallocation of market share from less productive to more productive firms - became more important than learning in driving aggregate productivity growth. In the longer run (1998-2004), learning once again became the more important factor in aggregate productivity
.<sup>6</sup>

# <u>6.3 Generalizability of findings</u>

There is good reason to believe the results presented in this paper will have good external validity across PNG. We draw on different survey platforms (that is, phone and online) to collect data about broadly representative samples of the population and they point to similar conclusions. The phone surveys capture responses in every province and distinct in PNG and the online experiment also has widespread geographic coverage. When weighting techniques are applied to adjust for differences between the demographics of respondents and the general population (for example, in terms of age, gender, education levels, and so on, and so forth) the overall conclusions are qualitatively similar. The weighted and raw results about the main outcome of willingness to get vaccinated are similar across the phone survey and online experiment. By drawing on administrative data based on the history of phone use in PNG, we were also better able to sample poorer respondents than would be the case by simply relying on random digit dialing, which is common practice in other low- and middle-income countries (Global Poverty Research Lab, 2020). The close to monopoly status of the main telecommunications company in PNG allowed this approach of oversampling poorer respondents to be more effective than efforts along these lines elsewhere in the world (Himelein and McPherson, 2021).

The findings of this study will be relevant to other low- and middle-income countries where low trust and limited knowledge about COVID-19 vaccines exist. They are consistent with a key theoretical explanation for people’s willingness to get vaccinated that has empirical support in more developed settings. Specifically, the results of the phone survey and the online experiment show that trust in the vaccine drives people’s willingness to get vaccinated as people balance whether the risks associated

> 6 This is likely to be required in settings like PNG as vaccinating adults is rather uncommon (for example, the seasonal flu vaccine is not widely available) and existing campaigns tend to
considering the possibility of informal housing. In our framework, individuals from income group 1 (the poorest) are eligible for public housing. Only a fraction of individuals from this income group, however, will benefit from the relatively

> 11Note that no central, authoritative registry of public housing is available in Cape Town documenting all housing delivered under the succession of government programs that were implemented since the 1920s (Wilkinson, 2000). Earlier public housing varies greatly in terms of typology, tenure arrangement and quality, and some of it has subsequently re-entered the formal housing market. For the purpose of this model, a series of explicit neighborhood, zoning, and physical attributes were used to delineate public housing characteristic of the RDP and BNG housing programs from overall housing stock in existence today.

> 12Although local surveys suggest that a significant proportion of beneficiaries resell the properties that were initially allocated to them under subsidized-housing programs (Tissington et al., 2013), for simplicity, we do not model this secondary market. Because the sales likely remain within the same income group, this has no impact on income sorting in the model.

7
Effects of Direct Democracy—A First Global Assessment. _Public Choice, 140(3-4),_ 431-461.

- Chinn, M., and H. Ito. (2006). What Matters for Financial Development? Capital Controls, Institutions, and Interactions. _Journal of Development Economics, 81(1)_ 163-192.

- Debrun, X., Moulin, L., Turrini, A., Ayuso-i-Casals, J., & Kumar, M. S. (2008). Tied to the mast? National fiscal rules in the European Union. _Economic Policy_ , 23(54), 298-362.

- Debrun, X., and Kinda, T., and Curisitine, T., and Eyraud, L., and Seiwad, J. (2013). The Functions and Impact of Fiscal Councils. _IMF Policy Paper_ , Washington DC: International Monetary Fund.

- Debrun, X., and Zhang, X., and Lledo, V. (2017). The Fiscal Council Dataset: A Primer to the 2016 Vintage _. IMF Note_ , Washington DC: International Monetary Fund.

- Fatas, A., and I. Mihov. (2013). Policy Volatility, Institutions and Economic Growth. _The Review of Economics and Statistics_ , 95(2),362-375.

- Frankel, J., C. Végh, and G. Vuletin. (2013). On Graduation From Fiscal Procyclicality. _Journal of Development Economics, 100(1),_ 32-47.

- Furman, J. (2018). The fiscal Response to the Great Recession: Steps Taken, Paths Rejected, and Lessons for Next Time. _Mimeo. Brookings Institution_ .

- Gavin, M., and P. Roberto. (1997). Fiscal Policy in Latin America. _NBER Macroeconomics Annual_ , vol. 12, pp 11-72, Cambridge, MA: National Bureau of Economic Research, Inc.

25
||(0.034)|(0.034)|(0.033)|(0.035)|
|---|---|---|---|---|
|<br>**Observations**|327|327|327|327|
|**Pseudo R2**|0.241|0.254|0.242|0.256|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

Testing how 2003 expenditures affect vulnerability in 2007 (Table 5) show that agricultural spending in 2003 has a statistically significant and positive association with 2007 VAM levels (Column 1). Results also confirm a larger association of restricted vis-à-vis extended spending, both statistically significant and, as found earlier, with the former roughly double in magnitude of the latter. Current spending in 2003 does not appear to have a significant relationship on the vulnerability in 2007, while investment spending continues to have a positive and statistically significant association. With a four-year spending lag, research and extension has a significant but negative effect on vulnerability. The link between research and extension and the reduction of vulnerability to food insecurity is stronger when a longer lag of spending is considered. Department-specific effects remain unchanged with respect to the 2007 contemporaneous effects.

**Table 5. Effects of 2003 Per Capita Agricultural Spending on 2007 Vulnerability to Food Insecurity**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**VARIABLES**|Total exp|Restricted and<br>extended<br>definitions|Current and<br>investment
# ALLOCATION AND MANAGMHENT OF PUBLIC OPENDINO

19. Where dilapidation has gone furthest, it makes the task of imprjving public expenditure management more difficult, in two ways:

- first, rehabilitation of basic budgetary and accounting functions becomes a prerequisite;

- secondly, there is an absence of the current and historic public expenditure data that should provide the starting point for rational public expenditure planning.

The latter point relates not only to whether ba.slc Information (such as recent actual expenditure data) Is available, but also to the credibility of information that emetges trnm a budgetary system that many participants have learned from experience not to take too seriously (see Box 3).

Page 9
, and (3) calculating the GTI index.

### Step 1: Development of the green dictionary

The first step was the development of a green dictionary, a database of terms that were used as the basis for the text analysis. The terms in the dictionary are not tasks, but rather words, word roots, and expressions directly linked to environmentally friendly concepts. Our assumption was that a task statement containing any of these key terms would be related to environmentally friendly tasks. The selection of terms was the result of a careful desk review of diverse sources. Naturally, since terms are short and general, most of them are present in multiple sources. Thus, the creation of the dictionary should be understood as a systematic iterative process of adding and confirming the appropriateness of terms as they consistently came up in the consulted sources. As we continued reviewing sources, fewer new terms were included for consideration in the dictionary.

For the creation of the dictionary, the starting point was to examine the terms commonly mentioned in the diverse material from the methodological approaches described in section 2, including the O*NET green project description, and from the labor environmental economics literature. First, the dictionary included terms that appeared in the BLS GTP survey questionnaire, most common terms from machine16