rafmacalaba/data-use-mentions-extended huggingface dataset:
infrastructure. Not surprisingly, a large body of recent literature is devoted to evaluating the impacts of transport investment using various innovative modeling and empirical strategies. However, much of the existing literature models transport improvement as a reduction in travel time or distance while taking the transport prices as given. Recently there is increasing realization in the literature mostly on international shipping that transport services are subject to thick market and network externalities which make transport costs themselves endogenous to trade flows. In this paper, we use half a million actual shipment data from a trucking portal in India to provide evidence on how trucking costs depend on route characteristics and affect the intensive and extensive margins of trade/shipment flow. The empirical analysis using the prepandemic period data confirms the presence of thick market externality along a route and spillovers across routes due to network externality, both of which confer advantages to origins and destinations with larger market sizes. These findings suggest that returns to the construction of a road would depend on not just the reduction in travel time but also the characteristics of the places that it is connecting. This may explain why studies find large benefits from the construction of the Golden Quadrilateral that connected major cities in India (Ghani et al, 2016; Asturias, Garcia-Santana and Ramos, 2018) but at best modest returns from India’s vast rural road construction projects (Asher and Novosad, 2020).

We utilize exogenous variations in value-added tax on gasoline across states to provide a causal estimate of the elasticity of shipment flow with respect to trucking cost. Using the VAT rate along the next probable route as an instrument while controlling for VAT along its own route, we find that a 1 percent increase in trucking unit cost reduces trade flow by 2.8-3.9 percent which is larger in magnitude than estimates from international shipping data by Brancaccio et al (elasticity=1.03) and but similar to Wong (2018) (elasticity=2.8-3.7). The implied elasticity of substitution derived from the above estimate of trade elasticity
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percent (in the euro area).<sup>57</sup> Among the global shocks, global demand shocks played the largest role in driving policy rates, accounting for 10 percent of the average variance in interest rates. The other global shocks (to monetary policy, supply, and oil prices) explained, on average, from 4 to 6 percent of the variation in rates.

In contrast, domestic shocks explained the lion's share of the variance of domestic policy rates, accounting for almost three-quarters of rate fluctuations on average over the full period. Among the domestic shocks, demand shocks were the main drivers of the variation in interest rates, with an average contribution of about 34 percent, followed by domestic monetary policy shocks (accounting for nearly 31 percent). Domestic supply shocks played a much smaller role, with an average contribution of nearly 10 percent.

change the global factor estimates. In addition, given the potential feedback effects, our FAVAR model with monthly data does not impose any restrictions between the global and domestic variables other than contemporaneous sign and zero restrictions. We also estimate the results excluding the US from the G5 sample, however, and the headline results for the averages across countries are robust.

> 57 Global shocks account for a smaller share of the variation in policy rates than domestic shocks in all five economies except the euro area. This finding likely reflects the euro area’s stronger global trade and financial linkages and much deeper integration with global supply chains. The dominant role of the global shocks in euro area interest rate cycles is consistent with other studies in the global business cycle literature. For example, Ha et al. (2020) and Kose, Otrok, and Prasad (2012) report a larger share of the global factor or global output factor in the euro area than other economies.

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Zenou 2006) and vacancy rates (Segú 2020).<sup>3</sup> Although high property taxes may result in wealth effects,<sup>4</sup> revenue from land taxes is less volatile than revenue from income or business taxes that vary with the business cycle, making projected public revenue from land taxes ideal for financing long-term local investment, as recognized by the fact that municipal bonds are often backed by property tax revenues.

Instead of a recurrent property tax, many countries levy high transaction taxes, partly as such taxes can be collected easily from landowners who come to register their transfers, thereby avoiding the fixed cost of an apparatus for property tax collection, including maintenance of land records and a tax roll and property valuation. In a digital environment, the cost of establishing the infrastructure for property tax collection is much lower while the benefits from having this infrastructure in place increase dramatically. Studies show that transfer taxes’ modest contribution to public revenue comes at very high cost in terms of governance risks and economic distortions as transfer taxes (i) push property transactions into informality, undermining the registry’s quality and currency; (ii) encourage corruption via underreporting of prices, often in return for a side payment to officials who turn a blind eye reducing the informational content of registry price data; and (iii) impose high deadweight losses.

For example, in the United Kingdom, transaction taxes caused large distortions in the volume, timing, and price of property transactions (Best and Kleven 2018) and negatively affected land markets and labor mobility (Eerola _et al._ 2021; Hilber and Lyytikainen 2017). A 1.1 percent land transfer tax in Toronto led to significant welfare losses (Dachis _et al._ 2012) and, if decisions on whether to own or rent are accounted for, it was estimated to incur a deadweight loss of 79 percent of revenue (Han _et al._ 2022). Replacing transfer taxes with an equivalent recurrent land tax on a
nm|(-1.230)||(-1.348)||(-1.224)||
|Time to resolve insolvency (years)<br>viro|0.270*||0.276*||0.265*||
|s en|(1.900)||(1.966)||(1.857)||
|Rule of Law (Kaufmann et al.,2010)<br>ines||-2.250***||-2.381***||-2.587***|
|Bus||(-2.685)||(-2.786)||(-3.007)|
|Constant|2.734***|3.746***|2.862***|2.476***|2.702***|3.204***|
||(2.906)|(4.848)|(3.086)|(2.717)|(2.866)|(3.910)|
|Observations|415|464|425|433|415|445|
|Number of country_code_|48|50|49|50|48|50|

_Note:_ Annual information between 2005 and 2017 for 50 EMDE countries. _Public debt_ includes debt data for general government. In case this data is not available, debt data for the central government is used. _St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y- o-y, using a one-year rolling window. _5-year St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y-o-y, using a 5-year rolling window. _Devaluation rate_ is the y-o-y change of the exchange rate vs. US dollar. Benchmark regressions in columns (14) and (15).

Panel data regressions with first-order error
change in that indicator for LGAs that actually received the project would have been the same as that observed in LGAs that did not receive the program. Some evidence on the parallel trend assumption was provided in the section above.

# **4.1 Results**

We now present the main results of the paper: did LGAs receiving the project present a different trend in indicators of state capacity when compared to the comparator areas? We highlight the trends within project areas, then present how the comparator areas perform, and finally compare the performance between treatment and comparator areas to explore the impact of the program.

# **4.2 How did outcomes change within ULGSP LGAs over time?**

We start by presenting how the responses of households about institutional capacity changed in project areas. Many indicators are presented in Table 4, Table 5 and Table 6. Columns (1) and (2) present average responses among households in project LGAs, in 2016 (first round) and 2018 (second round) surveys, respectively, while column (5) presents the difference in means.

Overall, the response improved across the board. In Table 4, we see the share of households reporting that the local council makes good use of revenues has increased from 44% to 66%, for example, while the share saying that local governments make good investment plans increased by 21 p.p. Responses about government performance in delivering services such as road maintenance and cleanliness have also improved, as have responses about ease of access to building permits, household services and medical treatment.

Table 5 reports indicators related to responses of transparency and accountability. The share of respondents agreeing that the local council provides information on budget, allows participation, consults other actors before decision. and handles complaints well have all increased by over 10 p.p. - for all these indicators less than 60 percent of respondents agreed with the statements by the second survey, but the performance increased significantly in the two years between surveys. It
effects of the Internet on employment in local labor markets. In each of the three different data sets that together cover 12 African countries with a combined population of roughly half a billion people, the authors find a significant and large relative increase in the employment rate in connected areas when fast Internet becomes available, with little to no job displacement across space. In 8 countries for which the authors use Demographic and Health Survey (DHS) data, they find a 6.9 percent increase in the probability that an individual is employed when fast Internet arrives. In 9 countries with Afrobarometer data, there is a 13.2 percent increase in the employment rate when fast Internet arrives. Finally, in South Africa, using Labor Force Survey (LFS) data, there is a 3.1 percent increase in the employment rate with the arrival of fast Internet. The impact is driven by increased employment in higher skill occupations, as less educated workers’ gains are smaller; estimated increase in employment in a skilled occupation is biggest for those with tertiary education. Overall, those with only primary schooling also see increased employment but only in unskilled occupations. The observed changes in use of Internet suggest two things: new types of jobs may have been created both via new Internet users as well as different use of Internet by existing users.

Bachas et al (2018) research the effects that payments systems can have on employment in Mexico, specifically studying a natural experiment in which debit cards tied to existing savings accounts were rolled geographically over time to beneficiaries of the Mexican cash transfer program, _Oportunidades_ . Prior to receiving debit cards, beneficiaries received transfers into a savings account every two months. After receiving debit cards, beneficiaries continued to receive their benefits in their savings account, but can access their transfers and savings at any bank’s ATM. In Mexico, transaction costs can be a significant barrier to the use of formal financial services; account opening fees and minimum balance 4
secondly the growth of foreign knowledge produces two contrasting effects on the accumulation of domestic knowledge, a positive one insofar as it favors the expansion of the knowledge intensive sectors through knowledge diffusion, a negative one insofar as it constraints such expansion through technological competition, i.e. we come once again across the fact that domestic and foreign technology are both complements and substitutes.

From (16.2) we can derive the conditions for _pT_ > 0, and PT = A Assuming that the denominator of (16.2) is positive27 a positive rate of growth of domestic knowledge requires.

P<sup>3</sup> AF+(P9 _/la6_ **_+ 13)AT>0 6 (01 4_** _+_ **_38 )XT_** The terms on the l.h.s. are the domestic and foreign factors affecting domestic knowledge accumulation (the foreign factors represent the complementarity of foreign and domestic knowledge), the terms on the r.h.s. represent the depressing effect of foreign knowledge accumulation (the substitution of foreign for domestic knowledge). Domestic and foreign knowledge will grow at the same rate (which, from (13)-(15) is a condition for global balanced growth) if {1 -[1PIO(P<sup>J</sup> 4 6<sup>-113)</sup> **_9]Ia6_** _+_ P6I14 | .I 3 }XT =|XF (18) The domestic effort on knowledge accumulation and the _net_ foreign 28 knowledge accumulation must grow at the same rate . In other words, if we assume (some form of) international knowledge diffusion, the condition for a uniform rate of growth of knowledge in all countries is not sufficient for assuring balanced growth.

We now turn to the aggregate trade share growth rates. The steady state growth rate of the aggregate export share implies _(PA +_ **_XA)_
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# <u>Table 2. Previous studies of out-of-pocket health expenditures</u>

|**Concept**|<br>**Multi-country studies**|**Global studies**|
|---|---|---|
|1<br>Expenditure in<br>absolute terms|Squires (2011) compares 12 OECD countries in terms of inter alia<br>per capita out-of-pocket expenditure in $US, using data from the<br>OECD’s Health Data database. He finds that Switzerland and the<br>US have the largest per capita out-of-pocket expenditures on health,<br>and France and the UK the smallest.|None|
|1<br>Dispersion (risk)|None|None|
|<br>3<br>Budget share|None|None|
|4<br>Progressivity|Wagstaff et al. (1992; 1999) report the progressivity of out-of-pocket<br>expenditures (and other financing sources) in 10 and 12 OECD<br>countries; the second study also analyzes trends. Out-of-pocket<br>expenditures emerge as regressive for all countries and years, except<br>in Spain in 1980, and the trend is towards greater regressiveness.|None|
|5<br>Catastrophic<br>expenditures|<br>Van Doorslaer et al. (2007) look at catastrophic spending in 10 Asian<br>territories. They find relatively low rates in Malaysia, Sri Lanka and<br>Thailand, and relatively high rates in China, Vietnam and<br>Bangladesh.|Xu et al. (2003; 2007) analyze catastrophic expenditures in 89<br>countries defining catastrophic as out-of-pocket expenditures<br>exceeding 40% of consumptionless a deduction for food costs. They<br>reported mean and median rates of catastrophic spending so defined<br>of 2.3% and 1.5% respectively.<br
Fifth, relative to advanced economies, EMDEs have become a more popular destination for foreign banks. While the number of foreign bank applications increased by 74 percent in EMDEs between 2005 and 2010, this figure declined by 51 percent for the advanced economies. Note that if we concentrate on a “matched sample” – those countries that responded to both survey – we observe a smaller contrast between advanced economies and EMDEs. Among the latter, foreign bank applications increased by 19 percent and they declined by 21 percent for advanced countries. Finally, both the number of total license applications and rejections increased between 2005 and 2010. The increase in rejections was mainly driven by a 15 percentage points increase in the rejection rate for domestic bank applications, which was in turn mainly due to an increase in rejection rates from 9 to 26 percent in EMDEs.

> 7 If we limit our analysis only to those countries that have responded to both surveys, the drop is slightly larger from 49 percent in 2005 to 16 percent in 2010.

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of total health expenditures, our results on the impact of recall periods on health expenditures remain tenuous.<sup>6</sup> The combination of large impacts on reported morbidity, doctor visits and whether a day was lost due to sickness together with smaller impacts on health expenditures suggests that much of what was underreported in the monthly surveys were less ―severe‖ illnesses that required smaller expenditures on the part of the household. Therefore, whether monthly or weekly reports should be the ―gold standard‖ for health surveys depends on the underlying question. If researchers are interested in the ability of households to smooth consumption after a major health shock, monthly (or even annual) reports may be sufficient—our data do not suggest that these episodes are easily forgotten. But if researchers want to look at the process through which sickness evolves from something ―minor‖ to something ―severe‖, monthly reporting may erase a significant fraction of the relevant action. Similarly, little is known about the industrial organization of markets for medical care in low-income countries. Since a large fraction of visits to doctors are for selflimiting minor problems—a fraction often guessed to be 80 percent—the degree of underreporting we find could substantially alter the formulation and tests of hypotheses related to these markets.

The remainder of our paper is organized as follows. Section I describes the data. Section II presents the evidence of the overall recall effect independent of its interaction with income. Section III explores the data further in order to illustrate the differentials across income groups and the robustness of our findings to age, education and respondent effects. The last section concludes with a brief discussion of the implications from these findings.

# **Section I: Data**

This paper uses data from two sources. The first is a long-standing panel study of health seeking behavior carried out by the Institute for Socio-Economic Research on Democracy and Development (ISERDD) in Delhi. The study followed 1621 individuals in 300 households from 7 different neighborhoods in Delhi between 2000 and 2002
or not. An analysis of this fifth (and imprecise) indicator would not yield any new insights because we already analyze the numerator and denominator separately.

> 6Despite having data on forest fires from 2003 onward, we do not include all pre-treatment periods as predictors. This is because if we were to do so, the “relevance weights” ( **_V_** _k_ ) that assign positive weights to the pre-treatment outcomes and zero to the the forest characteristics would minimize the pre-treatment RMSPE in Equation (B.5) (cf. Kaul et al. (2021)). Instead of including all pre-treatment outcomes, we aim to find synthetic controls that fit well to the project forests in the period leading up to the beginning of the FIP program.

> 7Forest size is expected to affect forest fire occurrences because of two reasons. First, man-made fires are more likely to be started on the forest fringe than in the interior (especially if agriculture is the main activity causing forest fires), so that larger forests may have, on average, fewer forest fires per grid cell. Alternatively, larger forests may have more fire occurrences because they are more difficult to monitor. And also the relationship between precipitation and forest fires is also ambiguous ex ante. In drier forests the vegetation is more prone to catching fire and the fire is likely to spread wider too. But then it may also be the case that agricultural activity is higher in areas with more precipitation, so that forest fire occurrences are more pronounced in forests with higher precipitation rates. Whichever of the two opposing mechanisms is dominant for each of these two variables, including precipitation rates and forest size in the SCM’s weighting process will improve the accuracy of the synthetic control.

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**Table 1.** List of Variables and Data Sources

|**Indicator**|**Description**|**Source**|
|---|---|---|
|Social trust|Percentage of respondents stating that, “Generally speaking,<br>most people can be trusted”; survey waves mapped to 4-year<br>periods as follows: survey 2005-2009 (period 1), survey 2010-<br>2014 (period 2), survey 2017-2019 (period 3)|World Values Survey|
|WBG engagement|WBG own commitment (% of GDP): Total amount committed<br>for projects related to domestic resource mobilization by<br>approval fiscal year as a share of the recipient country’s GDP<br>(scaled by factor 1,000 for better readability)|World Bank (public and internal<br>data) as reported in Table A3 in<br>the appendix|

_Source:_ Authors’ compilation.

**Figure 2.** Economic Fundamentals and Revenues, 2016-2019

<!-- Start of picture text --> 100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>6 8 10 12 0 20 40 60<br>Log GDP per capita Share of Agriculture in GDP<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 100 200 300 400 0 20 40 60 80 100<br>Trade Openness Age Dependency Ratio<br>Revenue, excl. grants ( % GDP) Revenue, excl. grants ( % GDP)<br>Revenue, excl. grants ( % GDP) Revenue, excl. grants ( % GDP)<br><!-- End of picture text --> _Notes:_
2 3 4 5 6 7 8 9 10<br>1-01994 1 6<br><!-- End of picture text -->

**Source: The Moscow LDngitudinal Household Survey in Dec-1994 and Jan-1996. Note:** 1 **- lowest** income **decile, 10 - highest** incmne decile

Note in the above figure that the lowest income group (decile 1) is also the group that exhibits by far the highest rates of privatization in both 1994 and 1996, which is consistent with the finding that pensioners, who generally have quite low incomes, are the demographic group most likely to have privatized their apartments.
**4 Data**

We use plant-level data from the Annual Survey of Industries **(ASI)** conducted **by** the Central Statistical Organization **(CSO),** a department of the Ministry of Programme Planning and Implementation of the Government of India to compute our -ighiin.-.iti.j indices for all consecutive years in the period **1980-81** to **1999-00,** with the exception of **1995-96** when the survey was not conducted. The length of our data allows us to cover all the Indian reforms of the 1980s as well as the major reform episode of **1991.** The survey covers all factories registered under the Factories Act of 1948. The **ASI** frame can be classified into 2 sectors: the 'census sector' and the 'sample sector'. Units employing more than **100** workers constitute the census sector. Units in the census sector are covered with a sampling probability of one while units in the sample sector are covered with sampling probabilities lower than one (onehalf until **1987-88** and one-third after **1987-88)."** As Bollard et al. (2010) explain in detail in their appendix, there are substantial caveats to the use of these data. Sampling schedule changes in **1997** led to drastic changes in sample size: the number of total plants covered and the number of plants in the census sector dropped sharply.<sup>2</sup> ' There is a sharp increase in the standard deviation of average employment in both the census and the sample sectors, causing us to worry about heteroskedasticity. In order to correct for the noise and heteroskedasticity, we will weight all descriptive statistics and regressions **by** 3-digit industry-year total employment. That is, we will give more weight to larger, presumably less noisy industries.<sup>2</sup> As
Life expectancy|Life expectancy at birth<br>in years for the total<br>population|Panel from 1960 to 2020<br>(61 years) of 208 countries|World Development<br>Indicators|
|Energy supply|Basic supply and demand<br>data for all fuels are required<br>to compare the contribution<br>that each fuel makes to the<br>economy and their<br>interrelationships through the<br>conversion of one fuel into<br>another.|Panel from 1971 to 2019<br>of 146 countries|OECD Economic,<br>Environmental and Social<br>Statistics, IEA World Energy<br>Statistics and Balances|

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