rafmacalaba/data-use-mentions-extended huggingface dataset:
of sending countries. For the two decades prior to 2010, the United Kingdom was the largest origin country in terms of numbers of outbound skilled migrants. It was supplanted in 2010 by India (2.1 million) and the Philippines (1.5 million), while China (1.4 million) also had high absolute numbers of high‐ skilled emigrants. In terms of the greatest increases over time (and considering only those large countries that sent at least 25,000 highly skilled workers abroad), Algeria (a rise of 954 percent), Russia (910 percent), Bangladesh (459 percent), Romania (428 percent), Venezuela (423

> 3 Data for Nobel prizes is derived from <u>https://www.aggdata.com/awards/nobel_prize_winners, supplemented with</u> data from the Nobel Prize website for the years 2011‐2015.

> 4 As a second example from our profession, the John Bates Clark Medal is awarded annually to a top “American” economist under the age of forty. Four of its nine winners since 2005 were born outside of the United States.

6
areas attracted less productive peasants or individuals with more egalitarian preferences into the areas (in-migration).

We explore these migration patterns empirically in Table 8 using data from the 2007 census. Columns 1–5 examine impacts on international migration. In particular, we estimate equation (1) for the share of international emigrants during the period of FMLN territorial control and afterward, the number of years since the international emigrant left the household, and the share of households receiving remittances. Unfortunately, the 2007 census does not include questions

> 31We also checked the robustness of these results using the Wealth Index from the 2007 Census at the census tract level (Table D.19). We do not find any difference across the boundary when using this measure to assess inequality. 32As mentioned in Section II.D, the agreement established that individuals occupying land in “conflict zones” could keep it. The state was in charge of administering land to anyone who claimed ownership; it gave current occupants a land title and 30 years to pay for the land.

> 33Results are available upon request.

37
and this greater heterogeneity of preferences may make it more difficult to reach consensus on growth enhancing reforms. Another potential problem with large countries is congestion or administrative costs that may escalate with country size.

Our focus on tax administration is motivated by two factors. First, as mentioned above, scale effects suggest that the cost of providing public goods may depend significantly on country size. However, it is also possible that congestion costs may lead to more complicated, inefficient and unwieldy tax systems among the relatively larger countries. Given these contrasting scale and congestion effects, what can we say about the quality of tax administration in small vs. large countries? The present paper attempts to answer this question. Second, anecdotal evidence suggests that tax administration is an important element of the business climate. However, very little is known about the quality of tax administration and the sorts of factors that determine it. For example, in the survey of over 11,000 firms in 30 countries in Eastern Europe and Central Asia conducted by the World Bank in 2008 that we use for the present study, more than 20 percent of the firms reported tax administration as a major or a very severe obstacle for their business and another 24 percent as a moderate obstacle. For a comparison, the corresponding figures for obtaining licenses and permits as reported in the survey are 17 and 18 percent, respectively.

The use of micro data complements existing studies that are exclusively focused on macro or country level data. Specifically, we use a survey of non-agricultural firms in 30 countries in Eastern Europe and Central Asia conducted by the World Bank in 2008. The survey reports firms’ experience with the quality of tax administration as measured by how severe is tax administration (on a 0 to 4 scale) as an obstacle to the current operations of the firm. Use of such experienced based measures is becoming increasingly popular in the literature. For example, 3
# **<<A>>** Acknowledgments

We thank Venture Strategies for Health and Development (VSHD), Marie Stopes International Uganda (MSI), Mbarara University of Science and Technology, Population Council, and the University of California at Berkeley for conducting the baseline and endline household surveys and secondary studies reported in this working paper. The GPOBA and Bill and Melinda Gates Foundation are gratefully acknowledged for their financial support of research activities and the data analysis of baseline and endline surveys. The authors are grateful to the peer reviewers: David Evans, Damien de Walque, Edit V. Velenyi, Ashis Das, Huihui Wang, Gandham N.V Ramana, and David Griffith for their valuable comments. Olusoji Adeyi and Magnus Lindelow (Health Global Practice) provided overall guidance.

2
instead of fourth grade in 2011. Another example includes India and China, where only certain states and cities participated in PISA. These variations are acknowledged by the underlying tests and the data is caveated with an asterisk in published reports. We preserve this information in our data, and include notes in the metadata for each case. We describe each case in detail in Patrinos and Angrist (2018). In the case of India, we verify that the state data is likely to be nationally representative using national assessment data.

We make an adjustment beyond the underlying tests in the case of China given the likelihood that China’s current PISA data is biased. The China HLO based on 2015 PISA data is from four cities (Beijing, Shanghai, Jiangsu, and Guangdong) and is 532. However, this data is likely biased upwards since the cities participating are urban and rich relative to the national average. We adjust the score based on socioeconomic information by city and across the nation and produce an average HLO of 462 at the secondary level, which is plausibly representative at the national level. The detailed procedure is described in Patrinos and Angrist (2018).

41
framework enables us to analyze the allocative efficiency consequences of implementing a hypothetical carbon tax at the firm level. Moreover, our model emphasizes the pivotal role of pre-existing distortions within the input market and their implications on the efficacy of such a policy measure.

In our paper, we extend the theoretical framework from Hsieh and Klenow [2009] to allow for differentiated energy inputs such as fuels and electricity and simulate the within-sector interactions of a carbon tax with existing market frictions. We hypothesize the introduction of a carbon tax levied on firms according to their total emissions from the consumption of fuels and electricity. A key insight gleaned from the model is the heterogeneous impact on firms of the carbon tax, contingent on the correlations between the energy distortions with the output and capital wedges, and the correlations between the energy distortions with firm productivity. Specifically, when more productive firms face lower distortions in the energy input markets and when there exists a positive correlation between the capital and output wedges with the energy wedges, the introduction of an environmental tax will decrease aggregate TFP. This occurs because introducing the carbon tax distorts higher-productivity firms more than lower-productivity ones, given that the former initially faced lower distortions. Consequently, resources are shifted from more productive to less productive firms, increasing misallocation and decreasing aggregate TFP. In contrast, if more productive firms face higher distortions or the correlations between the capital and output wedges with the energy wedges are non-positive, then the introduction of a carbon tax can either increase or decrease aggregate TFP.

Using detailed data on firms’ tax declarations and energy expenditures from the Direccion General de Impuestos Internos (DGII) of the Dominican Republic and aggregate information from the National Energy Information System (SIEN) between 2009 and 2018, we calibrate the model at the sector level. Using this data, we first provide a back-of-the envelope calculation of the revenues generated by the introduction of a carbon tax rate using plausible scenarios.<sup>8</sup> We find that, based on the most recent estimates in 2018, a
context of this evaluation, the results of the analysis are still very informative as they can be interpreted as lower bounds of the true impacts.

There are various problematic compositional changes in the sample that could take place. An example is that the FSSP likely induces the self-selection of lower–ability female students into middle schools. The marginal girl who is most likely to be brought into school by the

> 36 A limitation of this analysis is that PIHS 2001, the household survey used for the prebaseline period, is representative only at the province level. The survey was structured around three main domains: rural, major cities, and other urban areas. The first two domains (rural and major cities) were stratified by district and, thus, can be used to construct the treatment status of girls. The ―other urban‖ domain was stratified by division, a higher administrative level that may include both stipend and non-stipend districts. Therefore, the DD analysis is restricted to the ―rural‖ and ―major cities‖ domains and assumes that the sample statistics based on PIHS 2001 are representative for the two groups of analysis as a whole (that is, stipend and non-stipend districts).

26
- `sae proc stats||inds :` The stats and inds sub-routines are useful for processing Mata formatted simulation output and producing indicators with new thresholds or weights, as well as profiling.

The routines and sub-routines are described in the sections below.

# **6.1 Preparing the Target Dataset**

Due to the potential large size of the target data set, the `sae` command comes with an ancillary sub-command ( `sae data import` ) useful for preparing the target dataset and converting it into a Mata format dataset to minimize the overall burden put on the system. The command’s syntax is as follows: ``` saedataimport,datain(string)varlist(string)area(string)uniqid(string) dataout(string) ``` The options of the command are all required.

- The `datain()` option indicates the path and file name of the Stata format dataset to be converted into a Mata format dataset.

- The `varlist()` option specifies all the variables to be imported into the Mata format dataset. The variables specified will be available for the simulation stage of the analysis. Variables must have similar names between datasets. Additionally, users should include here any additional variables they wish to include, as well as expansion factors for the target data.

- The `uniqid()` option specifies the numeric variable that indicates the unique identifiers in the target dataset. This is necessary to ensure replicability of the analysis, and the name should match the one of the unique identifier from the source dataset.

- The `area()` option is necessary and specifies at which level the clustering is done, it indicates at which level the _ηc_ is obtained. The only constraint is that the variable must be numeric and should match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006).

- The hierarchical id should be of the same length for all observations. For example
# **5. STUDY CONCLUSIONS AND RECOMMENDATIONS**

# **Conclusions**

# **Regional Tenure Choice**

# **_Rental Housing Supply_**

Only a small supply of social rental housing “survived” the privatization waves of the 1990s. The rental sector in the region as a whole, including private and non-profit rental housing, is much smaller relatively than in developed market economies with similar patterns of demographics, urbanization, mobility needs, and building inventory. This is especially so in Armenia, Serbia, and Romania, where the formal rental sector is a barely traceable residual. In Russia the rental sector is significant, but only because it consists primarily of social rental housing slated for eventual privatization. In Lithuania social rental is decimated, but there is a lively, informal rental market in individual apartments. In Poland the significant rental sector is more diversified and includes the restituted prewar rental buildings, and the cooperative rental apartment stock (Figure 11).

**Figure 11** **_._ Building Structure and Rental Tenure in Selected Transition Countries**

<!-- Start of picture text --> 80<br>Apartment & hostel buildings Non ow ner-occupied tenure<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Hungary Serbia Romania Armenia Slovakia Czech Rep Lithuania Poland Russia<br><!-- End of picture text --> _Source_ : Households surveys (Table 1), national statistical offices.

_Note_ : Czech Republic and Slovakia: all units in housing cooperatives counted as non-owner-occupied because of lack of detailed data on renting.

With the notable exception of Poland, the apartment sector in the studied countries is characterized by the lack of clear ownership titles to common property of M-F buildings and 60
This “aggregate” finding across the four main categories of trade policy actions is unpacked in Table 3, which reports results for seven different types of instruments that are most frequently observed in the GTA data set. This more disaggregated focus reveals large countries focused more on import tariffs as well as export bans and export licensing requirements.<sup>18</sup> Distance to markets is positively associated with restrictive export measures and inversely correlated with liberalizing export measures on medical products; the former finding driven by measures still in force (see Table 2, column 4). Conversely, if the focus is on measures that were removed during the period, this variable is strongly correlated with re-imposition of import restrictions, with a coefficient estimate that is statistically significant at the 1% level (Table 2, column 6). Distance has a very similar pattern as country size does on the export side (Table 3).

Government effectiveness is positively correlated with all categories of trade policy measures, especially export licensing requirements (Table 3), except for imposition of import barriers, especially when it comes to measures still in force at the end of the sample period (Table 2). Higher initial tariffs are positively correlated with liberalization of imports (Table 1), which is expected as the higher the pre-crisis tariffs the greater the expected impact of removal of the tax on prices (or more realistically, attenuation of price rises for essential products). The result seems driven by liberalization of domestic taxation on imported medical products (Table 3). Conversely, when it comes to measures that are removed during the sample period, pre-crisis tariffs are strongly associated with re-imposition of import barriers – which may reflect a return to the initial level of protection for the products concerned (Table 2, column 6). This can also be inferred from the large positive correlation between pre-crisis tariff levels and the duration of import policy measures imposed on medical products (Table 4).

Supply-side capacity (proxied by a revealed comparative advantage in exporting medical
>(83)|28<br>(33)|
|Basel II: Will Implement by 2014|17|5|21|
|Percent of Countries Implementing Basel II by 2014|73|100|62|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries) 29
we know, the best data set to meet these requirements is Eurobarometer, from which we use repeated cross‐sections with annual frequency between 2004 and 2013 for 27 European countries.<sup>9</sup> An advantage of this data set is that it allows us to study perceptions towards a broader set of issues. The Eurobarometer survey includes the question “ _What do you think are the two most important issues facing (OUR COUNTRY) at the moment?_ ”, and respondents can choose within a set of 16 mutually exclusive categories. Figure 4 shows the average results for the top choices,<sup>10</sup> which includes general contextual considerations, such as unemployment, the economic situation and prices, followed by more specific subject areas, such as crime, health care, pensions and immigration. While this question does not specifically try to gauge preferences regarding the allocation

> 9 The group of countries includes Netherlands, Germany, Italy, Luxembourg, Denmark, Ireland, Great Britain, Greece, Spain, Portugal, Finland, Sweden, Austria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia, Bulgaria, Romania, Turkey and Croatia.

> 10 Other categories include Public Transportation, Defense, Foreign Affairs, Others, Don’t know.

12
Any cropland or livestock that coincides with the projected urban expansion area is considered part of the estimated overlap. These steps are replicated for each SSP scenario to arrive at estimated crop and livestock future overlap. However, it is important to note that this projection exercise assumes that the relevant food sectors will remain stagnant in terms of their technologies and productivity. While this allows us to estimate the overlap of urban expansion on crop and livestock production, it is important to recognize that actual overlap may differ if there are technological advances or changes in the economic or sociopolitical landscape affecting food production.

To determine current crop and livestock production, which are used as baseline values, we sum all crop and livestock production that that are _not_ located in urban areas in 2015. Note that the most recent crop and livestock global data are from 2010, instead of 2015. Moreover, since the crop and livestock data come from two independent sources with two different methodologies, it is possible for a certain area to have both cropland and livestock. After generating the baseline values, we compare them with the absolute values of projected overlap to calculate the percentages for potential food losses. This method is then repeated for each of the seven regions to derive the regional results, and for select cities.

Due to the above limitations and model uncertainty of climate projections on land use, we caution against over-interpreting precise quantitative results, but rather one should focus on broad magnitudes and directions.

# **2.5 Summary statistics**

## _In_

Table 2 we present grid-level summary statistics at baseline of urban extent (in 2015) and of crop and livestock production (in 2010) for the eight crop groups and the eight types of livestock, respectively. In Table 3 we present baseline total values for food crops and livestock globally and for each of the seven regions. For simplicity of the regional breakdowns, we aggregate the seven crop groups into four (fruits and vegetables, cereals and roots, sugars and oils, and pulses) and the eight livestock
Dague, DeLeire, and Leininger (2014) presented evidence on a significant reduction in employment and work hours as a result of the introduction of the food stamp and Medicaid programs. Several other papers, on the other hand, including Blau (2003), Currie (2003), Gruber (2003), and Olsen (2003), found no evidence of work disincentives for programs such as food stamps and nutrition benefits, childcare subsidies, or various in-kind programs.

The evidence from a number of recent studies on other countries is also inconclusive. On the one hand, Lemieux and Milligan (2007) examined the effects of a new policy in Quebec, Canada that tripled the benefits of a certain demographic group and found that the more generous benefits reduced employment significantly. The decline in the employment rate was found to be between 3 and 5 percent. Similarly, Fernandez and Saldarriaga (2014) found that the recipients of a conditional cash transfer (CCT) program (Juntos) in Peru reduced their involvement in the labor force by six to 10 hours per week, and Sahn and Alderman (1995) showed that a food subsidy program in Sri Lanka reduced the labor supply by 10 percent on average. Finally, Dabalen, Kiloic, and Wane (2008), using Albanian household-level panel data, demonstrated that the negative effect of social transfers on the supply of labor was sizable.

On the other hand, a number of other studies, including Skoufias and Di Maro (2008), Skoufias, Unar, and Gonzalez-Cossio (2008), and Freije, Bando, and Arce (2006), found no evidence of reduced work efforts in their evaluation of the CCT programs (Oportunidades-Progresa) and in-kind programs in Mexico. At the same time, these programs were found to be successful in reducing poverty and child labor. Alzua, Cruces, and Ripani (2012) examined the welfare programs of Mexico, Nicaragua, and Honduras and found small and insignificant effects on the labor supply, and Abdulai, Barrett, and Hoddinott
Policy Research Working Paper 6320

# **Abstract**

The infrastructure gap is one of the most significant impediments to India realizing its growth and poverty reduction potential. Although India’s transport network is one of the most extensive in the world, accessibility and connectivity are limited. Only 20 percent of the national highway network (which carries 40 percent of traffic) is four-lane and one-fourth of the rural population does not have access to an all-weather road. It is estimated that the transport sector alone will require an investment of nearly US$500 billion over the next 10 years. This paper investigates the impact of the Golden Quadrilateral highway project on the Indian organized manufacturing sector using enterprise data. The Golden Quadrilateral project upgraded the quality and width of 5,846 km of roads in India. The analysis uses a difference-in-difference estimation strategy to compare non-nodal districts based on their distance from the highway system. It finds several positive effects for non-nodal districts located 0–10 km from the Golden Quadrilateral that are not present in districts 10–50 km away, most notably higher entry rates and increases in plant productivity. These results are not present for districts located on another major highway system, the North-South East-West corridor. Improvements for portions of the NorthSouth East-West corridor system were planned to occur at the same time as the Golden Quadrilateral but were subsequently delayed. Additional tests show that the Golden Quadrilateral project’s effect operates in part through a stronger sorting of land-intensive industries from nodal districts to non-nodal districts located on the Golden Quadrilateral network. The Golden Quadrilateral upgrades further helped spread economic activity to moderate-density districts and intermediate size cities.

This paper is a product of the Economic Policy and Debt Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at EGhani