rafmacalaba/data-use-mentions-extended huggingface dataset:
-7-

budget for 1987 proposed selling all power generation and transmission facilities at federal dams while retaining ownership and control over the dams themselves. A new public service that has emerged at the local level is resource recovery which employs an innovative disposal technology. Private firms in the U.S.A. are financing, building and managing these capital intensive plants under contract with local governments. A recent study has concluded that local governments in the UJ.S.A. are more likely to resort to privatization when budgetary constraints become severe, and have some prior experience with alternetive service delivery methods.<sup>13</sup> Privatization of industrial and commercial public enterprises has been insignificant in the U.S.A. The main reason is that public enterprises are a minor feature of the governmental scene in this country. The public enterprises that exist are for the most part in the infrastructure sectors and not in manufacturing. While most privatization activities in the U.S.A. have so far been concerned with routine housekeeping or infrastructure services, the latest trend is to privatize more sensitive areas such as health and human services and public safety. The contracting out of child welfare services through competitive bidding has become increasingly popular. A growing number of local governments are now contracting out the management and operation of public hospitals to commercial hospital chains. A 1982 survey showed that 47 percen': of the cities and counties (responding to the survey) had sub-contracted emergency medical service to private operators. A sample survey showed that 44 13/ R. W. Poole & P.E. Fixler, "The Privatization of Public Sector Services in Practice: Experience and Potential," unpublished mimeo., 1986, p.3.
<!-- Start of picture text --> Crop and animal production, hunting and related...<br>Land transport and transport via pipelines<br>Wholesale trade, except of motor vehicles and...<br>Retail trade, except of motor vehicles and motorcycles<br>Electricity, gas, steam and air conditioning supply<br>Specialised construction activities<br>Manufacture of other non-metallic mineral products<br>Construction of buildings<br>Manufacture of basic metals<br>Manufacture of chemicals and chemical products<br>Food and beverage service activities<br>Wholesale and retail trade and repair of motor...<br>Human health activities<br>Manufacture of food products<br>Civil engineering<br>0 0.001 0.002 0.003 0.004<br><!-- End of picture text --> Source: Authors based on AECM survey; Eurostat.

**There are large differences in individual transition risk scores between CGSs.** For each individual CGS we calculate the sum of their transition risk scores for each sector _i_ . We find that within our NACE2 sample, the mean transition risk score is 0.03 with a standard deviation of 0.04. The highest scoring CGS has a transition risk score of 0.17 compared to 0.01 for the lowest scoring CGS. The highest scoring CGSs tend to be highly exposed to, and specialized in, crop and animal production. The lowest scoring CGSs have their highest exposures in retail and wholesale trade and financial service activities.

# _4.2 Physical risk vulnerability_

**Using the vulnerability data by Think Hazard, CGSs in Europe are exposed to a broad range of climate-related risks.** Our main results are provided in Figure 4. We find the main high-risk exposures to be for wildfire (56 percent of aggregate CGSs’ portfolios in our sample), coastal flood (41 percent), and river flood (6
will dampen fluctuations between different locations; likewise, estimates of unit transaction costs and transaction costs relative to sales prices on the basis of aggregate price data will be lower. Despite the clear rejection of the equality of shares, we find that the test reported in table 2 indicates that levels of predicted unit transaction costs and survey observations of unit transaction costs are of the same order of magnitude. Therefore, we conclude that predicted unit transaction costs may be considered good approximations for the assessment of market integration.

# _<<B>>Calculated regime probabilities_

With projected unit transaction costs, we can calculate the value of the market indicator, the price difference less projected transaction costs, for each location (and thus for both directions of trade) and for each month. A summary presentation of the results, the density function of the market integration indicator shown in figure 2, aggregates observations over months and buyer districts, partitioned into regions and periods with and without food shortages. The area under the density functions and below -κ, above +κ and between -κ and +κ characterizes the incidence of the various regimes.

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gram, based on lists of members obtained in advance, with responses given by the household head. We chose to sample equal numbers of households within each community because one goal was to obtain a community average effect on socioeconomic indicators (not weighted by population). Characteristics of the respondents are given in Table A4. The primary reported economic activity of over half of the respondent households was agriculture or livestock, most household heads have not completed secondary school, and 72% of respondents understood the survey questions “very well” (as assessed by the enumerator).

The sample is representative of the universe of possible applicants. When we compare the communities sampled with the universe of possible applicants in all states during these years, we find few differences in observable covariates. Table 2 shows the means and normalized differences in means across the sampled versus the not sampled applicants between 2011 and 2014. Most of the normalized differences are quite small (less than a quarter of a standard deviation). The covariate for which there is a substantial difference is percent indigenous in the municipality, which is larger for applicants in the sample both for beneficiary and non-beneficiary communities<sup>7</sup> . Sampled beneficiaries are also somewhat farther from major cities.

Leaders and households were surveyed separately, with some questions overlapping across the instruments. Leaders responded to questions about the PSA program, including the location of the parcel and its characteristics, a referendum question regarding how they would vote on program participation at different prices (see description below), the way in which payments were divided within the community, the types of forest management, and use of the PSA parcel. In addition, they responded to a battery of questions on community characteristics (infrastructure, productive activities, common property size, etc.). A significant number of questions were also asked with the intention of measuring social capital for use in a complementary study (Alix-Garcia, Sims, Orozco-Olvera, Costica, Medina & Monroy 2018).

The household survey also began with questions
in Latin America and the Caribbean:  Problems, Prospects and Options" , ECLAC Working Paper.

**International Labor Organization (ILO).** Global Employment Trends for Youth. 2013.

**International Labor Organization (ILO).** Youth Employment Statistics in Nepal. 2014. <u>http://apyouthnet.ilo.org/stats/youth-employment-statistics-in-nepal/at_download/file1 (accessed</u> September/1).

**International Labor Organization (ILO).** Labour market transitions of young women and men in

Nepal. Work 4 Youth Publication Series No. 12. May 2014.

**Kling, Jeffrey R. and Jeffrey B. Liebman.** 2004. _Experimental Analysis of Neighborhood Effects on Youth_ : John F. Kennedy School of Government, Harvard University.

**Kluve, Jochen.** 2006. "The Effectiveness of European Active Labor Market Policy" , IZA Discussion

Paper No. 2018; RWI Discussion Paper No. 37.

**Leuven, E. and B. Sianesi.** 2003. "PSMATCH2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing  and Covariate Imbalance Testing" , 4.0.6 17may2012.(http://ideas.repec.org/c/boc/bocode/s432001.html).

**Linn, M. W., R. Sandifer, and S. Stein.** 1985. "Effects of Unemployment on Mental and Physical Health" _American Journal of Public Health_ , 75(5): 502-506.

**Macours, Karen.** 2011. “Increasing inequality and civil conflict in Nepal”. _Oxford Economic Papers_ , 63 (1).

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55 and protection of consumers. From a world of shallow trade agreements, designed to deal with terms of trade externalities, to a world of deeper agreements involving coordination over behindthe-border policies. However, more work remains to be done on the implications of global supply chains, imperfect information and commitment problems for the desirability of deeper agreements. In addition, international cooperation in the presence of coordination externalities and the potential benefits (and costs) of regulatory harmonization are an obvious area of importance for future research. Developing new and better data on NTMs appears as an essential step to further improve our understanding of the trade impact of non-tariff measures and, hence, how changing rules overseeing NTMs eventually affect social welfare.

Finally, while no formal model has so far shown this, one could conjecture, as Bagwell et al. (2015, fn 119) do, that the two worlds in Lamy’s quote could be linked. The need for deep integration is likely the optimal development after terms-of-trade considerations have been removed from tariff choices (i.e., deep PTAs are the byproduct of GATT-like shallow integration). Indeed, Bagwell et al. (2015) note that, while the multilateral (single-undertaking) approach of the WTO was optimal for standard trade coordination involving terms-of-trade externalities, deeper international agreements aimed at commitment problems or coordination externalities might be better handled at a more disaggregate level under either a system of preferential or plurilateral trade agreements. While the world trading system appears to be moving already in this direction (e.g. Trans-Pacific Partnership, Transatlantic Trade and Investment Partnership), economic research is lagging behind. Addressing these questions will entail integrating the research on non-tariff measures (discussed in the chapter) with the literature on preferential trade agreements (discussed in chapter 14 of this handbook), particularly as little work has been done on the third-country effects of deep integration.
for a range of crops. Thereby, planting dates are computed for each crop and geographic region individually, depending on current rainfall data. Planting dates are computed as a sowing window start dekad<sup>41</sup> and a sowing window end dekad. To determine the planting dekad within this window, LEAP identifies two consecutive dekads in which certain rainfall thresholds are exceeded (these thresholds are crop and location specific). This is usually a good approximation of farmers’ decision to start planting.

- (ii) **_Calculation of Water Requirements Satisfaction Index (WRSI)_**<sup>**_42_**</sup> **_:_** The computation of the WRSI is based on a number of input parameters. Specifically, these include rainfall, the planting start and end dates computed in (i), potential evapotranspiration, soil water holding capacity, crop type, the water use rates of a growing crop, the geographical area where the crop is grown, and the length of the growing period. All of these parameters are static except rainfall, which

39 Additionally to the LEAP and LIAS tools and hotspots assessments, a range of other early warning data are collected regularly in Ethiopia. These include, for instance, Integrated Food Security Phase Classification data are collected by FEWS NET (see section IV.7).Moreover, the European Commission’s Joint Research Centre (JRC) uses remote sensing to monitor agricultural and pastoral vegetation in the Horn of Africa on a real time basis.

40 Initially, the LEAP project included the LEAP tool, a contingency finance facility, contingency plans, and national capacity building activities. LEAP was initiated following the implementation of a sovereign index insurance pilot conducted by WFP and GoE. With time, LEAP focused increasingly on the development of the LEAP tool and provision of the tools intermediary and final outputs.

> 41 A dekad is a time interval of approximately ten days. Every month has three dekads, such that the first
Policy Research Working Paper 5919

# **Abstract**

A revenue-neutral switch from trade taxes to domestic consumption taxes is fraught with implementation challenges in countries with a large informal sector. It is shown for a sample of low-income countries over 25 years that they have had a mixed record of offsetting reductions in trade tax revenue. The paper then analyzes the specific case of Nepal, using a unique data set compiled from unpublished customs records of imports, tariffs and all other taxes levied at the border. It estimates changes to revenue and domestic production associated with two sets of reforms: i) proportional tariff cuts coordinated with a strictly enforced value-added tax; and ii) proposed tariff cuts under a regional free trade agreement. It is shown that a revenue-neutral tax reform is conditional on the effectiveness with which domestic taxes are enforced. Furthermore, loss of revenue as a result of intra-regional free trade can be minimized through judicious use of Sensitive Lists that still cover substantially all the trade as required by Article XXIV of the GATT.

This paper is a product of the International Trade 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 swarnim@post.harvard.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the
7 60 for men. This could be explained by the development of, and easy access to the education sector and the low retirement age. At young ages, the labor force behavior observed in the FSU was close to the one observed in European countries in which large public investments in education took place and labor force participation rates were significantly below those observed in Anglo-Saxon countries (OECD, 1994).

The transition brought about several changes. As a response to the sharp income decline and cuts in public spending, **labor force participation rates** increased sharply for both men and women in the 15-19 age group and at old ages (see **Chart 1).** For example, in the age group 6064, rates increased from 32 to 77 percent between 1989 and 1995 for men, and from 14 to 67 percent for women.

**Chart 1. Ukraine Labor Force Participation Rates**

<!-- Start of picture text --> 100%<br>90%<br>70%<br>60%<br>30% \ -- Fmb18<br>50% A Male 1989<br>40% °Ot-°Male \ 1995<br>30% - -- Female 1989<br>20% -- Female 1995<br>10%<br>- N ~ ~ I?~ L D CD 9<br><!-- End of picture text --> Source: World Bank household Survey (1995) Another change has been the increased **importance of informal activities.** One important form of such activities in Ukraine consists of work on land plots, cattle/poultry breeding etc.. which represent significant sources of additional income. These activities are performed either as a complement to employment in the formal economy or have become the main occupation. In 1995, over 65 percent of men and women employed by a state or private enterprise report spending additional time working on land plots
example, facing online competition, many physical bookstores have closed (Goldmanis et al. 2010). However, there is a net market expansion effect for consumer electronics, although e-commerce diverts sales from offline, and consumers benefit more than firms from the introduction of e-commerce (Duch-Brown et al. 2017). The net impact of e-commerce depends upon the relative importance of the above factors.

In developing countries, a large proportion of people live in remote areas with limited access to offline retail stores. In these countries, e-commerce may have greater potential to reach a wider range of consumers who are otherwise constrained by limited access to markets than in developed countries. Building a multiregional general equilibrium model and using city-level data, Fan et al. (2016) show that e-commerce development in China disproportionately improves consumer welfare in remote cities. Drawing data from eight counties in three provinces where

> 1 <u>http://www.latimes.com/business/la-fi-amazon-history-20170618-htmlstory.html#</u>

> 2 https://www.businessinsider.com/black-friday-cyber-monday-vs-singles-day-sales-2018-12.

·· 2
# **2.3 Mobile Phone Metadata**

In a follow-up survey conducted in 2018, we requested informed consent from survey respondents to obtain their mobile phone CDR and match them to the survey data collected through the TUP project. CDR contain detailed information on:

- **Calls:** Phone numbers for the caller and receiver, time and duration of the call, and cell tower through which the call was placed

- **Text messages:** Phone numbers for the caller and recipient, time of the message

- **Recharges:** Time and amount of the recharge

For participants who consented, we match baseline survey data (collected November 2015 - April 2016) to CDR covering that same period, obtained from one of Afghanistan’s main mobile phone operators. For households with multiple phones and a designated household head (N=65), we match to CDR for the phone belonging to the household head. For households where the household head does not have a phone and someone else does (N=17), we match to CDR for one of the households’ phones selected at random. In total, for the 535 households in our sample, 629,543 transactions took place in the months of November 2015 to April 2016, broken down into 310,883 calls, 305,756 text messages, and 12,904 recharges.

From these CDR, we compute a set of 797 behavioral indicators that capture aggregate aspects of each individual’s mobile phone use (de Montjoye et al., 2016). This set includes indicators relating to an individual’s communications (for example, average call duration and percent initiated conversations), their network of contacts (for example, the entropy of their contacts and the balance of interactions per contact), their spatial patterns based on cell tower locations (for example, the number of unique antennas visited and the radius of gyration), and their recharge patterns (including the average amount recharged and the time between recharges
18 value of 0.387. For Dar es Salaam, rather than declining to 0.176, the simulation shows poverty rising to more than twice that level, 0.358.

It is unclear why using sectoral population growth rates produces such implausible results. We suspect this is related to population growth assumptions underlying the national accounts data. However, without a detailed understanding of how the national accounts data were constructed, we are unable to say conclusively. For purposes of this paper, we focus on the sectoral per capita growth rates calculated with the _national_ population growth rate, on the basis that they provide a much better match to the 2001 survey data.

**Figure 7**

<!-- Start of picture text --> Simulated changes in poverty<br>using alternative urban and rural growth rates<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.5 Simulation with Regional Growth Rates**

Another alternative is to apply growth data at the regional level, using annual GDP data by region. This data, in current prices, was converted to real values using the GDP deflator in Table 2, and the annual levels were used to calculate regional GDP growth rates for each year. Separately, regional population growth rates were calculated using the regional totals from the 1988 and 2002 national censuses. These regional GDP and population growth rate estimates are shown in Appendix Table A6. Regional GDP per capita growth rates were then calculated year-by-year by subtracting the population growth rates from the GDP growth rates.<sup>8</sup> Population changes were also taken into account to adjust
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