rafmacalaba/datause-encoder-data huggingface dataset:
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help them. Even a modest household expenditure survey could, for example, allow determining the amount of expenditure devoted to specific commodities by different groups and the impact of a likely change in the prices of those commodities on their overall welfare. In the Sudan, a quick household expenditure survey examined the expected impact of price liberalization on the poor (see Box 3). The surney was cheap and quick: the time between Box a Household Expendituro Survey: Sudan A quick household *xpenditur- survoy wos conducted to oxamine the expected social impact of tho proposed adjustment program. The household survey aimed at analyzing the Impact of price liberalization on the poor, and supplemented the report on the social dimensions of adjustment prepared by the Economic and Social Research Council (ESRC), Khartoum, for the Ministry of Finance, Economics and Planning. The survey concentrated on household expenditures for basic needs goods and on thoir prices. Four groups of commodities, considred to be particularly Important for the poor, were Identifoed: petroleum products (represented in the survey by kerosene), wheat products (represented by bread and flour), sugar and essential drugs, and sorghum, soap, milk, lentils, cooking oil, tea and mataches. Tho survey did not cover soclo-anthropological characteristics of the households, relying instead on the counterpar **t** eam$s work. In addition to information on prices and expenditures, information was also sought on the household's length of stay in Khartoum, province of orgigin, quality of housing, accoss to Identity and ration cards and main activity of household members. The survoy concentrated on four poor areas In the national capital which the ESRC had Identifled -- one where tho residents wero Opoor and three whoro they were Ov-ry poor. Thirty-two :c.ssehold were surveyed In each area making the sample size 128 housoholds. No sophisticated sampling procedure was cossiblo, there being no population frame available **as** a basis for sampling. However, prior flold obsorvations had confirmed that the areas were Internally relatively homogenous. The actual survey was conducted by staff and senior students of Ahfad College, Khartoum. The survey came to the conclusion that the main group expected to be advorsely affocted would be the Opoor' and not the *very poor. Since the former group **was** politically Important, the survey recommended care In liberalizing prices of commodities in their consumption basket. Targeted employment generating projects were also recommended for this group.
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The sample size is much smaller than the above household data: 30 passengers were surveyed along each RSDP road every year. While the total sample size is 600 over five years, the following analysis uses only 498 observations because of some missing values. Each passenger was asked about their travel time, frequency, costs and mode, as well as some basic individual characteristics, such as age and education attained ( **Table 10** ). In the sample, about half of the surveyed passengers travel twice to four times per month. About 35 percent travel only once a month.

|**Table 10. Summary statistics of passenger survey data**|
|---|

|<br>Variable|<br>Abb.|Obs.|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Frequency of travel per month|||||||
|Once||172|||||
|2 to 4 times||243|||||
|5 to 8 times||83|||||
|Dummy variable for program beneficiaries|D<sup>RSDP</sup>|498|0.227|0.419|0|1|
|from RSDP roads|||||||
|Distance to all weather road (minutes):|||||||
|15 to 30 minutes|FEED15-30m|498|0.235|0.424|0|1|
|31 to 60 minutes|FEED30-60m|498|0.098|0.298|0|1|
|1 to 2 hours|FEED1-2hr|498|0.056|0.231|0|1|
|2 to 3 hours|FEED2-3hr|498|0.044|0.206|0|1|
|More than 3 hours|FEED>3hr|498|0.052|0.223|0|1|
|Average travel time (minutes)|TIME|498|103.886|128.402|0|850|
|Average transport fares (EHB)|FARE|498|36.040|45.574|0|551|
|Average waiting time before boarding|WAIT|498|77.966|84.962|0|480|
|(minutes)|||||||
|Dummy variable for occupation:|||||||
|Farmers|FARM|498|0.112|0.316|0|1|
|Traders, bakery and other small businesses|SME|498|0.173|0.378|0|1|
|Government employees, nurse, teachers|GOVT|498|0.205|0.404|0|1|
|Students|STUD|498|0.122|0.328|0|1|
|Dummy variable for transport mode:|||||||
|Minibus|MMinibus|498|0.514|0.500|0|1|
|Bajaj, motorcycles|MBajaj, Motor|498|0.008|0.089|0|1|
|Age|AGE|498|33.558|11.717|14|87|
|<br>Dummy variable for male|MALE|498|0.624|0.485|0|1|
|<br>Dummy variable for education attained: Higher|<br>2EDU|498|0.390|0.488|0|1|
|<br>than secondary school|||||||
|<br>Dummy variable for living areas: Urban|URBAN|498|0.729|0.445|0|1|
|Time fixed effects:|||||||
|_t_=2013|T2013|498|0.175|0.380|0|1|
_Role of NGOs in Health Delivery Africa Health Policy Study_ 20to government, but due to lack of funds this role has been reduced. Neither organization, however, participates in training or serves as an authoritative body in policy Issues.

Despite the existence of umbrella organizations, there is little standardization of NGO activities, even within the same denomination. Many of the NGO facilities do not keep careful accounts. And despite the fact that the govemment subsidizes missions, there is not one locus In the government which has complete informatIon onl the size or activities of the NGO sub-sector. At present, there is no formai system of joint planning by NGOs and govemment. Reporting to the govemment is voluntary; some NGOs report, others do not. Until recently, there was incompatibility of personnel policy between government and NGOs making it unlikely that staff would switch from one to the other. The mission sector procures drugs separately from government. Because the mission sector does not have storage capacity, it does not buy drugs in the same quantities as the govemment, and therefore is not as efficient.

# _Sowaziland_

NGOs are very active in the health field in Swaziland.9 They were the earliest providers of health services In the country and continue to do so, providing today an estimated 30 percent of health services in the country. The range of their activities extends from providing hospital, clinic and rural health center care to work in providing clean water, in offering counselling to the handicapped or drug-addicted to providing nutrition rehabilitatlon. Indeed, NGOs interviewed by Planning Assistance indicated that they wanted to move into primary health care, but could only do so once government took responsibility for medical services currently being provided by NGOs.

The growth rate in numbers of NGOs in health is high; 26 out of _59_ NGOs interviewed by Planning Assistance in 1986 had been founded since 1980 and 15 out of those 26 have comelinto existence since 1983. This growth rate during a period of economic retrenchment is likely to indicate that their activities substitute for inadequate or limited government services.

Their relationship with government, however, tends to be constructive; 48 percent of the 59 NGOs interviewed by Planning Assistance received funds from the government and 14 out of the 59 received money from the government on a regular budgeted basis. Moreover, government often seconds staff to NGO facilities and a substandal proportion of all NGO staff are former government employees. The salaries In the two sub-sectors are competitive (exclusive of beneflts which government workers receive). As In other African countries, NGOs also receive substantial levels of funds from international sources and a majority are staffed by expatriates (52 percent in the 59 NGOs Interviewed by Planning Assistance).

9. This case study draws almost exclusively on Planning Assistance 1986.
# **Figure 3  The Evolution of Employment**

**by Foreign Enterprises, 2000-2007** (1,000 individuals)

<!-- Start of picture text --> 1800<br>1600<br>1400<br>1200<br>Male<br>1000<br>Female<br>800<br>600<br>400<br>200<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Source: Author’s calculation based on the _Enterprise Survey_ data 2000-2007 (GSO).

**Figure 4  The Number of Workers Employed by Foreign Enterprises: Seven Leading Provinces, 2000-2007** (1,000 individuals)

<!-- Start of picture text --> 500<br>450<br>400<br>350<br>HCMC<br>300 Binh Duong<br>Dong Nai<br>250 Hanoi<br>Long An<br>200<br>Hai Phong<br>Da Nang<br>150<br>100<br>50<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Source: Author’s calculation based on the _Enterprise Survey_ data 2000-2007 (GSO).
Consider, for example, an increase in income inequality between t:he nonpoor and the poor.

At given A one expects a partially compensating pro-poor re-allocation of progrram spending (given diminishing marginal utility of income). However, it seems implausible that higher inequality would leave A unchanged; more likely higher inequalit-y dissempowers the poor in terms of their influence on collective decision making wtithin the village.9 Suppose that the income of the nonpoor increases leaving that of the poo:r unchanged. The marginal utility of transfers to nonpoor can be assumed to fall, while the marginal utility of a transfer to the poor will be unchanged (or possibly rise). This will tend to increase the transfer to the poor. However, if the higher income for the nonpoor relative to the poor decreases the Pareto weight on the poor then the effect on the incidence of program spending is ambiguous. The necessary (and sufficient) condition for a higher income of the nonpoor to result in higher transfers to the poor is that Ax > _(UG_ - _AU ) / U_ in obvious notation.

# _2.2 The problen facing the center's Project Officoe_

The PO sets the budget allocation between communities, taking account of their behavior. The center has its own weight on the poor _2_ > 1, which it believes tc, be higher than many of the local Ai 's. The PO does not, however, have the same information set as is available locally. The PO has data supplied by the Central Statistics Office (CSO), represented by the vectors Zi for i=l,..,n but it is impossible to infer _(Xi, Hi)_ from Z,. So the center does not know how the community organizations have agreed to allocate their disbursements between the poor and nonpoor. We can write (Xi, _Hi)_ = _(Zi, 77i)_ where **17,** is a vector of random vaiiables unobserved by the center but with known joint distribution.

The project office's allocations G, (for i=1 ,..,n) solve the problem: 9 Bardhan and Mookherjee (1999) characterize the effect of inequality on the relative weight of the income groups in a model of electoral competition, where the nonpoor are organized in a lobby and can make campaign contributions: higher inequality lowers the level of awareness Df the poor, decreasing the, level of their political participation.

10
3 similarities and differences with those used by the OECD and by Krueger, Schiff and Valdés (K/S/V). It compares a synopsis of the indicators from K/S/V and the new study for the period to 1984, before summarizing the changing extent of price distortions across countries and commodities regionally and globally since then. The third section reports results from a global economy wide modeling exercise aimed at quantifying the trade and welfare effects of the reduction in price and trade distortions over the years since 1984 compared with the prospective effects of removing remaining distortions to agricultural and other merchandise trade. The final section concludes with some observations on what might influence the prospects of such reform in the years ahead.

# **Background**

For decades agricultural protection and subsidies in high-income (and some middle-income) countries have been depressing international prices of farm products, which lowers the earnings of farmers and associated rural businesses in developing countries. The 1958 Haberler report to Contracting Parties to the General Agreement on Tariffs and Trade forewarned that such distortions might worsen, and indeed they did between the 1950s and the early 1980s in East Asia (Anderson, Hayami and Others 1986). Such policies depress international prices for farm products, thereby adding to global inequality and poverty because three-quarters of the world’s poorest people live in poorer countries and depend directly or indirectly on agriculture for their main income (World Bank 2008).

In addition to this external policy influence on rural poverty, the governments of many developing countries have directly taxed their farmers over the past half-century. A well-known example is the taxing of exports of plantation crops in post-colonial Africa (Bates). At the same time, many developing countries chose also to overvalue their currency, and to pursue an import-substituting industrialization strategy by restricting imports of manufactures. Together those latter measures indirectly taxed producers of other tradable products in developing economies, by far the most numerous of them being farmers (Krueger, Schiff and Valdés 1988; 1991). Thus the price incentives facing farmers in many developing countries have been depressed by both own-country and other countries’ agricultural price and international trade policies.
simple average of the six attributes: _sij_ =<sup><u>1</u></sup> 6 ∑ _a_<sup>_sa_</sup> _ij_<sup>.17</sup> Table 9 presents the PPML regression results when the bilateral attraction variable _sij_ is included in the gravity regression. Column 1 has the original results and column 2 includes the attraction index. Comparing columns (1) and (2), the coefficients of distance and contiguity variables barely change and are robust to the inclusion of the attraction variable. More importantly, the state border effect remains strong. The attraction index is significant, suggesting that the listed pull factors lead to higher migration flows.

Column 3 presents the results when we interact the attraction index with each one of the contiguity variables. We first note that the gap between ‘same state neighbor’ and ‘different state neighbor’ dummies disappears and they are no longer statistically different, indicating that the state border effect is zero when _sij_ = 0 and the destination is not attractive at all relative to the origin. The coefficient of the interaction term between _sij_ and the ‘different state neighbor’ is greater than the coefficient of the interaction term with the ‘same state neighbor’ dummy. In other words, the state border effect becomes stronger as the bilateral attractiveness of the destination district increases.<sup>18</sup> Our next extension introduces other measures of distance which are highly relevant in the context of low-income countries with poor infrastructure. The analysis above relied on the flight distance between the geographic centers of origin and destination districts as a measure of distance and traveling cost. This measure may suffer from two measurement errors. First, flight distance does not account for the transport network across India, and thus distorts the actual cost of travel. For two districts that are not connected by highways, the flight distance underestimates the relative traveling time. If this measurement error is more relevant among district pairs that are in different states, the gravity estimation could overstate the state border effect. Second, the

> 17See Table A5 in the Online Appendix for the summary statistics of district characteristics included in the attraction index.

> 18Specifically, the state border effect is given by (2 _._ 420 _−_ 2 _._ 345) + [ _−_ 0 _._ 286 _−_ ( _−_ 0 _._ 576)] _· sij_ , and therefore increasing in _sij_ .

20
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-rt9

<!-- Start of picture text --> : ~~Polley Research<br>Country Operations<br>WPS 929<br><!-- End of picture text --> This paper a product of the Infrastructure Division, Country Department I, Latin America and the Caribbean - is part of a larger effort in the region to design specific reforms that can assist governments in decentralized economies, such as Brazil or Venezuela, in dealing with unusual sources of policy failures in the area of pollution control. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Antonio Estache, room El0-081, extension 81442 (July 1992, 46 pages).

Estache and Zheng make a case for federal monitoring of state environmental agencies' (SEPAs') performance - because of the tradeoff for the states between the need to raise revenue from taxes on local output and the need to limit pollution.

They also show that fines and taxes assigned respectively to the federal and state governments can improve firms' compliance and SEPA's - performance and hence environmental quality -without damaging state revenue, and perhaps even improving it.

They rely for their analysis on numce wal policy simulations based on an analytical framework designed as a multilevel Stackelberg game. This framework reproduces the hierarchical structure of pollution control policies in Brazil, where the federal environmental protection agency relies on SEPAs to ensure that federally defined minimum ambient standards are met locally.

The numerical simulations are based on a case study of the food and the printing and publishing industries.

ThePolicy Research Working PaperSeries disseminates the findings of workunder way in theBank. Anohiectiveofthe series is to get these findings out quickly, even if presentations are less than fully polished. The findings, terpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
# Appendix B: An extension: Remoteness and dispersion at the province level

The conceptual approach that we have outlined for measuring local-area level remoteness and countrywide population dispersion can also be applied at a sub-national regional level. For example, we can calculate the remoteness of each local area within a provincial context, and the associated dispersion of the population within each province. We can also derive a province-level indicator of the remoteness of each province within a country’s broader context.

In the analysis that follows, we derive indicators of remoteness and dispersion at the sub-national level for seven of our case-study PICs (Figure B1). We do not complete the exercise for Tuvalu due to its small size. We reconceptualize community-level remoteness as remoteness within the provincial context. Dispersion of the province population is then calculated as the population-weighted average remoteness of each local area within the province; that is, the average distance from that local area, to all other local areas (within the province), weighted by those areas’ populations. Remoteness of a province as a whole is conceptualized within the broader country context. For the calculations, we anchor the location of each province (or sub-national area) at the provincial capital (or other regional ‘urban hub’<sup>14</sup> ), and measure province remoteness as the (province) population-weighted average distance from that provincial capital to province capitals across the country.

For many countries, the results indicate that the more central provinces tend to have less dispersed (and often larger populations), while the most remote provinces often have widely dispersed (and small) populations. This likely reflects both geographical characteristics and (endogenous) settlement patterns, and the (endogenous) way in which administrative boundaries are determined. In Fiji, for example, Rewa Province (which captures the urban area of Suva, and had a population of around 101,000 at the time of the 2007 census) has a remoteness estimate (population-weighted average distance to all provinces) of 82km and an internal dispersion of 5km, while Rotuma Province (population 2,000) has a remoteness estimate of 318km and internal dispersion of 130km. Similarly, in Tonga, Tongatapu Province (which captures Nuku’alofa and had an estimated population of around 75,000 in 2016) has a remoteness estimate of 65km with internal dispersion of 9km, while Niuas Province (population 1,200) has a remoteness estimate of 569km with internal dispersion of 100km. The results hint at the extent to which agglomeration opportunities on the one hand, and service delivery challenges on the other, might vary across provinces within any given country.

> 14 See Appendix C for further details on the selection of regions and hubs.

31
worked in the same sector as in the year of the survey. Bilateral migration rates are then defined as the number of migrants from state _i_ to _j_ over the number of workers that stayed in state _i_ and declare working in sector _s_ at the time of the interview. InTable 3, we construct sectoral migration rates separately by educational attainment. The workers are treated as highly educated if they attended high school for at least one year; otherwise they are regarded as low educated.<sup>16</sup> Despite the presence of a relatively high number of zero migration flows among the states, the PNAD is considered to be representative of overall migration rates and thus adequate for studying migration patterns within Brazil (Fiess and Verner, 2003; Cunha, 2002). In robustness checks, we will also address the problem of unobserved flows by running Poisson-Maximum-Likelihood estimations including zero-flows.<sup>17</sup> In our final data set, close to 3 percent of the individuals have moved states at least once within five years prior to the interview. Even though most of the migrants are low qualified in absolute terms, the highly educated individuals are the more mobile throughout the years (2.75 percent versus 3.53 percent). Table A- 2 compares interstate mobility across sectors. Whereas less than 3 percent of the workers in basic metals, machinery, textile, and agriculture migrated within the last five years, this percentage is above 4 percent in the wood industry.

# **_Sector-State Specific Wages_**

Our key control variable is the sector-specific wage gap, ∆ _wijst_ <sup>. This variable</sup> accounts for most sector-state specific characteristics of the Brazilian labor market (such as sectoral and regional variations in employment regulations and labor productivity). Moreover, it controls for the indirect impact of market access on migration.

However, due to endogeneity concerns, we do not rely on the observed

> 16 In our empirical analysis, we exclude migration rates that are constructed with less than six observations. Results are robust when maintaining all observed flows and when omitting the top five and bottom five percent of migration rates. Also, using a sample limited to household heads yields overall very similar results (available upon request).

> 17 We have 7722 potential origin-destination-sector cells ( 27 × 26 × 11) but observe at least one positive migration rate for only 1748 cells. In the Poisson estimations we replace all missing values with zeros for these 1748 sectororigin-destination combinations.

13