rafmacalaba/data-use-mentions-extended huggingface dataset:
face as they attempt to replace an easyto-monitor price subsidy for everyone with a targeted social safety net that can deliver better impacts for the poor and vulnerable at a lower cost. Indeed, important episodes of civil unrest in Jordan in the past decade were linked to attempted reforms of utility or consumption subsidies (Atamanov, Jellema, and Serajuddin 2015).<sup>7</sup>

> 6 The Jordan Gives experiment was accompanied by a survey module added to the the Gallup World Poll on citizens’willingness to reform fuel subsidies, preferred design of reformed safety nets, and willingness to support the reform if preferred safety nets were implemented. The survey was conducted on nationally representative samples in Egypt, Jordan, Lebanon and Tunisia. Most respondents in all four countries preferred savings from fuel or diesel subsidy reform to be distributed to the poor and spent on improving social services. Half of the respondents in Jordan preferred the savings to be distributed only to poor families. More than 20 percent of Jordanians who initially opposed the idea of any subsidy reform would support fuel subsidy reform if the savings were to be distributed to the poor, alone or combined with education and healthcare spending.

> 7 A similar situation occured during energy subsidy reform episodes in many other countries (see IMF 2013). As in those cases, mitigating measures were considered as part of the reform in an attempt to generate public support for the reform and offset adverse effects on the poor. In November 2012, a few months after the Jordan Gives experiment, the petroleum subsidy was removed and an unconditional cash transfer was created to compensate the poor and vulnerable. To ensure transparent administration

4
found to vary considerably across the 153 economies (for those born in the 1980s), with a clear geographic pattern (Figure 1). Sub-Saharan Africa stands out as the region with some of the lowest rates of absolute mobility. In some of the poorest or most fragile countries in the region, the share of respondents that have more education than their parents is less than 20 percent, compared to over 80 percent in parts of East Asia. Of the 15

> 13 Two sources of information are used. The first source (http://uis.unesco.org/en/isced-mappings) is not available for all countries and for the most part only conveys the duration of ISCED categories in 1997 and 2011. This source is supplemented with information from UNESCO’s online database <u>(http://data.uis.unesco.org/), which outlines the</u> length of durations of ISCED categories by year from 1970.

> 14 The UNESCO information is not available before 1970, so for individuals who went to school before 1970 the durations from 1970 are applied. When information is missing in the UNESCO sources, additional country-specific information is used or the following rule for converting ISCED categories to completed years of schooling is applied: (i) ISCED 1: 6 years, (ii) ISCED 2: 9 years, (iii) ISCED 3: 12 years, (iv) ISCED 4: 13 years, (v) ISCED 5: 15 years, (vi) ISCED 6: 16 years, (vii) ISCED 7: 18 years, and (viii) ISCED 8: 21 years. In a handful of countries, a years of schooling variable is present but a categorical variable is not. In these cases, the reverse conversion using the country- and year-specific mappings is used.

13
then cash transfer programs or programs enhancing the delivery of basic services facilitating investments in physical and human capital are likely to be the most appropriate. The 4Ps program in the Philippines is a prime example of a cash transfer program aimed at addressing chronic or poverty-induced vulnerability. In contrast, if vulnerability is primarily “risk induced” (i.e., high uninsured income fluctuations) then an insurance type of program may be needed to increase resilience. The insurance provided would allow households to make production and investment choices based on their expected returns rather than the potential implications of these investments on welfare.

Extending some of the ideas of Hill and Porter (2017), the analytic framework adopted is operationally useful and complementary to the _Listahanan_ Proxy Means Test (PMT) as well as as the (PMT+) approach summarized by Mills, Del Ninno and Leite (2015) in their survey of effective targeting mechanisms in Africa (Del Ninno and Mills, 2015). In short, the approach adopted for identifying the vulnerable consists of the following three steps:

**Step 1:** The mean PCE of households is estimated by a linear regression model that summarizes household welfare at any given point in time as a function household observed characteristics and the recent typhoon shock experienced. Specifically, household welfare at any given time is specified by the equation below:

where ln hb denotes the log of real or cost-of-living-adjusted per capita expenditures (PCE) defined by the ratio the nominal ,.of household _h_ , in barangay or cluster _b,_ in year _t_ , in bb semester CC _s_ , adjusted by the Consumer Price Index in province in year _t_ , in semester hkkbb _s_ , hb denotes household observable characteristics, such as age and education level of the PPCCPP ppb CCPPCC pp household head, the total number, age and gender composition of household members, bb ownership of assets such as livestock, phone radio, TV etc., characteristics of the household XX residence including type and quality of water and sanitation facilities, main
Table 4: Simulation Results Using World Bank Measure of GW Volume

|Model|Crop<br><br>Sa<br>Co|Grids<br>tisfying<br>nstraints<br>(%)|Grids with<br>Production<br>Gains (%)|Median<br>Gains in<br>Production<br>(%)|Mean<br>Gains in<br>Production<br>(%)|
|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|
|SD|Barley|5_._47|2_._75|29_._57|101_._40|
|SD|Millet|5_._47|4_._31|76_._35|102_._88|
|SD|Maize|5_._47|43_._45|107_._96|184_._96|
|SD|Wetland Rice|5_._47|41_._41|108_._09|126_._08|
|SD|Sorghum|5_._47|18_._22|89_._49|112_._43|
|SD|Wheat|5_._47|8_._05|27_._97|46_._87|
|SRHS|Barley|5_._47|4_._68|33_._28|65_._23|
|SRHS|Millet|5_._47|0_._00|||
|SRHS|Maize|5_._47|57_._93|123_._26|221_._50|
|SRHS|Wetland Rice|5_._47|44_._04|114_._64|135_._14|
|SRHS|Sorghum|5_._47|40_._36|129_._42|242_._49|
|SRHS|Wheat|5_._47|14_._02|53_.
inspectors and tehsildars; and land registration, which is the responsibility of sub-registrars. Measures to coordinate these two services and enhance their efficiency through computerization, while at the same time reducing transaction costs to individuals in the land market, could go a long way towards stimulating the land market. Whether or not this would enhance access to land for the rural poor, however, depends on the degree of transparency with which land administration is conducted in practice. Access to information and public awareness of rights seem to be critical factors. A recent initiative of the Revenue Department, Government of Orissa, to disseminate a local-language 'how to' manual on matters of land transfers and access to land records, is a most welcome contribution in this area.

_Land survey and settlement operations:_ Survey and settlement operations evolved historically as a way to establish a record of rights in land, on which to base the assessment of land revenue. Periodic, revisional surveys, conducted every 25-30 years or so, serve as the major means to update land records. Since the process of mutation following an individual land sale-purchase transaction is burdensome, protracted and (for many) prohibitively expensive, many land holders prefer to wait until the next revisional survey to obtain title to their land. In practice, the survey and settlement process provides widespread opportunities for rent-seeking on the part of the govermnent officers involved, and it is not uncommon for poorer and less powerful landholders to 'lose' at least a proportion of their land in the official record. Land-grabbing by more powerful individuals, facilitated by exerting leverage over settlement officers, appears to be commonplace during survey and settlement operations. While the contested amounts of land are usually small, the net effect is systematically to discriminate against the rural poor and the socially excluded.

Four types of land transaction are considered in the main analysis. Land may be purchased, inherited, rented (leased) or, in the case of commons and public land, encroached upon. Each of these types of transaction, and the state
In the present study we utilize these data for two purposes. First, we empirically analyze the main factors determining the location of Venezuelan migrants across Ecuador’s _cantons_ .<sup>17</sup> Second, we use the data to classify _cantons_ according to the estimated density of Venezuelan migrants in the cantons population. This allows us to compare the within-canton changes in labor market outcomes for _cantons_ with varying densities of Venezuelans in their population.

At the country level, we estimate that about 470,095 Venezuelans were living in Ecuador in the first quarter of 2019. This amounts to about 3% of the population. Table 1 presents the top 10 _cantons_ by the estimated number of Venezuelan migrants (top panel) and by the share in the working-age population in the canton (bottom panel). As shown in the top panel, there is high regional variation, ranging from _cantons_ hosting almost 90,000 Venezuelan migrants (Guayaquil and Quito) to others having virtually none. In fact, four _cantons_ concentrate 52% of the 470,095 Venezuelan migrants estimated in the _Telefonica_ data: Guayaquil (87,505), Quito (86,386), Manta (32,405), and Santo Domingo de los Tsachilas (26,721).<sup>18</sup>

# **5 Location Choices of Venezuelans in Ecuador**

The goal of this section is to employ regression analysis to quantify the role played by a variety of geographic and economic factors in determining Venezuelans’ choice of region (defined as a canton) in the vein of Beine et al. (2019).<sup>19</sup> The geographic factors we consider are the distances from each canton to the main gateways into Ecuador for Venezuelans. According to the Ecuador’s official entry records, the majority of Venezuelans entered Ecuador by land from Colombia, through the Rumichaca bridge in the Tulcan canton (82%). Other land points of entry were San Miguel (5%) and
from different spending was collected from mid-2017 to mid-2018, so we have matched it to the 2018 fiscal data.

# Taxes

Tax revenues account for about 15 percent of GDP in 2018 (Table 1). The lines in bold type are included in this study. Indirect general taxes on goods and services are by far the largest component, with personal and corporate income taxes making up most of the rest. In this study we do not analyse corporate income tax, the incidence of which cannot usually be allocated out to households.<sup>3</sup> We also do not include taxes on the commercial sector. We similarly exclude indirect taxes on the commercial sector. As a result, we only capture just over half of total tax revenues.

Jordan’s GST standard rate is 16 percent, and there are reduced 4 percent and 0 percent rates, as well as items exempted from GST. Exempt items do not have GST charged, but there is an indirect ‘tax’ paid by consumers as producers cannot recover the GST paid on the inputs used to produce them, the cost of which is passed on to consumers. A Special Sales Tax (SST) applies to cigarettes and tobacco,<sup>4</sup> mobile phone subscriptions,<sup>5</sup> soft drinks,<sup>6</sup> and alcohol<sup>7</sup> based on the quantity purchased. We cannot distinguish between taxes charged to domestic and imported goods in household consumption and so apply the same rates to both.

PIT in 2018 was levied on gross income from all sources less exemptions and deductions.<sup>8</sup> The income tax rate is progressive on the income level: the first 12,000 Jordanian dinars (JOD) of an individual’s income is exempt; income between JOD 12,000 and JOD 24,000 is taxed at 7 percent; income between JOD 24,000 and 36,000 is taxed at
1997 to 2000. It should be noted that the OLC’s presented in Table 5 include two quarters. They thus differ slightly from the short-run effects in the tables above. The corresponding one-period effects convey a similar same message though the estimates are murkier given the high variation during economic crises – for this reason the two-period estimates are presented in the table.

Table 5 conveys a number of interesting messages. First, by removing agricultural employment from total employment the OLC estimates are pronounced for both Korea and Taiwan, China. This contrasts with Table 4 and suggests that, when refining the analysis, there is a relationship between growth and jobs even in countries where the overall OLC is weak. Second, Table 5 conveys no convincing evidence that the OLC differs considerably in times of crisis and times of calm – at least with respect to nonagricultural employment: in Korea, the magnitude of the non-agricultural OLC fell consistently over the periods of study; in the Philippines it was pronounced in the Asian crisis but not detectable in other periods; and in Taiwan, China the OLC was most pronounced in the period of calm. This supports the message from Table 4 that there is no compelling evidence that the OLC differs substantially across the business cycle.

Finally, Table 5 provides some evidence for the argument that agricultural employment functions as a shock-absorber in East Asia. In times of crisis, the agricultural OCL is negatively signed, suggesting that agricultural employment responds to growth counter-cyclically. The effect is visible in all three countries in times of crisis but not during periods of clam. Thus, agriculture appears to be a fall-back option for some workers who lose their jobs in the ‘modern’ sector during an economic contraction. To the extent that the data on agricultural employment can be understood as a proxy for informal employment, the results suggest that a similar counter-cyclical relationship may hold in the informal sector, which presents an interesting avenue for future research.

10
6.3|4.1|**1.6**|**-2.2**|
|Nigeria|Oil|6.1|6.7|0.9|**0.6**|**-5.8**|6.6|8.3|1.4|**1.7**|**-6.9**|
|Tanzania|Gold|5.5|6.5|6.3|**1**|**-0.2**|5.4|6.5|7.1|**1.1**|**0.6**|

Notes: Comparison of average growth rates pre and during the boom, SSA, RR countries only. Source: World Bank national accounts data, and OECD National Accounts data files (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG) and UN Statistics Division, National Accounts Main Aggregates Database (https://unstats.un.org/UNSD/snaama/formulas.asp)

# **Outcome 3: Rising poverty and inequality in resource-rich countries, putting Africa on a perilous trajectory**

The commodity price boom saw large inflows to African economies via resource companies exports and government revenues from production shares, royalties, and other resource sector taxes. However, despite the economic boom, the impact on poverty alleviation and inequality was disappointing.

The overall trend in resource-rich African countries was an increasing poverty headcount during the boom – indicated by the solid red line in Figure 14 – from about 280 million in 2003 to more than 310 million living in extreme poverty by 2019.<sup>11</sup> Meanwhile nonresource-rich Africa saw more modest increases – from 110 million to over 113 million over the same period. In part this was driven by a higher average (mean
facing stronger likelihood of promotion that would be enhanced by better economic performance coming with privatization, would be more eager to seek privatizing SOEs under their oversight.

# **3. Data**

Our data set is the Annual Survey of Industrial Firms (ASIF) from 1998 to 2009 collected by China’s National Bureau of Statistics. It includes _all_ SOEs and all non-state firms with sales above five million yuan in the industrial sectors, accounting for about 90% of total industrial output value in China.<sup>10</sup> Since our goal is to study the incentives of local politicians (i.e., politicians at the prefectural city level or below),<sup>11</sup> we only keep SOEs that are affiliated with local governments (defined as those SOEs with the share of local government ownership exceeding 50 percent) in our baseline regressions. In constructing our panel, we follow the best practices in fixing some identification inconsistencies.<sup>12</sup> We construct a dummy variable, _Privatized_ , that equals one for a firm for the privatization year and the subsequent years if it is privatized during the sample years and 0 otherwise. An SOE is classified as being privatized in two ways: staying in the database with the state share dropping below 50 percent; exiting the data set, which implies privatization, and its sales being below five million yuan or being merged with small private firms. Out of 50,030 SOEs in our sample, 40,557 had been privatized by the end of our sample period.

We match the ASIF data of SOEs with a data set on local politicians in China, i.e., party secretaries in the prefectural-level cities. The politician data set has details of their personal information (age, gender, and education), as well as their career path (the appointment date, the next deputation, and promotions/demotions). We use provincial yearbooks for the names of city-level party secretaries, and search
I ,, I D I 5 D. I,I C I - '- G I at I C I C I '- a I Q D :<br><!-- End of picture text --> Capital flows to the region were also disrupted. The indicator we used, the sovereign spreads of Brady bonds (Figure 6 )11, shows that recovery after the Tequila shock was 1 The Brady Bond sovereign spreads are available for the whole sample period only for Argentina, Brazil and Mexico. For the other countries we used the Latin American aggregate (LATEMBI), from JP Morgan.
Policy Research Working Paper 0

# **Abstract**

This study examines the major constraints of rural business entry and performance in Yemen. The Yemen rural investment climate survey made it possible to analyze rural investment climate constraints for rural businesses. The survey was used to investigate both farm and nonfarm rural enterprises. The rural investment climate was assessed using a combination of subjective impressions related by rural entrepreneurs, and a more objective, empirical set of analyses that employed indicators to rank the constraints to _“doing business”_ in the areas surveyed. These empirical analyses included application of the entry model, the performance model, the closure model, and the migration model. The migration model was introduced to identify how the rural investment climate variables at the community level increase migration and economic activities. Based on the assessment of the rural investment climate, this paper identifies and explains four critical areas in which the rural investment climate in Yemen can be improved: market demand, access to markets, access to finance, and the provision of business services. Because farm and nonfarm businesses often experience common or similar constraints, the climate in which they operate can often be improved with the same measures and policies. Addressing the constraints that affect rural women entrepreneurs in particular, who play a vital role in rural nonfarm enterprises, warrants clear priority as a means to generate income and employment. Security and labor issues are identified as the key such constraints that disproportionately affect women.

This paper is a product of the Agriculture and Rural Enterprises Unit, Social Development 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 nsawada@worldbank.org.

_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
**Figure 5: Average Utility Borrowing Rates vs. the Jurisdictions’ Commercial Rates**

<!-- Start of picture text --> 30%<br>26%<br>25% 24%<br>20% 19%<br>16% 17%<br>15%<br>15% 14% 13% 13%<br>10% 10% 10%<br>10% 8%<br>6% 5% 7% 6% 6% 6% 7% 7%<br>5% 3% 3.2% 4% 4% 4%<br>0%<br>Average borrowing rate for the utilities<br>Average commercial borrowing rate for the country<br>Public distribution company<br>Private distribution company<br>Average borrowing rate<br><!-- End of picture text --> _Source:_ Utility financial statements; World Development Indicators.

**Figure 6: Loan Sources and Average Borrowing Rates**

<!-- Start of picture text --> 25% 23.0%<br>20%<br>15%<br>10% 9.0% 8.4%<br>7.5%<br>6.6%<br>5% 2.93% 3.6% 4.2% 4.0%<br>0.6%<br>0%<br>Tanzania Kenya Senegal Tajikistan Uganda<br>Government Commercial IFI<br>Average Borrowing Rate<br>Percent of Total Loans<br><!-- End of picture text --> _Source:_ Utility financial statements; World Development Indicators.

19
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