rafmacalaba/datause-encoder-data huggingface dataset:
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# Table 18 :Annual Percentage Change in Poverty Gap Ratio for Rural Poor Growth,lnequality and Total Effects

|States|73-74to<br>Growth|77-78<br>'Ineq|Total|77-78to<br>Growth|83<br>'Ineq|Total|83 to 86<br>Growth|-57<br>'Ineq|Total|
|---|---|---|---|---|---|---|---|---|---|
|ANDHRA PRADES|-8|1.4|-6|-7.9|-0.7|-9.1|-6.7|4.2|-2.3|
|ASSAM|4.3|-3.8|1.2|-10.7|1.3|-8.9|-4.9|4.6|0|
|BIHAR|-0.7|-1.3|-2|0.4|-0.2|0.3|-10.8|-0.4|-11.5|
|GUJRAT|-8.4|6.7|-0.2|-7|-4.5|-13.4|-2.1|15.1|13.6|
|Haryana|-3.6|-2.1|-5.6|-5.9|-3.4|-10.1|-8.1|10.9|1.4|
|Jammu-Kashmir|-4.6|-2.2|-6.2|-13.9|-0.6|-14.2|-12.5|16.8|6.2|
|KARNATAKA|-6.4|4.1|-1.4|-5.2|-1.8|-7|1.8|-1.7|0.3|
|KERALA|-8.2|3|-4.2|-5.9|-2.3|-9.2|-6.8|1.1|-5.5|
|MADHYA PRADES|<br>-3.4|3.1|0|-4.8|-2.3|-7.6|-4.3|1.6|-3|
|MAHARASHTRA|2.5|2|4.4|-9.2|-0.2|-9.8|-2.1|2|-1|
|ORISSA|-4.8|1.9|-2.7|-1.3|-1.9|-8.1|-2.8|9.3|-2|
|Punjab|-18|7|-11.1|0.4|-6.3|-6.3|-2.1|1|-1|
|RAJISTHAN|-9.5|5.9|-2.4|-3.6|1.7|-1.7|-0.3|-8.2|-8.2|
|TAMIL NADU|-2.9|4.1|1.7|-2.1|0.5|-1.5|-7|-2.5|-9.5|
|UTTAR PRADESH|-11.9|5.7|-4.3|-0.3|-0.7|-0.9|-11.6|2.1|-9.4|
|WEST BENGAL|-3|-0.2|-3.2|-2.5|-8.2|-3|-8.5|-4.5|-14.3|
|ALL INDIA|-4.6|2.3|-1.9,|-3.3|-1.6|-4.6|-6.7|1.1|-6.7|

# Table l9:Annual Percentage Change in Poverty Gap Ratio for Rural Ultra Poor Growth,lnequality and Total Effects

|States|73-74t<br>Growth|o77-78<br>'Ineq|Total|77-78t<br>Growth|o83<br> **'Ineq**|Total|83to86<br>Growth|-87<br>**'Ineq**|Total|
|---|---|---|---|---|---|---|---|---|---|
|ANDHRA PRADES|-9.2|2.7|-6.5|-9.4|-1.7|-11.6|-6.7|5.7|-1|
|ASSAM|5.6|-8.7|-2.1|-14.6|2.4|-11.6|-C.4|8.8|2.9|
|BIHAR|-0.9|-2.2|-3|0.6|-0.4|0.1|-14.1|-0.9|-16.9|
|GUJRAT|-10.1|12.1|3|-9|-9|-19.3|-3.1|28.7|27.1|
|Haryana|-5|-1.3|-5.7|-6.3|-5.5|-12.9|-11.9|10.7|0|
|Jammu-Kashmir|-6.5|-3.9|-8.8|-18.9|-1.5|-17.8|-16.2|25.8|9.7|
|KARNATAKA|-9|7.3|0|-6.1|-2.1|-8.7|2.1|-2.7|0|
|KERALA|-10.2|4.9|-4.3|-7.3|-4.5|-13.3|-8.8|2.9|-5.2|
|MADHYA PRADES|-4.6|4.6|0.4|-6.3|-3.5|-10.3|-5.6|0.8|-5.2|
|MAHARASHTRA|3.5|3.3|6.7|-12.1|-0.6|-13.4|-3|2.8|0|
|ORISSA|-6.8|2.4|-3.6|-1.8|-3|-10.4|-3|11|-4.5|
|Punjab|-35.6|8.4|-10|0|-12.7|-11.7|-5.1|1.2|-0.6|
|RAJISTHAN|-12|10|0|-4|1.7|-2.6|0|-12.2|-12.2|
|TAMIL NADU|-3.4|6.6|3.1|-2.9|1.1|-1.5|-8.6|-3.1|-11.8|
|UTTAR PRADESH|-17.2|9.9|-3.8|-0.3|-0.5|-0.8|-14.7|2.8|-12.9|
|WEST BENGAL|-3.8|-0.5|-4.6|-3.3|-10.1|-3.8|-10.4|-7.8|-19.3|
|ALL INDIA|-5.6|3.8|-1.7|-4|-2.3|-5.9|-9|0.4|-9.5|
# **6. EVALUATING POLICY IMPACT AND COMPARING ALTERNATIVE METHODS OF MEASURING RESTRICTIVENESS**

This section has three related objectives: to illustrate that services policy matters for outcomes that we care about; to show how restrictiveness of policy may be inferred econometrically, as discussed in Section 3.3; and to demonstrate that our simple STRI produces as economically sensible results as more complex methods of measuring policy restrictiveness.

While collecting and publishing information on regulatory measures affecting trade in services is the primary purpose of this database, we are ultimately interested in evaluating the economic impact of such policies. Due to the idiosyncratic nature of individual services sectors, a full-fledged analysis is clearly beyond the scope of one paper, and for the purposes of this section we assume policy restrictiveness to be exogenous data.<sup>30</sup> We will, however, present two examples of the impact of services trade policies: on mode 3 flows, i.e. inward foreign investment, and on access to services. The value of foreign investment is a uniform metric across heterogeneous sectors and thus is a natural dependent variable for a broad cross-sectoral investigation in Section 6.1. In contrast, ̳performance‘ assumes a different meaning in each sector. In Section 6.2, we use the example of bank lending to illustrate how services trade policies affect access to financial services as measured by domestic credit extended to the private sector.

## **6.1. Cross-sectoral analysis based on FDI flows**

We assess the impact of policy barriers on FDI using a comprehensive dataset on global mergers and acquisitions (M&A) spanning the period 2003-2009.<sup>31</sup> The dataset consists of

> 30  In their study of telecommunications and transport sector policies, Borchert et al. (2012) go further and account for the endogeneity of policy choices.

> 31  It would be of interest eventually to take into account the impact on greenfield investment as well but data concerns prevent us from incorporating greenfield flows into the present analysis.  First, the best greenfield data with global coverage that is currently available relies heavily on _estimated_ rather than actual FDI flows, thus greenfield data is not of a quality comparable to the M&A data we use here.  Second, the sectoral breakdown is

37
**Table** 4. **Other Exogenous Variable Definitions for the USAGMKTS Model.**

Variable Definition YEAR - Two digit year (e.g., 1985 - 85) D70 - Dummy variable, 1 if 1970, 0 if not D71 - Dummy variable, I if 1971, 0 if not D72 - Dummy variable, 1 if 1972, 0 if not D73 - Dummy variable, 1 if 1973, 0 if not D74 - Dummy variable, 1 if 1974, 0 if not - Ql Quarterly dummy variable for first quarter - Q2 Quarterly dummy variable for second quarter Q3 - Quarterly dummy variable for third quarter Q4 - Quarterly dummy variable for fourth quarter WPRDFG - World production of feed grains (corn, sorghum, oats, barley), million metric tons N - U.S. total population in millions POP - U.S. noninstitutional population over 16 years in millions from the FAIRMODEL 27
Lastly, the program’s staggered rollout in municipalities could potentially be used as a source of quasiexogenous variation. The program was implemented initially in all municipalities more or less simultaneously without any clear selection criteria. By the time the Consorcio Colombia Mayor was established, pretty much all municipalities had some beneficiaries. Unfortunately, according to the data made available to us by the consorcio, the subsequent expansion of the program with the aim of enrolling the entire target population proceeded in a parallel fashion across municipalities (meaning that there was no clearly identifiable subgroup of municipalities or other criteria that determined the speed of the expansion). Given that no objective estimate of the number of individuals in the target population—that is, individuals who fulfill the age and Sisben requirements—is available at the municipal level, we calculated an estimate of coverage by dividing the number of beneficiaries reported by the consorcio by the number of people who are age eligible according to Colombia’s population projections.<sup>4</sup> We have complete yearly data on 1,101 municipalities in 2010–14. The first observation is that, of the 5,505 individual observations, 55 are larger than 1 and, in many cases, by a substantial amount (the largest value is over 12). This points to the unreasonably large number of beneficiaries in the consorcio data. (Keep in mind that the denominator also includes noneligible individuals of the appropriate age.) We have run a simple regression of the estimated coverage—the share of beneficiaries in the ageeligible population—on a set of year-specific dummy variables and a set of municipality-specific dummy variables. This yields an R<sup>2</sup> value of 0.7305. With almost three-quarters of the variation explained by municipality and year fixed effects, the remainder may be too weak to act as a statistically strong source of variation. This problem can be visualized in figure 5 for the 298 municipalities that are included in any of the ENCVs over 2010–13 and that are used in our analysis. With the exception of a few sudden spikes in some municipalities, which are likely to be caused by errors in the data on beneficiaries made available to us by the Consorcio, the trends over time are almost parallel in all municipalities.

> 4 The National Administrative Department of Statistics of Colombia provides population projections for five-year groups. (See “Demografía y Población: Proyecciones de Población” (database), National Administrative Department of Statistics, Bogotá, Colombia) The total number of age-eligible residents per municipality has been constructed by assuming a constant age distribution within each group.

17
(Ancey, 1974; Anderson and Leiserson, 1980; Chernicovsky *t al., 1985; Delgado, 1979; ILO, 1985b; Liedhola and Chuta, 1976; Hatlon at al., 1979; Mueller and Zevering, 1970; Norman, Simmons and Hays, 1982; Okafor, 1983; Thomi and Yankson, 1983). Focusing exclusively on agricultural households, farm management surveys in numerous ; :tcan countLies have found 15 to 65 percent of farmers with secondary employment in nonfarm enterpr.ses, and time allocation studies of farm households have found 15 to 40 percent of total family working hours devoted to income generating nonfarm activities (Anthony et al., 1979; Cleave, 1974; Hill, 1977; ILO, 1985b and 1985c; Luning, 1967; Norman, 1972; Okafor, 1983).

B. Income Africa's rural inhabitants typically derive between 25 and 30 percent of their income from nonfarm sources (Table 2). And because nonfarm activities are monetized to a much larger extent than is agricultural production, nonfarm earnings constitute an even larger share of cash income. Panel B of Table 2 indicates that nonagricultural income regularly accounts for 30 to 50 percent of rural cash incomes.

C. Income Distribution Available data on African rural nonfarm earnings do not show the clear pattern of equity enhancement that analysts have regularly reported in Asian studies (e.g., Bell, Hazell and Slade, 1982; Kilby and Liedholm, 1986). Researchers in Northern Nigeria (Matlon, 1979) and Lesotho (Van der Weil, cited in ILO 1982h) have found that high-income rural dwellers derive a greater share of their earnings from nonfarm sources than do the poor. Less detailed evidence from rural Tanzania (Collier, Radwan and Wangwe, 1986) and from farm families in Zambia (Ghai and Radwan, 1984), Uganda (ILO, 1985d) and Zimbabwe (Weinrich, 1975) suggest similarly inequitable trends. But support for the equity enhancing view of rural nonfarm incomes does emerge from some studies: from rural Botswana
||industries) and firm-size group is small (11-20<br>full-time permanent workers) or medium (21-<br>99 full-time permanent workers).The size<br>groups here are based on workers at the firm<br>three fiscal years ago (or two fiscal years ago<br>in some countries) from the date of the<br>Enterprise Survey.<br>Source: Enterprise Surveys.<br>|
|---|---|
||www.enterprisesurveys.org|
|Courts Obstacle (City-industry-firm size level<br>cell average)|Firms were asked how severe is the (lack of<br>proper) functioning of courts as an obstacle for<br>their current operations. Responses were<br>recorded on 0-4 scale as: no obstacle (0), minor<br>obstacle (1), moderate obstacle (2), major<br>obstacle (3), or a very severe obstacle (4). We<br>take the average value of the reported scores<br>where the average is taken over all other firms<br>(that is, excluding the firm in question) in the<br>same city-industry-firm size group or cell. City<br>is the sub-national region or city where the<br>firm is located. Industry is defined at the 2-<br>digit ISIC Rev. 3.1 level (7 industries) and<br>firm-size group is small (11-20 full-time<br>permanent workers) or medium (21-99 full-<br>time permanent workers).The size groups here<br>are based on workers at the firm three fiscal<br>years ago (or two fiscal years ago in some<br>countries) from the date of the Enterprise<br>Survey.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Population (lagged, logs)|Total population in the country. Values of the<br>variable used are one year prior (that is, lagged<br>by 1 year) to the fiscal year covered by the ES.<br>In cases where the fiscal year covered by the<br>ES spans more than one calendar year, the<br>population figure is the weighted average over<br>the calendar years covered (lagged by 1 year),<br>where the weights used are the proportion of<br>months covered in the calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|Inflation (CPI, annual %)|Annual percentage rate of inflation based on<br>the consumer price index. Values of the<br>variable used are for the fiscal year covered by<br>the Enterprise Survey. In cases where the fiscal|

57
First, picking nine binding environmental targets in the 12th FYP also means neglecting other important environmental targets and issues, such as binding targets for PM 2.5 or water efficiency in the agricultural sector. As binding environmental targets cascade downward through the administrative hierarchy, targets set by upper level governments might not fully represent local conditions or local environmental priorities. As a result, the most urgent local environmental protection challenges might be untouched by the target system. For example, heavy non-ferrous mining industries in Chenzhou municipality (Hunan), caused severe pollution in multiple counties over the last two decades. Yet because there was no binding target addressing non-ferrous metal pollution until the most current 12th FYP, the most pressing local pollution issue was not tackled until recently (Kostka, 2014). Moreover, targets can be inappropriate since targets are usually distributed based on administrative boundaries, which do not necessarily match the unit of protection (see _Box 1_ ).

# _(b) Unscientific targets_

As targets get distributed at each level, bureaucrats need to make decisions as to how to share the burden of implementation. Yet, this decision-making process requires a constant flow of high quality information in order to identify the “right” target level for subordinate governments and enterprises. In the absence of such information, the use of one-size-fits-all targets can distribute the implementation burden very unequally between different reporting units. For example, within the same municipality in Hunan, one EPB reported that air pollution targets were “easy” to achieve while two neighboring counties felt they were “difficult.” Such scenarios can generate resentment and supply incentives for heavily-burdened localities to misreport data on difficult targets.

Setting appropriate targets that factor in localities’ capabilities and implementation burden is a time and information-intensive process and can be a very politicized task (see _Box 2_ ). A government official at a municipal Water Resource Bureau notes how shortages in staffing and lack of department coordination frustrates the process of setting scientific and differentiated targets: In our bureau, I am the only person in charge of water management, and I do not have time to go to enterprises and counties to do checks. I also cannot get enterprise data on industrial value added figures from the Statistical Bureau, hence it is very difficult for me to estimate scientifically how much water is consumed by enterprises at the county level. While the provincial government gives differentiated targets to the bureaus, our bureau cannot give differentiated targets for the counties and instead all the counties get the same target for water consumption. [Given our constraints] my main job is to sit in my office and write documents, or, you can also call it, I “play with words” ( _wan wenzi_ ) (INT29052012).

11
good out of total wealth at the end of their ...rst period of life. The adult generation at the beginning of the period puts savings (capital) from the previous period into the ...nancial sector and obtains a return at the end of the period. At the end of the period the adult generation decides the amount to transfer out of total wealth as an inheritance to their o¤spring and the amount to consume in licit goods. The young generation receives these savings and invests them in capital (the only asset in the economy), obtaining a return on them at the end of the period. We assume households have perfect foresight and that the only heterogeneity among households is their moral cost when young of working in illicit activities.

ii) Licit ...rms, that produce licit consumption goods, demanded by households when young and when adult. These ...rms operate in a perfectly competitive environment using a constant returns to scale technology that uses capital and licit labor and have perfect foresight. To simplify the analysis we assume that licit ...rms in the model are not corruptible.

iii) Illicit activities are of two types: i) those that produce an illicit good, such as drugs, for which the price and demand is determined by world markets, and ii) those that redistribute income, i,e. common crimes such as burglary, larceny, theft, motor vehicle theft, arson, shoplifting, robbery, kidnapping and extortion, all of which generate insecurity in the economy and aim to apprehend income from licit workers. Both types of illicit activities use a techonology that is labor intensive, consistent with the assumption that capital accumulation in this sector generates perfect detection from the government. Drug tra¢ cking operates in an imperfect competition environment where workers are not paid according to their marginal productivity but by a sales commission based on average production compatible with income sharing. Illicit labor markets are segmented in the sense that drug tra¢ cking ...rst hires the labor it needs to meet foreign demand and the remaining illicit workers are allocated to common criminal activities. Illicit income is not subject to income tax.

iv) A government that raises ...scal revenues from both taxes (from licit income) and con...scation proceeds (from illicit activities). It then uses this revenue to produce public goods for all households and to fund a police and judicial system that can detect illicit activities and con...scate their income, such that it maximizes a utilitarian social welfare function under a balanced budget in every period.

In the next section we outline the precise mathematical assumptions for each of these participants in the economy, and derive their corresponding optimal behavior, specifying the variables that each of these agents take as given.

# 4.1 Households

We ...rst describe licit and illicit households in order to characterize their behavior in terms of their optimal savings (in assets) and consumption decisions. Then we study the way they self select into illicit and licit activities. Assume that there are Ht households in period t and that the population 13
- 30 -

34. It is important to note that fiscal deficits in the 4.5-5% of GDP range are sustainable in the sense that such deficits are consistent with other macroeconomic targets/variables (GDP growth rate, export growth rate, inflation rate, real interest rate). If these targets/variables change, the level of sustainable fiscal deficit will also change. [n particular, external shocks could reduce economic growth, weaken export performance and raise real interest rate. For example, if a sustained external shock were to reduce medium-term export growth to 4% p.a., and GDP were to falter to 3-4% p.a., sustainable fiscal deficit will fall below 4% of GDP. Similarly, as noted in footnote 12, GOP's ability to mobilize 2.6% of GDP in terms of seignorage revenue at 5% inflation rate could be reduced with the greater monetization of the economy and improvements in financial services. This is very likely, as suggested by the experience of more financially developed economies where the ratio of base money to GDP is much lower. To guard against these risks and uncertainties, a prudent policy stance would be to aiqn for an even lower fiscal deficit than 4.5-5 % of GDP.

35. Seipnora2e and Ouasi-Fiscal Deficits: The arguments underlying the result of Table 17 is based on the key assumption that the full amount of seignorage revenues are available for financing the fiscal deficit. However, as noted, this has not been the case in the past. A part of the seignorage revenue has been used to finance "quasi fiscal deficits" resulting from transactions between the State Bank of Pakistan and the private sector. There are three sources of quasi fiscal deficits: (1) subsidized credit schemes funded by the SBP; (ii) exchange -ate losses resulting from SBP forward cover operations; and (iii) write-off on loans that are not recovered. The cost of these transactions are estimated at around 1.3 % of GDP in 199019l. The diversion of seignorage revenues to finance quasi fiscal deficits has required additional domestic borrowing to finance the ordinary fiscal deficits. Apart from the resource misallocation implications of the quasi-fiscal deficits, their macroeconomic implications in terms of additional domestic borrowing needed to fiance the ordinary fiscal deficits suggests an urgent need to review and rapidly phase out these quasi-fiscal tansactions. Without this policy change, financing of fiscal deficits even in the 4.5-5 % range would require positive net domestic borrowing with adverse financial consequences.

Table 17. Pakistan: Sustainable Fiscal Deficits as a Function of Inflation Rate (% of GDP)

|Inflation Rate<br>.<br>____________|**Seignorage**<br>_______________|Net**External**<br>Borrwing|**Net**Domestic<br>Borrowing|**Sustainable Fsal**<br>Deficits|
|---|---|---|---|---|
|_5_|2.6|**2.2**|0|4.8|
|10|3.0|**2.2**|0|5.2|
|15|3-1|2.2|0|5.3|
|20|3.2|2.2|**0**|**5.4**|
|**25**|3.0|**220**||5.2|
|30|2.8|2.2|0|5.0|

Source: Own **esdma**
weather station data interpolated using covariates such as altitude (http://csi.cgiar.org/Aridity/).

- (3) _Land surface temperature_ (Figure 2c). NASA’s MODIS also generates high-resolution satellite imagery on land surface temperature (LST).

- (4) _Brightness of nighttime lights_ (Figure 2d). This information comes from the Defense Meteorological Satellite Program Operational Linescan System’s (DMSP OLS’s) annual composite satellite data for nighttime lighting in 2009 (https://ngdc.noaa.gov/eog/). These data allow regions to be differentiated by the density of their population and also the degree of the electrification of their dwellings, commercial and industrial premises, and infrastructure.

- (5) _Estimated travel time to nearest functioning water source_ (Figure 2e). This covariate was created for the current study by first creating a “friction surface” that estimates the time required to traverse each 1x1 km pixel across Nigeria. This varies according to the type of land cover, topography, and the layout of the road and the wider transport network across the country. The friction surface was then used in a least-cost path algorithm to estimate the likely travel time from the center of each 1x1 km pixel to the nearest functioning improved water source (such as a well, bore hole, or pump). The latitude and longitude, as well as the level of functionality, of every such water point and water scheme in Nigeria was recorded as part of the NWSS 2015.

- (6) A final category of data used in the analysis was a gridded map of estimated population density across Nigeria (Figure 2f) constructed from satellite-derived settlement maps and available census data as part of the AfriPop project (www.afripop.org) (Linard et al. 2012). An alternative population grid, from the Global Rural Urban Mapping Project (GRUMP, <u>http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-density)</u> was also investigated. These gridded population surfaces were not used as covariates but were used to calculate population-weighted mean and count estimates for the various modeled indicators.

# **Defining and implementing a standardized grid format**

The geo-spatial data sources described above were obtained in a variety of spatial resolutions and geographic extents. The land-sea templates inevitably varied, so the precise definition of 6