rafmacalaba/datause-encoder-data huggingface dataset:
Financial globalization could, in principle, be behind the poor domestic development if financial activity (of domestic assets) moved overseas. In a world of financial integration, transactions do not have to take place domestically; that is, firms and households can transact in any market, domestic or foreign. But this does not seem to be the whole story. Some of the domestic development indicators take into account the activity that happens both domestically and abroad. Moreover, internationalization does not seem to be compensating for poor domestic development. Internationalization is positively correlated with financial development within and across regions. Thus, it complements rather than substitutes for domestic markets. Furthermore, globalization is important for many other countries and regions and thus does not explain the cross-country or cross-regional differences. And developed countries, with more domestic financial development than emerging economies, are even more globalized.

Part of the problem seems to lie in the financial intermediation process, since many assets available for investment are not purchased by banks and institutional investors. These institutions hold large resources that were expected to be invested long term and in many parts of the financial sector, not just in a few firms. However, institutional investors seem to shy away from risk, investing short term and following herding and momentum trading strategies, among other practices. Moreover, banks have moved from financing large corporations to financing standardized retail products and some specific lines of credit to SMEs that are easy to commoditize, that can be done on a large scale, and that involve relatively low risk, like leasing and collateral lending. Part of this trend might be due to a regulatory emphasis on stability. However, managers’ risk-taking incentives seem to play an important role. For example, evidence from Chile on mutual funds, pension funds, and insurance companies seems to reinforce this point. In sum, while it could be the case that more assets would help those investors take more risk, the evidence and the literature indicate that the overall functioning of financial systems is not contributing to the degree of financial development envisioned by the pro-market reformers.

To the extent that part of the problem lies in the financial intermediation process, policy makers face a difficult road ahead. The role of institutional investors is emblematic in this respect. For example, it is not clear how to generate incentives for more risk taking to foster innovation and growth while preserving the stability of the financial system. This problem is particularly acute because households are often forced to allocate a substantial portion of their savings to pension funds. On the one hand, to the extent that funds invest too conservatively, 31
**_Energy demand models for policy_** **_<u>formulation</u>_**

Table 8: Comparison of energy demand forecasting models

|Criteria|Kuwait model|DTI|ERASME|NEMS|MAED/<br>MEDEE|LEAP|POLES|
|---|---|---|---|---|---|---|---|
|Type|Top-down|Top-Down|Top-down|Hybrid|Bottom-up|Bottom-up|Hybrid|
|Purpose|Energy demand<br>forecasting|Energy system<br>analysis|Energy demand<br>forecasting|Energy market<br>analysis|Energy demand<br>forecasting|Energy system<br>analysis|<br>Energy market<br>analysis|
|Approach|Econometric|Econometric|Econometric|Econometric<br>with<br>rich<br>technology<br>representation|Accounting|Accounting|Accounting<br>with<br>econometric-<br>style equations|
|Geographical<br>coverage|National|National|Regional level<br>but aggregated|National|Flexible|Flexible|Global|
|Activity<br>coverage|Main<br>end-use<br>sectors|Demand<br>and<br>supply sectors|Main<br>demand<br>and<br>supply<br>sectors|Supply<br>and<br>demand sectors|Demand sectors|Demand<br>and<br>supply sectors|<br>Demand<br>and<br>supply sectors|
|Level<br>of<br>disaggregation|<br>Industry,<br>residential,<br>transport,<br>commercial and<br>others|Domestic,<br>transport,<br>service<br>and<br>industry|?|Industry,<br>residential,<br>commercial and<br>transport|Industry,<br>transport,<br>household and<br>service|Industry,<br>transport,<br>household and<br>service|<br>Industry,<br>transport,<br>household,<br>service<br>and<br>agriculture|
|Technology<br>coverage|Conventional|Both renewable<br>and<br>conventional|?|Both<br>conventional<br>andrenewable|Both<br>conventional<br>andrenewable|Both<br>conventional<br>and renewable|Both<br>conventional<br>and renewable|
|Data need|Time<br>series<br>data<br>for<br>econometric<br>estimation|Time<br>series<br>data<br>and<br>technology data|Time<br>series<br>data<br>for<br>econometric<br>estimation|Time<br>series<br>data,<br>technology<br>data,<br>survey<br>and census data|Data<br>and<br>survey/<br>estimates<br>for<br>base<br>year<br>information and<br>estimation<br>parameters|Historical,<br>socio-<br>economic,<br>technological<br>and<br>other<br>information|<br>Time<br>series,<br>socio-economic<br>data,<br>technological<br>data<br>and<br>survey/<br>estimates<br>for<br>various<br>parameters|
|Skill|High<br>for|High<br>for|High|High<br>for|Low|Medium|High<br>for|

91
these road data with the boundaries of Mexican allows us Intersecting municipalities to obtain municipality-level panel data of differences in road length for four periods between 1993 and 2012, namely 1993-1998, 1998-2003, 2003-2008, and 2008-2012.<sup>9</sup> For each period, the differenced data capture the flows of new roads in each municipality. We are able to track different road classes from the AAA typology: multi-lane divided, two-lanes, divided, pavement, gravel, and earth. To illustrate the evolution of the road data over time, we show in Figure 1 the expansion of the road network for the four periods. The changes between 1993 and 1998 (panels A and B) occurred exclusively during the PRI presidency (1929-2000), while the changes between 2003 and 2012 occurred exclusively under the PAN presidency (2000-2012), as shown in panels C and D.

We present in Table 1 the length of new roads in kilometers built in each period. To Table 1: New Physical Investment by Road Category, 1993–2012

||Multi-lanes|Two Lanes|Divided|Pavement|
|---|---|---|---|---|
|1993–1998|2,259|421|514|2,541|
|1998–2003|1,814|786|1,189|3,647|
|2003–2008|9,697|1,452|960|1,606|
|2008–2012|434|169|911|1,553|
|Total|14,204|2,828|3,574|9,347|

_Notes:_ Authors’ calculations based on DeLorme (2014) intersected with the AAA maps for the years 1993, 1998, 2003, 2008, and 2012.

calculate these changes, we first calculated changes in each road category in each municand summed them In so we in road ipality up. doing dropped negative changes length that in some cases might reflect either an upgrade from low to high quality roads or the deterioration of existing networks. As this is what is most relevant to do, we thus restrict our analysis to positive changes in road construction. Our data on the physical extent of roads suggest that 14,204 km of multi-lanes, 2,828 km of two-lanes, and 3,574 of divided roads were built over the 1993-2012 period. As for other types of pavement, we are able to track 9,347 km of new roads over the same period. These important investments in

> 9 To determine the municipality boundaries, we use the shapefiles from INEGI.

7
used panel data on 28 countries in Europe and Central Asia over the period of 1990-2004 to explore the impact of the shift from historical budgets on hospital throughputs, national health spending, and mortality from causes amenable to medical care. Specifically on the impact at the hospital level, the authors found that compared with historical budgets, FFS increased inpatient admissions while patient-based payment (i.e. based on diagnosis, age or insurance status) had no effect (Moreno-Serra and Wagstaff 2010). Another well-designed study comes from an earlier experience in Hainan province, China (Yip and Eggleston 2001). Provider payment reform in this context involved moving from FFS to negotiated prepayment for bundled services. It was found to have slowed the growth rate for overall expenditures, in particular growth in spending on expensive drugs (Yip and Eggleston 2001; Yip and Eggleston 2004). The results led the authors to recommend “cautious optimism regarding the effectiveness of prospective payment for controlling costs” (Yip and Eggleston 2001).

Utilizing cross-sectional data from 52 health centers in Rwanda, Schneider and Hanson (2007) estimated the cost impact of user fees paid by the uninsured patients and capitation paid by insurance. They found significant differences in cost between the two payment forms. However, it was not clear if the difference is due to incentive differences or a less severe case mix among the insured population. Capitation effects appear to be more pronounced in the case of Thailand, where it was employed as early as 2000 by the Thai Social Security Scheme. Here, Mills et al. (2000) found that compared with FFS, capitation decreased the total value of prescriptions by 0-24% and hospital days by up to 80%. The response appeared to be stronger in the private hospitals, which were reported to have different pay schedules for doctors seeing insured versus FFS patients, sometimes even providing fewer drugs per chronic case or developing strategies to ‘dump’ higher cost Social Security patients and deter them from re6
**Table 20.  Village Liabilities in 2004**

|_RMBper capita_|Mean|Percentage >0|Percent of total|
|---|---|---|---|
|1 Total|253|86|100.0|
|2 Financial institutions|120|58|47.5|
|3 Higher levels ofgovernment|21|25|8.2|
|4 Enterprises|36|46|14.1|
|5 Unpaid wages|14|41|5.7|
|6 Farmers|42|53|16.4|
|7 Other|21|25|8.2|
|_8 Estimated % to be repaid_|_55_|||

Source: 2005 village survey.

Selling assets and borrowing are common mechanisms used by governments around the world to finance capital investments. If managed properly, these are considered efficient and equitable means of finance for long term capital. As long as the investments yield returns greater than or equal to the financing costs of these debts, the debts should be financially sustainable, and there is no intergenerational transfer of burdens. In all countries, however, the challenge is to keep a close watch over local borrowing to ensure macroeconomic stability and sustainability. What is worrisome in the Chinese case is the lack of transparent information on the scale of local government debt and its growth. Based on data from 57 counties in three provinces, Wang (2006) estimates county government debt to be as high as 14 percent of GDP. As for the sale of land use rights, the need to impose limits is urgent, to safeguard farmers rights and ensure adherence to national priorities for urbanization and food security. Given the current ambiguous property rights over agricultural land, the risk of abuse of farmers is high.

# **4 Institutional Impediments to Improving Rural Public Finance**

The discussion above has highlighted the diverging trends between recurrent and capital spending at the village and township levels: while reforms were reducing the role of these two levels in the provision of social services and moving resources and responsibilities to the county, policies to improve rural infrastructure were having the opposite effect – creating growing burdens for villages and townships and exacerbating their fiscal gaps. This section turns to the institutions and organization of government to identify some impediments to the 37
**Table 4.4:                           Adult Illiteracy**

|**Region/State**|**1991**|**2000**|**1991-2000**<br>**Change**|
|---|---|---|---|
|**North**|**75.9**|**83.7**|10.2|
|Rondônia|80.4|87.0|8.2|
|Acre|65.7|75.5|14.9|
|Amazonas|76.2|84.5|10.9|
|Roraima|78.3|86.5|10.5|
|**Pará**|**76.4**|**83.2**|**8.9**|
|Amapá|80.7|87.9|8.9|
|Tocantins|69.9|81.2|16.2|
|**Northeast**|**63.5**|**73.8**|16.2|
|**Southeast**|**88.2**|**91.9**|4.2|
|**South**|**88.7**|**92.3**|4.1|
|**Mid-West**|**83.9**|89.2|6.4|
|**Brazil**|**80.6**|**86.4**|7.2|

_Source_ : Census 1991 and 2000.

**Figure 4.3:                  Life Expectancy, 1970-2000** **<u>(years)</u>**

<!-- Start of picture text --> 90.0<br>1970 1980 1991 2000<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>Source: Human Development Indicators 1970-2000.<br>10.0<br>0.0<br>North NortheastSoutheast SouthCenter-West Brazil<br><!-- End of picture text --> The increase of Pará’s life expectancy corroborates the improvement in monetary and non-monetary indicators **.** In Pará, life expectancy reached 69 years in 2000. Pará’s life expectancy at birth increased 34 percent or 17 years, the same as in the North region as a whole, during 1970-2000 (see Figure 4.3). There exits a gender differential in life expectancy. Men in Pará, as in many parts of Brazil, have significantly lower life expectancy than women.

21
least developed nations and one of the poorest countries in its region. The IMF (2009) estimates that gross domestic product (GDP) per capita was $350 in 2007 and $457 in 2008 (current US$).<sup>14</sup> Based on the broader set of development indicators used in the UNDP human development index (e.g., health, education, living standards), Afghanistan ranks 181<sup>st</sup> out of 182 countries.<sup>15</sup> And UNICEF (2009, p. 11) reports that Afghanistan has the highest prevalence of stunting in the world among children under five years old.<sup>16</sup> The Afghan economy is largely based on agriculture; major crops include wheat, rice, maize, barley, vegetables, fruits, and nuts. Approximately 70% of cultivated crop area is devoted to wheat and about 15% is devoted to rice, barley and maize.<sup>17</sup> Wheat is both a major production crop and the main staple of the Afghan diet, with wheat flour contributing 57 percent to the total caloric content of the average bundle of food items of the relatively poor (i.e., the 20<sup>th</sup> to 50<sup>th</sup> percentile of the total consumption distribution, which is the basis for the official Government of Afghanistan poverty line). Due to large fluctuations in weather and insecurity, however, wheat production is highly volatile and the country is dependent on its trading partners to meet any shortfalls.<sup>18</sup> Pakistan is the major supplier of wheat (mostly in the form of flour) due to close historical ties and a shared 1,600 kilometer border; estimates of Pakistan’s share of the Afghan wheat and flour import market range from 59% (Chabot and Dorosh, 2007) to 79% (Maletta, 2004).

Seasonality plays an important role in food security in Afghanistan. Temperatures vary dramatically across seasons, with hot summers and frigid winters; and the climate in the highlands varies with elevation. In many cases, severe winter conditions affect transportation, and in high mountainous areas roads are often blocked throughout the winter due to heavy snow accumulation. In these communities in particular, households are forced to rely only on food supply stored before the winter.

> 14 In a country like Afghanistan though, where the drug economy is large, the official National Income Accounting data are likely to significantly understate GDP. UNODC (2008) estimates that in 2007 the farm gate value of opium cultivation was US$1 billion, but this dropped to US$730 million in 2008. The potential export value in 2007 of opium, morphine and heroin (at border prices in neighboring countries) was $4 billion (or, in per capita terms, about $160).

> 15 UNDP (2009)

> 16 Based on data from the 2004 National Nutrition Survey.

> 17 Chabot and Dorosh (2007)

> 18 Afghan wheat production has been estimated to be over five times as variable as Pakistan’s wheat production. (Persaud, 2010)

6
37 do not trust the quality of the water sold from the govenmment also significantly deters households from connecting to the public water system.

Based on these results, Whittington et al. propose supplying public water to villages from a central kiosk o. set of kiosks, where kiosks will be metered and water purchasers would be charged for the water that they purchase. This system avoids the substantial costs of metering all houses, and allows households to purchase the exact amount of publicly supplied water that they want. A side benefit is that the public water system can demonstrate its reliability to the households.

Food and Agricultural Programs In many developing nations the agricultural sector plays a fundamental role in development. Much of the population is directly or closely involved with agriculture (including many of the most disadvantaged members of society) and a large fraction of GDP typically orginates in the agricultural sector. Also, development theory has long stressed the importance for growth of transferring agricultural resources to the industrial sector (Lewis, 1954; Fei and Ranis, 1964; Sah and Stiglitz, 1987).3° Finally, government food distribution programs can have impotant welfare effects by increasing food consumption among the poorest households and by reducing wage rates in nonagricultural occupations. We discuss agricultural programs before tuming to food programs.

While any govemrnment program may differentially affect the agricultural sector through urban-ural variauon, a more narrow list of goverunent expenditures in agriculture includes: (i) subsidization of ferllizer, seed, pesticides, credit, and technology adoption; (ii) govenmment purchases (sales) of agricultural outputs; (iii) public works development of agricultural infrastructure; and (iv) land redistribution. Benefit incidence studies that explicitly examine agriculture include Meerman for Malaysia, Foxley et al. for Chile, and Bahl et al. for Korea.

Due to the scarcity of household survey data on program participation, most studies assume that agricultural benefits are distributed in proportion to farm income, though Bahl et al. allocate benefits partly in proportion to farm income and partly in proportion to food consumpdon, and some authors make different distributional assumptions for land reforn, small
28 with export taxes on tea and rubber, and found that these have pro-poor incidence effects.

# _Import Duties_

Results in Table 3 also suggest that incidence effects of import duties in C6te d'Ivoire are large, with mixed effects across household groups ranked by income range. Removing duties positively and substantially affects agricultural export sectors, since import duties operate akin to export taxes. Since prces of domestic import competing products increase behind the tariff wall, removing tariffs raises domestic prices of exportables relative to import substitutes, which affects returns to fixed factors in export sectors. Subsistence food croppers selling food products to the domestic and urban market suffer, as agricultural wages are bid up by agricultural exporters. Government workers lose since they must pay the new replacement tax. In other developing country incidence studies, import duties are usually assumed to be fully forward shifted to consumers of imported products, with a regressive or proportional incidence outcome the standard result (see Jeetun 1978).

# _Stabilization Payments_

Results in Table 3 also reveal incidence effects for the Stablization Fund covering coffee and cocoa. In part, this is a reflection of the 1986 base-year data used in the model, since in the year large net receipts accrued to the Fund which acted as a tax on export croppers. In the early 1990s, the Fund is close to break-even if not a net payer of revenues, and hence in model terms more like a subsidy. The gain to export croppers from removing the 1986 tax is nearly 30 percent of income. Interestingly, inactive households share in this
food prices induces only a 3.2 percent seasonal gap in caloric intake. However, the extent of this seasonal variation varies substantially across households. Two groups suffer most – poorer urban households and rural net food sellers. Both see seasonal swings in caloric intake of about 10 percent, linked to seasonality in maize and rice prices. These variations occur at low levels of caloric intake and do not account for intra‐household distribution. Substantial detrimental effects on child malnutrition from food price seasonality cannot be excluded. Rural net food buying households, who form the largest group in the country, appear to be able to largely smooth their caloric intake across the crop year.

Together these findings suggest that, while seasonal price variation is not a major contributor to food price volatility, the issue of seasonality remains relevant for African livelihoods, despite larger domestic market integration and two decades of economic growth, structural transformation and urbanization, and poverty reduction. Seasonal variation in staple prices is substantially larger than on the international markets, which is consistent with the view that storage is inefficient and points to the need to improve farm‐ and village‐level storage, among others. Turning to food consumption, the evidence suggests that households who are net food buyers do not experience major problems with intra‐annual consumption smoothing. This does not imply that they are unaffected by high food prices but, on average, they do appear to be able to cope with regular seasonal price variations. Poor urban households and net food sellers suffer from much greater seasonal consumption variation.

From a broader measurement perspective, the findings underscore the need to account for seasonality in prices and consumption in measuring poverty. The importance of doing so has been illustrated before by Muller (2008), but remains largely ignored in practice (Beegle, Christiaensen, Dabalen and Gaddis, 2016). With many of our results, especially on consumption, based on a short sample and one country, replication and extension of the findings using longer time periods and across different settings is important to strengthen their internal and external validity. New waves of the LSMS‐ISA surveys in Tanzania and other countries are now becoming available, opening up exciting opportunities to do so, including along a wider range of welfare indicators and indicators of longer term impacts such as child growth and nutrition.

24
Table 6: Inequality measures for per capita household earnings, based on actual and simulated earnings within racial/ethnic group and within gender

|||**Simula**<br> <br>|**ting returns on**<br>|**ly**<br> <br>|**Simulating ret**<br> <br>|**urns and chara**<br>|**cteristics**<br>|
|---|---|---|---|---|---|---|---|
||Observed|equal treatment<br>|Equal treatment|all<br>|equal treatment<br>|equal treatment|all|
|||by<br>race/ethnicity<br>within gender|by gender<br>within<br>race/ethnicity|segments<br>treated as<br>white men|by<br>race/ethnicity<br>within gender|by gender<br>within<br>race/ethnicity|segments<br>treated as<br>white men|
||(a)|(b)|(c)|(d)|(e)|(f)|(g)|
|**Theil(1)**||||||||
|Bolivia|0.77|0.55|0.57|0.50|0.59|0.63|0.57|
|Brazil|0.72|0.65|0.73|0.67|0.66|0.72|0.66|
|Guatemala|0.87|0.76|0.82|0.79|0.75|0.79|0.76|
|Guyana|0.37|0.33|0.31|0.33|0.32|0.31|0.32|
|**Theil(0)**||||||||
|Bolivia|0.93|0.67|0.75|0.60|0.78|0.82|0.71|
|Brazil|0.68|0.63|0.69|0.65|0.63|0.68|0.64|
|Guatemala|0.97|0.86|0.92|0.86|0.85|0.89|0.84|
|Guyana|0.35|0.30|0.28|0.31|0.31|0.28|0.30|
|**Gini**||||||||
|Bolivia|0.62|0.55|0.56|0.53|0.57|0.58|0.56|
|Brazil|0.60|0.58|0.60|0.58|0.58|0.60|0.58|
|Guatemala|0.65|0.61|0.63|0.63|0.61|0.62|0.61|
|Guyana|0.44|0.40|0.39|0.40|0.41|0.39|0.40|
|**90th percent**<br>|**ile wages /10**<br>|**th percentile wa**<br>|**ges**<br>|||||
|Bolivia|62.0|29.0|43.6|23.6|44.6|49.4|37.6|
|Brazil|17.9|16.4|19.1|17.6|16.8|18.1|17.1|
|Guatemala|49.5|33.9|42.8|34.5|37.0|40.9|35.5|
|Guyana|6.9|5.3|5.2|5.7|5.6|5.2|5.2|
|**50thpercent**|**ile wages /10**|**thpercentile wa**|**ges**|||||
|Bolivia|14.5|7.7|11.7|6.3|11.4|12.2|10.1|
|Brazil|4.0|3.8|4.2|4.0|3.9|4.1|4.0|
|Guatemala|10.5|8.1|9.8|8.0|9.5|10.1|9.0|
|Guyana|2.6|2.5|2.3|2.5|2.4|2.4|2.3|

32