rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1b. Non-migrants, Hukou holders, and total population (growth)**

||1||2||3||
|---|---|---|---|---|---|---|
|Dep. Variable:|ln non-|migrants|ln tot|.pop|D tot. Pop|2000-10|
|ln real wage|0.2<br>[0.1|49<br>00]*|0.3<br>[0.04|71<br>6]**|0.0<br>[0.02|51<br>2]**|
||Urban|rural|urban|rural|urban|rural|
|ln ruggedness|-0.122|-0.174|-0.128|-0.166|-0.012|-0.009|
||[0.005]***|[0.001]***|[0.006]***|[0.001]***|[0.278]|[0.172]|
|ln cooling days|-0.072|-0.031|0.012|-0.028|0.032|-0.093|
||[0.814]|[0.911]|[0.968]|[0.919]|[0.628]|[0.266]|
|ln heating days|0.263|0.439|0.271|0.419|-0.028|-0.070|
||[0.043]**|[0.001]***|[0.073]*|[0.002]***|[0.626]|[0.003]***|
|ln rainfall|-0.027|0.677|-0.011|0.670|-0.079|-0.017|
||[0.847]|[0.012]**|[0.943]|[0.012]**|[0.178]|[0.555]|
|D yangtze|0.045|-0.194|0.041|-0.247|0.022|0.007|
||[0.763]|[0.331]|[0.788]|[0.241]|[0.52]|[0.842]|
|nat.res.index|-0.001|-0.039|-0.002|-0.036|-0.002|0.009|
||[0.932]|[0.062]*|[0.87]|[0.112]|[0.465]|[0.181]|
|% hh water|-0.756||-0.560||0.104||
||[0.012]**||[0.066]*||[0.008]***||
|% hh toilet|1.121||1.259||0.109||
||[0.023]**||[0.012]**||[0.098]*||
|% pop gas|0.397||0.337||-0.182||
||[0.029]**||[0.081]*||[0.004]***||
|nr.obs|662||662||662||
|R2|0.735||0.709||0.544||

_Notes: all regressions include province-urban and province-rural fixed effects, as well as controls for a prefecture’s ln(x-coordinate), ln(y-coordinate), ln(area) and four dummy variables denoting whether the dominant language spoken in each prefecture corresponds to one of four of China’s main languages spoken (Mandarin, Yue, Wu and Jinyu). p-values, based on standard errors clustered at the province level, in brackets. ***, **, * denotes significance at the 1%, 5%, 10% respectively. In all columns we allow the coefficients for all geography-related variables to differ between the urban and rural parts of prefectures respectively (effectively this means that we include each geography variable interacted with our rural dummy as well as interacted with our dummy indicating the urban part of each prefecture)._ Both the significance, as well as the size, of the estimated coefficient on real wages falls substantially. This is very different for the geographical characteristics, where we find similar results as when considering migrant stocks. The same holds for our three public amenity variables, except for a surprising negative effect of the percentage of households that have tap water in their home. This most likely reflects the fact that providing tap water is easier when serving a smaller population. The results when considering total population shares (migrants + non-migrants) in column 2 are more similar to those using non-migrant shares, which is not that surprising given that migrants typically make up only a small part of a prefecture’s population. Finally, when considering population growth instead (column 3), we again find a positive effect of real wages. It is not as significant as in our “migrant-regressions”, 17
<!-- Start of picture text --> —_/ me<br>|<br>4<br>P<br>a vf<br>(v*. =<br>i oN<br>MAL Kut }<br>a. \ ) }<br>a a . ra<br>f/ & = EN -<br><!-- End of picture text -->

<!-- Start of picture text --> BB Large wale wale IRR <= <= 12%<br>BB Laree scale IRR <= 20% <= 20% 20%<br>BD Large scale IRR> IRR>> 20%<br>BB sma scale RR = RR = = 10%<br>Bi smat scale RR <= 30% RR <= 30% <= 30% 30%<br>WE Small scale IRR<= 50% scale IRR<= 50% IRR<= 50%<= 50% 50%<br>> 50%<br><!-- End of picture text --> BB Large wale wale IRR <= <= 12% BB Laree scale IRR <= 20% <= 20% 20% BD Large scale IRR> IRR>> 20% BB sma scale RR = RR = = 10% Bi smat scale RR <= 30% RR <= 30% <= 30% 30% WE Small scale IRR<= 50% scale IRR<= 50% IRR<= 50%<= 50% 50% BE small scale IRR<sup>>50%</sup>
<!-- Start of picture text --> YACUIBA<br>500<br>400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text --> _Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

26
linear (see, for example, Brazil and Colombia). Eligibility for social tariffs is usually confined to households consuming below a certain limit that ranges from 75 kilowatthours per month (Paraguay) to 500 kilowatt-hours per month (Venezuela) and takes an average value of around 200 kilowatt-hours. In a handful of cases additional eligibility criteria are applied based on household characteristics (some Argentine provinces), neighborhood characteristics (Colombia), or whether the family is a beneficiary of other welfare programs (Brazil). The average discount provided to beneficiaries of the social tariff is equivalent to around 40% on a monthly bill of 100 kilowatt-hours.

**<u>Table 11: Overview of tariff structures for residential electricity</u>**

||General resid<br>|ential tariff<br>|s<br>|Social tariffs<br>|<br>|||
|---|---|---|---|---|---|---|---|
||Volumetric<br>charge|Time<br>sensitive|Load<br>charge|Volumetric<br>charge|Consumption<br>limit<br>(kWh)|Other<br>eligibility<br>criteria|Discount<br>on<br>100kWh/m<br>bill(%)|
|Argentina|DBT/IBT|||IBT|100-200|�|-50|
|Brazil|Linear|||IBT|100-200|�|-50|
|Chile|Linear|�|�|||||
|Colombia|Linear|||IBT|200|�|-50|
|Ecuador|IBT|||IBT|130||-16|
|El Salvador|Linear|||Linear|200||-3|
|Honduras|IBT|||IBT|500||-3|
|Guatemala|Linear|||Linear|300||-45|
|Mexico|IBT|||||||
|Nicaragua|IBT|||||||
|Paraguay|IBT|||IBT|75||-55|
|Peru|IBT|||||||
|Uruguay|IBT/Linear|�|�|||||
|Venezuela|IBT|||IBT|200||-85|

Source: CIER, 2003 It is interesting to examine the design of the IBT tariff structures in more detail, and examine to what extent they repeat some of the design errors that were observed in the IBT structures used in the water sector (Table 12).

First, the size of the subsistence block ranges from 25 to 300 kilowatt-hours per month, with an average value of around 90 kilowatt-hours per month. Based on patterns of appliance ownership and use in Latin America, a subsistence consumption threshold for rural households is generally thought to lie around 40 kilowatt-hours per month (which is sufficient for a few light bulbs and a radio), while the equivalent threshold in urban areas is probably closer to 120 kilowatt-hours (which supports a few light bulbs, a small refrigerator and a modest television). In this sense, the average value of 90 kilowatt-hours per month does not appear unreasonable, although thresholds of 200-300 kilowatt-hours per month as practiced in Colombia and Venezuela are probably excessive.

Second, the gradients of the IBT structures for electricity tend to be steeper for electricity than those observed for water. As a result, in nearly half of the cases considered the average tariff reaches cost recovery levels well within the typical consumption range of a residential household. This can be compared with only 18% of the cases considered in the water sector.

30
<!-- Start of picture text --> oO<br>2lo) ,<br>N |<br>2I<br>© |<br>6) |<br>oO2||<br>3-®|<br>gO eh|<br>e |<br>row |<br>59 |<br>oS<br>oy |<br>8 |<br>ne |<br>3|<br>o |<br>iS |<br>° I<br>~[S)<|I<br>|<br>1995 2000 2005 2010<br>year<br><!-- End of picture text -->
disaggregated capital stock. Table 4 presents the assumed average life service lines for each of these assets groups. The average service life for a given class of asset is considered to be identical for all kinds of economic activities. The service lives are arrived at by considering the nature of these asset groups, consulting experts, and a careful review of the average service lives used by other countries, as reported in OECD (2001b).

Based on average service life estimates, geometric depreciation rates are applied. With geometric depreciation, the market value in constant prices is assumed to decline at a constant rate within each period. The implicit depreciation factor for each asset group is set at a value that ensures that the initial value will have been reduced to 10 percent of the original value by the time it reaches the end of its expected service life. The main drawback of geometric depreciation is that it will never exhaust the full value of an asset. That is, the depreciated value of the asset falls asymptotically, approaching, but never reaching, zero. While the infinity problem is somewhat troublesome, geometric depreciation has the practical advantage of being suited better for benchmark estimates, such as in the present study.

**Table 4. Guatemala: Asset Classes and Average Service Lives**

|Asset Class|Average Servi|ce Life (Years)|
|---|---|---|
||Private Sector|Public Sector|
|Construction|50|50|
|Machinery and Equipment|...|15|
|Imported Capital Goods|15|...|
|Domestically Produced Capital|10|...|
|Goods|||
|Cultivated Assets and Major|6|...|
|Improvements to Land|||
|Other Assets|10|...|

_Source:_ Based on OECD (2001b) and expert consultation.

# **4.4 Quality Indices of Capital and Labor**

Based on the previous calculations, quality indices can be elaborated. The quality index of the labor force will only be used in the later growth accounting exercise since it already reflects improvements in human capital. However, the estimate for the quality of capital enters into the regressions. The following paragraphs are concerned with the construction of the indices for the quality of capital and labor, respectively, and a brief comparison over both indices for 1970-2002. The capital and labor quality indices yield interesting outcomes.

# **_Quality of Capital_**

One can calculate a quality index of capital by using the disaggregated capital stock data. The estimate follows the methodology advocated by Laurits et al. (1980) and

-25-
12 <!-- Start of picture text --> Figure 2:Tariff Rates and Imports of Tea,<br>tariff tea Britain 1741-1750<br>.3 3000.<br>_ * 7 , \ g ~~~~~imports<br>.2 2000w<br>arff<br>.1 1000.<br>o 0o T<br>1740 1745 1750<br>Year<br><!-- End of picture text --> Source: McCulloch (I'4S5S), page 332. Tea is measured in thousands of pounds
<!-- Start of picture text --> Figure 8: Tertiary Gross Enrollment Rate by Income<br>Quintiles, circa 2001 and 2009<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2001 2009 2000 2009 2000 2006 2001 2009 2001 2009 2001 2009<br>Costa Rica Panama Guatemala El Salvador Honduras Nicaragua<br>Quintile 1 Quintile 5 Mean<br>Source: Authors’ calculations based on several household surveys circa 2001 and circa<br>2009<br>Gross enrollment rate<br><!-- End of picture text --> The relatively high enrollment rates of the richer section of the population are related partly to the growth of the private sector. As Table 4 shows, the private sector accounts for a high share of tertiary education enrollment<sup>15</sup> in many countries: the shares are close to 70 percent in Nicaragua, 60 percent in El Salvador, 55 percent in Costa Rica and 48 percent in Guatemala. The lowest share is in Honduras at about 35 percent of total enrolment<sup>16</sup> . In all countries, **Table 4: Percentage enrolled in private** the top quintile constitutes the overwhelming **tertiary education, circa 2009** share of private tertiary enrollment.

|**Country**|**All**|**Quintile 1**|**Quintile 5**|
|---|---|---|---|
|**Costa Rica**|54.7%|33.8%|58.9%|
|**Guatemala**|47.5%|0.0%|50.0%|
|**El Salvador**|63.2%|55.0%|72.4%|
|**Honduras**|33.9%|24.1%|36.4%|
|**Nicaragua**<br>Source:Authors cal|70.8%<br>culations base|66.7%<br>d onseveralhouse|74.1%<br>hold surveys circa|

Of the relatively small share of the poor who enroll in tertiary education, a surprisingly large proportion is enrolled in private institutions, even though public universities offer free education. Data for Nicaragua show that about two-thirds of those from the first quintile attend private institutions. In El Salvador about 55 percent of students in tertiary education attend private institutions Amongst students from quintiles 1 to 3, the private enrollment share is about 40 percent, while in the richest quintile, this share <mark>Source: Authors calculations based on several household surveys circa 2009</mark> rises to 60 percent (not shown in table) In Costa Rica, about 34 percent of students from quintile 1, and 40 percent form quintiles 2 and 3 attend private institutions (not shown in table). The equity objectives of public financing of tertiary education are drawn into question when a

> 15 Tertiary education enrollment defined as population attending any education level with 13 years of education of more. For Nicaragua is 12 years of education or more.

> 16 Household surveys in Panama do not distinguish between enrollment in public and private tertiary institutions.

17
rate is almost identical to that on Java if Aceh is excluded, reflecting the continuous nature of reported conflict in that province.

**Table 2: Local Conflict**

||Conflict<br>Reported (%)|Violent<br>Conflict<br>Reported<br>(%)|New<br>Conflict<br>(%)|Total<br>Villages/Neighborhoods|
|---|---|---|---|---|
|National|7.1|4.4|3.9|68,815|
|Rural|6.7|4.2|3.6|56,748|
|Urban|8.8|5.8|5.7|12,067|
|Java|5.4|3.6|4.0|24,962|
|Off-Java|8.0|4.9|3.9|43,853|
|Off-Java(ex. Aceh)|5.7|3.5|4.1|38,118|
|Source:<br> PODES2003 (|Questions17.03)||||

The highest rates of villages and neighborhoods reporting conflict by province were in Aceh (23.4 percent), Maluku (15.7 percent), and NTB (13.8 percent) (see Table 3). The lowest incidence of conflict was reported in South Kalimantan (1.4 percent), South Sumatra (2.3 percent), and Bengkulu (2.4 percent) (see Table 3).

**Table 4: Type of Local Conflict**

||National<br>(%)|(% of<br>which<br>violent)|Rural<br>(%)|Java<br>(%)|Off-Java<br>(%)|Off-Java,<br>non-Aceh<br>(%)|Aceh<br>(%)|
|---|---|---|---|---|---|---|---|
|Civil/Group Strife|52.1|64.6|48.5|76.2|42.9|67.1|3.6|
|Strife w/ GovApparatus|7.0|66.5|7.6|3.6|8.4|5.4|13.2|
|SchoolStrife|2.7|45.9|1.8|3.9|2.2|2.8|1.2|
|Ethnic Strife|3.3|51.8|3.2|1.0|4.2|6.5|0.5|
|Other|34.9|58.4|38.9|15.3|42.4|18.2|81.4|
|_(N)_|4,958|61.7|3,875|1,376|3,582|2,213|1,369|
|Source<br>:PODES2003 (Qu|estions17.0|4a)||||||

14
**Appendix Table 1:** Descriptive statistics

|Variable|Additional description|Mean|Std.<br>Dev.|
|---|---|---|---|
|Growth rate of female enrollment (grades 6-10) in the region|Ln(enrollment in 2003) - Ln(enrollment in 1999)|0.21|0.29|
|Growth rate of female enrollment (grade 6) in the region|Ln(enrollment in 2003) - Ln(enrollment in 1999)|0.11|0.35|
|Growth rate of female enrollment (grade 10) in the region|Ln(enrollment in 2003) - Ln(enrollment in 1999)|0.31|0.45|
|Total female enrollment in 1999 in the region|in natural logs|6.56|0.78|
|Total female enrollment in grade 6 in 1999 in the region|in natural logs|5.27|0.76|
|Total female enrollment in grade 10 in 1999 in the region|in natural logs|4.53|0.83|
|# of co-educational madrasas in the region, 1999||1.73|1.66|
|# of girls-only madrasas in the region, 1999||3.54|2.17|
|# of boys-only madrasas in the region, 1999||1.50|1.40|
|# of co-educational schools in the region, 1999||0.19|0.49|
|# of girls-only school in the region, 1999||0.03|0.17|
|# of boys-only schools in the region, 1999||2.74|1.70|
|Δin # of madrasa (1999-2003)|Change in # of madrasas in the region,, 1999-2003|0.70|0.89|
|Δin # of school (1999-2003)|Change in # of schools in the region,, 1999-2003|0.10|0.37|
|Mean age of schools||0.03|0.17|
|Mean age of madrasas||0.08|0.31|
|Fraction of madrasas in the region (in 1999) who are converts|Converts are madrasas established before 1980|0.39|0.41|
|Urban|Fraction of urban schools|0.13|0.31|
|Govt|Whether any government school in the region|0.05|0.22|
|**Number of BRAC schools**||||
|**Number of BRAC branches**||||
|**Number of BRAC female members**||||
|N||4784||

Note: Region refers to “union”, an administrative unit which is smaller than sub-district (Upazila) but bigger than village (usually comprising of 10-15 villages).

16
incidentally, refers to a situation in which trade is not monopolized by the government, but rather is permitted to private firms and citizens as well – so China began to shift to a policy of free trade in 1979.) The specific measures in China included allowing a growing number of firms, including private ones, to trade directly and opening a foreign exchange market to facilitate this trade.

Figure 4. Trade Reforms and Trade Volumes China 1978 - 2000 <!-- Start of picture text --> Trade/GDP (log) Average tariff<br>2 0.5<br>Average tariff rate<br>(right axis)<br>1.6 0.4<br>Trade/GDP<br>(left axis)<br>1.2 0.3<br>0.8 1988 8,000 trading companies 0.2<br>1986 Forex swap market<br>0.4 1984 800 trading companies 0.1<br>1979 Open SEZs to FDI, forex retention<br>1978 Trade monopolized by MOFERT<br>0 0<br>1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> 10
the relevance of the first question. Several studies have recently addressed this same issue. In addition to thc World Bank study of the East Asian Miracle (World Bank 1993), the Economic Development Institute has conducted a dctailed study of the main bank system, while senior officials of the Japan Dcvelopmcnt Bank and Japan Economic Research Institute have compicted a comprehensive study of policy-based finance in postwar Japan (JDB/JERI 1994). This paper, which is part of the World Bank research project on the Effectiveness of Credit Policies in East Asia, focuses on the links between policybased finance and the evolution of the Japanese financial system. It draws extensively on these three studies but more especially on the JDB/JERI study.

The remainder of the paper is divided into four sections. The next section sets out the evolution of the Japanese financial system. Section III reviews some important aspects of policy-based finance, while the following section discusses the importance of credible visions for the success of policy-based finance. The last section summarizes the paper and offers some conclusions on the relevance of the Japanese experience for developing countries.

5
WPS3177

# **DOES INFRASTRUCTURE REFORM WORK FOR THE POOR? A CASE STUDY ON THE CITIES OF LA PAZ AND EL ALTO IN BOLIVIA**<sup>**1**</sup>

**VIVIEN FOSTER**<sup>**2**</sup> **AND OSVALDO IRUSTA**<sup>**3**</sup>

World Bank Policy Research Working Paper 3177, December 2003 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This research was originally prepared as a background paper for the Public Private Infrastructure Advisory Facility Country Framework Report for Bolivia.

> 2 Vivien Foster is an Economist in the Office of the Director for Finance, Private Sector and Infrastructure in the Latin America and Caribbean Region of the World Bank.

> 3 Osvaldo Irusta is a graduate student at the Universidad Católica de Bolivia and Adviser to the Superintendent of Electricity in Bolivia.

1
-15-

# iii) Speculative Assets = Marketable Securities + Investments and Other Assets + (1-h) (Buildings and Land),

where h is a parameter denoting the proportion of the land and buildings category that can be attributed to productive use.

The parameter h is not, of course, directly observable. To estimnate h we postulate a linear relation between a firm's productive use of land and buildings and the level of firm output. This relation is estimated based on pooled time-series cross section data for the sample of 241 companies over the 1983-88 period. resulting in 1,446 estimates of h.<sup>15</sup> The overall mean and median of these estimates during the sample period are 0.135 and 0.078.16 To illustrate a typical distribution of h, Figure 3 portrays the distribution for 1988, which has a mean and median of 0.126 and 0.075 respectively. These results confirm our earlier conjecture that land and buildings are one of the most popular means of speculation for Korean corporations.

Using the above estimates of h, Table 2 decomposes total assets into productive, liquid, and speculative assets for each of the years 1983 to 1988. The table reveals little variation in the composition of corporate assets from year to year. On average, firm asset allocation consists of 42.3, 43.5, and 22.2 percent in productive, liquid, and speculative assets, respectively.<sup>17</sup> The 43.5 percent for liquid assets appears large compared to U.S. data. the average ratio of liquid assets to total assets for U.S. manufacturing, mining, and trade corporations during the 15Specifically, we estimate the following equation: where BL = value of building and land assets, Q = output, and D-, j=1,...26, are industry dummy variables. Note that (1io + **Ti** ) measures the marginal capital dand and building)/output ratio for iadustry j. We choose the kadustry with the lowest marginal capital/output ratio and denote it by il*. We then generate estimates for h as hit = (fl*)(Qit/BLit),<sup>for t equal to 1983-</sup> 1988, and i=l,..., 241.

16Out of the 1446 estimates, 34 estimates were greater than 1 and one estimate was negative. We assign a value of h=1 for those that were greater than one, and h=O for the negative estimate.

- 17The surn of the ratios exceeds one hundred percent because the ratios are obtained by dividing the end of year balance of each asset category by the beginning of year book value of total assets.