rafmacalaba/datause-encoder-data huggingface dataset:
based on the survey data. Specifically, we correlate ranking on land with rank on the sum of land area, ranking on the likelihood of hunger with a nutrition index, and ranking on the need for help with an index constructed based on the survey data about recently experienced shocks. We use Kendall correlations, a special case of standard Pearson correlation evaluating the degree of similarity among sets of ranks (Abdi, 2007).

To check robustness, we use several measures for each of the dimensions of poverty captured in ranking exercise. For land, in addition to the sum of residential and agricultural plots, we create a composite z- score of variables related to land and building ownership, as well as the first principal component of these variables. For the likelihood of being hungry, we use a composite z-score and the first principal component of all nutrition-related questions in the survey. For the likelihood of needing help, we create a composite z-score of all survey variables related to economic shocks, and also use the first principal component of these variables. These variables provide a unique household ranking in nearly all villages. Table 4 presents the results.

Village heads’ rankings of land ownership are weakly but significantly correlated with households’ reported land size. Their rankings of likelihood of hunger are correlated with nutrition measures but the correlation is weaker. Rankings of need for help are not correlated with shocks. The consistent strength of land measures across ranking exercises may be explained by the fact that land is the most directly observable of the three criteria for someone outside the household.

**Table 4: Correlations of rankings and household variables**

||Lowest to highest<br>land holdings|Highest to lowest<br>likelihood to be<br>hungry|Highest to lowest<br>need for help|
|---|---|---|---|
|**Land measures**||||
|Size of all plots (res. and agr.)|0.149***|0.098***|0.103***|
|Composite z-score of land and buildings|0.150***|0.133***|0.136***|
|Principal component 1 of land and buildings|0.148***|0.133***|0.137***|
|**Nutrition measures**||||
|Composite z-score of nutrition|0.008|0.004|0.017|
|Principal component 1 of nutrition|0.088***|0.108***|0.090***|
|**Shocks measures**||||
|Composite z-score of shocks (all inverted)|-0.049**|-0.031|-0.023|
|Principal component 1 of shocks (all inverted)|-0.031|-0.028|-0.008|
|Observations|1,245|||

* p<0.1 ** p<0.05 *** p<0.01

The weakness of the correlations between the three criteria and their corresponding observable characteristics indicates that village heads do not determine these rankings based solely on observable land, nutrition, and shock characteristics. Rather, the village heads may base their rankings on other information about household welfare that overlaps with the information on land, nutrition, and shocks.

16
# **1. Introduction**

This paper draws lessons from a randomized experiment in rural Malawi, which has one of the highest population densities for a Sub-Saharan African country<sup>2</sup> . In order to understand why rural roads – passable for vehicles in rural areas – may not necessarily lead to the provision of regular bus services, a minibus service was subsidized over a six-month period over a distance of 20 kilometers to serve five villages<sup>3</sup> .

Like Hine and Rutter (2000) mentioned, “in the quest to tackle rural poverty, feeder road investment is a favoured solution of many donors.” Feeder roads are proposed as a potential way to take people out for poverty in rural areas (AICD 2009). With this end in view, a benchmark called the rural access index (RAI), which is the proportion of rural people who live within two kilometers (typically equivalent to a 20-minute walk) of an all-season road, has been set.<sup>4</sup> Measuring the cost of being isolated is a growing subject of research in economics and development. Ravallion and Jalan (1997) confirmed the existence of spatial poverty traps and the need for households to reach some asset thresholds before they will participate in markets. Therefore, rural roads investments seem to be critical. Indeed, rural road development enhances access to markets for both inputs and output through a reduction in transaction and trade costs (transport and logistics costs). The greater availability (both monetary and physically speaking) of inputs increases their use by farmers. Consequently, agricultural productivity can increase. Rural roads also allow producers to achieve additional productive opportunities, leading to a rise in production that is highlighted by numerous studies. Stifel and Minten (2008) find, in the case

> 2 According to official population estimates in 2002, population density was more than 90 inhabitants per sq.km.

> 3 This location was selected in order to make multiple trips per day, and given that the road was not in a very good condition, there was a need to be close enough to the market center.

> 4 An all-season road is a (gravel or bitumen paved) road that is passable all year by the prevailing means of rural transport (often a pick-up or a truck which does not have four-wheel-drive). Predictable interruptions of short duration during inclement weather (e.g. heavy rainfall) are acceptable, particularly on low volume roads (Raballand et al. 2010). Despite major measurement difficulties of the RAI, it is required for World Bank project teams to report it on a bi-annual basis and assess the number of people covered at 2 kilometers in the project area.

2
The study was conducted between the July and December 2009 in a rural area in central Malawi. The sample site includes a cluster of five villages located 17 kilometers from a market town which offers a large weekly market, a health clinic, and access to further means of transport. In total, 542 households were listed in the five villages (in a complete census). A circular circuit track connects three of the five villages, with the other two villages within 10 minutes walking distance. This track was identified to serve as the bus route to use in picking up passengers. While there is some evidence of irregular transport on the road between the village cluster and the market town, there was no evidence of a bus or any other mode of transport intended for passengers that operated along the road connecting the village cluster and the market town.<sup>8</sup> We report on the second phase of a two part design.<sup>9</sup> Of the originally listed 542 households, 514 households were successfully interviewed and were eligible to participate; 406 of these households are included in the second phase analysis. Participation in phase one and phase two was by random assignment. After selection, a baseline survey was conducted interviewing both men and women. After the completion of the baseline survey, a meeting was called at each of the five villages. At the meeting, a household-level lottery was explained. One member of each household was asked to select a ticket from a bucket. Each ticket contained a number to signify the price of one round trip on the project bus. Prices of a round trip included the following categories: MK 0, MK 10, MK 20, MK 50, MK 100, MK 300, MK 500.<sup>10</sup> After the household member was assigned the lottery ticket, the number was recorded and the price

> 8 This road was identified as in a good condition for buses to run (during the dry season).

> 9 The first Phase included conducting a listing of all households in the village cluster area and randomly sampling 100 households in four villages to participate in the first phase of the project which did not randomize the price, rather randomized overall accessibility.

> 10 All households that participated in the first phase (randomly selected) were allocated MK 0.

6
_Figure 13: Financial Transfers from LCT Pioneers to Less Developed Countries with Large, Untapped Fuel Reserves_ <!-- Start of picture text --> 6000 5451<br>4788<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>-1000 -663<br>GDP losses from cooperation in Gross surplus of LCT Pioneers Net surplus of LCT Pioneers after<br>the two most vulnerable FFDCs financial transfers<br>groups<br>Source: World Bank and Purdue University (ENVISAGE).<br>Notes: LCT = low-carbon transition. Surplus to the pioneers (large emitters) is calculated as the difference between the net present value of GDP under a<br>cooperative scenario and the net present value of GDP under the unilateral (noncooperative) scenario with Nordhaus taxes. Losses are calculated as the<br>total discounted GDP lost under cooperation to low-income countries with large unexploited fuel reserves and middle income coal exporters.<br>3.5. Political Economy of Low-Carbon Transition<br>Low carbon transition represses the value of almost all fossil fuel reserves in FFDCs below BAU<br>in almost all scenarios (figure 14). Accumulated rents decrease even in the scenarios that increase<br>GDP and consumption. This suggests that economic growth and welfare in FFDCs can be<br>decoupled from the declining value of fossil fuel assets. But, long-term interests of individual<br>extractive sectors in FFDCs are not always aligned.<br>Figure 14: Cumulative Losses of Discounted Resource Rents in FFDCs as a percentage of BAU<br>5.0 Unilateral Cooperative Unilateral Cooperative Unilateral Cooperative<br>0.0<br>-5.0<br>-10.0<br>-15.0<br>-20.0<br>-25.0<br>-30.0<br>-35.0<br>-40.0<br>-45.0<br>Coal Gas Oil<br>Traditional diversification Asset diversification<br>US$ Billion<br>(2015 - 2050 s a % of BAU)<br>Reduction of NPV of resource rents in FFDCs<br><!-- End of picture text --> Low carbon transition represses the value of almost all fossil fuel reserves in FFDCs below BAU in almost all scenarios (figure 14). Accumulated rents decrease even in the scenarios that increase GDP and consumption. This suggests that economic growth and welfare in FFDCs can be decoupled from the declining value of fossil fuel assets. But, long-term interests of individual extractive sectors in FFDCs are not always aligned.

_Source:_ World Bank and Purdue University (ENVISAGE). _Note:_ BAU = business as usual; FFDCs = fossil fuel–dependent countries; NPV = net present value. The bars indicate the difference between the cumulative absolute losses of discounted resource rents (value of underground assets) for different fossil fuel reserves over the period 2021–50 under various climate scenarios and the corresponding values under the BAU scenario, expressed as a percentage of the BAU figures (discounted at 6 percent). Cooperative scenario 22
13

# II. Consequences of regulatory reforms

How did these regulatory episodes affect the development of the telecommunication **s** ector? In particular, what effect did they have on services, prices, returns on investment, and overall welfare? And, what effect did the lack of definition of market boundaries in recent reforms have on the behavior of firns?

## _Services, prices, profit and economic welfare_

_Telecommunication_ **_s_** _ervices._ Whatever measure of telephone expansion is used--numbers of telephones, installed lines, lines in service, density, automatization and digitalization, or investment--expansion is most dramatic in the 1980s and lowest in the 1970s (figures 1 through 4). The number of telephones grew at an annual rate of nearly 9 percent during 1981-91, but at only 4 percent during 1971-81 (table 1). In the period before 1970, network expansion was uneven, proceeding fairly rapidly at 7.3 percent a year in 193448, slowing to 2.6 percent in 1948-57, and picking up again to 6.8 percent in 1957-70. As would be expected, pending applications for telephone installations (as a share of lines in service) were high in the periods preceding expansion. Somewhat unexpectedly, pending applications have increased in recent years, even as the network was being doubled, reflecting a surge in demand and higher expectation by consumers of getting a telephone installed.

**Table 1. Telephone expansion in Chile, 1934-91** (annual growth rates)

|1|934-60|1960-70|1971-81|1981-91|
|---|---|---|---|---|
|Telephone in service|5.6|6.7|4.2|8.9|
|Line in service|N.A.|7.5|3.9|9.9|
||1960|1970|1981|1991|
|Telephone applications<br>(% of Lines in service)|54|25|42|24|
|Telephone density|2.7|4.1|5.4|10.8|

Source: CTC.
- Leechor C (1996) Reforming Indonesia’s Pension System, Policy Research Working Paper 1677, World Bank, Washington, D.C.

- Maurer-Fazio M (2007) The role of education in determining labor market outcomes in urban China, in _Education and Reform in China_ Hannum E and Park A eds. London and New York: Routledge: 260-275.

- Maurer-Fazio M, Connelly R, Chen L and Tang L (2011) Childcare, Eldercare, and Labor Force Participation of Married Women in Urban China, 1982-2000. _Journal of Human Resources_ , 46(2): 261-94.

- McGeary K (2009) How do health shocks influence retirement decisions? Review of Economics of the Household 7: 307-321.

- McKee D (2006) Forward thinking and family support: explaining retirement and old age labor supply in Indonesia, Working paper 005-06. CCPR, UCLA.

- National Bureau of Statistics (2001) “Communiqué of the National Bureau of Statistics of People's Republic of China on Major Figures of the 2010 Population Census (No. 1)” <u>(http://www.stats.gov.cn/english/newsandcomingevents/t20110428_402722244.htm)</u> National Bureau of Statistics of China, Beijing.

OECD (2006) _Live Longer, Work Longer_ , Paris: OECD Publishing.

- Pang, L, de Brauw, A and Rozelle, S (2004) “Working until you drop: The elderly of rural China,” China Journal 52: 73-96.

- Park, A and Porter, M (2011) Housing windfalls and intergenerational transfers in China, Paper presented at the Annual Meetings of the Royal Economics Society, April 2011.

- Ravallion, M and Chen, S (2007) “China's (Uneven) Progress Against Poverty,” Journal of Development Economics, 82(1) (January 2007): 1-42.

- Robalino DA, Zylberstajn E, Zylberstajn H, Afonso LE (2009) Ex ante methods to assess the impact of social insurance policies on labor supply with an application to Brazil, World Bank Social Protection Discussion Paper No 0929, Washington D.C.

- Sohn MJ (2007) The characteristics of the recent trend of self-employment. SERI Economic Focus 146: 39-55.

- Vere JP (2011) Social security and elderly labor supply: evidence from the Health and Retirement Study, Labor Economics (in press).

- Venti, S and Wise, D (2004) “Aging and housing equity: Another look” in _Perspectives on the Economics of Aging_ , David Wise (eds). Chicago: University of Chicago Press.

28
both for men and women, but positively correlated with the share of workers completing primary school. Although the estimated coefficient estimates are modest, these findings are consistent with firms substituting more educated workers for less educated workers. Why this is happening is not clear as one might expect firms to hire more skilled workers when forced to pay higher wages. The fact that such substitution happens among both men and women, suggests these patterns are not driven by gender-specific tradeoffs. Importantly, our results are at odds with the notion that minimum wage increases adversely impact women’s relative employment prospects.

# **8 Conclusion**

The paper adds to the literature on the gender effects of minimum wages by looking at how different firms adjust their wages and the composition of employment within the firm in response to minimum wage changes. Using manufacturing firm-level census data, this paper demonstrates sizeable gender wage gaps among production workers. These gender pay differentials are to a large extent driven by sorting across firms and by differences in firm productivity, but persist even after firm characteristics and differences in value-added per worker are accounted for. They are also robust to controlling for firm fixed-effects. Thus gender pay differences are driven by differences in pay across as well as within firms.

Gender differences in the impact of minimum wage increases on wages are strongly correlated with educational attainment. Women with the least education did not benefit from increased minimum wages on average, whereas women higher in the education distribution (i.e those who completed at least junior high school) benefitted the most. Average gender wage gaps 24
# Table of Contents

Page No.

# PART I: HYPERINFLATION AND CHRONIC INFLATION

|**I***|Introduction<br>**..........**|**I**<br>1|
|---|---|---|
|II.|Stopping Chronic Inflation and Hyperinflation<br>**................**|7|
||1. Stopping Hyperinflation:TheCase of Bolivia<br>**............**|7|
||2. Credibility and Stabilization<br>**............................**|9|
||3. Elimination of Inertia through Hyperinflation<br>**............**|<br>13|
||4. Stabilization, Inertia and External Shocks<br>**...............**|<br>15|
||5.The Ability to Prevent Hyperinflation<br>**....................**|16|
||6.TheConsequences ofLimitedControl Over Inflation<br>**.g....**|20|
||PART II: ORTHODOX STABILIZATION<br>IN CHRONIC INFLATION COUNTRIES||
|III.|Inflationary<br>Rigidities under Alternative Orthodox £.rategies|23|
||l. Fiscal Deficit and Monetary Accommodation**................**|23|
||2.Fiscal-based<br>Stabilization<br>**...............................**|28|
||a.TheMexican Stabilization<br>of 1983<br>**.....................**|28|
||bo<br>Brazil<br>1983-4<br>**...............**<br>**.**|30|
||3.TheFiscal-Monetary<br>Combination<br>**..........................**|<br>35|
||a. TheChilean<br>Experi-ent<br>**eeegcgeceegeee.geeeeee**<br>**...**|36|
||b.Argentina<br>1976-8<br>**.............**<br>**.....................**|**...**39|
||4.ExchangeRate Based Stabilization<br>**.cegee.eeeeeeeeeece.eeee**|41|
||a.<br>General<br>Considerations<br>**...............................**|41|
||b. Lessonsfrom Argentina's Stabillzations..........e..c|47|
||cc<br>The Chilean<br>Experiment<br>**g...eceeeceeeeeeeeeeeeeeceeeg**|53|
||5. Inertia, Credibility and Exchange Rate Policy**.. ceeeecee..**|58|
|IV.|Concluding<br>Remarks<br>**........**<br>**..........................**|60|
|Tables|<br>**..**<br>**c... eec...**<br>**gee..............ge..ge**<br>**.....**<br>**geg**<br>**g..**|63|
|Figur|es<br>**.egeeegeggeeeeeeeceeeeegeeege..ecccgecggeeceeegegeee.eeec**|72|
|Refere|nces<br>**gcgc.ggecgeeeeggeggec.geg.egg**|80|
|Appen|dix<br>**.ccge.......e.......**<br>**e**<br>**.......... .................... e **<br>**c**|83|

Appendis *******-*-e***-*-*****e-e****-*@@@-*@-@@v*****s*ve@@@*+* 83~~-
individual survey module was administered to one male and one female member of each household (usually, husbands and wives).

Consistent with previous literature, the analysis focuses on ever-married women in the working age range. In our dataset, this includes 1,076 women ages 15–49 years who reported being married, widowed, separated, or divorced.<sup>7</sup> While it is possible for women who have never been married to have children, this is not common in Bangladesh or in the sample studied in this paper. We then follow a threestep process to identify our target groups (Figure 1). First, we identify female respondents who have children. As expected, a large proportion of the women included in the sample, 87.3 percent, have at least one child. Then, we categorize women who have children into two groups—women whose youngest children are 0–5 years old, and women whose youngest children are age 6 or older. This age threshold is informed by previous literature indicating that closely supervised childcare needs are greatest in the first 5 years of life, when children are dependent on their caregivers and have a high risk of injury if they are left unsupervised (Morrongiello and McArthur 2018, Maryland State Department of Education 2012). Also, in Bangladesh, children start primary school at age 6, after which schools partially fulfill the parents’ childcare needs (World Bank 2020, World Health Organization 2013). In our sample, 46.3 percent of mothers have young children ages 0–5 years.

**Figure 1. Sample distribution of ever-married women ages 15–49 years by the availability of childcare**

||Ever-married women age 15-49 years<br>(N= 1076)<br>||
|---|---|---|
|No child|Has child|Do you have children?|
|(N=137; 12.7%)|(N=939; 87.3%)||
|Youngest child aged 6<br>years and older|Youngest child aged<br>0-5 years|What is the age of your<br>youngest child?|
|(N=504; 53.7%)<br>No one:|(N= 435; 46.3%)<br>HH/Non-HH members/Institutes:|Who took care of your|
|No childcare available|Childcare available|youngest child if you|
|(N=206; 47.4%)|(N=229; 52.6%)|need to go to work?|

Source: Authors.

working-age male and female members; (b) households with only working-age females; and (c) households with only workingage males.

> 7 This paper’s focus on ever-married females ages 15–49 years is consistent with the Demographic Health Survey (DHS). We determine the marital status of women by matching the marital status of women from the household roster with women’s own responses in the individual surveys.

6
Table A-l(a). China: Basic Indicators by Province

||Popu.<br>1994<br>(Mil.)|GDP<br>per cap<br>1994<br>(Th. Y)|Area<br>(Th. Mu)|Aged<br>popu.<br>(%)<br>1987|Urban<br>unemplmt.<br>rate<br>1994|
|---|---|---|---|---|---|
|All China|1198.50|3.76|9590.09|6.23|2.8|
|Beijing|11.25|9.64|16.80|7.66|0.4|
|Tianjini|9.35|7.76|11.31|7.79|1.2|
|Hebei|63.88|3.36|187.95|6.34|2.8|
|Shanxi|30.45|2.80|156.30|5.98|1.2|
|Inner Mongolia|22.60|3.02|1192.04|4.26|3.7|
|Liaoning|40.67|6.35|146.15|6.54|2.5|
|Jilin|25.74|3.76|186.81|5.56|1.8|
|Heilongjiang|36.72|4.41|451.47|4.17|2.5|
|Shanghai|13.56|14.54|6.30|11.52|2.8|
|Jiangsu|70.21|5.78|102.52|8.23|2.1|
|Zhejiang|42.94|6.21|101.77|8.20|3.1|
|Anhui|59.55|2.50|139.06|6.35|3.1|
|Fujian|31.83|5.29|121.12|6.17|2.4|
|Jiangxi.|40.15|2.57|166.86|5.39|2|
|ShandorLg|86.71|4.47|156.57|6.72|3.1|
|Henan|90.27|2.44|167.01|6.33|2.3|
|Hubei|57.19|3.29|186.10|6.14|3|
|Hunan|63.55|2.67|212.10|6.43|3.8|
|Guangdcing|66.89|6.34|177.99|7.28|2.4|
|Guangxi|44.93|2.76|237.34|6.20|3.6|
|Hainan|7.11|4.66|33.98|6.36|3.6|
|Sichuan,|112.14|2.48|570.31|6.79|3.8|
|Guizhou|34.58|1.51|176.04|4.95|5.5|
|Yunnan|39.39|2.47|393.33|5.70|2.7|
|Tibet|2.36|1.94|1220.01|6.67|5|
|Shaanxi|34.81|2.43|205.52|5.26|3.5|
|Gansu|23.78|1.90|456.55|4.62|5.3|
|Qinghai|4.74|2.92|742.82|3.93|6|
|Ningxia|5.04|2.66|51.73|3.69|5.3|
|Xinjiang|16.32|4.13|1683.98|4.62|3.2|

---------------------------------------------------------------

Source: State Statistical Bureau (1995). Cost data are from Wu Renhong, 1995, "China's Inflation and Regional Disparity," and World Bank, 1994, China. Tnternal Market and Rpgiulations. Social indicators are from Yasuko Hayase and Seiko ]Kawamata, 1991, Pnpiilat-ion Policiy anci Vital StAtisting in China, Institute of Developing Economies, Tokyo, Japan. Author's own calculation.

59