rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: World Development Indicators and LSMS-ISA (Current USD, 2010)

# (a) Macro Data: World Development Indicators, 2010

||Malawi|Tanzania|Uganda|Thailand|Mexico|US|
|---|---|---|---|---|---|---|
|Income per capita|359|524|471|4,802|8,920|48,377|
|Agricultural share (% Income)|29|28|25|12|3|1|
|Consumption per capita|257|328|376|2,577|6,023|32,783|
|Rural population (%)|84|71|85|56|22|19|
|Life expectancy|53|59|57|73|77|79|

# (b) Micro Data: LSMS-ISA, 2010

||Malawi|Tanzania|Uganda|
|---|---|---|---|
|Income per household|1,384|1,625|1,623|
||[1,314; 1,453]|[1,465; 1,786]|[1,358; 1,888]|
|Income per capita|343|378|509|
||[328; 358]|[342; 414]|[335; 682]|
|Agricultural share (% Income)|43|34|23|
|Consumption per household|1,601|1,931|2,366|
||[1,569; 1623]|[1,869; 1,994]|[2,240; 2,491]|
|Consumption per capita|416|456|589|
||[407; 425]|[436; 475]|[545; 633]|
|Rural households (%)|82|69|77|
|Sample size|12,015|3,012|2,337|

_Notes_ : Statistics in panel (a) are provided by the World Development Indicators at the World Bank and are based on national accounts data. Statistics in panel (b) are produced from the ISA household surveys data provided by the World Bank and adjusted as described in Section 2. Brackets denote 95% confidence intervals.
# _4.1. The dataset_

We use data collected during four rounds of the DHS in Ghana: 1993-94 (5,822 households surveyed), 1998-99 (6,003 households), 2003 (6,251 households), 2008 (11,778 households).<sup>6</sup> The four rounds include data for each household on the main source of drinking water and the time it takes to walk to this source, fetch water, and return. It is thus a perround-trip measure. The number of trips, by each household per day, to collect water is not available in the data. Characteristics of all household members are also available, in particular age, sex and whether each member is or is not in school. The information available for each household however differs somewhat from one round to the other. Each household belongs to a unique cluster which is spatially identified by GPS coordinates data. There were 400 such clusters in 1993-94 and 1998-99, and 412 clusters in 2003 and in 2008.

These four rounds do not represent a pure panel, as the households surveyed in these four rounds are not identical. However, and as will be explained in the next paragraph, the GPS coordinates data still allow us to build a panel of clusters.

# _4.2. Using the GPS coordinates to build a panel of communities_

The DHS data provide GPS coordinates of the surveyed clusters in each of the four rounds (1993-94, 1998-99, 2003 and 2008). For the 2008 sample, GPS coordinates are missing for seven clusters, leaving us with a total of 405 GPS-identified clusters for that year. For each of these clusters we identify the closest neighbor (belonging to the same region) in the other three rounds by calculating great distance circles between all pairs of clusters.<sup>7</sup> Table 1 reports the average distance (in miles) between two matched clusters in each region.<sup>8</sup> For example, in the Ashanti region, the average distance between a cluster surveyed in 2008 and the nearest cluster surveyed in 2003 is 3.4 miles. The average distance between matched clusters is small in regions such as Ashanti and Greater Accra with large numbers of clusters, varying here between 1.1 miles and 3.4 miles. The average distance between matched clusters is greater in the North where there are fewer clusters (about 14 miles in the Northern region).

# _4.3. Descriptive statistics_

In Table 2 we report the number of surveyed households, the percentage of households with the main source of drinking water on the premises (in dwelling or in yard), the average

> 6 For greater details on the sampling procedure in each of the four rounds, see the Ghana DHS final reports available at http://www.measuredhs.com/.

> 7 The great circle distance is the shortest distance between any two points on the surface of a sphere.

> 8 All Tables 1-7, and Figures 1-2, are found in Appendix F.

8
details.<sup>18</sup> We follow Miguel et al. (2004) and focus on internal armed conflicts between the government and an internal party with and without outside intervention, which accounts for 88.5% of the conflicts recorded in the database. We define a project as being carried out in a conflict state if the state is in conflict in the year the road work activity is recorded; a country is likely not to return to full stability after the end of a conflict, so we also create a variable that defines the country as being in a post conflict period for five years after the end of a conflict, or until the country reverted back into conflict. There are 187 conflict and post-conflict periods in the countries covered in our data.

Second, we use data from the Worldwide Governance Indicators (WGI) which are based on data from household and firm surveys, commercial business information providers, nongovernmental organizations, and public sector organizations. Six indicators capture different aspects of governance in two hundred countries since 1996. We use the variables on “control of corruption” and “political stability and absence of violence/terrorism”.<sup>19</sup> These indicators are measured between -2.5 and 2.5, where higher numbers reflect lower levels of corruption and political instability. We multiply the variables by (-1) and rename the variables “Corruption” and “Political Instability” so that higher numbers reflect higher levels of corruption and political instability.

Third, we use Transparency International’s 2008 Corruption Perception Index which allocates scores to countries from one to ten, where zero equals the highest level of perceived corruption and ten equals the lowest level of perceived corruption. We rescale the variable so that ten is the highest level of corruption. Graf Lambsdorff (2005) and Thompson and Shah (2005) underscore that the Corruption Perception Index is inappropriate for comparison of countries across time, due to changes in methodology, as well as the data sources underlying the index. We use 2008 because this is the first year with the highest number of countries covered. We have also assembled the index for the years 1998–2011, and our

> 18UCPD defines conflict as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths”.

> 19The control of corruption variable measures “perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ’capture’ of the state by elites and private interests,” and the variable political stability and absence of violence/terrorism reflects “perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism” (Kaufmann et al. 2010).

12
15

# _Extemnal Sector_

As discussed in Whalley and Yeung (1984), the treatment adopted for the external sector in general equilibrium models is important because it can significantly affect results. The term 'external sector closure rule' refers to the assumptions made on the export demand and import supply behavior of the foreign entity (rest of the world) with whom the country being modelled is assumed to trade.

There are two popular variants of external sector closure used in the literature. The first is the small open price taking economy treatment (SOPTE) with homogeneous goods. In this model variant, import and export prices are taken as given, and hence there are no endogenous changes in the terms of trade as policies or other parameters in the home country change. Commodity trade imbalances are balanced by exogenously specified capital inflows in the current account.

This small open price-taking economy (SOPTE) formulation provides a simple description of the rest of the world. Interdependence with the rest of the world is reflected in the prices faced by the smaller economy. With no insulation from the world economy, world prices determine domestic factor prices and production. The implication of this assumption is that there are no terms-of-trad **e** ffects which affect model welfare results. However, because only net trades enter the SOPTE implementation, it cannot account for cross-hauling found in the international trade statistics. Also, because it explicitly assumes infinite elasticity of foreign excess demand functions, sensitivity analysis on trade elasticity parameters is not possible. The SOPTE assumption also becomes key to how the model evaluates the impacts of policy changes. For example, given CBte d'Ivoire's large position
**Marital status** Richest 16.54 20.59 Never married 56.75 52.68 _Total 100.00 100.00_ Married 38.56 34.38 Widowed 1.77 7.61 Divorced 2.93 5.34 _Total 100.00 100.00_

# 2. Migration from the Households’ Perspective

## _Determinants of migration_

The results of our fieldwork indicate that female migration in Lesotho is primarily driven by economic “push” factors, often due to shocks to the family household, such as job loss, death, or bad crops, rather than “pull” factors. These push factors are closely linked with overall economic circumstances in rural Lesotho and many households’ existing dependence on remittances.

Livelihoods in rural Lesotho typically revolve around subsistence agriculture. Outside of farming, local work consists of odd, piecemeal jobs that are inconsistent and pay little. For most families, however, this is not enough. They struggle to feed their families and keep their children in school, much less pay for major expenses or recover from economic shocks. Given limited local options, most households look to employment options outside their village. Rural communities understand these opportunities to vary by sector, location, salary, and stability. Rural households previously opted to send males away for work and often to the mines in South Africa. This is in part due to historically patriarchal norms, and in part because these jobs are seen to be the most lucrative and attractive: they pay the best and are more stable and longer‐term than other “male” jobs, such as in construction or agriculture, which tend to be project‐based and/or seasonal. Given Lesotho’s historical gender norms and work opportunities, rural communities understand “migration” as being mostly to South Africa and being done by males. However, in response to retrenchment from the mines, or men becoming ill and returning home, households send other members away for work both to recover from lost remittances, and to diversify their sources of income.

Consistent with the nationally‐representative data, rural communities understand job prospects to be moving more in favor of women: domestic work is in high demand both in South Africa and urban Lesotho and textile jobs are now perceived to be easier to get than mining or construction jobs. In contrast to male migration to South Africa, communities understand internal moves for work – typically for factory or 17
Indonesia (see Figure 2), we see that countries with coverage similar to LAC in the 1960s, ended with higher access rates.

Figure 2. Spread of educational attainment over the last 50 years <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>1960s 1970s 1980s 1990s 2000s<br>LAC Indonesia<br>Thailand<br>Coverage (in percentage)<br><!-- End of picture text -->

**Source:** IPUMS and own elaboration.

Although alternative hypotheses could explain the continuous expansion of coverage, such as the irreversibility of the fixed investments required to provide coverage of basic public services, or an unobserved decline in the quality of these, the data available in this paper do not allow us to test for these competing explanations. However, the stylized facts show that, on average, coverage increases steadily in the region.

Figure 3 shows that, although there is an overall positive trend in access to basic public services and education, the opportunities that presented greater and continuous growth were access to electricity, school attendance and access to clean water. In the 1960s, most of the countries in LAC had electricity access rates below 40 percent. Fifty years later, access to electricity is above 85 percent for almost every country in the region. Similarly, school attendance evolved positively 12
This study contributes to several academic literature strands. Most directly, our study contributes to the literature on the informal sector that is reviewed comprehensively by Schneider and Enste (2000), Guha-Khasnobis et al. (2006), and Kanbur (2009, 2011).<sup>3</sup> Our paper documents how gender advancement can help explain persistence in the Indian context, and we hope that this link can be evaluated in other countries, too. Our work also builds upon prior studies of gender differences in entrepreneurship (e.g., Estrin and Mickiewicz 2011). Parker (2009) and Klapper and Parker (2011) offer a comprehensive review of this literature and appropriate references. Existing research mostly employs cross-country studies of gender ratios in entrepreneurship (e.g., Minniti 2010, Minniti and Naudé 2010) or provides insights into gender-based network linkages (e.g., Ghani, Kerr, and O’Connell 2011c). This paper traces out a related economic consequence of female entrepreneurship in developing economies. These contributions finally constitute an important input to the growing body of work on entrepreneurship and economic advancement in developing countries (e.g., Ardagna and Lusardi 2008, Khanna 2008, Schoar 2009, Klapper et al. 2010).

These findings can also aid Indian policy makers in the evaluation of economic trends. Despite significant economic advancement since liberalization began, the role of women in the Indian economy still lags well behind that of advanced economies (e.g., Dunlop and Velkoff 1999, Mammen and Paxson 2000, World Bank 2011). Cross-country data from the World Bank Entrepreneurship Snapshots find that India’s rate of entrepreneurship is lower than its stage of development would suggest; similar comparisons also highlight that India’s gender ratio among entrepreneurs is lower than its peers. This dual under-performance has cultural and economic antecedents, but it is starting to change. Women are making economic gains in the Indian economy, and further progress represents a tremendous growth opportunity for the country. This paper identifies how this process can contribute to persistence of the informal sector, which is an important consideration for how policy makers view and treat the informal sector.

> 3 Work regarding India includes Kundu (1999), Chakrabarti and Kundu (2009), Nataraj (2011), Kar and Marjit (2009), Amin (2010), Kathuria et al. (2010), Siggel (2010), Ghani, Kanbur, and O’Connell (2013), and Ghani, Kerr, and O’Connell (2013). Work regarding other developing economies includes Chen et al. (1999), Chen (2001), Schneider (2002), Maloney (2004), Gulyani and Talukdar (2010), and Kweka and Fox (2011). Basu et al. (2011) offer a recent theoretical model of the formal-informal sectors.

5
# How OECD Policies Affected Latin America in the 1980s

# by Chris Allen, David Currie, T. G. Srinivasan, and David Vines

# Table of Contents

|1.|Intro|duction|1|
|---|---|---|---|
|2.|The|Latin American Model|1|
||2.1<br>2.2|Short Run<br>The Longer Run: Asset Accumulation Effects|2<br>4|
||2.3|Policy Framework|5|
||<br>2.4|<br>Stability, Comparative Statics, and Simulation Exercises|6|
|3.|Effe|cts of Global Shocks on Latin America|10|
||3.1|A U.S. Monetary Contraction|11|
||3.2|A US. Fiscal Expansion|12|
|4.|Re-R|unning the 1980s|13|
||4.1|Re-running G-3 Monetary Policy in the 1980s|13|
||<br>4.2|<br>Re-running G-3 Fiscal Policy in the 1980s|14|
|5.|Con|clusions|16|
|Refe|rences||17|
|Foot|notes||19|
|Tabl|es||21|
**Table A2.CU.B: Intergenerational Persistence in Schooling in Urban China -** **<u>(18 30 Years Age Cohorts)</u>**

|||**LINEAR CEF**||**Q**|**UADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.26|0.35|0.089|0.067|0.34|0.28|
||(0.019)***|(0.020)***|(0.027)***|(0.052)|(0.055)***|(0.076)***|
||[0.025]***|[0.024]***|[0.028]***|[0.052]|[0.077]***|[0.075]***|
|Quadratic Coefft.||||0.014|0.00048|-0.014|
|||||(0.0034)***|(0.0034)|(0.0048)***|
|||||[0.0030]***|[0.0045]|[0.0049]***|
|Intercept|9.19|8.58|-0.61|9.59|8.6|-0.99|
||(0.17)***|(0.18)***|(0.25)**|(0.21)***|(0.23)***|(0.31)***|
||[0.20]***|[0.28]***|[0.33]*|[0.23]***|[0.36]***|[0.38]**|
|No. Observations|2230|2205|4435|2230|2205|4435|
|||**Estima**|**tes of Mobility**|**from Quadrati**|**c CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|9.59|8.58||**_IGME0 _**|0.067|0.35|
|**_ES6 _**|10.50|10.68||**_IGME6 _**|0.237|0.35|
|**_ES9_**|11.33|11.73||**_IGME9 _**|0.322|0.35|
|**_ES16_**|14.25|14.18||**_IGME16_**|0.521|0.35|

Notes: (1) The data used are CFPS 2016 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling, (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7a. Benchmarking water and sanitation indicators for Tanzania against Sub-Saharan Africa, 2006**

||Unit<br>Low-i|ncome countries|Tanzania|Middle-income<br>countries|
|---|---|---|---|---|
|Access to piped water|% pop|10.1|7.4|56.4|
|Access to stand posts|% pop|16.1|25.2|20.4|
|Access to wells/boreholes|% pop|38.3|41.0|6.3|
|Access to surface water|% pop|33.8|24.4|13.9|
|Access to septic tanks|% pop|5.3|2.8|44.0|
|Access to improved latrines|% pop|9.3|3.7|0.9|
|Access to traditional latrines|% pop|47.9|79.2|33.0|
|Open defecation|% pop|37.1|14.3|15.8|
|Domestic water consumption|liter/capita/day|72.4|62.0|Na|
|Urban water assets in need of rehabilitation|%|35.5|42.0|25.0|
|Revenue collection|% sales|96.0|121.0|99.2|
|Distribution losses|% production|33.0|45.0|23.1|
|Cost recovery|% total costs|56.0|36.0|80.6|
|Labor productivity|connections per<br>employee|176|109|203|
|Total hidden costs as % of revenue|%|130.0|144.8|84.9|
|US cents per m<sup>3</sup>|Tanzania|Scarce|water resources|Other developing<br>regions|
|Residential tariff|39.8||60.26||
|Nonresidential tariff|117.0||120.74|3.0 – 60.0|

_Source:_ Banerjee and others 2009.

Data on utility performance are from 2007 and are a weighted average of performance indicators from the seven largest utilities in the country namely Arusha, Dawasco, Dodoma, Mbeya, Morogoro, Moshi, and Mwanza Derived from AICD water utilities database, http://www.infrastructureafrica.org/aicd/tools/data.

**Table 7b. Benchmarking water and sanitation indicators across Tanzanian utilities, 2007**

|Utility|Water<br>connections|Water delivered|System<br>losses|Collection<br>ratio|Average<br>total cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|---|
||(‘000s)|(mns m3/year)|(%)|(%)|(US$/m3)|(US$/m3)|(US$m/year)|(% revenues )|
|Arusha|23.8|16|33|95|0.68|0.28|6|193|
|Dodoma|16.1|7|34|113|0.93|0.45|3|127|
|Mbeya|19.8|9|44|85|0.69|0.31|3|245|
|Morogoro|18.0|9|34|100|0.81|0.32|4|195|
|Moshi|15.2|8|34|115|0.78|0.27|4|206|
|Mwanza|21.6|12|34|99|0.57|0.34|3|99|
|Dawasco|127.1|101|52|139|0.90|0.29|47|237|
|Tanzania|241.7|162|45|121|0.84|0.30|69|209|

Data on utility performance are from 2007 and are a weighted average of performance indicators from the seven largest utilities in the country namely Arusha, Dawasco, Dodoma, Mbeya, Morogoro, Moshi, and Mwanza and were provided by World Bank country staff.

# **Challenges**

Access to safe water has been declining during the 2000s. According to household survey evidence, access to safe water decreased from 90 percent in 2000 to 80 percent in 2007 in urban areas and from 46 20
3 proxies that measure policy actions. Instead of measuring executable policies, cross country regressions use policy indicators, such as the average ratio of exports to GDP or the average ratio of broad money to GDP over the past 30 years. Cross country regressions, therefore, do not typically link executable policies with growth.' The inherent statistical end conceptual obstacles to interpreting cross country studies limit what economists can learn about policy and growth from cross country regressions. Even if cross country regressions yield very "strong" results, these results should be viewed as suggestive empirical regularities, not as stylized facts nor as behavioral relationships on which to measure responses to policy changes. Cross country regressions, however, can be very useful. Along with other analytical methods, demonstrating that certain policy-growth relationships hold well across countries will influence beliefs about policy and economic performance. Similarly, beliefs about policy and growth that are not supported by cross country evidence will tend to be viewed skeptically.

# 2. Back to the "facts"

To examine the strength of the empirical relationship between longrun growth end various policy indicators, we slightly modify the approach taken in Levine and Renelt (1992), henceforth LR. LR examine whether the conclusions from existing cross-country studies of growth and policy are robust or fragile to small alterations in the conditioning set of information, i.e., do slight alterations in the right-hand-side iariables change the results for the variables of primary interest? LR examine the relationship between economic growth and a wide assortment of fiscal expenditure, fiscal revenue, monetary, trade, and exchange rate policy indicators as well as political and economic stability indexes for a broad cross-section of countries over the 1960-89 and 1974-89 periods.