rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Descriptive statistics on relevant outcomes in different samples**

|||**Male**|||**Female**||
|---|---|---|---|---|---|---|
||**Total **|**Reform**|**Non reform**|**Total **|**Reform**|**Non reform**|
|**Generation II**|||||||
|Year of birth|1960|1959|1960|1964|1964|1964|
|Father died after 1994 or alive|0.62|0.62|0.62|0.76|0.76|0.76|
|_Individual characteristics_|||||||
|Years of schooling|6.62|5.88|7.01***|3.51|3.81|3.36*|
|Education gap with spouse|3.10|2.08|3.65***|-3.10|-2.08|-3.65***|
|Share of gifts received at marriage|0.54|0.52|0.55**|0.46|0.48|0.45**|
|<br>Share of time on productive work|0.33|0.34|0.33|0.11|0.17|0.07***|
|<br>Share of time on household work|0.15|0.14|0.15***|0.39|0.36|0.42***|
|Share of time on leisure|0.52|0.52|0.51|0.49|0.47|0.51***|
|Have an individual bank account||||0.25|0.25|0.25|
|Having a sanitary latrine (household level)||||0.44|0.65|0.29***|
|_Reproductive behavior_|||||||
|<br>Number of children born||||3.18|2.78|3.46***|
|Share of daughters born||||0.44|0.45|0.44|
|Survival rate of daughters||||0.97|0.97|0.96|
|<br>Survival rate of sons||||0.95|0.97|0.94|
|No. of observations|1,209|421|788|1,209|421|788|
|**Generation III**|||||||
|Year of birth|1992|1991|1993***|1992|1991|1992**|
|Years of schooling|7.76|8.03|7.65|6.63|6.80|6.56|
|No. of observations|1,656|470|1,186|1,537|446|1,091|
|**Sample I (6-18)**|||||||
|<br>% of the total sample|39.31|36.60|40.39|37.41|34.75|38.50|
|<br>Currently enrolled|0.84|0.79|0.85*|0.84|0.81|0.85|
|Expenses on education (Rs)|1,327|1,767|1,180***|1,103|1,363|1,011**|
|of which on books/stationery|0.81|0.73|0.84***|0.85|0.78|0.87***|
|of which on transport/hostel|0.11|0.26|0.06***|0.10|0.20|0.06***|
|of which on private coaching/tuition|0.08|0.02|0.10***|0.06|0.03|0.07**|
|Mothers’ expenses on education (Rs)|121.72|284.60|67.43***|89.51|158.97|65.07***|
|<br>of which on books/stationery|0.89|0.78|0.95***|0.92|0.85|0.95***|
|<br>of which on transport/hostel|0.09|0.21|0.02***|0.05|0.11|0.02***|
|of which on private coaching/tuition|0.02|0.00|0.03|0.03|0.04|0.03|
|Share of time on productive work for age 6-14|0.01|0.01|0.01|0.01|0.01|0.01|
|Share of time on household work for age 6-14|0.07|0.06|0.07|0.09|0.08|0.09|
|Share of time on study for age 6-14|0.28|0.28|0.27|0.29|0.30|0.28|
|<br>Share of time on leisure for age 6-14|0.64|0.65|0.64|0.62|0.61|0.62|
|Share of time on productive work for age 15-18|0.09|0.12|0.07***|0.04|0.05|0.04|
|<br>Share of time on household work for age 15-18|0.13|0.11|0.14***|0.21|0.20|0.21|
|<br>Share of time on study for age 15-18|0.22|0.21|0.22|0.23|0.24|0.23|
|<br>Share of time on leisure for age 15-18|0.56|0.55|0.57|0.52|0.51|0.53|
|<br>**Sample II (15-34)**|||||||
|<br>% of the total sample|67.93|72.55|66.10|68.12|69.96|67.37|
|<br>Completed primary education age 15-30|0.72|0.75|0.71|0.56|0.65|0.52***|
|<br>Completed secondary education age 19-34|0.48|0.55|0.45***|0.27|0.32|0.25**|
|**Sample III (0-18)**|||||||
|<br>% of the total sample|44.69|41.49|45.95|42.88|40.81|43.72|
|Treatment forpreventable diseases|0.50|0.43|0.53**|0.56|0.54|0.57|

_Source:_ Own computation from 2011 REDS follow-up survey.

16
# IMPROVING WATER RESOURCE MANAGEMENT IN BANGLADESH

# **Introduction**

The demand for water and the ability to control its location, timing, quality and quantity are becoming critical with the growing population of Bangladesh. Starting with simple agricultural needs the pace of development has been placing increasing demands for industrial and municipal use of fresh water. The changing characteristics and magnitude of flood in the country has also changed the ways in which people used to respond to it in the past. Traditional activities like inland fishing has been affected by many human efforts to contain flood. Increasing erosion of river banks have enhanced environmental problems and have assumed major importance due to land and settlement pressures. The harnessing of water upstream in the Ganges plain and associated changes in river morphology has added new dimensions to its availability, in time and location. With all these current problems and the high probability of further deterioration of the situation, it is imperative for the country to develop a strategy and a long-term plan for management of this critical resource.

The Bangladesh Water and Flood Management Strategy, prepared in October 1995, presents a strategic framework for the development of a national water management system for the country. [ see Box 1]. The strategy outlined will require a sound system for managing and developing water resources in Bangladesh. This system has to serve the water sector goals, keeping in perspective the resource constraints and the need for dynamism to make it responsive to the changing times. In Bangladesh, where water is a prime economic resource for agriculture (contributing 30 percent to GDP), water transport (contributing 4 percent to GDP), energy, water and sanitary services (contributing 2 percent to GDP), its value as an economic good cannot be underrated and the need for efficient management overemphasized.

The management of water resources needs to focus on both supply and demand aspects. Investments in dams, reservoirs, wells, pumps and canals have in the past provided adequate access to fresh water supplies. But with the growing demand imposed by population and modern usage of water, the supply-side approach to water planning is meeting its limitation. The availability of surface water in Bangladesh has declined with greater upstream use by India, in parts of the years, and groundwater potentials are fast depleting with increasing usage, particularly agricultural. The option remaining is to look for improved demand managemen **t** o bring water use into balance with supply. It has also been now recognized that the country has a better chance of combating seasonal flood (when excess supply occur) through better flood management rather than flood control measures.

1
model. By contrast, bank finance is less likely to be available for this purpose due to the limited pledgeability of income and the non-contingent nature of debt contracts for risky investment, especially in developing countries where financial markets are less developed. We thus expect that establishments that have a higher share of retained earnings in financing their working capital are more likely to innovate.<sup>17</sup> Moreover, we use total investment to examine the impact of inflation and corporate liquidity holdings on establishments’ overall investment. We also include the following establishmentlevel control variables: Establishment size, measured by the log of the number of employees; the establishments’ age; a dummy variable indicating if it is a subsidiary of a larger firm having access to intra-firm credit; and a dummy variable indicating if it has any formal bank loan (that is, different from credit lines for working capital).

**_Other data sources._** We measure inflation by consumer price inflation from the World Bank World Development Indicators (WDI) for all countries and years included in the WBES.<sup>18</sup> We remove inflation outliers by excluding country-year observation with excessive rates above 100% as well as significant deflation episodes with rates below -2% since firms’ incentives to adjust their investment behavior might be driven by different factors during these extreme episodes. Qualitatively, our results remain unchanged if we do not impose any range for inflation or use alternative restrictions (only including inflation episodes above 0% and below 20% or 50%). The average (median) inflation rate in the WBES sample is 7% (5.8%). We also use real GDP per capita in constant US dollars from the WDI as a macro-level control variable. Our empirical specification works with the first lag of both inflation and real GDP per capita since the variables from the WBES refer to the previous year.

Moreover, we use two-digit sector-level information to test the ‘difference-in-difference’ predictions (ii)-(iv) in Proposition 1. Specifically, the sector variables allow us to examine whether the adverse effect of inflation on innovation is more pronounced in more productive sectors, in

> 17While there are several potential financing sources for working capital, we show (cf. Table 1 below) that retained earnings are the predominant form. By contrast, formal bank loans are available only to relatively few firms, and their share in working capital finance is minor.

> 18Data on consumer price inflation are available for all countries and time periods. Alternative measures for the cost of liquidity such as nominal deposit or lending interest rates are available only for about two-thirds of the country-year pairs. Where available, however, these interest rates are highly correlated with our inflation measure.

16
where _L_<sup>_i_</sup> _t_<sup>denoteslaborusedinsector</sup><sup>_i_inperiod</sup><sup>_t_andwesuppressthefixedfactorineach</sup> industry to simplify notation. Assume that _Q_<sup>_i_</sup> ( _·_ ) is increasing, continuously differentiable, and strictly concave.

The domestic price of good _i_ , _p_<sup>_i_</sup> , is treated as exogenous. A central example, which will be treated explicitly, is of a small open economy in which the domestic price is equal to the given world price plus a tariff (export subsidy) for a good that is imported (exported). For now, we will assume that domestic goods prices are constant over time, but later we will extend the analysis to the case of an unanticipated or anticipated change due to a trade liberalization. Cameron, Chaudhuri and McLaren (2007) and Artu ̧c, Chaudhuri and McLaren (2010) allow for the more general case of a stochastic process for domestic prices. The wage in sector _i_ at date _t_ is competitively determined: where _w_ � _t_<sup>_i_denotesthenominalwagepaidbysector</sup><sup>_i_atdate</sup><sup>_t_and</sup><sup>_pi_denotesthedomestic</sup> price of good _i_ . Thus, the competitive employers in each sector each take the wage as given and maximize profit; the wage adjusts so that this wage just clears the spot labor market in that sector.

# **2.2 Labor Mobility**

A worker _θ_ who is in industry _i_ at the end of period _t_ receives a non-pecuniary benefit _ε_<sup>_i_</sup> _θ,t_<sup>.</sup> This can be thought of as enjoyment of the work or of living in the region where industry _i_ is located, for example. We assume that the _ε_<sup>_i_</sup> _θ,t_<sup>areindependentlyandidenticallydistributed</sup> across workers and sectors and over time, with cdf _F_ ( _·_ ), pdf _f_ ( _·_ ), and full support: and mean zero: 8
## **Population**

The Mexico City Metropolitan Area (with a population of 17 million in 1995 _(Instituto Nacional de Estadistica, Geografia e Informatica,_ INEGI, 1997) is composed of the Federal District (containing Mexico City and its 8.5 million inhabitants) and part of the State of Mexico. Demographic information used for the population distribution was obtained from Mexican National Institute of Statistics, Geography and Information (INEGI). Figure 4.1 presents a population distribution map of the ZMVM. The GIS working group at the Federal District Government provided political boundaries and geographic definitions. Each locality (represented by a point) is assigned to a municipality (in the State of Mexico) or to a delegation (in the Federal District).

**Figure 4.1 Population Map for ZMVM by Municipality, 1995** (in the State of Mexico) or Delegation (in the Federal District)

<!-- Start of picture text --> J -<br><!-- End of picture text -->

**Population 1995** _7 **14975 - 90543 90544 - 255838 266839 - 552183 552184 - 839692 839693- 1696609**

# **Exposure of the Population**

By combining the map of population distribution and the maps of air quality it was possible to assess exposure of the population to air pollutants. This assessment of pollution exposure should correspond with the format of exposure defined and used in the epidemiological studies that our exposure-response models are based on (section 5). Exposure-response relations are constructed from epidemiological information and air quality measurements made at monitoring stations. The statistical exposure-response relationships reflect several factors in the cause-effect chain between air quality and health effects.

_13_
For instance, CHS (2009) estimate that US total business investment in intangibles was approximately one trillion dollars in 1999. Including intangible investment thus raises estimated US GDP in 1999 by roughly this amount, or 12 percent. Using the same methodology, Marrano and Haskell (2007) find that as a result of excluding intangibles, market sector gross value added in the UK was understated by 13 percent in 2004. Studies replicating the CHS methodology in other developed OECD countries also report large magnitudes in intangible investment, ranging from 5 to 12 percent of GDP.<sup>6</sup> Another common finding is that since intangibles have become increasingly important over time, ignoring them distorts the _trends_ in measured investment and output. For example, the share of intangible investment (or the extent of underestimation of GDP) in the US has risen from about 7 percent in 1970 to 14 percent in 2007 (Corrado and Hulten 2010). Between 1995 and 2005, business investment in intangibles increased faster than investment in tangibles in almost all countries of the EU (Jona-Lasinio et al. 2011). In the UK, intangible investment more than doubled as a share of market sector gross value added between 1970 and 2004, overtook fixed capital investment in the mid 1990s and is now roughly 50 percent bigger. In the US, Finland and Sweden, intangible investment is now also greater than tangible investment. And in Canada, Denmark, France, Germany and Japan, it comes close (OECD 2010).

Does intangible investment display similar magnitudes or trends in developing countries? This is difficult to answer because apart from a recent study on China (Hulten and Hao, 2011), there has been no systematic attempt to quantify intangible assets in low and middle income countries. Yet, the question is of interest to policy makers because intangible investment is arguably a key driver of innovation and growth for all countries, and likely is not promoted by the same policy instruments as tangible investment. Our study begins to redress this gap by adapting the CHS methodology to measuring intangible assets in Brazil. While this methodology is not without shortcomings, it has compelling advantages compared to the alternatives. For one, the resulting estimates can be meaningfully compared to existing ones for developed countries and for China. Moreover, the methodology explicitly identifies and measures the components of intangible investment, which is more useful for policy purposes than just backing out economy-wide aggregate intangible investment as a black-box residual from aggregate output and tangible investments.

> 6 See Barnes and McLure (2009) for Australia, Belhocine (2009) for Canada, Hao et al. (2009) for Germany, France, Italy, and Spain, Fukao et al. (2009) for Japan, van Rooijen-Horsten et al. (2008) for the Netherlands, and Edquist (2009) for Sweden. OECD (2010) summarizes the results of various countries.

3
# **Table 8.  Distribution of Assets and Premium Payments for Life and Pension Insurance among major Swedish Insurance Companies, 2006**

|**_Company_**||**_P_**|**_ercent of assets_**<br>|**_of all_**<br> <br>|**_Percent of total_**|**_premium_**|
|---|---|---|---|---|---|---|
||||**_companies_**||**_paymen_**|**_ts_**|
|1<br>Skandiakoncerne<br>|n||25 %||19 %||
|2<br>SEB Trygg Liv|||19 %||17 %||
|<br>3<br>AMF Pension|||18 %||11 %||
|4<br>Handelsbankskon<br> <br>|cernen<br>||12 %<br>||12 %<br>||
|5<br>Länsförsäkringar|||10 %||8 %||
|<br>6<br>Folksam|||9 %||12 %||
|7<br>Swedbank (Robu<br> <br>|r) Insurance||4 %<br>||10 %<br>|<br>|
|8<br>Others|||3 %||11 %||
|9<br>All companies|||100 %||100 %||
|All companies (e|xcluding Alecta|,|||||
|10<br> <br>FPG, SPV), billio|<br>ns of kronor||1550||124||
|11<br>All companies (e|xcluding Alecta||||||
|<br> <br>FPG, SPV), % G|<br>DP|,|54.7 %||4.3 %||
|12<br>Alecta, billions o|f kronor||300<sup>a</sup>||19||
|13<br>FPG – Reinsured|company book|assets|125||0.2||
|<br>14<br>SPV - Civil serva|<br>nt scheme||295||50||
|14<br>All companies, bi|llions of kronor||2 270||193||
|15<br>All companies %|GDP||80 %||6.8 %||
|,<br>aAuthor’s rough approximation.<br>Source: Swedish Insurance Federat<br>**Table 9. Overview of Key Fina**<br>1<br>|<br>ion and autho<br>**ncial Data fo**<br>2<br>|r’s calculatio<br>**r Swedish L**<br>**kronor**<br>3<br>|ns.<br>**ife Insurance**<br>4<br>|**Companies**<br>5<br>|<sup>**1)**</sup>**, 2006. Mill**<br>6<br>|**ions of**<br>7<br>|
|Premium<br>|Investment|Unrealized|Insurance|Net|Surplus|Operating|
|payments<sup>1</sup>|Income|gains on|benefit|addition to|paid out to|surplus/deficit|
|||investments|payments|technical|the insured||
|+|+|+|<br>-|reserves/ins<br>urance|-|+|
|||||liabilities|||
|**Individual Insurance**||||-|||
|Traditional life insurance<br>16 228|28 010|15 694|17 977|2 279|1 666|26 316|
|Unit linked life insurance<br>26 857|1 809|18 544|20 382|19 892|1 203|1 341|
|Permanent health insurance<br>1 107|1 138|501|703|-83|213|1 443|
|Group life insurance<br>5 224|1 288|294|3 853|-271|540|1 461|
|Health and accident<br>458|628|172|718|-162|-5|413|
|insurance|||||||
|**Occupational Group Insurance**|||||||
|Defined benefit<br>16 145|38 291|13 803|10 845|-1 106|0|45 826|
|Defined contribution<br>28 427|38 949|19 744|9 250|12 324|24|48 257|
|,<br>traditional<br>|||||||
|<br>Defined contribution, unit-<br>25 323|1 654|8 586|2 919|31 583|0|698|
|linked|||||||
|<br>Health & accident insurance<br>6 795|1 367|399|1 915|108|87|5 599|
|**Total**<br>126 564<sup>1</sup>|113 134|77 737|68 562|64 564|3 728|131 354|

<u>1) Taking into account deposit share distribution and a couple of other small factors, total premium payments, adjusted for this, were</u> 118 905 in 2006.

Source. Swedish Financial Supervisory Authority and Statistics Sweden.

29
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 12 Benchmarking power indicators**

||Unit<br>Fragile low-income<br>countries|**Côte d’Ivoire**|Middle-income<br>countries|
|---|---|---|---|
||Mid 2000s|**2005**<br>**2008**|Mid 2000s|
|Installed power generation capacity|MW/mil. people<br>45.7|**58.3**<br>**58.3**|798.6|
|Power consumption|kWH/capita<br>165.3|**235.6**<br>**240.5**|4,479.3|
|Power outages*|Day/year<br>11.1|**45.6**|5.9|
|Firms‘ reliance on own generator*|% consumption<br>16.2|**15.1**|10.9|
|Firms‘ value lost due to power* outages|% sales<br>5.4|**5.0**|1.6|
|Household connections (CIE)|% population|20.0||
|Access to electricity (DHS 99)**|% population<br>15.0|**49.7**|59.9|
|Urban access to electricity (DHS 99)|% population<br>57.6|**89.7**|85.2|
|Rural access to electricity (DHS 99)|% population<br>3.9|**26.6**|31.8|
|Growth in access to electricity|% population/year<br>3.3|**3.3**|1.5|
|Revenue collection|% billings<br>33.6|**66.1**<br>**88.0**|100.0|
|System losses|% production<br>40.0|**17.5**<br>**23.4**|10.1|
|Cost recovery|% total cost<br>100.0|**100.0**<br>**80.6**|100.0|
|Total hidden costs as % of revenue|%<br>442.5|**62.8**<br>**136.5**|0.1|
|U.S. cents|**Côte d’Ivoire**|Predominantly thermo<br>generation<br>Oth|er developing regions|
||**2005**<br>**2009**|Mid2000s|Mid 2000s|
|Power tariff (residential at 75 kWh)|**11.9**<br>**9.6**|14.5|5.0 – 10.0|
|Power tariff (commercial at 900 kWh)|**16.9**<br>**18.5**|18.8||
|Power tariff (industrial at 50,000 kWh)|**10.7**<br>**9.3**|14.2||

* Based on Enterprise Survey 2009 ** Based on DHS 1999 Survey and benchmarks for nonfragile low-income countries. Source: Eberhard and others 2009, derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

# **Challenges**

The power supply in Côte d’Ivoire has become unreliable in recent years. As the Ivorian economy recovers from crisis and economic growth rates are picking up again, demand for power has grown. At the same time, power sector investments were neglected during the crisis. This has led to an overall shortage of supply. Transmission and distribution networks become overloaded, and there is no capacity margin to serve growing demand, both domestically and in neighboring countries. As a result, load shedding increased from 16.6 gigawatt-hours in 2007 to 30.0 gigawatt-hours in 2009. This is still a relatively small proportion of the total load served of around 5,500 gigawatt-hours during this period. Quality of service has deteriorated, with more than 20 percent losses, 36 hours of average outage duration in 2008, and 52 hours in 2009. No country-specific information on the value of lost load was available for Côte d’Ivoire, but based on an average value for other African countries of around $2.50 per kilowatthour of lost load, the total cost of outages in 2009 was $75 million, or around 0.3 percent of GDP. While worrisome in absolute terms, these losses are still towards the lower end of what has been observed in 22
# **4. Data and Econometric Methodology**

To test the predictions from the model we use survey data collected by us from the same caste _of_ potters, from three villages in the South Indian State of Karnataka, viho were the focus of the qualitative interviews. All three villages exchange brides indicating that they are all part of the same marriage market. The survey consisted of a three part questionnaire, a family questionnaire directed to the head of the household, a woman's questionnaire for all women above the age of fifteen, and a husband's questionnaire. All 149 potter families in the three villages were interviewed, which included 177 adult women, and 130 married men. After dropping the never-married and widowed women we are left with a sample of 142 women of which 137 answered the violence question'<sup>4</sup> . In addition to questions relating to socio-economic characteristics and marriage transactions, all the married women were asked if they had ever been severely physically assaulted by the husbands at any time during the marriage, along with questions about the extent and severity of the violence. The question on the incidence of domestic violence elicited a much more accurate response than the questions about its extent and severity". Thus in this paper we will focus on the determinants of a binary variable indicating whether the wife has ever been severely physically assaulted by her husband. Men were not asked about wife-abuse in the survey, but were interviewed about it in the qualitative work. As far as possible, all the women were interviewed by female interviewers and the men by male interviewers.

It is clear that while our data are detailed and intimate, these advantages are traded off with the size of the sample'<sup>6</sup> and its representativeness. However, a more conventional sampling frame would not have permitted us to collect sensitive information like the incidence of wifeabuse, as we would not have established the level of trust necessary to gather such information with reasonable accuracy.

**14** The model suggests that some of these women are in bad "separated" marriages where links between the husband's and wife's families have been severed, but where violence is absent. NVhile we have anecdotal evidence from a sub-sample of 40 interviews suggesting that about fifteen per cent of marriages fall into this category, the survey instrument did not include questions on the quality of the links between the two families.

**_15_** The questions on the extent and severity of violence were answered by only 70 per cent of the sample. Even when the questions were answered the responses had strong interviewer effects with some interviewers being able to elicit more complete and consistent responses than others. On other the hand, the dichotomous question on whether a woman had ever been physically assaulted by her husband was answered by all the respondents without significant interviewer effects.

**16** This sample size is comparable to the widely analyzed ICRISAT sample, the core of which consists of 120 households from three villages.

17