rafmacalaba/datause-encoder-data huggingface dataset:
_Table 7: Impact of Electrical Conductivity on Net Primary Productivity, alternative thresholds, India_

||(1)|(2)|(3)|
|---|---|---|---|
|DepVar: Δ log(netprimary productivity)|EC > 75 mS/m|EC > 100 mS/m|EC > 125 mS/m|
|Electrical Conductivity exceeds threshold|‐0.0315<br>(0.00477)***|‐0.0656<br>(0.00600)***|‐0.0752<br>(0.0102) ***|
|Precipitation (m/year)|0.329|0.325|0.326|
||(0.0212)***|(0.0212)***|(0.0212)***|
|Precipitation^2 (m/year)|‐0.0779<br>(0.00682)***|‐0.0776<br>(0.00682)***|‐0.0771<br>(0.00679)***|
|Temperature (C)|‐0.419<br>(0.0590)***|‐0.407<br>(0.0592)***|‐0.392<br>(0.0585)***|
|Temperature^2 (C)|0.00587<br>(0.00113)***|0.00563<br>(0.00114)***|0.00540<br>(0.00112)***|
|Year Fixed Effects|Y|Y|Y|
|Gridcell Fixed Effects|Y|Y|Y|
|Geographic Time Trends|State|State|State|
|Distance to nearest station|100km|100km|100km|
|Cropland Threshold|>30%|>30%|>30%|
|N|26830|26830|26830|
|R‐sq|0.475|0.476|0.476|

Notes: Table shows results from estimating equation 1 for the India sample via ordinary least squares. Each column is from a separate regression, and regressions vary by the threshold of EC used in the EC indicator variable. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001 23
investment‐to‐output ratios only differ slightly in the forced repatriation case, at most 1 percentage point in the first decade.

# _4.2.1.  Post‐conflict GDP projections across the different scenarios under voluntary mobility_

The results of our simulations for Syria’s post‐conflict GDP in the different scenarios under the voluntary mobility case are shown in Graph 11(a). Under the baseline/moderate scenario, average GDP growth is 8.4 percent over 2019‐2038. As can be observed from the top right of Graph 11(b), with the inflow or reconstruction funds, the main growth driver over the 20‐year period is capital accumulation. As I/Y reverts to something close to pre‐conflict trends especially after the 20‐year annual inflow of reconstruction funds, the contribution from human capital growth and TFP are just as relevant as physical capital growth. In this scenario, Syria reaches its pre‐conflict GDP level by 2031, and its pre‐conflict GDP per capita level by 2033, thus losing about two decades.

In the optimistic scenario, average GDP growth is 10.9 percent over 2019‐2038 reaching a peak of 20.9 percent in 2022 with exceptionally high investments, and stronger contributions from other growth drivers relative to the moderate scenario (see Graph 11(a) and Graph 11(b), bottom left). Even with this strong performance, it would take Syria 9 years, that is by 2027, to surpass its 2010 GDP level and 11 years to surpass its pre‐conflict GDP per capita. In the pessimistic scenario, GDP growth averages 5.1 percent across the next two decades, only slightly higher than pre‐conflict levels, amid limited reconstruction funds from external sources (see Graph 11(b), bottom right, for the difference in growth drivers vis‐à‐vis the moderate scenario). In this case, it would take Syria at least 22 years to pass its pre‐conflict GDP level and almost 29 years to meet its GDP per capita level. This finding echoes the simulation in World Bank (2017b) where under the assumption that the conflict ends in its sixth year (2017), with investment recovering but remaining below its pre‐conflict level, Syria’s GDP remains below its pre‐conflict level even 20 years after the conflict.

28
(RICS-Amhara), which is representative of four zones of the Amhara region or about half of Amhara’s population (18 million). As shown in detail in Loening, et al. (2008) the results obtained for Amhara are fairly similar to those obtained for Ethiopia at large, even though the data are in a technical sense not representative at the national level. In order to be able to analyze how the performance of the non-farm enterprise sector is affected by agricultural outcomes, the RICS-Amhara was augmented with wereda (i.e. district) level indicators of predicted agricultural performance based on new rainfall information, based on NOAA’s Africa Rainfall Estimates Climatology dataset 1995-2006.<sup>2</sup> The urban data are drawn from the 2006 Ethiopia Productivity and Investment Climate Survey (PICS) which covered 14 major cities located in seven regions of Ethiopia, with approximately half of the data coming from Addis (see the Appendix in Mengistae and Honorati (2007) for more information). The PICS comprised a survey of 360 manufacturing firms, supposed to employ at least five employees, as well as a survey of 126 microenterprises, 84 of whom were engaged in manufacturing activities, supposed to exclude firms with five employees or more. The former group of firms is referred to as “large” enterprises, while firms in the microenterprise survey are referred to as “small” enterprises. In practice, due to measurement error and changes in size in between being documented in the registry and the timing of the survey, these size boundaries were not strictly adhered to when administering the survey.3 The surveys mainly differ in sampling frame. For the large manufacturing firms, the national manufacturing census provided the sampling frame whereas informal firms were sampled by means of direct enumeration in key urban clusters such as the Merkato in Addis. Firms without a fixed business location are not covered in the

> 2 See World Bank (2007b) and Love et al. (2004) for details.

> 3 The  microenterprise data consequently contain a substantial number of firms with more than 5 employees, while the “large” urban manufacturing data contains some firms with fewer than 5 employees.  For the purpose of our productivity analysis, we exclude firms with more than 10 employees, the conventional cutoff used to define a microenterprise, from the microenterprise sample. For the analysis of growth, we did not use this cutoff, since curtailing the sample at 10 employees might bias our growth estimates downwards.

-8-
also a panel of countries over time, Calderón and Servén (2010, 2012, 2014) argue persuasively that infrastructure can have positive effects on both growth and distributive equity. These beneficial effects, however, require a framework that regulates, organizes, and coordinates the governments and companies that build public infrastructure and provide its services. Moreover, as highlighted by Pritchett (1996) and Devadas and Pennings (2018), the amount of infrastructure spending is not necessarily an indication of effective infrastructure investment. The quality of spending matters, and this seems to be highly related to the strength of public institutions (World Bank 2003, 2017r).

# **Institutions**

_Public Institutions_ – in the regulatory, justice, policy, and political systems – can promote social and economic stability, provide a safe living and working environment, defend property rights, and safeguard basic civil rights. The environment and policies that public institutions provide have a large, fundamental impact on economic development (North 1990; Acemoglu, Johnson, and Robinson 2004). The evidence that good governance (reflected in political stability, the rule of law, the protection of property rights, bureaucratic quality, transparency and accountability, and the absence of corruption) has a positive effect on productivity and economic growth is large, comprehensive, and convincing.

– Barro (1991) shows in a study of around 100 countries for 1960 85 that economic growth is positively related to political stability and inversely to government-induced market distortions. Using ethnolinguistic fractionalization as an instrumental variable for measures of government corruption, Mauro (1995) finds that corruption has a statistically significant and economically large negative effect on economic growth. Knack and Keefer (1995) find that property rights, proxied by contract enforceability and risk of expropriation, has a substantial impact on economic growth, even after accounting for capital accumulation. Rodrik, Subramanian, and Trebbi (2004) show that the quality of institutions, measured by a composite indicator of the protection of property rights and the rule of law, has a positive impact on income levels across a large sample of countries. Chanda and Dalgaard (2008) find that the quality of institutions (proxied by a composite index of the rule of law, bureaucratic quality, corruption, the risk of expropriation, and the government repudiation of contracts) is positively related to 10
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# _C. Modeling the Determinants of Nutritional Status_

After illustrating the targeting efficiency we then model child nutritional status using a framework derived from a household model in the tradition of Becker (1991). Assume that the household maximizes a quasi-concave utility function that takes as its arguments consumption of commodities and services, x, and the leisure, 1, and health status, 9 (of which a child's anthropometric measurement, h, is one dimension), of each household member. The household solves the following problem, maxu(x,1,9;A,Z), X,/,@ where _A_ and Z respectively represent household and community characteristics, some of which are not observed. Allocation choices are conditional on the budget constraint: _px =w(T -1)_ **_+_** _y,_ where p is a vector of prices, w is a vector of household members' wages, T is a vector of the household members' maximum number of work hours, and y is household nonwage income. The nutritional status of children, _h,_ is determined by a biological health production technology: where I is a vector of health inputs and **_,ui_** represents the unobservable individual, family, and community characteristics that affect the child's nutritional outcomes. Household characteristics (e.g., demographics, educational levels, etc.), _A,_ can have an impact on health by affecting household allocation decisions. Community characteristics, Z, such as access to clean water, can also have direct impacts on nutritional outcomes. Note that the input vector, _I,_ includes consumption goods which contribute positively to household welfare both directly through x, and indirectly through _h._ This represents the simultaneous choice of consumption goods and health inputs.

Solving the household's optimization problem leads to reduced-form demand equations including those for consumption, nutrition inputs, and child nutrition. The nutrition functions for each child conditional on per capita expenditures (quasi-reduced form) can be represented as follows: _hi = h(X,A,Z,6i),_ where **c,** is the child-specific random disturbance term, which as such is assumed to be uncorrelated with the other elements of the demand function. Since consumption, x, is a choice variable, it is unlikely that it is uncorrelated with the disturbance term, and instrumental variables approaches are typically employed. This is the model that we estimate, substituting an asset index for x as instruments, using the 1996 and 2000 DHS data.

_9_
the effect of the interaction between drought in _t_ ́ 1 and polygyny is positive, indicating a resilience of polygynous communities to the adverse effects of past droughts.

## **_(ii) Alternative Measure of Polygyny_**

Here, we use an alternative definition of polygyny based on geographical considerations. In Mali, polygyny is more prevalent in the Kayes and Sikasso regions, where 37.2% and 37.5% of men are polygynous respectively (Census, 2009). We use these regional differences to construct our polygyny variable. The variable takes the value 1 if the household resides in the Kayes or Sikasso region and 0 otherwise. The region is not entirely exogenous; for example, an individual might decide to migrate to a particular because that is more to Our data indicate that internal region region open polygyny. migration seems to be a rare event in rural Mali. Indeed, only 0.349% of heads of households migrate within the country. Therefore, the choice of region can be considered as determined at the time of birth. We specify the model in equation (2), always controlling for the year and EA fixed effects.

Table 6 reports the results of our estimations. These results are nearly identical to those reported in Table 3. The negative effect of drought in _t_ is less important and is significant at a level of 10%. Finally, the effect of drought in _t_ ́ 1 and its interaction with polygyny is greater. These differences could be explained by the fact that the region is not a perfect indicator of polygyny, as other social factors such as the evolution of certain norms in the region could influence crop yields.

# **5.4 Mechanisms**

Our results suggest that polygyny can reduce (or even reverse) the adverse effects of drought on crop yields a year after its occurrence. We find that the interaction between polygyny and drought in _t_ ́ 1 is positive and statistically significant. A logical question therefore arises: what mechanism underlies the resilience effect of polygyny?

The literature outlines a number of resilience strategies for farmers living in droughtprone agricultural communities. This includes the use of modern agricultural inputs (like 26
**<u>Table 3: Extreme Shocks Effects on Poverty –Robustness</u>**

|**DV**|(1a)|**Food-Poor**<br>(1b)|(1c)|(2a)|**Absolute-Poor**<br>(2b)|(2c)|
|---|---|---|---|---|---|---|
|Extreme Shocks|0.311***(0.088)|0.291**(0.111)|0.370**(0.119)|0.327***(0.088)|0.313*(0.135)|0.365**(0.131)|
|All 23 Strategies|-|Yes|-|-|Yes|-|
|Savings (26.1%)|||0.007(0.035)|||0.017(0.024)|
|Do Nothing (20.8%)|||-0.039**(0.012)|||-0.045**(0.013)|
|Spiritual (12.1%)|||0.021(0.011)|||0.065(0.027)|
|Sale Animals (8.8%)|||0.032(0.039)|||-0.003(0.053)|
|Social Networks (6.9%)|||0.027(0.032)|||-0.011(0.026)|
|Adjust Consumption (5.4%)|||0.050(0.087)|||0.037(0.046)|
|R-/Pseudo -squared (%)|0.35|2.72|0.91|0.87|3.95|2.01|
|Observations|3,059|3,059|3,059||||
|Fixed Effects|Households +<br>Year|Households+Year|Households+Year|Households+<br>Year|Households+Year|Households+Year|
|Model|Linear Prob.|Linear Prob.|Linear Prob.|Linear Prob.|Linear Prob.|Linear Prob.|

**Notes:** Table reports estimates from regressions of households poverty e.g., food versus absolute poor status on extreme shock variables, and vectors of shockcoping strategies. Frequencies of strategy-use are shown in parentheses. Shock realizations are based on estimated shock quantities in section 2 which range between -1.12 and 7.83. Extreme shocks correspond to realizations above 80th percentile of shock estimated shocks. The columns are differentiated by outcome variables and implied regression models. All columns include both household level and year fixed effects. All standard errors are clustered at the region level and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

17
The establishment of a poverty line for a country or region requires the availability of national or regional household survey information. The research findings of this study are based on a nationally representative rural household survey in Bangladesh conducted in 2004. One reason for choosing this survey for developing an energy poverty line is that it includes a representative picture of all types of energy use in rural Bangladesh. The survey was conducted in all four traditional divisions of Bangladesh, namely Dhaka, Chittagong, Khulna and Rajshahi. From these four divisions, 40 _thanas_ or _upazilas_ (sub-districts) were randomly selected. From each _thana_ , 3 villages were randomly selected, and from each village 20 households were randomly selected. The sample for this survey is 2,388 households from 119 villages that is representative of rural Bangladesh (Asaduzzaman, Barnes and Khandker 2007).

The survey provides information on income, education, health, housing, consumption, assets, and farm production and home enterprises. These are important variables for estimating poverty and are the main explanatory variables in the demand analysis presented later in this paper. The results from this survey are quite consistent with other surveys conducted in Bangladesh. In addition, there was a village level survey that collected information on prices for consumer goods, wages of males, females and children, village irrigation patterns, and different infrastructure information.

The survey is a rich source of information on energy, which is the main focus of this study. In rural Bangladesh the evidence from the survey indicates that there is a high degree of reliance on the biomass fuels (Table 1). As in other developing countries, in rural Bangladesh most households use biomass fuels for cooking and such fuels are commonly collected from the local environment. In fact, fuelwood is considered a superior fuel for cooking because it is much preferred over other biomass fuels such as crop residues or dung. However, other biomass fuels are burned in great quantity for cooking in rural Bangladesh. It is surprising that the use of tree leaves for cooking is so 12
**FIGURE 4: SHARE ADJUSTED SECTORAL CONTRIBUTIONS TO GDP GROWTH**

<!-- Start of picture text --> Financial Services 2.56<br>Health & Social Services 1.29<br>Education 1.17<br>Communications 3.28<br>Transportation 0.81<br>Commerce 0.76<br>Construction 1.43<br>Manufacturing 0.49<br>Agriculture 0.18<br>0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50<br>2006‐2013 1995‐2005<br><!-- End of picture text --> Source: Senegal Country Sources and authors’ estimates Senegal can be classified as having entered the information society since the early 1990s based on its telecommunications infrastructure, radio network coverage, diversified audiovisual landscape, packaged satellite programming, connection to the information superhighway, and the use of information technology in different segments of society (Sagna 2000). Recent advances in ICT, including cellular communications, mobile banking, and wireless broadband internet, have served to deepen the economy. Cellular networks have developed rapidly since the 2000s, and geographic coverage reached 106.5 percent in 2014. Mobile coverage in Senegal is on par with regional economic leaders such as Cape Verde (100.1 percent) and Ghana (108.2 percent) and greatly exceeds the SSA average of 66 percent (Figure 5).

13
# **1. Introduction**

Large numbers of households in many less developed countries (LDCs) continue to cope without good quality water and sanitation services (WSS). As is true for many other services, the poor are more likely than the non-poor to receive poor quality WSS services. Bosch et al. (2000) find that fewer poor households are connected to water networks and many poor households have access to lower-quality services than non-poor households. Poor households bear a higher burden, as measured through time, health and energy, than non-poor households. WSS service provision in Sri Lanka is subject to similar problems. As discussed below and in more detail in Pattanayak et al. (2005a), contrasted with the rich, the poor consume less water per capita, and they are less likely to use safe water sources or engage in preventative measures such as treating their water or purchasing storage tanks. As a result, the poor are more likely to suffer from water-related diseases.

In the early 2000s, Sri Lanka, like many other LDCs at the time, was considering the possibility of inviting the private sector to operate WSS services, specifically around three coastal towns: Negombo, Kalutara and Galle. There were during the preparation process for these public-private partnerships concerns that such arrangements in WSS services may exacerbate the existing inequalities in WSS between poor and non-poor households. In 2004, the government abandoned its plan for setting up such a public-private partnership.

In this paper, we describe the results of a stated preference conjoint survey administered in Sri Lanka to assess demand for WSS services not just with respect to price, but with respect to important WSS service features. Our analysis contrasts the responses of the poor<sup>1</sup> and non-poor, providing valuable information for those who seek to improve WSS services in a cost effective manner. Without a detailed understanding of why and how people desire WSS services, it difficult to increase coverage of these services and achieve the resulting social, health and economic benefits as it will not be possible to adapt policies to consumer preferences. Microeconomic studies can be used to evaluate factors that drive an individual’s demand for WSS services. Estache et al. (2002) argue persuasively that inputs from microeconomic

> 1 Poor households are defined as those households in the bottom two deciles, or the bottom quintile, of the income distribution; poor households spend less than or equal to SLR 3,356 per capita per month.

2