rafmacalaba/datause-encoder-data huggingface dataset:
- 30 -

59. Another area that requires priority attention is to<sup>develop</sup> policies and programs to effect the transition from the<sup>public sector to</sup> the private sector of the provision and distribution of farm inputs and for the marketing of agricultural products. The underlying logic is to strengthen government action in areas aimed at the provision of essential public goods that can be expected to have catalytic effects on development while reducing the use of scarce government resources for essentially commercial activities for which private firms have a comparative advantage.

60. Advocates of the basic needs approach argue that the required strategy of rural development is to concentrate upon the poorest<sup>rural</sup> groups (i.e. those who cannot afford or produce enough food or secure access to adequate health care and those least served by existing infrastructure and services). From a short-run humanitarian point of view there is clearly much to be said for putting the last first. Experience<sup>in</sup> SSA during the past two decades is, however, a powerful reminder<sup>of the</sup> importance of considering the feasibility, as well as the desirability, of alternative policies. Politicians and bureaucrats rarely<sup>are the ones to</sup> suffer hardship when imbalance between public sector responsibilities and the resources available gives rise to severe budget and foreign<sup>exchange</sup> crises and systemic failures in the implementation of the rural development programs. The polarization of the development debate into a misleading choice between growth or equity or between a basic needs<sup>as opposed to a</sup> trickle down development theory has undoubtedly contributed<sup>to the failure</sup> to give more vigorous support to a rational small farm development strategy capable of furthering both the growth and equity objectives of development.
To make the comparison similar we successively restrict the HIES sample to rural areas, rural areas in the survey divisions, and further to those with a male member between the ages 20 and 45. As expected, our study samples have a higher propensity to have a migrant and receive remittance income. A quarter of the households in our sample have had a migrant in the past 5 years, compared to 15 percent of the households in the rural sample in the survey provinces and 9 percent nationwide. This translates to the differences in remittance receipts as well. Partly because of remittances, our study sample has higher expenditures and incomes, and lower poverty. The $1.90 poverty rate in our sample is 4 percentage points lower compared to the rural households in survey provinces, and 9 percentage points lower compared to the national rural sample. However, these differences could also reflect the selection of people into the lottery. Our study sample is slightly younger and more educated than other comparable national samples, and more likely to be entrepreneurial. About 44 percent of our sample operate a household business compared to 15-20 percent in comparison samples. Our sample is also more likely to have and operate farmland and also to have taken out a loan in the past year. In addition to the differences in entrepreneurial capacity, these differences may also reflect better access to credit or collateral (farmland) among our sample. This comparison validates that migrants, and potential migrants, have different observable (and potentially unobservable) characteristics than the population. This further casts doubt, as has been pointed out in the literature, on non-experimental estimates of the impact of migration due to the important selection issues.

However, even if the study sample is different from the population at large, our study sample is likely to be representative of the policy-relevant sample: those who would participate in international migration and have some borrowing ability to finance migration.

# **Balance**

Moreover, among the interviewed applicants, time-invariant or retrospective characteristics are balanced across treatment groups. Though we did not have a baseline survey collected before the outcome of the lottery, we collected data on several individual specific characteristics that are unlikely to change over time. We also collected retrospective data on outcomes before the lottery was conducted. As **Table 1** shows, except for a couple of characteristics, most are balanced across treatment groups. Group T1 is 0.22 inches (0.3 percent) taller on average than the control group. Group T2 is 0.16 inches (0.2 percent) taller than the control group. Similarly, group T2 is 2.5 percentage points less likely to be a Muslim, whereas group T1 is 1.5 percentage points more likely to be a Muslim compared to the control group. A joint test across all of these outcomes, however, fails to reject the null that the characteristics are balanced across the lottery outcomes.

13
**_Figure 12_** _Impact of the 2011 Export Ban in Dar es Salaam_

<!-- Start of picture text --> TZ Shillings/kg<br>900<br>600<br>300<br>Dar es Salaam<br>Nairobi<br>Counterfactual<br>0<br>Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12<br>Source : Authors’ calculations based on parameter estimates<br><!-- End of picture text -->

**_Figure 13_**

_Impact of the 2011 Export Ban in Songea_ <!-- Start of picture text --> TZ Shillings/kg<br>900<br>600<br>300<br>Songea<br>Nairobi<br>Counterfactual<br>0<br>Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12<br><!-- End of picture text -->

**Source** : Authors’ calculations based on parameter estimates

39
Compiling the data for two indicators in Zambia reveals discrepanclec * between sources. For example, Source 1 indicates that education's share of the public budget increased after 1980, whereas Source 2 suggests that education's share declined through 1983. Source 3 shows a serious decline in enrollment rates in 1980, whereas Source 4 describes only a marginal decline in 1981. Variation between the sources for a single yenr is eight percentage points. Discrepancies may result from differences in definitions or in methods of extrapolation. Conclusions in different sources may therefore contradict one another, and it is difficult to judge which data are more likely to be correct.

Education shar-e of total goverment budget .(% 1975 1977 1979 1980 1981 1982 1983 1984 1985 Source 1 11.5 13.8 12.1 8.9 11.0 13.0 15.2 Source 2 17 (75-77) 14.2 (78-80) 13.8 (81-83) 142 (84-85)

# Gross pnmary enrollment rat _L_

1970 1975 1980 1981 1982 1983 1984 1985 1986 Source 3 90 97 90 93 .9699 97 Source 4 98 97 98 101 103 Sources: 1. Auerhan, et.al. (1985); 2. Colclough and Lewin (1990); 3. World Bank (1987a); and 4. World Bank (1988e). ._________________.. .

2.09 Data Availability: An important obstacle to assessing the effects of adjustment policies on the educatio:l sector is the lack of disaggregate data. The literature on education and adjustment (both aggregate analyses and country studies) most frequently provides data on public education financing levels and enrollment rates. Data on quality and equity indicators are much more scarce, and information on factors that influence the demand for education is virtually nonexistent, 2.10 Pata Aggregation: Many sources compare indicators for country groups to show crosscountry trends, but data coverage is inconsistent. First, the selection of indicators varies among reports. Second, the time period for which data are provided vary: some reports give annual figures, others give averages for periods of between two and ten years, still others average figures for "before" and "after" adjustment. Third, reports group countries alternatively by region (World Bank regions or geographic), by income level, or by differentiating "adjusting" from "non-adjusting" countries. Colclough and Lewin (1990) provide education sector statistics and trends for the 35 countries with gross primary 16
|**_Table 13: Average casesper_**<br><br>**_1000glob_**<br>|**_ally a_**<br>**_ee_**<br>**_fter s_**<br>|**_ding_**<br>**_Ha_**<br>**_in_**|**_noi_**<br>**_tober_ **(Colizza_et al_. 2007)<br>**_in Oc_**|
|---|---|---|---|
|_%)_<br>Antiviralpolicy scenario (_pAV= 50_|_0_<br>_.5_<br>_R_<br>_= 1_|_R_<br>_9_<br>_0_<sup>_= 1._</sup>|_R_<br>_3_<br>_0_<sup>_= 2._</sup>|
|Maximalcoverage|0.01|35|184|
|Limited coverage, uncooperative|16|222|397|
|Limited coverage, cooperative‐I|0.01|126|305|
|L<br>ooperative–II<br>imited coverage, c|0.01|97|287|

_Discussion_ : Containment success is poor across the distribution scenarios for any _R0_ greater than 1.5. If sufficient stockpiles of antivirals are available (2‐6% of the global population) and a cooperative strategy is maintained with timely redistribution of the stockpiles of prepared countries, then mitigation even at a _R0_ of 1.9 is possible. Based on details not shown in Tables 12‐13, the cooperative system results in delays of the peak of the global pandemic for as much as a year for _R0_ between 1.5‐1.9, which is sufficient for the development of a well‐matched pandemic vaccine. The amount of redistribution of antiviral stocks involved is modest, reducing the availability of stockpiles in the prepared countries to 8‐9% of the population from the prior 10%.

34
# **Table A2: Robustness to Including Judge Fixed Effects**

|||Non-win<br>|ners<br>|||Winne<br>|rs<br>||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Survival|(2)<br>Employment|(3)<br> Sales|(4)<br>Profits|(5)<br>Survival|(6)<br> <br>Employment|(7)<br> Sales|(8)<br>Profits|
|**Panel A: Without Judg**|**e Fixed Effec**|**ts**|||||||
|Business Plan score|-0.0002|-0.0021|-0.0056|<br>-0.0041|0.0000|0.0897**|0.0011|<br>0.0009|
||(0.0014)|(0.0168)|(0.0196)|<br>(0.0180)|(0.0006)|<br>(0.0310)|(0.0127)|<br>(0.0127)|
|Sample Size|1,078|1,078|1,059|1,059|1,062|1,062|1,047|<br>1,047|
|R<sup>2</sup>||0.014|0.039|0.036||0.023|0.013|0.013|
|**Panel B: With Judge F**|**ixed Effects**||||||||
|Business Plan score|-0.0012|0.0015|-0.0135|<br>-0.0150|-0.0004|<br>0.1253***|-0.0059|<br>-0.0051|
||(0.0016)|(0.0175)|(0.0217)|<br>(0.0197)|(0.0008)|<br>(0.0338)|(0.0145)|<br>(0.0142)|
|Sample Size|1,067|1,078|1,059|1,059|963|1,062|1,047|<br>1,047|
|R<sup>2</sup>||0.028|0.058|0.058||0.051|0.053|0.046|

Robust standard errors in parentheses, *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. See Appendix A.1. for variable definitions. Employment, Sales and Profits are unconditional outcomes, coded as zero for non-operating firms
access and quality remain unequally distributed. These challenges are aggravated in the context of diminishing financing after 2014. Healthcare outcomes, such as maternal and child health, remain relatively low in comparison to other countries in the region. For instance, malnutrition rates remain higher than in Peru.<sup>9</sup>

# **4 Data**

We merge data from two sources: Ecuador’s Quarterly Labor Force Survey (known as ENEMDU), which provides the data for the labor market outcomes at the regional level, and novel data on the geographical distribution of the Venezuelan population in Ecuador based on mobile phone records by _Telefonica Ecuador_ .

## **4.1 Household Survey Data: ENEMDU**

ENEMDU is the official labor market survey for Ecuador.<sup>10</sup> It is a nationally representative household survey that collects information on individual characteristics, employment status and sources of income for individuals living in Ecuador.

Our analysis uses data for the fourth quarter of the ENEMDU for years 2008-2019, which we aggregate to the _canton_ level.<sup>11</sup> But our regression analysis focuses on the period 2013-2018, which provides a tighter window around the onset of the arrival of Venezuelans into Ecuador.

Our analysis is based on the working-age population, defined as all individuals age 15 or above.<sup>12</sup> We analyze a wide range of canton-level labor market indicators: the participation rate, the employment-to-population ratio, and two indicators of the quality of employment (the rate of adequate employment and the rate of Informality). In addition, we also examine effects on monthly earnings and on hourly wages.<sup>13</sup>

> 9Likewise, social assistance in Ecuador remains below the 2012 level. To this date more than 50% of the population considered to be in extreme poverty lacks coverage by the system and social integration of the poor population is still very limited.

> 10 _ENEMDU_ stands for _Encuesta Nacional de Empleo, Desempleo y Subempleo_ , that is, _National Survey on Employment, Unemployment and Underemployment_ . For details on the methodology used in the ENEMDU survey, see ENEMDU Methodology.

> 11We eliminate the 21 smallest _cantons_ in terms of population. Specifically, we drop cantons with fewer than 4,000 individual observations in the ENEMDU. As a result, our analysis is based on 200 of the 221 overall number of _cantons_ . The data for 2019 corresponds to the second quarter, which was the most recent one available at the time of conducting the analysis.

> 12In constructing these indicators we use individual observations for all (working-age) individuals, regardless of nationality or country of birth.

> 13Our definition of the rates of adequate employment and informality use the working-age population

8
# Methodology and Findings for the Exposure Analysis of the Chinese Wastewater Sector to Flooding and Earthquakes Hazards

**Xi Hu**<sup>**1,2,3**</sup> **, Raghav Pant**<sup>**2**</sup> **, Conrad Zorn**<sup>**2**</sup> **, Weeho Lim**<sup>**2,4**</sup> **, Elco Koks**<sup>**2,5**</sup> **, Zhimin Mao**<sup>**6**</sup>

1. Labor and Worklife Program, Harvard Law School, Harvard University, Cambridge, MA, USA

2. Environmental Change Institute, University of Oxford, Oxford, UK 3. National Bureau of Economic Research, Cambridge, MA, USA 4. Chinese Academy of Sciences, Beijing, China 5. Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, The Netherlands 6. The World Bank, Washington DC, USA

**JEL classification:** L95, O18, Q54

**Keywords:** infrastructure; water; sanitation; climate change
1 Introduction “Do countries with lower barriers to international trade experience faster economic progress? Few questions have been more vigorously debated in the history of economic thought, and none is more central to the vast literature on trade and development.” (Rodriguez and Rodrik 1999) The relationship between liberalization and economic performance has been strongly debated since Adam Smith and it is still today at the center of disputes. Even more than academic economists, policy makers consider the subject central to their concerns. In particular developing countries, that during the 1990s have undergone profound liberalizations<sup>1</sup> with the objective of improving their economic performance and accelerating their growth rates, have recently started to put these policies under discussions because of mixed results.<sup>2</sup> . In this study we will try to explain why liberalization can result in outcomes that are different depending on the context and, more in particular, are uneven for heterogenous firms.

In this paper we develop a model that shows how the impact of liberalization can be asymmetric for different types of firms and test its main predictions focusing the liberalization that Mexico underwent during NAFTA. This provides an excellent opportunity to study a deep process of liberalization and integration with the global economy that increased the pressures from import competition and entry threat of foreign competitors. We will analyze it and show that, consistent with our theoretical model, the impact of NAFTA was highly asymmetric on different types of firms and, while productivity growth “on average” is larger for those firms in sectors where tariffs dropped more, at the same time, this effect is weaker for plants more distant from the production technology frontier.

A number of previous studies using firm-level<sup>3</sup> data have already shown that an increase in import competition tends to have a positive impact on economic performance (Tybout and Westbrook 1995, L ́opez-C ́ordova 2003, Pavcnik 2002, De Hoyos and Iacovone 2006, Nickell 1996, Fernandes 2007). However, these studies normally focused on the “average” impact of liberalization and implicitly as1As discussed in a recent World Bank report over the last few decades almost all developing countries have liberalized their trade regimes (Bank 2006)

> 2“In many developing countries, there has already been a de-industrialisation process in which lowering of tariffs has led to imports displacing local industries” (Martin Khor 1999, Director of Third World Network)

“Some trade critics are bothered by the disappointing performance of Latin America since it slashed tariffs in the 1980s and 1990s while more protectionist China and Southeast Asia sped ahead” (“Pains from free trade spurs second thoughts”, Wall Street Journal, 30 March 1997).

> 3In the paper we refer interchangeably to “firm” and “plant”, however it is important to emphasize that in our empirical analysis the unit of observation will be the plant consistently with the unit of observation of our data.

2
Table A3: Summary statistics for the variable used (2009‐2016)

|**Variable**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Cambodia**||||||
|Index * ln(D/L)|324|‐1.03|0.26|‐1.64|0.14|
|h1|316|0.14|0.35|0.00|1.00|
|h5|300|0.34|0.48|0.00|1.00|
|e6|86|0.22|0.42|0.00|1.00|
|h8|306|0.11|0.32|0.00|1.00|
|**China**||||||
|Index * ln(D/L)|2,492|‐2.16|1.15|‐4.13|0.97|
|h1|2,543|0.46|0.50|0.00|1.00|
|h5|1,535|0.65|0.48|0.00|1.00|
|e6|1,529|0.24|0.43|0.00|1.00|
|h8|1,525|0.42|0.49|0.00|1.00|
|**Indonesia**||||||
|Index * ln(D/L)|2,307|‐1.16|0.48|‐2.39|0.56|
|h1|1,142|0.10|0.30|0.00|1.00|
|h5|1,139|0.17|0.38|0.00|1.00|
|e6|1,877|0.21|0.41|0.00|1.00|
|h8|1,144|0.05|0.22|0.00|1.00|
|**Malaysia**||||||
|Index * ln(D/L)|834|‐0.71|0.33|‐1.29|0.30|
|h1|830|0.07|0.25|0.00|1.00|
|h5|827|0.51|0.50|0.00|1.00|
|e6|416|0.23|0.42|0.00|1.00|
|h8|831|0.20|0.40|0.00|1.00|
|**Mongolia**||||||
|Index * ln(D/L)|627|‐0.79|0.29|‐1.30|0.28|
|h1|326|0.26|0.44|0.00|1.00|
|h5|325|0.42|0.49|0.00|1.00|
|e6|160|0.18|0.38|0.00|1.00|
|h8|327|0.19|0.39|0.00|1.00|

32
HOowever, as Fer roni **ainld Kanhur** (19')0) poinit out, goi ug, be vul I( t his generatl conclusion **requ;** res more **inat** s is. The iitt **eract** i ori; **between** thedifferent di **me ns** ions of basic needs achievements cre st i II **ur Ide rirtsearchted, aiid** we lack convintcinrg quanti fication ot the impact of extra public expeniditure in one category rather than arnotheer. Ulti mately, ret;ouic(-! are0 scarce, **and** tradeoffs have to be faced. Is the marginial dol lir better Speclt on primary health care or b.asic edu(catiotn? Withlin primary health care, is t!e marginal dollar better spenlt on personnel or on drugs? Would it be better to have cost recovery (risking some loss<sup>of access) and use the money raised to</sup> ensure drug availability, rather<sup>than have no cost recovery but limited drug</sup> availability? Design issues within<sup>specific programs become important, as do</sup> the political questions raised<sup>by targeting--how will the previous</sup> beneficiaries of social expenditures<sup>react, and can their opposition be</sup> overcome?

This basic question, and others,<sup>arise also in the targeting of income</sup> transfer programs that can act<sup>as safety nets. While these programs are more</sup> prevalent in developed countries,<sup>they do make an appearance in developing</sup> countries also. The most obvious<sup>example are food subsidies, and here again</sup> the WDR and the HDR arrive at<sup>a consensu3. Thus the HDR notes,</sup> Food subsidies can do much to stabilize<sup>food prices, transfer</sup> income to the poor and maintain political<sup>and social stability...</sup> The design of food subsidies always<sup>demand care. The budgetary</sup> burden should be kept manageable.<sup>Incentives for food production</sup> should not be discouraged. Targetting<sup>should ensure that benefits</sup> reach the poor to make the programme<sup>cost-effective. To reduce</sup> costs the subsidies need to be targetted<sup>towards low-income</sup> households -- by subsidizing food<sup>mainly consumed by low-income</sup> households or sold in low-income areas
(Tasselled Cap Brightness index, TCB), as measured by NASA’s MODIS system <u>(http://modis.gsfc.nasa.gov/); aridity, as measured by the CGIAR project (http://csi.cgiar.org/Aridity/);</u> brightness of night‐time lights measured by the DMSP OLS project (https://ngdc.noaa.gov/eog/); and a delineation of urban versus rural areas constructed by the Nelson Institute (https://nelson.wisc.edu/sage/data‐and‐models/schneider.php). A final covariate was constructed based on physical access to improved water sources. This covariate was created for the current study by, first, using a ‘friction surface’ that estimates the time required to traverse each 1×1km pixel across Tanzania.<sup>34</sup> This varied according to the type of landcover, topography, and the layout of the road and wider transport network across the country. The friction surface was then used in a least‐cost path algorithm to estimate the likely journey time from the center of each 1×1km pixel to the nearest functioning water point (such as a well, bore hole, or pump). The latitude and longitude, as well as level of functionality, of every such water point in Tanzania were recorded as part of the Water Point Mapping project undertaken by The Ministry of Water and Irrigation, Government of Tanzania.<sup>35</sup>

# _Model‐based Geostatistics framework_

The predictive approach used in this study to generate a fine‐scale map of stunting prevalence across Tanzania was based on a body of statistical theory known as model‐based geostatistics (MBG)<sup>36,37</sup> . MBG models are a class of generalized linear mixed model, with an approximation of a multivariate Normal random field (i.e., a Gaussian process) used as a spatially autocorrelated random effect term. The proportion of children who were recorded as being stunted , _Y_  _xi_ , at each location in Tanzania was modeled as a transformation of a spatially structured field superimposed with additional random variation . <mark>The count of individuals stunted from the total sample of in each survey cluster was modeled as a conditionally independent binomial variate given the unobserved underlying</mark> _Y_  _xi_ value. The spatial component was represented by a stationary Gaussian process with mean and covariance . <mark>The unstructured component was represented as Gaussian with zero mean and variance . Both the inference and prediction stages were coded using the INLA framework,</mark><sup>38</sup> <mark>primarily in the R programming language.</mark><sup>39</sup> The mean component was modeled as a linear function of the _n_ geospatial covariates described above,<sup>,wherewas a vector consisting of a constant and the</sup> covariates indexed by spatial location , and was a corresponding vector of regression coefficients. Each covariate was converted to _z_ ‐scores before analysis. Covariance between spatial locations was modeled using a Matern covariance function: 4