rafmacalaba/datause-encoder-data huggingface dataset:
colonial empire, is located in the South of the country. Overall, therefore, our estimates suggest that the e¤ects of migrant networks on export participation tend to be more pronounced among the ...rms located in the region where such networks are likely to be stronger, in line with the model presented in section 2.

# 6.2 Networks and export intensity

We now turn to the e¤ect of migrant networks on the export intensity of each ...rm, conditional on exporting to that market. From (8), we specify the revenue estimatingequation as: where xjc<sup>fob</sup> is the revenue obtained by ...rm j in country c, and the remaining regressors have the meaning de...ned above.

The theory we adopt and the empirical analysis of the previous section suggest that ...rms self-select into export markets. An important di¢ culty in estimating (12) is therefore that non-zero export revenues are only observed for the subset of destinations that ...rm j serves. Due to sample selection, OLS estimation on non-zero export revenues may deliver biased estimates on the e¤ect of migrant networks on export intensity.

We will employ two alternative methods to account for potential selection bias in the estimation of (12). First, we will adopt the Tobit procedure proposed by Crozet et al. (2011). In the context of a similar theoretical framework, Crozet et al. (2011) show that the minimum value of positive ...rm-level exports observed in country c can be used as a theory-grounded censoring point for xjc<sup>fob</sup> in Tobit estimation. Replacing xc(j) with xc<sup>fob</sup> (j)� c in (6), we see that ...rm j ...nds it pro...table to serve country c if xc<sup>fob</sup> (j) > �Fc=� c. For each destination, there is therefore a minimum value of xc<sup>fob</sup> (j) that is consistent which non-negative pro...ts. Under the assumption of a log-normal distribution for ac(j), this yields a Tobit structure where the minimum value of xc<sup>fob</sup> (j) in country c serves as a theoretically-consistent censoring point.

The main advantage of the Tobit estimator is that the correction procedure relies solely on the link between theory and observed trade ‡ow data. For robustness, we will also use a Heckman correction approach similar to Helpman et al. (2008). To avoid identi...cation based on functional form, the Heckman selection model requires identifying at least one variable that a¤ects export participation but not export intensity. From (7) and (8), we see that this requirement is ful...lled by a variable that in‡uences solely ...xed costs of exporting. In line with Helpman et al. (2008), we will use measures of entry costs based on World Bank data that may plausibly satisfy this requirement. As a benchmark, we will also present the results of OLS estimates of (12) on non-zero export revenues.

14
Policy Research Working Paper 5618

# **Abstract**

Using household survey data, this paper estimates the mortality impact of improved water and sanitation access in order to evaluate the potential contribution of water and sanitation investment toward achieving the child mortality targets defined in Millennium Development Goal 4. The authors find that the average mortality reduction achievable by investment in water and sanitation infrastructure is 25 deaths per 1,000 children born across countries, a difference that accounts for about 40 percent of the gap between current child mortality rates and the 2015 target set in the Millennium Development Goals. According to the estimates, full household coverage with water and sanitation infrastructure could lead to a total reduction of 2.2 million child deaths per year in the developing world. Combining this analysis with cost data for water and sanitation infrastructure, the authors estimate that the average cost per life-year saved ranges between 65 and 80 percent of developing countries’ annual gross domestic product per capita. The results suggest that investment in water and sanitation is a highly cost-effective policy option, even when only the mortality benefits are taken into consideration. Taking into account the additional expected benefits, such as reduced morbidity, time spending, and environmental hazards, would further increase the benefit-cost ratio.

This paper is a product of the rospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at isabel. guenther@nadel.ethz.ch.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
## **ANNEX** Ii: **UST** OF **REPORTS REVIEWED**

# Africa

|**Botswana**|6031-BT: Public Expenditure and Development in Botswana (June 3,<br>1986)|
|---|---|
|**Burldna Faso**|Cogts, Financement et Politique de l'Education au Burkina Faso (May<br>7, 1987)|
|**Camer.oon**|7451-CM: Adapting Public Finances to a Changing Macroeconomic<br>Environment (October 6, 1988)|
|**Central Afrin**|Public Expenditure Review (October 23, 1989)|
|**Cote**dvoire|6051-IVC: The C6te d'Ivoire in Transition: From Structural<br>Adjustment to Self-Sustained Growth (March 9, 1987)|
|**Ghana**|5824-GH: Priorities for Public Expenditures, 1986-1988 (August 13,<br>1985)|
||Possibilites Budgetaires et Besoins Financiers du Secteur de l'Education<br>en R6publique du Guinee, J. Orivel et J. Perrot (November 1988)|
|Kenga|7508-KE: Public Expenditure Issues (April 14, 1989)|
|Lnsoth|7243-LSO: Public Expenditure Priorities in Lesotho (September 28,<br>1988)|
|**Madagascar**|7803-MAG: Public Expenditure, Adjustment and Growth (December<br>20, 1989)|
|**Malawi**|7281-MAI: Public Expenditure Review (February 10, 1989)|
|Mali|Coaits, Financement et Efficacite des Enseignements Superieur et<br>Secondaire au Mali (November 1988)|
|**Moafmbique**|7615-MOZ: Public Expenditure Review (September 5, 1989)|
|Nier|Cofts, Financement et Politique de l'Education au Niger (July 13,<br>1988)|
|Nigeia|Education Sector Expenditure Review (July 1985)|

64
for adaptation to climate change have been foreclosed.<sup>14</sup>

# **4 Climate Impacts on Prices**

## **4.1 Climate data**

The NSS household data that we describe below were collected using clustered sampling, but the precise location of primary sampling units has not been released. Thus, the district is the lowest level of geographic disaggregation at which we can link household and climate variables. By 2003, India had 576 districts, excluding those in Lakshadweep, Andaman and Nicobar Islands, and in the mountainous northern state of Jammu/Kashmir, with an average land area of around 5000 km<sup>2</sup> .

We use monthly temperature readings from 370 weather stations throughout India with at least 20 years of records during 1951-1980. These data are available from the India Meteorological Department (IMD), which also provides a high resolution daily gridded (on 1<sup>_◦_</sup> latitude by 1<sup>_◦_</sup> longitude cells) rainfall dataset covering 1951-2003 based on daily records from more than 1800 weather stations.<sup>15</sup> Normal precipitation during the 1960-2000 period for a district are interpolated from the 296 cells covering India using the proportion of the district’s area in each cell as weights. District-level temperature normals are an average of records from the three nearest stations to the district’s geographical center, weighted by the inverse of the squared distance to the district centroid.

Figure 1 illustrates the tremendous variation in climatic conditions across India, ranging from the desert-like western Rajasthan to the eastern foothills of the Himalayas, one of the wettest regions on earth. Most precipitation falls during the southwestern monsoon, extending from June through September. The monsoon plays a critical role in determining the success of the kharif (summer season) harvest; rabi (winter) season production depends to a much greater extent on irrigation.

## **4.2 Land values and climate**

Our cross-sectional Ricardian analysis uses reported land values collected from a farm household survey as a measure of _π_ . In principle, the value of land should equal the expected

> 14Ideally, one would want a land productivity panel spanning many decades over which _climate_ has actually changed, and changed to different extents across regions, while the menu of technologies has remained relatively stable. Such data are, at present, unrealistic.

> 15Since the IMD release does not extend past 2003, weather data for our panel analysis are taken from the Center for Climate Research, University of Delaware. Though available at a higher grid resolution (0 _._ 5<sup>_◦_</sup> _×_ 0 _._ 5<sup>_◦_</sup> cells), these data are interpolated using much fewer weather stations than those of IMD.

10
**Table 4: Philippine’s export and import survival by products and destination during the lockdown**

|||**Exports**<br>|||
|---|---|---|---|---|
|**Category**<br>||**Product**<br><br>|**Destin**|**ation**|
|**Survival dynamics**|**Intermedi**|**ate**<br>**Other Products**|**Top ten**|**RoW**|
|1<sup>st</sup> month<br>|51%|55%|47%|40%|
|6<sup>th </sup>month<br>|25%|24%|19%|12%|
|12<sup>th </sup>month<br>|24%|22%|17%|10%|
|Mean (months)|3.2|3.1|2.6|2|
|Median (months)|1|1|1|1|
|||**Imports**|||
|1<sup>st</sup> month<br>|64%|65%|51%|38%|
|6<sup>th </sup>month<br>|39%|39%|20%|10%|
|12<sup>th </sup>month<br>|37%|37%|18%|8%|
|Mean (months)|4.4|4.5|2.8|1.8|
|Median (months)|2|2|1|1|

Note: RoW is the rest of the world. Source: Authors’ compilation using PSA data

# **4 Methodology**

An event-study approach is employed to establish the effect of COVID-19 lockdown policies on international trade in the Philippines. This approach is preferred over impact-evaluation estimators such as the difference-in-differences (DiD) and Propensity Score Matching (PSM). DiD and PSM compare the outcome before and after a treatment has been imposed on a control and treatment group. Given that all countries in our sample imposed lockdowns, we do not have a non-treated control group (Majune and Addisu, 2021). Another advantage of the event-study approach is that it addresses concerns of lockdown anticipation. Since East Asia was the first epicenter of COVID-19, most countries started imposing restrictions earlier than the rest of the world (see Figure 1) which could have triggered the reaction of exporters and importers in the Philippines in anticipation of the imposition of lockdowns in partner countries. The event-study also enables us to observe the reaction of international trade to lockdown policies at high frequency (months) which often changed depending on the spread/wave of the virus in a country. The event-study model is specified as follows: 19
# **5. Returns to Education**

The returns to education can be estimated using the human capital model (Becker, 1975 and Mincer, 1974), wherein one can estimate the returns to additional years of schooling by using the wage data collected from persons who have different levels of education. The model assumes that wage earners are paid according to their marginal product, which rises as more human capital is accumulated. Using the earnings function approach, we can write the wage earnings (W), a function of years of schooling(S), years of experience(E) and other factors.

ln(wi) _=aO+CClSi+ Uc2 Ei+cx3E2 +E_ (1) Years of schooling can be interpreted as an aggregate measure of human capital obtained from formal schooling, while experience is an indicator of human capital acquired while employed (or on the job training). One can interpret Cal as the private rate of return to schooling, based on mincer's earnings function. If we assume that the cost of additional schooling is simply the wages foregone (Ws-,), then the private rate of return (CC ) can be shown to be equal to the annual increase in income (W, - Ws-,) divided by the cost of investment (which is the wages foregone, W,_l). A more general variant of (1) allows different levels of schooling (e.g. primary, secondary and higher) to have different private returns, and can be written as: _ln(wi)= co+acpSpi +aSs,±i+C,Si +a2 Ei+ 3 E E+ui_ (2) where Sp;, S,i and Std are the number of years in primary, secondary and tertiary education ith respectively for the i individual.

**Table 29: Mean Wage Earnings by Educational level in Ghana (in cedis).**

|Education level|1|987|1|991|
|---|---|---|---|---|
||Earnings|mean<br>schooling|earnings|mean<br>schooling|
|No education|72398.36||213786.6||
|Primary|57152.43|3.1|200805.01|6.1|
|Middle|62512.13|6.0|264004.8|9.2|
|Secondary|90653.82|11.1|357161.7|13.6|
|Higher|141900.6|.20.9|591255.8|19.7|
|All|84462.72|7.0|297584.9|7.1|

Mean earnings estimates by educational level are presented in Table 29. It shows significant earnings differential by level of education. In 1991, workers who have completed tertiary level education earned 2.7 times more than do illiterate workers. In an attempt to explain the earnings variance of the sample we estimate earnings function, as shown in Table 30. As discussed earlier, the model is of the Mincerian type with continuos years of schooling and experience as the independent variable. The signs of the 42
**Figure 5: Competition in SSA (1990, 2008)**

<!-- Start of picture text --> Sub-Saharan Africa (1990) Sub-Saharan Africa (2008)<br>Competition No competition Competition No competition<br><!-- End of picture text --> Source: Delpeuch and Leblois (2011) SSA includes Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Democratic Republic of the Congo, Gambia, Ghana, Guinea, Guinea-Bissau, Ivory Coast, Kenya, Madagascar, Malawi, Mali, Mozambique, Niger, Nigeria, Senegal, Sudan Togo, Uganda, United Republic of Tanzania, Zambia and Zimbabwe.

23
# **_5.1. Disaster Impacts and Pre-Disaster Assets_**

# **5.1.1. Bangladesh**

In Bangladesh we have three-round panel data in 1998-1999 collected immediately after the 1998 flood with information on both the number of school days and days actually attended in school. Therefore we can construct the proportions of days attended in rounds 1 to 3 (within a year). In the analysis we investigate changes in the proportion of days attended in school in a year. Age and female dummies are included in all specifications. Union fixed effects and age and female dummies control for variations in trend.

# Table 2 to be inserted

Table 2 reports estimation results on the change in school attendance over a year using alternative flood exposure measures such as water depth, the number of days covered by water, repair cost, and the number of days evacuated from home.<sup>13</sup> Columns 1 to 4 show that repair cost significantly reduces school attendance, but other measures are insignificant. In Columns 5 to 8, we include interaction terms with land size and the maximum education in the household, to take into account the possibility that households with higher levels of physical and human resources are better able to cushion the effects of flood. In the estimations with water depth, the number of days covered by water and repair cost, holding land helps to mitigate the negative impacts of the flood. In the specification using repair cost, household education significantly mitigates the impacts of flood. The direct effect on school attendance is significantly negative only in the case of repair cost. Overall the impact seems smaller among girls, but the effect is insignificant in many specifications.

# Table 3 to be inserted

From Table 3, we summarize empirical results on school progression measured by change in years of grades completed from 1998 to 2004, which captures long-term impacts of the 1998 flood. In Table 3, we use the four measures of the 1998 flood separately to assess the impacts. Results show that the number of days evacuated from home has a significant and negative effect on change in school attendance. This is in contrast to the finding on transition from pre-school to school stages (Yamauchi, Yohannes and Quisumbing, 2008).

# Tables 4 and Figure1be inserted

Columns 1 to 4 in Table 4 include interactions with total asset value. Consistent with the notion that households with more resources are better able to weather shocks, asset holding helps mitigate the negative impact of the 1998 flood on school progression

> 13Potentially repair cost and the number of days evacuated from home are endogenous, being correlated with schooling shocks and asset holding. In preliminary analysis, instrumenting these by water depth and the number of days covered by water did not singnificantly change the results. This is because we use the first differenced specification, wiping out the time-invariant effect of household assets.

12
mar & Vollmer, 2011). The authors find access to improved sanitation reduces diarrhea by 2.2 percentage points (pp) corresponding to a 17 percent drop in prevalence. Another study (Bose, 2009) using similar methods from Nepal looks at access to sanitation using a 2006 Demographic and Health Survey (DHS) finding reductions of 5 percent from mean prevalence of 8 percent, a substantially larger relative reduction. A third study, Spears (2013), estimates the effect of open defecation in anthropometric measures between and within countries, where it finds positive benefits from externalities and direct effects of eliminating open defecation. Finally, Gunther and Fink (2010) analyze data from 172 DHS surveys and find that households having flush toilets have 13 percent lower odds of diarrhea than households without - in line with the estimates from Kumar and Vollmer (2012) but much less than estimates from meta-analyses. Moreover the study finds the benefits at the cluster level<sup>5</sup> are twice as large as those at the household level, suggesting there may be positive externalities to improved sanitation.

# **4 Model**

In this paper we seek to answer two questions: (i) what is the private benefit of a household’s access to sanitation infrastructure on early childhood health outcomes?; and (ii) what is the benefit produced through an external effect, or externalities, of a neighbor’s access to this sanitation infrastructure? In this paper we define external effect or externality as the change in final utility, in this case health status, originated from the access to sanitation infrastructure by others in the surroundings.<sup>6</sup> We define this surrounding as a village, which we assume ensures a minimum level of isolation from other surroundings. In order to answer these important questions, especially the role of externalities of sanitation access, we propose a theoretical model presented in Equation (1) where a health outcome _Dijk_ of a child _i_ of the household _j_ that is in the village _k_ is a function of household sanitation access _Sjk_ , an externality _Rk_ produced by his neighbor’s sanitation access, and a vector _X_ of child and household characteristics. _ijk_ We define the externality _Rk_ as the ratio of households in the village with access to sanitation _Sjk ∈_ (0 _,_ 1). The rationale behind this definition is that lack of full sanitation coverage could cause inter-household contamination (as described in Section 3). So it is natural to

> 5A cluster in the DHS is typically a village for rural areas and district for urban areas.

> 6This follows the classical defitinition of externality in economics as a change in the utility level produced by other actions.

7
The survey reveals that around 17 percent of adults who applied for a traditional account found it difficult to obtain the required documents, versus 11 percent of adults who applied for JDY accounts (Figure 7). Although they were significantly less likely to report documentation woes, we expected a much lower figure for JDY applicants. The JDY policy was designed to make it easier for unbanked adults to get an account by easing documentation requirements. To qualify, a person only needs to produce a 12-digit identification number issued by the government, either in person, or electronically. The question remains whether eased documentation requirements will encourage adults to open accounts (see Box 2). In some states, the share of JDY applicants citing documentation costs was significantly higher, including Uttar Pradesh (26 percent) and Chattisgarh (31 percent).

# **<mark>Box 2: Are easy documentation requirements enough to encourage account ownership?</mark>**

The JDY policy has attempted to make documentation requirements relatively lenient. The only documentation required to open a JDY account is a biometric 12-digit individual identification required to open a JDY account is a biometric 12-digit individual identification number, also called an _Aadhaar_ number, issued by the Unique Identification Authority of India on behalf of the government. The government's goal was to have universal coverage, and as of mid-2016, 93 percent of adults in India had an _Aadhaar_ number.<sup>21</sup> For adults without an _Aadhaar_ number, at least one other official document- voter ID card,<sup>22</sup> driving license, PAN card, passport, or MGNREGA<sup>23</sup> card- can be submitted to open a JDY account. If none of these documents are available, a temporary “small” account is opened for a period of one year. Upon successful submission of documents, these accounts are converted to permanent accounts.<sup>24</sup>

> 21 Press Information Bureau, 2016.

> 22 Permanent Account Number (PAN) is a unique, 10-character alpha-numeric identifier, issued to all judicial entities identifiable under the Indian Income Tax Act, 1961.

> 23 Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is an Indian labor law and social security measure that aims to provide at least 100 days of wage employment in a financial year to every household whose adult members volunteer to do unskilled manual work.

> 24 “Small Account”, Official Pradhan Mantri Jan Dhan Yojana website

> (http://pmjandhanyojana.co.in/small- account-pmjdy/ ).

21