rafmacalaba/datause-encoder-data huggingface dataset:
year) series. An adverse temperature event is similarly defined. The long series covers the available years 1971-2001 for the 2002 year and the years 1981-2011 for the 2012 year.

Next, we define a measure of weather adversity. Let _ai,τ_ takes the value of 1 if an adverse event happened at time _τ_ in district location _i_ . _Ni,t_ counts the total number of events within district boundary _i_ during the five years under consideration (this is usually 5, unless there are missing data). Then, the percentage of adverse events (for either temperature or rainfall) is defined as:

# **Agro-climatic zones, roads and yields: ICRISAT**

We obtained information on the agro-climatic zone from the ICRISAT mesolevel database.<sup>30</sup> We use a standardized measure of crop water requirements, the yearly evapotranspiration (measured in mm), and the length of the growing period (defined as the period when normal precipitation exceeds 0.5 ETo).<sup>31</sup> These time-invariant data are presented using the 2011 district census boundaries. We imputed missing values in these ICRISAT data using information on the nearest neighbour using 2011 district census boundaries.<sup>32</sup> In addition, we obtained the 2001 and 2011 ICRISAT time-variant data on the total road length and the number of markets in the district (in km). For both years, we also obtained measures of crop (output) price (Rs/100 kg), crop

> 30See: http://data.icrisat.org/dld/

> 31ICRISAT records what it terms the normal monthly and annual potential evapotranspiration. Most documents refer to this concept as the ”reference crop evapotransporation” denoted ETo. This refers to a hypothetical crop and relies on climatic inputs only, not taking into account the specific crop or soil conditions. See Allen et al. 1998. We use the monthly values and compute an annual value from this.

> 32There are 421 districts in the ICRISAT file, compared to a total of 640 in India 2011 NSS. So about 35% are missing. However, our final dataset has about 575 districts (because we don’t use all the states), so according to this 27% districts are missing in ICRISAT.To deal with the missing, we assigned the value of the nearest non-missing neighbor out of 4 closest neighbors.

55
WPS4195

**USING THE GLOBAL POSITIONING SYSTEM IN HOUSEHOLD SURVEYS FOR BETTER ECONOMICS AND BETTER POLICY***

John Gibson, _University of Waikato_ David McKenzie, _Development Research Group, World Bank_

# **Abstract**

Distance and location are important determinants of many choices that economists study. While these variables can sometimes be obtained from secondary data, economists often rely on information that is self-reported by respondents in surveys. These self-reports are used especially for the distance from households or community centers to various features such as roads, markets, schools, clinics and other public services. There is growing evidence that self-reported distance is measured with error and that these errors are correlated with outcomes of interest. In contrast to self-reports, the Global Positioning System (GPS) can determine almost exact location (typically within 15 meters). The falling cost of GPS receivers (typically below US$100) makes it increasingly feasible for field surveys to use GPS as a better method of measuring location and distance. In this paper we review four ways that GPS can lead to better economics and better policy: (i) through constructing instrumental variables that can be used to understand the causal impact of policies, (ii) by helping to understand policy externalities and spillovers, (iii) through better understanding of the access to services, and (iv) by improving the collection of household survey data. We also discuss several pitfalls and unresolved problems with using GPS in household surveys.

_JEL codes:_ C81, O12, R20 _Keywords:_ Global Positioning System; Distance; Location; Survey Measurement; Networks; Externalities.

# World Bank Policy Research Working Paper 4195, April 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We are grateful to Kathleen Beegle, Chris Bennett, Piet Buys, Geua Boe-Gibson, Alan de Brauw, Uwe Deichmann, John Hoddinott, and Ben Olken for helpful comments and advice. Financial support from Marsden Fund grant UOW0503 is gratefully acknowledged.
# **APPENDIX A: RESULTS FROM BASELINE SCENARIO**

# **_<u>Installed Generation Capacity (GW)</u>_**

- Coal would remain as the predominant energy resource to generate electricity in South Asia. It would account for more half of the total installed capacity in the region by 2040 because of its cost competitiveness as compared to other technologies to generate electricity;

## **Table A1a: Installed Capacity (GW)**

||**2020**|**2030**|**2040**|
|---|---|---|---|
|**Afghanistan**|1.1|6.0|7.0|
|**Bangladesh**|27.2|48.7|67.8|
|**Bhutan**|6.9|14.7|14.8|
|**India**|387.2|586.5|783.9|
|**Nepal**|3.4|7.9|9.3|
|**Pakistan**|57.5|108.7|173.0|
|**Sri Lanka**|5.8|9.1|11.7|
|**Total**|489|782|1067|

## **Table A1b: Installed Capacity Mix in 2020 and 2040 (%)**

||Year|Diesel|Gas<br>Turbine|Hydro|Solar|Coal|Wind|Combin<br>e Cycle|Biomass|Nuclear|
|---|---|---|---|---|---|---|---|---|---|---|
||**2020**|19.2%|54.1%|24.6%|0.0%|0.0%|2.2%|0.0%|0.0%|0.0%|
|**Afghanistan**|**2040**|3.0%|8.5%|88.1%|0.0%|0.0%|0.3%|0.0%|0.0%|0.0%|
||**2020**|14.8%|9.2%|1.5%|17.3%|19.4%|0.4%|37.5%|0.0%|0.0%|
|**Bangladesh**|**2040**|5.9%|3.7%|0.6%|6.9%|66.8%|0.1%|15.0%|0.0%|0.9%|
|**h**|**2020**|0.1%|0.0%|99.9%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Butan**|**2040**|0.1%|0.0%|99.9%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
||**2020**|0.3%|3.1%|15.5%|3.2%|59.9%|9.7%|3.8%|1.4%|3.0%|
|**India**|**2040**|0.1%|6.9%|12.7%|2.6%|57.7%|9.3%|4.2%|2.9%|3.7%|
||**2020**|1.6%|0.0%|97.8%|0.6%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Nepal**|**2040**|0.6%|0.0%|99.2%|0.2%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Pki**|**2020**|0.8%|4.3%|29.8%|0.0%|34.3%|16.7%|10.7%|0.8%|2.6%|
|**astan**|**2040**|0.3%|1.6%|22.1%|0.0%|45.4%|20.0%|6.0%|0.3%|4.3%|
||**2020**|3.1%|10.7%|32.7%|1.5%|16.3%|17.8%|17.1%|0.8%|0.0%|
|**Sri Lanka**|**2040**|0.8%|5.7%|17.6%|1.1%|37.1%|24.5%|12.3%|0.8%|0.0%|
|**Tl**|**2020**|1.2%|3.7%|18.4%|3.6%|52.7%|9.9%|6.6%|1.2%|2.7%|
|**ota**|**2040**|0.6%|5.7%|15.9%|2.3%|54.3%|10.3%|5.1%|2.2%|3.4%|

37
our modi...cations to standard TFP even more important if one wants to use TFP data to capture welfare. As is the case with taxes, welfare rises with increases in output holding inputs constant, even if there is no change in actual technology.

Note that we would get a qualitatively similar result if, instead of labor market rationing, we assumed that the household has monopoly power over the supply of labor, as in many New Keynesian DSGE models. As in the example above, we would need to construct the true labor share by using the household’s marginal disutility of work, which would be less than the real wage. In this environment, we would obtain the welfare-relevant labor share by dividing the observed labor share by an assumed value for the average markup of the wage over the household’s marginal rate of substitution between consumption and leisure.

The second example shows that there are situations where our previous results in sections 2 and 3 are exactly right and need no modi...cation, even with multiple wages and labor market rationing. Consider a household that comprises a continuum of individuals with mass Nt. Suppose that each individual can either not work, or work and supply a ...xed number of hours L<sup>b</sup> . In this environment, the household can make all its members better o¤ by introducing lotteries that convexify their choice sets. Suppose that the household can choose the probability qt for an individual to work. The representative household maximizes intertemporal utility: where qtU (Ct<sup>0;</sup> L � L<sup>b</sup> ) + (1 � qt)U (Ct<sup>1;</sup> L) denotes expected utility prior to the lottery draw. Ct<sup>0</sup> and Ct<sup>1denoterespectivelyper-capitaconsumptionoftheemployedandunemployedindividuals,</sup> while average per-capita consumption, Ct is given by: Assume that the individuals that work face an uncertain wage Pt<sup>L, which is observed only after labor</sup> supply decisions have been made. More speci...cally, individual wages in period t are iid draws from a distribution with mean EtPt<sup>L.Noticethat,bythelawoflargenumbers,thehouseholddoesnot</sup> face any uncertainty regarding its total wage income. Thus, the budget constraint for the household becomes: Pt<sup>IKt+Bt= (1 ��) P I</sup> t<sup>Kt�1+(1 + it) Bt�1+qtNtEtP L</sup> t<sup>Lb+P K</sup> t<sup>Kt�1+�t�P C</sup> t �qtCt<sup>0+ (1 �qt)C</sup> t<sup>1</sup> � Nt (54) Following Rogerson and Wright (1988) and King and Rebelo (1999),<sup>15</sup> we can rewrite the per-period utility function as:

> 15 In obtaining this result we use the fact that the marginal utility of consumption of the individuals in the household <u>1</u> needs to be equalized at the optimum. This implies: c<sup>0</sup> t<sup>= c1</sup> t � �(�L(�L)L<sup>b</sup> <u>)</u> � � :

22
dataset. Therefore, _CIF_ , the measure of aggregate capital inflows by foreigners, is equivalent to the sum of the following inflows: direct investments in the reporting economy, portfolio investment liabilities, and other investment liabilities. Similarly, _COD_ is the aggregation of outflows of direct investments abroad, portfolio investment assets, other investment assets, and international reserve assets. As our aim is to shed light on both how large and how volatile capital flows are, we scale _CIF_ and _COD_ and their components by trend GDP throughout the paper.<sup>15</sup> Our sample of countries is based mostly on data availability. However, we exclude countries that are either very small or very poor. Small countries are a concern because they might display an artificially high volume of financial transactions due to their role as offshore financial centers or tax havens. A country is considered small if its gross national income (GNI) in 2005 was less than four billion U.S. dollars, PPP adjusted. Thirty countries are excluded from the analysis for this reason, among them Belize, Guyana, and Maldives. Poor countries generally depend heavily on official aid flows that behave differently than private capital flows, and are thus beyond the scope of our analysis. We exclude 46 countries with GNI per capita smaller than 2,000 U.S. dollars (PPP adjusted) in 2005, among them Bangladesh, Ethiopia, and Niger.<sup>16</sup> We classify our final sample of 103 countries into groups according to their income levels as measured by their GNI per capita in 2005. In particular, we classify _low-income countries_ as those with GNI per capita below 7,500 U.S. dollars. _Middle-income countries_ include those with GNI per capita between 7,500 and 15,000 U.S. dollars. These two groups,

> 15 Trend GDP is calculated by applying the Hodrick-Prescott filter to the series of nominal GDP in U.S. dollars. Nominal GDP is obtained from the _World Development Indicators_ . If data for the last years of the sample was not available, we complemented our dataset with data from the _World Economic Outlook 2009_ . 16 We used 2005 data on both GNI and GNI per capita as using more updated data would reduce significantly our sample coverage. Moreover, the ranking of countries relative to the thresholds used in this paper does not change considerably over time.

9
# 2.2. Potential Drivers of the Trends in Labor Shares

For our quantitative analysis in Sections 4 and 5, we will adopt the view that the differential trends in routine and non-routine labor income shares are driven by different interactions with ICT. Of course, there are other potential causes for these trends. While far from conclusive, this section discusses three plausible alternative explanations and offers some evidence suggesting that these 1. are not likely the dominant forces behind the observed trends displayed in panel A of Figure First, at least since the seminal work of Piketty and Saez (2003), a growing literature and heated public debate have put much emphasis on the rising gap between the earnings of the top 1% of earners relative to the remaining 99% (e.g., Jones and Kim, 2014; Atkinson, Piketty and Saez, 2011). To gauge at the potential impact of this recent divergence on our reported trends, Panel B of Figure 2 illustrates the impact of our top-code adjustment relative to the raw values reported in the CPS.<sup>14</sup> It is important to note that, despite the fact that we adjust top-coded values at the individual level, our adjustment effectively results in a level shift in aggregate income shares; all the original trends are preserved after the adjustment. If the divergence of the top 1% were really driving the differential trends for routine and non-routine income shares, then we should expect a strongly rising gap between the adjusted and raw numbers for non-routine workers but not for routine workers. As this is not readily apparent in panel B of Figure 2 we argue that “the one percent” are not likely the main driver of our results. Nevertheless, panel B of Figure 2 highlights that the top-code adjustment is crucial in order to construct quantitatively meaningful aggregate income shares based on earnings reported in the CPS.

Second, it also appears plausible that the decline in routine labor’s importance in production might merely be a symptom of the decline in manufacturing jobs. However, Figure 6 illustrates that the strong divergence between routine and non-routine income shares is not only due to the disappearance of classic blue collar jobs—which are primarily concentrated in the manufacturing sector. Specifically, at least since 1988, both service and managerial/professional occupations gained about 20% in income share. Thus, both abstract and manual occupations contributed about equally to the rise in the non-routine income share since 1988. Similarly, while traditional blue col-

> 14Note that these shares are not re-scaled to match the BLS’s estimate of the aggregate labor share.

13
**_Table 3: Gender Disaggregated Impact of COVID-19 School Closures on Intrahousehold Time Spent on Domestic and Care Activities in 2020_**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||Time Spent on|(Hoursper day)...||Share of Care|
||||||Intrahousehold|
||Care and Domestic<br>Work<br>(Self/All)|Care and Domestic<br>Work<br>(Self/Single)|Care and Domestic<br>Work<br>(Self/Married)|Care and Domestic<br>Work<br>(Spouse/Married)|<br>Share of Care<br>(Self/Self+Spouse<br>if Married)|
|Female Respondent|1.073***|0.770***|1.159***|-1.647***|0.209***|
||[0.07]|[0.14]|[0.08]|[0.08]|[0.01]|
|School closure due to COVID-19|-0.003|0.101|-0.060|0.423***|-0.008|
||[0.06]|[0.13]|[0.07]|[0.09]|[0.01]|
|School closure due to COVID-19*Female|0.519***|0.333**|0.639***|-0.551***|0.032***|
||[0.08]|[0.17]|[0.10]|[0.10]|[0.01]|
|Academic Break|-0.062|0.170|-0.155*|0.040|0.001|
||[0.07]|[0.15]|[0.08]|[0.10]|[0.01]|
|Academic Break*Female|0.302***|0.049|0.420***|-0.094|0.012|
||[0.09]|[0.19]|[0.11]|[0.11]|[0.01]|
|Change in movement workplaces|-0.009***|-0.004|-0.012***|-0.008**|0.000|
||[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|
|Change in movement retail & rec|-0.008***|-0.006*|-0.009***|-0.003|-0.000*|
||[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|
|GDP per capita (Ln)|-0.103***|0.026|-0.152***|-0.284***|0.011***|
||[0.03]|[0.05]|[0.03]|[0.04]|[0.00]|
|Constant|3.457***|2.522***|4.087***|6.931***|0.284***|
||[0.31]|[0.56]|[0.36]|[0.41]|[0.03]|
|Observations|79631|21996|57635|43556|29141|
|Adjusted R-squared|0.13|0.10|0.15|0.19|0.30|
|SC+SC*FEMALE(Coeff)|0.52|0.43|0.58|-0.13|0.02|
|<br>SC+SC*FEMALE(SE)|0.08|0.14|0.09|0.09|0.01|
|SC+SC*FEMALE(P-value)|0.00|0.00|0.00|0.15|0.00|
|Mean Male<br>Notes:* significant at 10% level, ** significant at 5% lev|3.65<br>el, *** significant at 1% level.|3.47|3.72|5.31|0.41|

<mark>[1] Female is a dummy variable where 1 = female-led business and 0 if male-led business.</mark>

[2] School Closure due to COVID-19 is a dummy variable where 1 = schools closed in a country at the time of survey wave.

[3] OLS pooled regressions include Region of the world Fixed Effects and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents).

39
category (last column in Table 3) is not very clear - this could be a unit driven by a generator (own or rental), geothermal, or wind power.

Unlike the rural households in many other developing countries who get their electricity from the grid, a good number of rural Vietnamese uses a variety of sources, notable among which are mini grid, pico-hydro, and diesel. A mini grid system uses a mechanism that diverts some of the water flow of a river through civil works (intake weir, fore bay, etc.). Mini grid technology is simple, and so is its operation and maintenance. It can be built locally at a low cost and has a moderately reasonable capacity to serve private investors.<sup>3</sup> A pico (also known as pico-hydroelectric) power plant is of very small capacity (1 kilowatt or less), uses a technology similar to that of mini grid and incorporates all of the electro-mechanical elements into a portable device. It is easy to install and the power output is sufficient for a single house or small business. A diesel engine can provide a wide range of power (from several hundred watts to 20 megawatts), has a low initial cost, is easy to install, and is reliable enough to be extensively used. Besides these three sources, generator (own or rental), geothermal, or wind power are also used by a small number of households.<sup>4</sup> Given the proliferation of household electrification, it is worth investigating the factors that determine a household’s grid connectivity. A household’s decision to get grid electricity may be influenced by factors of its own and its commune, including availability of electricity at the commune level. An equation for household’s access to grid can take the following form:

> 3 A mini grid can provide as much as 5 megawatt of capacity.

> 4 An extensive discussion of a wide range of electrification technologies can be found in World Bank (2006).

11
_S_ ( _ai_ ) = _λ_ Pr _−i_ ( _yi_ = _h | ai_ ) _._ (3.1)

In this equation, Pr _−i_ ( _yi_ = _h | ai_ ) represents the posterior probability that the members of individual _i_ ’s reference group think that her income is _h_ , conditional on observing individual _i_ undertaking action _ai ∈{_ 0 _,_ 1 _}_ . The parameter _λ_ measures how much individuals care about being perceived as being of type _h_ . In the context of our first experiment, the null hypothesis that individuals care only about the material benefits of a product and derive no separate utility from its status component implies that _λ_ = 0. The alternative hypothesis that individuals care about the pure status signaling component of a product, on the other hand, would imply that _λ >_ 0.

Our first experiment is designed to test these competing hypotheses. In the following sections, we will return to and extend this simple theoretical framework in order to generate testable predictions and elucidate the experimental design.

# **3.2 Experimental Design**

## **3.2.1 Set-up and Experimental Protocol**

The sample for this experiment consisted of 1,260 customers identified by the bank.<sup>17</sup> The customers on this list were randomly drawn from the set of current gold card holders with a credit limit of at least Rp 20 million (US$1,420), who were current on their credit card payments, and were not bank employees. Essentially, these were customers whom – for the purpose of our relatively small experiment – the bank was willing to offer an upgrade to the platinum card, even though they may not have normally qualified for it. Customers in this sample were then assigned to one of the treatment conditions described below. Treatment status was assigned randomly at the individual level, stratifying on income (below Rp 300 million per year, between Rp 300 million and Rp 500 million, or above Rp 500 million) and on customers’ current annual card fee (equal to Rp 240,000 or waived). Appendix Table A.1 displays sample characteristics for all experiments. In the sample for our first experiment, 24% of participants are female, and the average age is 47 years.

To implement the experiment, the bank made marketing calls to customers in this sample. In the calls, all customers were offered a paid upgrade to the benefits, services and credit limit available to the bank’s platinum card holders. However, customers were randomly and individually assigned to one of two treatment arms, described in greater detail in Section 3.2.2 below, which varied the details of the script, as well as the characteristics of the product that was being offered. Customers in a treatment group were offered an upgrade to an actual platinum card, while customers in a control group were informed that they were offered an upgrade to all the benefits and services usually reserved to the platinum card, but as an add-on to their current gold card.

> 17This experiment was registered in the American Economic Association Registry for randomized control trials under trial number AEARCTR-0000828.

8
Table 2: Changes in job categories by country income

||(1)<br>Upper-tier<br>self emp.|(2)<br>Formal<br>wage|(3)<br>Upper-tier<br>informal wage|(4)<br>Lower-tier<br>informal<br>wage|(5)<br>Lower-tier<br>self emp.|(6)<br>Unemployed.|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Year 10s|-0.008***|0.154**|-0.038**|-0.076**|-0.017***|-0.000|-<br>0.015***|
|(in HIC)|(0.001)|(0.029)|(0.009)|(0.017)|(0.002)|(0.000)|(0.000)|
|Year 10s|0.005**|0.027***|-0.001|-0.026***|-0.011**|-0.000|0.006|
|(in UMIC)|(0.002)|(0.005)|(0.001)|(0.005)|(0.005)|(0.007)|(0.005)|
|Year 10s|-0.001|0.034***|-0.001|-0.010|-0.004|-0.002|-0.016|
|(in LMIC)|(0.003)|(0.011)|(0.002)|(0.008)|(0.003)|(0.003)|(0.015)|
|Year 10s|-0.021*|0.052|0.002|-0.010|-0.015|-0.003|-0.005|
|(in LIC)|(0.011)|(0.057)|(0.004)|(0.020)|(0.038)|(0.006)|(0.026)|
|Observations|493|493|493|493|493|493|493|

Notes. Standard errors are in parentheses and are clustered at the country level. Country fixed effects are included in all regressions. Coefficients are marginal effects, not interaction terms, meaning the coefficients are directly interpretable as the average change for each income status. The country income classification is fixed based on the income classification of the country for the year the first survey is available. * p<0.10 ** p<0.05 *** p<0.01.

Which region showed the largest increases in formal wage employment? Figure 3 answers this question graphically. The largest increases for formal wage employment happened in East Asia and the Pacific (in our sample, Australia and the Republic of Korea dominate this increase), followed by Europe and Central Asia and Latin America and the Caribbean. Sub-Saharan Africa saw increases, but much more modest. The only clear decrease was in the Middle East and North Africa. Figure A2 in the appendix shows changes by gender and Figure A3 shows changes by age group graphically. The empirical results for gender are in Table A6 and A7 in the appendix. Overall results are generally similar, with the largest differences being labor force participation status. The empirical results for age groups are in table A8, A9 and A10. Interestingly, movements out of lower tiered employment and into formal wage employment is most pronounced for prime-aged adults. The youngest and the oldest see relatively less “positive” movements along the jobs ladder.

18