rafmacalaba/datause-encoder-data huggingface dataset:
job location (3.1%); (2) persons who work at home (5.6%); (3) workers whose workplace location was not adequately described (this includes all persons commuting to a job outside of the GMR) (4.6%); (4) commuters who chose bicycle, auto-rickshaw, taxi, or shared ride as their as their main mode (due to the low frequency of such choices) (5.4%); (5) a small number of workers who claim they commute by two-wheeler or car but do not own such a vehicle (0.3%). Excluding these persons resulted in 4,958 commuters.<sup>14</sup> Assumptions made in computing the cost, in-vehicle and out-of-vehicle travel times for modes not chosen are described in the Appendix.

The joint model is estimated using the data for the principal earner in each household. Most households who own a vehicle have only one car or two-wheeler, and it is usually the primary income earner who uses them for commuting. All other sampling details are the same as the commute mode choice models, resulting in a sample of 3,786 households.

# **_D.  Estimation Results_**

Tables 4 presents the results of estimating the multinomial logit and mixed logit models and Table 5 the resulting elasticities.<sup>15</sup> Several results are worthy of comment. The value of travel time, both in-vehicle and walking, is greater than the wage. In model 1, the value of out-of-vehicle travel time (walking time) is 1.2 times the wage; the value of in-vehicle travel time is equal to the wage. This is a common result in mode choice studies in developing countries (Deaton et al. 1987). The high value of walking time is in

> 14 Approximately 24% of commuters were dropped.  The percent of commuters dropped is approximately the same for all income and education categories; however, commuters with vehicles were less likely to be dropped than those without.

> 15 Price and income elasticities are calculated as simulated arc elasticities corresponding to a 50% increase in rail fare, bus fare, gas price and income, respectively.

15
The university’s ability to attract more students into its S&T graduate programs and to raise quality will be strongly influenced by the demand for high level skills reflected in better entry level salaries and career prospects for graduates and Ph.D. holders. Although employers complain of shortages and of turnover, it is not apparent that the salaries of the most highly skilled workers and particularly of researchers have risen sufficiently to elicit the desired supply response.

# **Diversifying into Linked Services**

Improving the quality of instruction provided by USM and expanding graduate and doctoral programs would start the process of enhancing USM’s reputation in Malaysia and abroad. This would begin drawing more foreign students to Penang which would contribute to the heterogeneity of the student body and could be a welcome source of earnings for the university as well as for local providers of services to the school. Elite universities are important exporters of services and can be significant revenue generators for the local economy.<sup>33</sup> University teaching and research in the life sciences has the potential to generate synergies with Penang’s hospital sector, itself a growing exporter of medical services, mainly to visitors from Indonesia although there is ample scope for diversification.<sup>34</sup> Medical services providers, like universities can be significant revenue earners for the local economy but too often universities and hospitals remain as island sectors in the urban economy with few linkages and low employment multipliers (Florida, Mellander and Stolarick 2008). It is when these sectors connect with the real sector as in the case of Boston and San Francisco that the multiplier effects start to grow (see Figure 17). The backward linkages from medical services can be to bio-informatics, to IT based firms, to producers of diagnostic and imaging equipment, medical implants, and to the pharmaceutical industry supplying hospitals with medications and using their services to

> 33 See for instance Appleseed (2003) on the economic impact of research universities in the Boston area; Appleseed (2004) on Harvard; CBRE consulting (2008) on the University of California-San Diego; Wisconsin Technology Council (2004) on the University of Wisconsin-Madison; and Econsult Corporation (2006) on the University of Pennsylvania.

> 34 Nearly 200,000 foreign visitors used Penang’s hospital facilities in 2006 for everything from routine physical exams to heart related procedures to hip and knee replacement.  Each visitor spent approximately 2.5 days in a hospital (not including any additional time spent in Penang or other resorts to convalesce) and was accompanied by at least one additional person who generated revenues for local merchants and hotels.

34
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30
-3-

# II. THE STUDY AREA AND FIELD PROCEDURES

2.01 The field work for this study was conducted in May 1989 in three large Igbo villages in the Nsukka district of Anambra State: Edem, Ekwegbe, and Umunko. These villages are believed to be representative of conditions in many areas of Nsukka district. The villages were 10 to 30 kilometers from the town of Nsukka, the district center and the site of the main campus of the University of Nigeria. The population of these villages is difficult to estimate because **-** the dwellings are widely dispersed often with several concentrations of housing and commercial activity- -and because the only census estimates are more than two decades old. However, the study villages probably range in size from about 10,000 (Edem) to 25,000 (Ekwegbe).

2.02 All three villages are predominantly agricultural communities. The majority of the population are farmers (62 percent of the sample respondents in Edem; 69 percent in Ekwegbe; 80 percent in Umunko; see Table 1). The principal crops grown in the area are cassava, yams, and maize. The second and third largest occupational groupings are "small traders" and "skilled laborers." None of the study villages had electric services, though a handful of households in each village had their own generators. All villages had primary schools and basic market facilities. Table 1 summarizes some of the other socioeconomic characteristics of the three villages.

2.03 The household questionnaire was developed and then pretested in a nearby village similar to the three study villages. The questionnaire was administered by a team of ten enumerators, who received almost two weeks of training. The enumerators were all secondary school graduates from the Nsukka district who were familiar with local customs and water use practices. In order to facilitate execution of the surveys, the chief and local government leaders for each village were consulted, and their cooperation in the etudy was secured before work began.

2.04 In total, 132 households were interviewed in Edem, 128 in Ekwegbe, and 135 in Umunko. It was impossible to construct well-defined sample frames in any of the villages within the time and resource constraints of this study. Enumerators were simply dropped in different parts of the communities and instructed to walk in a specified direction and to interview every other household. Interviews were conducted in the afternoon and early evening, when people were likely to be back from their work in the fields. Although this sampling procedure appears to us to have yielded a relatively representative sample of households, it may have introduced biases of which we are unaware. However, we are unable to speculate about how such biases may have affected the survey results.
|**Table 7: Poisson regressions for th**|**e number of op**|**erating projec**|**ts in bilateral**|**relations**||||
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|**Bilateral variables**||||||||
|Distance|-0.7253***|-0.7243***|-0.7066***|-0.7121***|-0.7140***|-0.7209***|-0.7107***|
||[0.097]|[0.094]|[0.094]|[0.095]|[0.091]|[0.093]|[0.091]|
|Colonial relationship|0.8934*|0.8914*|0.8718|0.9003*|0.8981*|0.9057*|0.8766|
||[0.530]|[0.513]|[0.578]|[0.516]|[0.513]|[0.513]|[0.579]|
|**Investor country variables**||||||||
|Net food imports per inhabitant ($)|1.9477*|2.2305**|2.1871**|2.2658**|2.2132**|2.2243**|2.1912**|
||[1.080]|[0.926]|[0.953]|[0.923]|[0.923]|[0.924]|[0.953]|
|Population|0.7522***|0.7379***|0.7285***|0.7388***|0.7344***|0.7364***|0.7289***|
||[0.083]|[0.083]|[0.085]|[0.084]|[0.082]|[0.083]|[0.085]|
|**Host country variables**||||||||
|Food exports|0.0705|0.0783|0.1318**|0.0640|0.0981|0.0864|0.1386**|
||[0.061]|[0.059]|[0.065]|[0.064]|[0.067]|[0.062]|[0.063]|
|Suitable non-forest land (mn ha)|0.7015***<br>[0.149]|||||||
|Suitable forest land (mn. ha)|0.0154<br>[0.092]|||||||
|Max. possible output value non-||||||||
|<br>forest area||0.7000***|0.6124***|0.7121***|0.6769***|0.6872***|0.6218***|
|||[0.126]|[0.149]|[0.129]|[0.138]|[0.132]|[0.151]|
|Max. possible output value forest<br>area||-0.0251|-0.0415|-0.0441|-0.0186|-0.0251|-0.0432|
|||[0.072]|[0.077]|[0.074]|[0.072]|[0.073]|[0.078]|
|Yield gap|0.5036|1.1073|0.4565|1.1729|.7851|0.9373|0.5484|
||[0.681]|[0.754]|[1.002]|[0.817]|[0.886]|[0.811]|[0.983]|
|Land governance index|||-0.1930**||||-0.2296**|
||||[0.095]||||[0.117]|
|Weak investor protection||||-0.0015||||
|||||[0.003]||||
|Control of corruption (%)|||||-0.0057<br>[0.008]|||
|Political stability (%)||||||-0.0041<br>[0.006]|-0.0048<br>[0.007]|
|No. of observations|25,704|26,838|20,223|25,515|26,848|26,838|20,223|
|Pseudo R<sup>2</sup>|0.254|0.231|0.217|0.228|0.231|0.231|0.218|

**_Notes:_** Variable in logs. Robust standard errors in brackets. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. Constant included but not reported.

26
metal mining (-48.7%/+1<sup>_◦_</sup> C) and timber (-41.2%/+1<sup>_◦_</sup> C) (Chen and Yang, 2019). Losses in agricultural TFP in China are estimated at 2.6% for every additional day with a temperature of more than 33<sup>_◦_</sup> C during the year (Chen and Gong, 2021).

These findings are consistent with county-level payroll evidence from the US indicating that labor productivity in highly-exposed non-agricultural industries, such as utilities, mining, and manufacturing, declines by as much as 50% on a workday above 95<sup>◦</sup> F (35<sup>◦</sup> C), a ninefold impact relative to less exposed industries, although there seems to be adaptation when evaluating cross-county differences (Behrer and Park, 2017).<sup>xxxv</sup> In indoor controlled environments, productivity tends to be similarly negatively associated with temperature, often in a non-linear, U-shaped way. A meta-analysis of indoor office, laboratory, and classroom settings finds task performance to increase until reaching a ceiling at 21<sup>_◦_</sup> C- 22<sup>_◦_</sup> C, with further increasing temperature from 23<sup>_◦_</sup> C to 30<sup>_◦_</sup> C resulting in a 9% productivity loss (Seppanen et al., 2006). Student performance among college entrants in China declined by 2.9% of a standard deviation for a 1<sup>_◦_</sup> C increase in mean temperature of 23<sup>_◦_</sup> C during the exam period—with a more pronounced effect for highperforming students (Graff Zivin et al., 2020). These impacts are larger than those found among students of similar age in New York (-1.60%/+1<sup>_◦_</sup> C) (Park, 2022), possibly due to lower unobserved adaptation, like access to air conditioning.

Health is increasingly seen as an important mediating factor. When heat exceeds certain thresholds, negative physiological responses such as respiratory disease and cardiovascular strain, higher levels of fatigue and exhaustion, heat strokes, and cognitive impairment can affect body and brain functioning, which typically reduce work capacity while on-the-job (Zander et al., 2015). One adaptive response to cope with these effects is to reduce labor effort. DHS interviewers reduce their labor effort on hotter and more humid than usual days, completing 13.6% fewer interviews per hour (LoPalo, 2023). Rice harvesters in India self-report heat exhaustion, pain, signs of cardiovascular strain, during work on hot days, which 16
prices rose sharply. Since the harvest in 2010 is close to trend, expectations of a bad harvest in 2010 are unlikely to have caused the price spike, unless these expectations were completely off.

One possibility is that the 2010 price increase was due to a bad Vietnamese, or regional, harvest in 2010, as is claimed by FAO (2011). This conclusion does not seem to be supported by the data on the (de-trended) harvests in Lao PDR and Vietnam, which suggest that both harvests stayed close to the trend (Fig. 10). While we have few data points, it appears that the size of the harvests in Lao PDR impacts on the exports to Vietnam; large harvests have been found to increase subsequent exports (Fig. 11).

The conclusions drawn from the visual inspection of the graphs are corroborated by re-estimating the error correction model for Lao PDR (from Section 3), with exports (lagged six months) and harvest size (lagged two and eight months) added as independent variables. The choice of lags was based on the graphs. Good harvests are expected to reduce prices as supply increases in the short-run, but may then also increase prices later in the year as rice stocks start running out prior to the next harvest.

Column 1 in Table 4, confirms that exports have a highly significant effect on prices ( _t_ -value = 3.9). A one standard deviation (1.409 million kg) increase in exports is associated with roughly a 25 percent annualized inflation rate (0.015*1.409*12=0.254). This should be considered as a rough indication, however, given that the export variable is heavily skewed with many observations close to zero. Column 2 reports the same regression with only harvests added, using data for 2001:9-2012:12. The second lag is negative as expected, though the _p_ -value is 0.15, while the eighth lag is positive and significant. Thus, the larger the harvest, the larger the price increase at the end of the year, 21
19 areas are found in the largest cities, while 29% are in towns with less than 15,000 inhabitants. Similarly, 27% of underweight urban children reside in the largest cities, but 30% reside in the smallest towns.

In a final robustness check, we ask whether our observed gradient between poverty and city-size is somehow an artifact of the focus, in this paper, on small area estimates of poverty for each city rather than direct measures of poverty for such localities.<sup>21</sup> We have already noted that direct measures of poverty for individual towns and cities are not generally available in developing country settings. The household surveys that underpin poverty analysis in these countries do not generally cover sufficiently large samples to permit poverty measurement at this detailed level. As was described in Section 2, the small area estimation procedure applied in the present paper combines household survey with unit-record population census data in an effort to circumvent this small sample problem. The approach takes advantage of the full population coverage of the population census and then applies statistical techniques to insert into the census an indicator of per capita expenditure or income for each household. This is necessary because in most developing (and developed) countries, the population census fails to collect detailed income or expenditure data.

India offers an opportunity to probe the contention that our findings are merely an artifact of the methods we have employed. The Indian National Sample Survey Organization (NSSO) fields a very large sample survey every five years with a sample size that is sufficiently large to permit a breakdown of urban areas into city-size categories.<sup>22</sup> Table 9 draws on a World Bank study (World Bank 2010) to illustrate that at the national level for the years 1983, 1993 and 2004/5, National Sample Survey data show a clear gradient in poverty by city size. This gradient holds both at the national

> 21 Tarozzi and Deaton (2007) have recently expressed a concern that the small area estimation procedure employed by ELL (2002, 2003) may overstate the precision of local level poverty estimates. They base their argument on Monte Carlo simulation results. Elbers, Lanjouw and Leite (2008) examine this issue with data for the state of Minas Gerais in Brazil, and find little evidence in that specific setting for concern. It remains, though, that the ELL procedure _estimates_ poverty, rather than directly _measuring_ it, and as such there is interest in assessing whether the findings reported in this paper would also hold had poverty been directly measured.

> 22 Every five years the NSS fields a “thick round” with a sample size of around 120,000 households, The “thin rounds” fielded in the other years have sample sizes of around 30-40,000 households.
**Table 5: Including Turnout with GOVFRAC**

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|govfrac|-0.087<br>(0.039)**|-0.053<br>(0.028)*|-0.017<br>(0.014)|
|Turnout|0.062<br>(0.035)*|0.064<br>(0.035)*|0.010<br>(0.016)|
|Regular<br>elections|-0.026<br>(0.016)|-0.004<br>(0.010)|-0.015<br>(0.009)*|
|Presidential|-0.046<br>(0.018)**|-0.028<br>(0.013)**|-0.009<br>(0.009)|
|Majoritarian|-0.001<br>(0.016)|-0.015<br>(0.012)|-0.005<br>(0.006)|
|Observations|207|206|239|
|Adjusted R-squared|0.32|0.46|0.15|

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in the sample are listed in the Appendix 5.

31
Policy Research Working Paper 9241

# **Abstract**

The spread of COVID-19 and implementation of “social distancing” policies around the world have raised the question of how many jobs can be done at home. This paper uses skills surveys from 53 countries at varying levels of economic development to estimate jobs’ amenability to working from home. The paper considers jobs’ characteristics and uses internet access at home as an important determinant of working from home. The findings indicate that the amenability of jobs to working from home increases with the level of economic development of the country. This is driven by jobs in poor countries being more intensive in physical/manual tasks, using less information and communications technology, and having poorer internet connectivity at home. Women, college graduates, and salaried and formal workers have jobs that are more amenable to working from home than the average worker. The opposite holds for workers in hotels and restaurants, construction, agriculture, and commerce. The paper finds that the crisis may exacerbate inequities between and within countries. It also finds that occupations explain less than half of the variability in the working-from-home indexes within countries, which highlights the importance of using individual-level data to assess jobs’ amenability to working from home.

This paper is a product of the Social Protection and Jobs Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at hwinkler@worldbank.org.

_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