rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The spread of COVID-19 and the implementation of “social distancing” policies around the world have raised the question of how many jobs can be done at home. Most of the existing efforts to estimate these figures rely on US-based measures of the type of tasks required by different occupations (Dingel & Neiman, 2020a; Avdiu & Nayyar, 2020; Mongey, Pilossoph, & Weinberg, 2020; Leibovici, Santacreu, & Famiglietti, 2020).<sup>1</sup> However, the task content of jobs exhibits substantial variation across countries (Lo Bello, SanchezPuerta, & Winkler, 2019; Hardy, Lewandowski, Park, & Yang, 2018). Differences in the organization of production or in the level of technology adoption across countries imply that the same occupation may be more intensive in face-to-face interactions or in physical tasks in poorer economies. As a result, using USbased measures to estimate the amenability to _working from home_ (WFH) in developing countries may lead to biased conclusions.

To overcome this challenge, this paper uses skill and household surveys from 53 countries at different levels of economic development with rich information on the type of tasks carried out by people at work. We estimate indexes of the task content of jobs to rank them by their vulnerability to social distancing measures according to their amenability to a remote setup. In addition, given that the task data vary at the individuallevel—and not by occupation, as in the Occupational Information Network (O*NET) classification—we show how the likelihood of being able to work from home correlates with other characteristics of the individual and his or her job.

We build on the literature by estimating jobs’ amenability to WFH, as opposed to estimating the fraction of jobs that can be done at home. Estimating the latter is challenging since choosing the tasks that determine whether a person can work from home is largely arbitrary, specifically if one does not have a model linking such tasks to the probability of WFH during a pandemic. Dingel & Neiman (2020b) and Saltiel (2020) consider that an occupation cannot be performed from home if at least one of several conditions holds. For instance, in the Dingel & Neiman (2020b) study, some of categories that are sufficient to consider that an occupation cannot be done at home include “Performing for or Working Directly with the Public is very important”, “Handling and Moving Objects is very important” or “Repairing and Maintaining Electronic Equipment is very important.” However, occupational requirements can change during exceptional conditions. For example, while for professionals in communications or in law it is very important to have contact with the public, they can still carry out some (but not all) of their tasks using ICT (Information and Communication Technologies); craft workers for whom handling and moving objects is crucial may still be able to sell their products through e-commerce; individuals repairing equipment can still work on portable objects at home, to name a few examples. More generally, occupations comprise a bundle of tasks, and while

> 1 An exception is Saltiel (2020), who uses STEP surveys.

2
# **_Case study of a failure (to date):  The effect of political affirmative action for former untouchable castes in north India_**

For thousands of years, the low castes of India—historically called untouchables and today called Scheduled Castes (SCs)—were institutionally excluded from basic social, political, and economic rights. They were denied access to temples and schools, forced to live in segregated quarters of the villages, and denied the right to own assets and enter most occupations. In some parts of India, untouchables could not even walk through higher caste neighborhoods. After the independence of India, an end was put to the legal recognition of the rules of caste, but the social behavior of high castes is today still largely governed by the norms of the caste system.

What can one expect from political affirmative action in village governments for the low castes? The research findings discussed next, based on Pandey (2005, 2010), are from a different state of India than the research findings, discussed above, on political affirmative action for women. The two sets of findings use different outcome measures. Thus the findings are not directly comparable. Nonetheless, they suggest that political affirmative action for a stigmatized group need not mitigate its social exclusion but can actually lower the quality of the public services that the group depends on.

The state of Uttar Pradesh had two distinct sets of land tenure institutions under British colonial rule: _elite control areas_ ( _elite areas,_ for short) and _non-elite control areas_ ( _non-elite areas,_ for short). In elite areas, the landlords were responsible for paying the land tax to the British colonial authority. The landlords had a free hand to exercise political, economic, and judicial authority over the villages. In contrast, in non-elite areas, the cultivators were responsible for paying the land tax to the British colonial power, and the authority of the landlords was much more circumscribed. The land tax was abolished after India’s independence, but the legacy of elite control is still evident in the greater political presence of individuals from dominant classes in local government. Local governance and public school outcomes are worse in villages situated in regions with a history of elite control.

Pandey’s evaluation of the impact of political affirmative action for SCs focuses on the impact on public schools because most SC families depend on public schools, and most high14
# **_Data_**

The shift-share Bartik analysis draws on labor market indicators from the _Cambodia SocioEconomic Survey (CSES)_ and on trade flows from the United Nations (UN) COMTRADE database. Combines, these surveys provide information on employment, informality, wages, and trade flows in 2009, 2014, and 2019. We include the distribution of employment across provinces in Cambodia in the appendix (Table A1).

Annual bilateral trade flow data come from the UN COMTRADE database. This analysis focuses on Cambodian exports, or its analog (world imports of the rest of the world from Cambodia). We merge these trade data with labor market indicators using the concordance between ISIC rev 3.1 (from CSES) and HS0 – 1988/92 trade classification (used by UN COMTRADE). We include the instrumented change in export exposure between 2009 and 2014 and 2009 and 2019-20 by district in the appendix (Table A2).

# **_Results_**

# <u>Informality</u>

Our Bartik second stage estimates in Table 4.1 show that an increase in trade exposure (measured by exports per worker) correlates with a decrease in informality rates in Cambodia.<sup>3</sup> More specifically, a foreign demand shock of US$100 in the period 2009–2014 is related with a decrease of nearly 0.14 percent in informality rate, which dissipates overtime to 0.03 percent for the 2009–2019 period. These results are consistent with the hypothesis that increasing exports help decrease informality and imply that districts with higher exposure to trade have indeed experienced a decrease in their informality rate. The results for 2014-2019 are not statistically significant.

**Table 4.1: Estimated effect on informality rate from a US$100 increase in exports per worker in Cambodia**

|VARIABLES|**Informality**<br>**rate,**<br>**2014-2009**|**Informality**<br>**rate,**<br>**2019-2009**|**Informality**<br>**rate,**<br>**2019-2014**|
|---|---|---|---|
|Foreign demand shock<br>(USD 100), 2014-2009|-0.135**<br>(0.0624)|||
|Foreign demand shock<br>(USD 100), 2019-2009||-0.0254*<br>(0.0143)||
|Foreign demand shock<br>(USD 100), 2019-2014|||-0.0165<br>(0.0119)|
|Observations|183|185|183|
|R-squared|0.203|0.176|0.160|

3 We show the first stage of our estimations in the appendix.

24 | P a g e
externalities (e.g., Farrell and Saloner, 1986; Katz and Shapiro, 1994; Laffont _et al_ ., 1997). No one would have a telephone installed if there had been few connected numbers. Using cross-country data on the subscription rate for cellular phone services, Ahn and Lee (1999) estimate access demand for mobile networks and show that per capita GDP and the size of the existing fixed-line network increase the probability of people subscribing to mobile telephone services. Okada and Hatta (1999) also find interdependent network externalities between the mobile and ground-based telephone networks, with regional panel data on aggregate income and consumption in Japan.

The price structure of telecommunications services appears to become increasingly complicated, including peak/off-peak and multipart pricing and strategic discriminatory price schedules. In general, unregulated price competition enhances social welfare, as long as the access prices are reciprocal and reasonably low. A variety of price schedules could induce telephone subscribers to reveal their preferences and thus improve economic efficiency.<sup>8</sup> Termination-based discriminatory pricing, which is among the most common price mechanisms in advanced telecommunications markets, may be a powerful instrument to increase the number of telephone subscribers due to its indirect network externalities (Laffont _et al_ ., 1998b; Fu, 2004).<sup>9</sup> However, there may be a raisingeach-other’s-cost effect under the assumptions of the balanced calling pattern and reciprocal access pricing (Laffont _et al_ ., 1998a).

A structural model with micro data, rather than conventional reduced-form cross-country regressions, needs to be employed for investigating into the causality issue among income, prices and penetration. Apparently, an increase in teledensity could stimulate economic growth, because telecommunications is an important determinant of economic

> 8 A simple pricing model suggests that many forms of price discrimination, such as a two-part tariff and quantity-dependent prices, work to expand the output chosen by firms, thus lowering the deadweight loss of the economy. However, price dissemination more or less transfers surplus from consumers to firms. Therefore, the resultant distributional inequality may remain open to argument. See, for instance, Pepall _et al_ . (1999).

> 9 It is also referred to as tariff-mediated network externalities or pecuniary externality (Economides _et al_ ., 1996).

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|**Table 2**<br>Correlates of thelevelof|poverty.Depen|dentvariable:|_Poverty _|||
|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|
|Income per capita|−4.732<sup>∗∗∗</sup>|−4.363<sup>∗∗∗</sup>|−4.440<sup>∗∗∗</sup>|−4.452<sup>∗∗∗</sup>|−4.431<sup>∗∗∗</sup>|
||(0.864)|(0.869)|(0.868)|(0.855)|(0.854)|
|GovExp|<br>−0.159<sup>∗∗∗</sup>|<br>−0.171<sup>∗∗∗</sup>|<br>−0.170<sup>∗∗∗</sup>|<br>−0.155<sup>∗∗∗</sup>|<br>−0.169<sup>∗∗∗</sup>|
||(0.0499)|(0.0499)|(0.0500)|(0.0495)|(0.0498)|
|Mortality||<br>0.0365<sup>∗∗∗</sup>|<br>0.0367<sup>∗∗∗</sup>|<br>0.0404<sup>∗∗∗</sup>|<br>0.0407<sup>∗∗∗</sup>|
|||(0.0103)|(0.0103)|(0.0101)|(0.0101)|
|PolChange|||−0.318<br>|−0.254<br>|−0.269<br>|
||||(0.453)|(0.449)|(0.447)|
|Population growth||||2.613<sup>∗∗∗</sup>|2.656<sup>∗∗∗</sup>|
|||||(0.641)|(0.640)|
|Inflation|||||<br>−0.0509<sup>∗</sup><br>(0.0263)|
|2005.year|0.912|0.929|0.907|1.020|<br>0.939|
||(0.672)|(0.671)|(0.675)|(0.668)|(0.667)|
|2006.year|0.160|0.306|0.365|0.401|0.339|
||(0.723)|(0.722)|(0.726)|(0.718)|(0.716)|
|2007.year|0.328|0.470|0.504|0.557|0.541|
||(0.772)|(0.770)|(0.772)|(0.763)|(0.760)|
|2008.year|<br>0.746|<br>0.943|<br>0.986|<br>1.006|<br>1.133|
||(0.879)|(0.876)|(0.877)|(0.866)|(0.866)|
|2009.year|<br>0.999|<br>1.284|<br>1.333|<br>1.217|<br>0.994|
||(0.982)|(0.979)|(0.980)|(0.967)|(0.970)|
|2010.year|0.677|1.264|1.324|1.415|1.326|
||(0.907)|(0.915)|(0.916)|(0.904)|(0.902)|
|2011.year|<br>−0.00962|<br>0.515|<br>0.572|<br>0.651|<br>0.697|
||(0.968)|(0.976)|(0.977)|(0.968)|(0.966)|
|2012.year|<br>0.673|<br>1.214|<br>1.286|<br>1.272|<br>1.170|
||(1.061)|(1.066)|(1.066)|(1.051)|(1.049)|
|2013.year|0.719|1.315|1.384|1.277|1.067|
||(1.147)|(1.153)|(1.154)|(1.139)|(1.140)|
|2014.year|−0.751|−0.314|−0.247|−0.290|−0.465|
||(1.211)|(1.217)|(1.217)|(1.201)|(1.200)|
|Constant|78.72<sup>∗∗∗</sup>|65.02<sup>∗∗∗</sup>|65.44<sup>∗∗∗</sup>|57.39<sup>∗∗∗</sup>|57.84<sup>∗∗∗</sup>|
||(5.522)|(6.817)|(6.807)|(7.008)|(6.999)|
|Observations|546|539|539|538|538|
|Number of cc_num|118|117|117|117|117|

Standard errors in parentheses; regional dummies included but not reported. ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1.

Next, we rerun the regression using the overall measure of business regulation and the four alternative measures of property rights: the difficulty of starting a business, acquiring licenses, getting credit and contract enforcement (Table 3). The choice of indicators is dictated by the literature on property rights and their enforcement, which suggests that laws and regulations that change the underlying incentives for market participants – firms, workers and investors – are most difficult to achieve as they require legislative and sometimes judicial support.

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# **Dynamic Effects of Microcredit in Bangladesh**

## <u>1. Introduction</u>

Microfinance has received donor endorsement as an anti-poverty program, because it targets and reaches the poor, especially women, as well as small producers and entrepreneurs who often have limited access to formal financial institutions. Despite its overwhelming success in reaching the poor, induced benefits of microfinance (measured in terms of income and consumption and other dimensions of household welfare) are debated. Some studies found substantial positive effects, while others found no or even negative effects. The most substantive evaluation of microcredit was carried out two decades ago by researchers at the World Bank, which examined three well-known credit programs in Bangladesh using the 1991/92 cross-sectional survey data. This evaluation found that microcredit helped promote household welfare, and the impacts of credit are higher for women than men (Khandker 1998; Pitt and Khandker 1996; 1998; Pitt et al. 1999; Pitt et al., 2006).<sup>2</sup> The findings have been debated because of restrictive statistical assumptions for model identification of program benefits (Morduch 1998; Roodman and Morduch 2009; 2014). However, the claims of statistical limitations were also invalidated (Pitt 1999, 2014; Pitt and Khandker, 2012).

A number of studies that employed less restrictive assumptions such as randomized control trial (RCT) methods find limited or no benefits of microfinance.<sup>3</sup> The RCT studies are often based on assessments of short-term program effects (such as 18 months of intervention). But a certain minimum length of program membership is necessary to measure the effects of microcredit since

> 2A study from Guatemala suggests that female entrepreneurs benefit from microcredit programs as they move from selfemployment or perhaps from a single hired laborer, to two or more hired laborers. As this performance is replicated across a large of borrowers in a given area, this leads to local economic growth in an economy (Kevane and Wydick 2001).

> 3 Studies include the following, among others: Karlan and Zinman 2010; de Mel, McKenzie and Woodruff 2008; Augsburg et al., 2012; Attanasio et al., 2011; Banerjee et al., 2010; Crepon et al., 2011; Karlan and Zinman 2011).

2
gram in both 2021 and 2022. The findings underscore a significant variability in outcomes, with the Average Treatment effect on the Treated (ATT) surpassing the Average Treatment effect on the Untreated (ATU). Our analysis investigates the program and highlights the characteristics that correlate with higher program effect.

# **4.1. Model**

We begin this section with a high-level overview of the C-RCF model. Our approach will be guided by the Neyman-Rubin Causal Model, as described in Rubin, 1978 and further elaborated in Imbens and Rubin, 2015.

Every property is indexed as _i_ . We consider a binary variable for treatment assignment, _Zi j_ , which can take values in the set {0, 1}. Here, _Zi_ = 1 signifies that the unit _i_ was visited, and _Zi j_ = 0 means otherwise. Potential outcomes for each unit are denoted as ( _Yi_ (0), _Yi_ (1)). The term _Yi_ (1) represents the outcome for unit _i_ when it undergoes treatment, corresponding in our study if a property was ever visited. Conversely, _Yi_ (0) represents the outcome without treatment exposure. The outcome variable is continuous, meaning ( _Yi_ (0), _Yi_ (1)) ∈ _R_<sup>2</sup> . The vector _Xi_ contains the covariates, among them the categorical variable _ui_ that indicates the neighborhood where the property is located. We proceed we a cluster robust estimation procedure.<sup>11</sup> We start by proposing a partially linear model, assuming that a unit’s baseline outcome can be described by an unknown (potentially complex) function _f_ , with a treatment assignment introducing a constant shift in the outcome by an amount _τ_ ( _Xi_ ): Our analysis will describe 4 outcomes variables: (i) Tax Participation - _Y_<sup>_Pa_</sup> - Dummy that assumes value 1 if any positive payment; (ii) Compliance - _Y_<sup>_Co_</sup> - The ratio of paid to payable; (iii) log Value Paid - _Y_<sup>_lnP_</sup> ; and (iv) Value Paid - _Y_<sup>_VP_</sup> . We are not able to find any statistically significant

> 11For a more detailed explanation, see Athey and Wager, 2019, section 1.2.

17
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may enhance the productivity of individual projects in the tradable sectors by reducing operating costs, increasing demand for their products, and diminishing downside risks; where these services are absent, the economic efficiency costs can be large.<sup>23</sup> Yet as the public sector extends itself into lower priority areas (where the public good component is nonexistent and/or the private sector can provide these services more effectively), productivity for individual investments may not be enhanced. Maintaining an appropriate balance between the shares of public and private investments in total investment is also important. Public investments in certain priority areas are complementary to the efficiency of individual investments; in other areas, they may supplant private investments.

# A. Basic statistical results

Data for agricultural and industrial projects demonstrate the importance of overall public investments for investment productivity in the tradable sectors. The productivity of private and public tradable projects increases significantly as the share of public investments in GDP grows--but only up to a point. Figure 1 depicts simple range averages from the raw data<sup>24</sup> : the average ERR for investment projects increases by about 5 percentage points as the share of overall public investment in GDP increases from 5 to almost 10 percent. However, as the share of overall public investment increases beyond the 10 percent public investmentlGDP ratio, the average ERR eventually drops.

23 See Lee and Anas (1990) for documentation of the costs of under-provision of public infrastructure services on manufacturing enterprises in Nigeria. 24 For figures I and 2, the points represent ERR averages for each segment.
sometimes with (proxies of) controls for student ability and/or school quality. Equation (1) is the standard approach, as in, for example, Patrinos and Psacharopoulos (2020) with the easy interpretation that  3 is closely related to the rate of return to one additional year of schooling. However, this approach forces the proportional increase in labor earnings from one year of schooling to be the same whether that year is at the primary level or the postsecondary level. Many studies, including in Ghana, find that returns are higher for higher levels of schooling, especially postsecondary schooling. See, for example, Urzúa (2019) or Turkson and Baffour (2020) for Ghana. To account for this, we estimate a semiparametric version of the same model: 2 ln(earnings) =  0 +  1  experience +  2  experience + _f_ ( years of schooling ) +  , (2) adding controls for region and area (urban/rural) of residence and mother’s and father’s level of schooling.

There is an extensive literature criticizing regressions such as equations (1) and (2), the most common criticism being ability bias: those with greater innate ability are both more likely to complete more years of schooling and have greater labor earnings. Since neither equation (1) nor equation (2) includes ability, the omitted variables bias for  3 is positive. Despite this intuitive argument, an early review of the literature by Griliches (1977) argues that ability bias is likely to be small in equation (1) and may be overwhelmed by larger negative biases. Card (1999) echoes that argument and uses twins data to estimate that the bias in the average return to schooling is about 10 percent when using OLS. Card also finds that many instrumental-variables estimates are _higher_ than OLS estimates, casting doubt on the ability to find an easy econometric fix for the OLS bias.

Another potential source of bias in equations (1) and (2) is the absence of controls for school quality. As first argued by Behrman and Birdsall (1983), if better-quality schools increase both the number of years students stay in school and their labor earnings later in life, then again, 14
|end Variable: Log real hourly wage<br>Exchange rate changes<br>(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>-0.072<br>(52.4)***<br>-0.033<br>(4.7)***<br>-0.023<br>(1.4)<br>0.06<br>(20.6)***<br>0.105<br>(17.5)***<br>0.036<br>(5.6)***<br>0.024<br>(1.9)*<br>-0.119<br>-0.11<br>-0.089<br>-0.077<br>(59.31)***<br>(54.38)***<br>(55.60)***<br>(46.59)***<br>0.006<br>0.007<br>(24.6)***<br>(28.29)***<br>0.018<br>0.017<br>(10.86)***<br>(10.42)***<br>0.039<br>0.038<br>(12.03)***<br>(12.02)***<br>2,300,567<br>3,868,747<br>3,868,747<br>3,868,747<br>3,868,747<br>0.147<br>0.226<br>0.005<br>0.001<br>0.003<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>he period of analysis is 1986-1992 in columns 1-4 and 1991-2000 in<br> standard errors clustered by individual. All specifcations include<br>oductivity, frm age, and percentage of foreign capital. In addition,<br>p88 and post89, the specifcations in columns 3-4 include the vari-<br>nclude the variable schooling, and the specifcations in columns 7-8<br>ed by 10, and age-square and Herfndahl index by 1000. Dependent|
|---|
|Dep<br>1989-92 appreciation<br>(1)<br>(2)<br>(3)<br>indop88*post89<br>-0.083<br>-0.085<br>-0.073<br>(31.5)***<br>(31.8)***<br>(55.6)***<br>indop88*schooling<br>-0.004<br>-0.007<br>(2.9)***<br>(4.9)***<br>post89*schooling<br>0.012<br>0.01<br>(52.8)***<br>(45.9)***<br>indop88*post89*schooling<br>0.002<br>0.003<br>(4.5)***<br>(5.6)***<br>indop88*mskill<br>-0.029<br>(4.1)***<br>indop88*hskill<br>0.01<br>(0.6)<br>post89*mskill<br>0.067<br>(23.3)***<br>post89*hkill<br>0.118<br>(19.4)***<br>indop88*post89*medskill<br>0.041<br>(6.6)***<br>indop88*post89*highskill<br>0.022<br>(1.7)*<br>IC<br>IC*schooling<br>IC*medskill<br>IC*highskill<br>Number of observations<br>2,300,567<br>2,300,567<br>2,300,567<br>R-square<br>0.215<br>0.164<br>0.187<br>Year+Industry+Regional dummies<br>Yes<br>Yes<br>Yes<br>Worker fxed efects<br>Yes<br>No<br>Yes<br>Worker-frm fxed efects<br>No<br>Yes<br>No<br>_Notes_: * signifcant at 10%; ** signifcant at 5%; *** signifcant at 1%. T<br>columns 5-8. Absolute value of t-statistic in parentheses, based on robust<br>the variables age, age square, tenure, Herfndahl index, frm size, labor pr<br>the specifcations in columns 1-2 also include the variables schooling, indo<br>ables mskill, hskill, indop88 and post89, the specifcation in columns 5-6 i<br>include the variables mskill and hskill. The variables age, tenure are divid<br>variable: log real hourly wage.|

15