rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Home-based work across countries**

Our first exercise is to calculate the share of jobs that can feasibly be performed at home for all the 107 countries for which data are available in the ILO database. On average, 23.9% of all jobs can be done from home based on the standard DN2020 measure. However, once we account for internet access, this share drops to 18.7%. Put differently, failing to account for internet access would cause us, on average, to overestimate the share of jobs that can be performed from home by almost 30 percent.

Measures of the feasibility of home-based work that do not consider internet access are thus upwards biased, and this bias is especially large in low income countries. Figure 1a plots both the DN2020 and our modified home-based work (HBW) measure against GDP per capita. The distance between the two fitted lines is a measure of the magnitude of the bias associated with ignoring ICT and internet access constraints. The bias is largest in the poorest countries. For example, 5.5% of all jobs in Ethiopia can be performed from home according to the DN2020 measure, while accounting for internet access reduces the prevalence to 2.1%. Even more strikingly, in Nepal the number of jobs that can be performed from home drops from 14.7% to 6.3% once internet constraints are accounted for. By contrast, in rich countries such as Switzerland, Sweden, the United Kingdom, the Netherlands, and Luxembourg—where the DN2020 measure would be between 40 and 55 percent—internet access constraints hardly matter.<sup>6</sup> How large is this bias in different parts of the global income distribution? Figure 1a does not provide a direct answer since it does not consider country size. Figure 1b presents the share of telecommutable jobs by level of income of the countries, weighted by the size of their employed population and separating between types of home-based work. The portion of jobs requiring internet, but lacking access is the bias of the DN2020 measure. It shows that the share of telecommutable jobs that do _not_ require internet access is consistently very low. The share is slightly over 3 percent on average and is no more than 5 percent in any country. In other words, few jobs can be effectively done from home without internet. Developing countries are doubly disadvantaged; not only they have fewer telecommutable jobs, but also internet access is far more

> 6 Recall from section 2 that the association between GDP per capita and the share of jobs amenable to working from home depends critically on what we assume about the ability of agricultural workers to do their jobs from home; assuming that the agricultural self-employed can work from home would result in a negative association between GDP and the prevalence of work amenable to working from home (Gottlieb, Grobovsek, and Poschke, 2020).

9
processes, and female empowerment (Dreze and Khera 2011; Khera and Nayak 2009). Critics note the program’s high cost, inefficiency in transferring resources, and serious corruption (Niehaus and Sukhtankar 2012). They point out that high program wages not only cause leakage and corruption that may undermine program impacts but, instead of helping people move out of agriculture, may in fact encourage return migration to rural areas. To better understand whether these arguments are justified, empirical analysis of NREGS impacts is needed.

Beyond a large body of descriptive and case study evidence, most quantitative studies of NREGS have focused on general equilibrium impacts through price and wage effects, using the program’s phased rollout to identify treatment effects based on repeated cross-sections or administrative data. While this approach has provided important insights, it is not well suited to (1) assessing the extent to which the program and its benefits are targeted toward the poor, (2) identifying the channels through which program effects materialize, and (3) assessing behavioral responses to determine whether, for example, the program crowds out other forms of employment. The use of before/after household panel data allows assessment of program effects on the treated, to answer some of the policy issues raised in this context, but this assessment is demanding in terms of data.

This paper focuses on partial equilibrium impacts of NREGS on direct beneficiaries. We study NREGS impact on key welfare indicators and the channels through which potential impacts materialize, using a three-round panel of some 4,000 households in the Indian state of Andhra Pradesh. The fact that data were collected in 2004, before NREGS had been conceived; in 2006, when the implementation was just starting; and in 2008, when the program was fully operational throughout the state, together with the program’s phased roll-out, allows us to distinguish short- and medium-term effects. Data at the household level are combined with administrative data on households’ participation and work records.

We find that, in the setting studied, the program was reasonably well targeted and had significant impacts, the magnitude of which exceeded the value of direct transfers. While short-term effects focus on higher nutritional intake, accumulation of nonfinancial assets is visible in the medium term. Benefits are concentrated with scheduled castes and tribes and those relying on casual labor. Land-related investment is one plausible channel for medium-term benefits to materialize, and there is little evidence of NREGS crowding out other types of employment or investment.

The paper is structured as follows. Section 2 describes key features of NREGS, its implementation in Andhra Pradesh, and evidence on its impact from the literature. Section 3 introduces administrative and household survey data, descriptive statistics, and our methodology. Section 4 presents estimates of program impacts in the short and medium term, heterogeneity of impacts by caste and labor market 3
# _4.3. Impacts on Education for Children Ages 7-15_

The analysis of the educational and child labor outcomes is presented in Table 3. In particular, columns one to three analyze the effect on enrollment while columns four to seven examine the impact on unconditional attendance, grade progression, learning and total hours of child labor. The standard errors in this table, as well as in all subsequent tables, are clustered at the unit of randomization, which is the village.

To analyze the impact of cash transfers on school enrollment, we use three measures of enrollment as dependent variables. The first comes from school administrative ledgers we collected at each school (column 1). The second comes from the household survey where we asked parents to provide evidence of school registration by either showing the student ID card/number or other evidence such as official school receipt of registration documents (column 2). This additional evidence of school registration was only requested at our last survey round, round 3. The third measure comes from parental self-reports in the household survey (column 3) and is potentially prone to self-reporting bias as highlighted by Baird and Özler (2012). However, by using three measures, two collected at the household-level – including one asking for documents confirming actual registration - and one collected at the school-level, we reinforce the robustness of our analysis. At round 3, the correlation between the parental self-reports (column 3) and the school-based measure (column 1) is 0.80, it is 0.84 between the parental selfreports and the registration evidence provided by the households (columns 2 and 3) and 0.68 between columns 1 and 2.

Focusing first on the results at the end of the two year experiment (round 3), we find that the cash transfers increase enrollment. In the comparison between the transfers made to the mothers and the fathers (panel A), the coefficient on “treatment” can be interpreted as the impact of cash transfers given to mothers. For example, with the school-based measure in column 1, the coefficient estimates the impact of cash transfers to mothers as a 13.8 percentage point increase in enrollment. Compared to a mean of 38.3 percent of children enrolled in the control group, this corresponds to 36 percent improvement. The coefficients on the CTF indicator are negative but not significant, suggesting that the gender of the recipient did not matter much in terms of enrollment impacts. The p-values reported for “Treatment + CTF” also suggest a combined coefficient different from zero, implying that cash transfers given to father have a positive and 12
**Table 5. COVID-19 impacts on formal and informal employment across sectors**

|||Formal<br>|||Informal<br>||
|---|---|---|---|---|---|---|
||Agriculture<br>(1)|Industry<br>(2)|Services<br>(3)|Agriculture<br>(4)|Industry<br>(5)|Services<br>(6)|
|**Panel A: Female**|||||||
|COVID|0.002***|-0.005***|-0.017***|0.019***|0.002*|0.012***|
||(0.000)|(0.001)|(0.002)|(0.003)|(0.001)|(0.002)|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.165]|[0.000]|
|Pre-pandemic|0.010|0.038|0.186|0.168|0.033|0.104|
|average|||||||
|**Panel B: Male**|||||||
|COVID|0.001|-0.023***|-0.026***|0.013***|0.005***|0.008***|
||(0.001)|(0.003)|(0.002)|(0.003)|(0.001)|(0.002)|
||[0.661]|[0.000]|[0.000]|[0.000]|[0.001]|[0.000]|
|Pre-pandemic|0.039|0.123|0.227|0.253|0.063|0.097|
|average|||||||
|Test:βm= βf<br>(p-value)|0.299|0.000|0.000|0.074|0.099|0.06|

Notes: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Parentheses indicate standard errors, while square brackets indicate the False Discovery Rates (FDR). Data are taken from Sakernas 2016–2020 (both February and August rounds). Dependent variables are unconditional (of the whole 15–64-year-old population) dummy variables of being employed across different formality-sector combinations. The main variable of interest in the above results is the COVID dummy, which takes on the value of 1 if the time period is August 2020. Pre-pandemic averages are population averages of the employment outcomes throughout 2016–2019 (both February and August rounds). Regressions are of the 15–64-year-old population, consisting of 1,614,217 male and 1,640,149 female individuals. Control variables include age, squared age term, marital status, household size, education level dummies, training experience dummy, and urban/rural dummy. All specifications include year, province and month fixed effects.

33
|**Table 1: Descriptive statistics across surveys and samples**|**IHPS**|**IHS4**|**IHS4**|
|---|---|---|---|
||||**restricted**|
|**Panel A: Household level information for owners of agricultural land**||||
|Household size|5.010|4.457|4.409|
|Head’s age (years)|44.382|44.824|44.971|
|Head female†|0.264|0.311|0.342|
|Head reads & writes Chichewa†|0.685|0.672|0.661|
|<br>Head’s education (years)|5.560|5.298|5.230|
|Somebody reads & writes Chichewa†|0.911|0.874|0.868|
|<br>Highest educ.by any member (years)|7.881|7.332|7.244|
|Christian†|0.785|0.827|0.829|
|<br>Household asset and dwelling wealth index|0.247|0.184|0.183|
|Household agricultural asset access index|0.208|0.195|0.195|
|<br>Area of agricultural land (ha)|0.647|0.569|0.577|
|No. of parcels|1.537|1.399|1.420|
|Uses org. manure†|0.297|0.257|0.263|
|Cultivates cash crop†|0.097|0.073|0.075|
|Has formal document to agric. land†|0.023|0.016|0.016|
|No. of households|1,308|8,203|7,129|
|**Panel B: Parcel level information for owned agricultural land**||||
|Parcel size (ha)|0.421|0.407|0.410|
|Organic manure applied†|0.233|0.214|0.216|
|Cash crop planted†|0.070|0.055|0.055|
|Soil is sandy|0.197|0.188|0.189|
|<br>Soil is between sandy and clay|0.527|0.566|0.570|
|Soil is clay|0.276|0.245|0.241|
|Slope is flat|0.502|0.56|0.562|
|Slope is small|0.370|0.334|0.331|
|<br>Slope is moderate|0.097|0.070|0.070|
|Slope is steep|0.031|0.036|0.037|
|<br>Parcel in swamp/wetland|0.131|0.124|0.124|
|No. of parcels|2,011|11,472|9,668|
|**Panel C: Household level information for owned dwelling & residential**|**land**|||
|Property size (ha)|0.055|0.058|0.059|
|<br>Years dwelling has been occupied|7.451|7.245|7.185|
|Improved dwelling†|0.397|0.393|0.385|
|<br>Has formal document to non-agric. land†|0.065|0.035|0.030|
|No. of households|1,354|7,099|6,239|

_Source:_ Own computation from 2015/16 Malawi IHPS and IHS4.

_Note: ‘_ IHS4 restricted’ refers to the subsample of respondents reporting to be owners or co-owners of the parcel or dwelling. † Underlying variable is dichotomous variable.

16
Figure 5: Stationary Distribution of Education by Ancestry <!-- Start of picture text --> Average Education<br>Natives: 11.90<br>Non-natives: 12.47<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Education<br>Natives Non-Natives<br>25<br>20<br>Percentage 1510<br>5<br>0<br><!-- End of picture text -->

# **4 Differences in Socioeconomic Outcomes across Groups**

While in previous the sections we focused on the differences between Natives and nonNatives, in this section we focus on differences across the different native groups.

The panels in Figures 6, 7, and 8 show the average outcome by group for each of the outcomes we study.<sup>11</sup> The last panel in Figure 8 shows the average standardized outcome by group (with higher numbers denoting better outcomes). These figures also show the average outcome for Natives and non-Natives, and the outcome at the first and ninth deciles by department as benchmarks for comparison.

There is large variation across groups in all outcomes. In fact, one-third of the groups have average years of education above the national average.

The magnitude of the differences in outcomes across groups can be appreciated in several ways. First, it is always the case that the difference in the average outcome between the first and last group is greater than the average difference between Natives and nonNatives. Consider for example the case of years of education. While non-Natives have 9.86 years of education on average, the Charr ́ua have 11 years of education on average and the Vilela have 5.4 years of education on average. Second, it is always the case that the difference between the first and last groups is greater than the difference between a

> 11 Table A4 in the Appendix provides these numbers.

16
variables measuring different aspects of institutional quality and the index proxying the importance of corruption in the public sector confirm the findings in the previous section. All the alternative institutional variables have important positive impact on both the value of exports and the probability of exporting.

# **4 Summary**

Recent empirical evidence supports the characterization of developing countries as belonging either to a cohort of countries that are deepening linkages with the global trading system (globalizers), or to those that are not (Dollar and Kraay, 2004). The globalizers (like China and India) have seen rapid growth in trade, and this growth has been linked to accelerating growth rates, pushing incomes on a catch-up path with the OECD and driving poverty rates down in the process (Sala-i-Martin, 2006). At the same time, there is another cohort of developing countries (many in Africa) with a very different story to tell. For a raft of reasons, they are being left behind. While trade and growth may be wrapped up in a positive cycle for some countries, those left behind have not experienced rapid trade growth, or the related mechanisms that signal deeper integration into the global economy.

How important are tariffs, and how important are factors like infrastructure and institutions in explaining the failure of non-performing developing countries to integrate into the trading system? To address this set of questions, we have explored the evolution of trade across a panel spanning bilateral trade flows from 1988 to 2002. We have examined not just trade volumes where trade is observed, but also the determinants of zero trade flows. This has involved estimating a selection model 21
<!-- Start of picture text --> 10_COA xp_OTP<br>xp_COA 10. TRD<br>oeEL<br>= xp_KES<br>10_OIL 1O_KE5 1O_PPP a<br><!-- End of picture text --> <!-- Start of picture text -->
|IO_XMN |O_PFD 1O_ATP<br>lO_hhd | 10_AGR | 10_OME | fp_AGR<br>1O_LEA<br>xp_P_C | xplGAS | 10_ELE<br>Woe<br>xp_CRP<br>=<br>10 TRQ. | IOLONS<br>10_XSV<br>|O_WAP |O_WTP 1O5TEX | |O_OMN xp_AGR<br><!-- End of picture text -->
# **Table 12a.  Tariff Rates, Export Tax Rates, Estimated Ad Valorem Equivalence of Barriers to FDI in** **<u>Services Sectors and Estimated Improved Market Access</u>** <u>(ad-valorem in %) -- by sector</u>

|(ad-valorem in %)-- bysector|**Tariff**|**rates**||Estimated<br>change in|Equivale<br>t|nt % barriers<br>o FDI|
|---|---|---|---|---|---|---|
||2011-pre WTO<br>accession|2020-final<br>commitments|Export tax<br>rates|world market<br>price|Base<br>Year<br>|Post-WTO<br>Accession|
|Electric industry|0.0|0.0|0.0|0.0|||
|Oil extraction|1.7|1|7.9|0.0|||
|Oil processing|5.1|4.9|4.6|0.0|||
|Gas|4.7|5|18.8|0.0|||
|Coalmining|4.4|4.4|0.0|0.0|||
|Ferrous metallurgy|8.6|5.9|0.4|1.5|||
|Non-ferrous metallurgy|10|7.4|5.3|1.5|||
|Chemical & oil-chemical industry|7.4|5.2|1.6|1.5|||
|Mechanical engineering & metal-working|8.9|5.7|0.0|0.0|||
|Timber & woodworking & pulp & paper industry|14.3|8.2|6.9|0.0|||
|Construction materials industry|12.7|9.9|1.6|0.0|||
|Textiles and Apparel|12.3|8.2|4.1|0.5|||
|Food industry|18.2|13.6|3.1|0.5|||
|Other industries|10.4|7.4|0.0|0.5|||
|Agriculture & forestry|7.7|5.7|0.6|0.0|||
|Other goods-producing sectors|14.2|10.7|0.0|0.5|||
|Telecommunications|||||33.0|0.0|
|Science & science servicing (market)|||||33.0|0.0|
|Financial services|||||36.0|0.0|
|Railway transportation|||||33.0|0.0|
|Truck transportation|||||33.0|0.0|
|Pipelines transportation|||||33.0|0.0|
|Maritime transportation|||||95.0|80.0|
|Air transportation|||||90.0|75.0|
|Other transportation<br>Source: Shepotylo and Tarr (2014) for tariff rate<br>authors'estimates for change in world market p|s; Kimura et al.<br>rices.|(2004a,b,c) for|barriers to F|DI; Roskomst|33.0<br>at for exp|0.0<br>ort tax rates;|

48
_COTE D’IVOIRE: Volatility, Shocks and Growth_

|**Forecast Error Variance Decomposition (FEVD**<br><br>|**) Variable:**<br>|**D.LnYL**<br>|
|---|---|---|
|**Forecast Step**<br>**REERShock **|**SE **|**OIRF**|
|**1**<br>0.16%|0.01|1.00%|
|**2**<br>4.49%|0.06|0.68%|
|**3**<br>5.49%|0.07|0.26%|
|**4**<br>5.62%|0.07|0.40%|
|**5**<br>6.06%|0.07|0.16%|
|**6**<br>6.08%|0.07|0.08%|
|**7**<br>6.03%|0.07|0.12%|
|**Source:**Authors' calculations. Each impulse variable w<br>standard deviation.|as shocked by|one|

Further, we provide an illustrative summary linking the response of output to price ( ) and REER shocks ( ).

**Figure 12: Summary, Response of growth rate to cocoa price and real exchange rate shocks**

|Impulse Variable|Shock Magnitude|Response of<br>worker(D.ln|the Growth<br>yl)|rate of GDP per|
|---|---|---|---|---|
|||**1**|**2**|**3**|
|Cocoa Prices|**0.1627**|**0.96%**<br>(0.007278)|**1.46%**<br>(0.00768)|**0.21%**<br>(0.005464)|
|Real Exchange Rate|**0.1233**|**0.67%**<br>(0.006862)|**1.1%**<br>(0.006794)|**0.48%**<br>(0.004725)|

**Figure 13: VAR FEVD of GDP/worker in response to cocoa price and RER shocks**

|Impulse Variable|Shock Magnitude|Forecast Er<br>of the Grow<br>(D.lnyl)|ror Variance<br>th rate of G|Decomposition<br>DP per worker|
|---|---|---|---|---|
|||**1**|**2**|**3**|
|Cocoa Prices|**0.1627**|**0.5%**<br>(0.013205)|**4.42%**<br>(0.06099)|**11.5%**<br>(0.097643)|
|Real Exchange Rate|**0.1233**|**0.18%**<br>(0.008075)|**2.09%**<br>(0.040829)|**6.2%**<br>(0.069355)|

**Source:** Authors' calculations. Each impulse variable was shocked by one standard deviation of the orthogonalized errors. The standard errors of the forecasts are shown in parenthesis.
- 44 -

Table 10 (Con't). Consolidated Buget Base Infrastructure Regressions with Time Dummies

|Variables|Model 1|Model 2|Model 3|Model 4|Model 5|
|---|---|---|---|---|---|
|GASTILSQ|1.32-<br>|1.40-<br>|1.38-<br>|1.55-<br>|1.48-|
||(2.95)|(3.25)|(3.21)|(3.67)|(3.61)|
|PCOM|1.12E-1<br>(0.11)|1.66E-1<br>(0.17)|2.31E-1<br>(0.24)|-1.66E-1<br>(-0.18) (-0.5|-5.61E-1<br>9)|
|NFFCAP1K|-|3.43E-2~<br>(3.34)|3.30E-2-<br>(3.14)|3.35E-2<br>(3.35)|3.03E-2-<br>(2.98)|
|NOFGDP1|-|-|3.1OE-1<br>(0.64)|-||
|NCBGDPSQ|-|-|-|2.94E-1l<br>(2.59)||
|DFIGDP|-|-|-|-|-4.62<br>(-2.56)|
|DFIGDPSQ|-|-|-|-|1.64-<br>(3.50)|
|Y81|-5.86E-1<br>(4..19)|-1.22<br>(-0.41)|-1.43<br>(-0.48)|-1.65<br>_(-0.57)_|-2.75<br>(-0.96)|
|Y82|3.00E-2<br>(0.01)|-2.27E-1<br>(-0.08)|-3.02E-1<br>(-0.10)|-1.01<br>(-0.34)|-2.54<br>(-0.85)|
|Y83|-1.06E-1<br>(-0.03)|-6.86E-1<br>(-0.21)|-6.81E-1<br>(0.21)|-9.42E-1<br>(-0.30)|-3.51<br>(-1.10)|
|Y84|-1.71<br>(-0.54)|-1.04<br>(-0.34)|-9.06E-1<br>(-0.30)|-9.68E-1<br>(-0.33)|-2.45<br>(-0.83)|
|Y85|2.32E-1<br>(0.07)|2.80<br>(0.84)|3.17<br>(0.93)|2.32<br>(0.71)|-1.OOE-l<br>(-0.03)|
|Y86|-4.21<br>(-1.24)|-8.84E-1<br>(-0.26)|-4.50E-1<br>(-0.13)|-3.29E-1<br>(-0.10)|-4.10<br>(-1.22)|
|R<sup>2</sup><br>|0.7381<br>|0.7627|0.7636|0.7767|0.7889|
|Adjusted R<sup>2</sup><br>|0.6853<br>|0.7122<br>|0.7186<br>|0.7267<br>|0.7391<br>|
|F-Value<br>N|13.965<br>132|15.091<br>132|14.401<br>132|15.511<br>132|15.841<br>132|

Significant at the .01 level Significant at the .05 level Significant at the .10 level
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