rafmacalaba/datause-encoder-data huggingface dataset:
1950 to 2003 for the majority of countries.<sup>41</sup> The indicator we used for measuring health outcomes is life expectancy.

We excluded from our sample small islands or territories as well as the former Soviet Union countries and East European countries because the data is unavailable or inconsistent. This still leaves us with gaps in the data which had to be imputed using linear interpolation. The final sample contained longevity and GDP per capita data for 122 countries over 44 years (1960-2003).<sup>42</sup> A plotting of the cross-section data for 2003, reveals a clear non-linear relation between GDP per capita and a country’s life expectancy (see Figure 1). As per capita income increases, the improvement in health outcomes measured as life expectancy slows down.

**Figure 1: Scatter Plot of Life Expectancy and GDP per Capita in 2003**

<!-- Start of picture text --> Scatter Plot of Life Expectancy and GDP Per Capita in 2003<br>0 10000 20000 30000 40000<br>GDP Per Capita (1990 International Geary-Khamis dollars)<br>Scatter Plot Fitted Line Lowess Smoothing<br>90<br>70<br>Life Expectancy at Birth<br>50<br>30<br><!-- End of picture text --> 41 The Maddison data, however, do not include the data for Democratic Republic of the Congo and Republic of the Congo, Cyprus, Fiji, Guyana, Iceland, Luxembourg, Macao (China), Maldives, Malta, and Papua New Guinea. They are relatively small countries, so omitting them does not bias the results. In addition, Maddison only have data for former USSR countries and East European countries from 1989 to 2003.

42 In the scatter plot shown above, former Soviet Union countries and East European countries are not included.

20
in each country.

We then introduce a triple interaction term between amenability of remote work at the sector level, internet penetration at the country level, and an exporter dummy variable at the firm level. This helps establish whether or not there is an exporter premium on the effect of a country’s digital infrastructure on firm performance, conditional on a sector’s amenability to remote work.

# **4. Results**

This section presents the baseline results, robustness of those results and the heterogeneity in the effects.

## **4.1 Baseline specification**

The results from estimation of equation (1) presented in Table 1 show that firms in sectors more amenable to remote working experienced, on average, a more resilient sales performance during the pandemic in countries with better digital infrastructure compared to other countries, when controlling for country and sector fixed effects. For the full sample, the interactions between the amenability of remote work at the sector-level (measured as the share of remote workers) and the country-level quality of digital infrastructure (measured as the percentage of population using the internet) yields a positive and statistically significant coefficient of 0.979 for the change in firms’ sales (Table 1, column 1).<sup>5</sup> Table 1: **Change in Sales**

||Full|Exporters|Non-Exporters|Full|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exporter||||1_._739<sup>_∗∗∗_</sup><br>(0_._550)|
|WfH x digital infra.|0_._979<sup>_∗∗∗_</sup>|1_._607<sup>_∗∗_</sup>|0_._923<sup>_∗∗∗_</sup>|0_._980<sup>_∗∗∗_</sup>|
||(0_._254)|(0_._626)|(0_._280)|(0_._278)|
|WfH x digital infra. X exporter||||0_._076<br>(0_._605)|
|Country F.E.|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|
|R-squared|0_._262|0_._308|0_._241|0_._262|
|Observations|68,007|13_,_145|54_,_862|68_,_007|

The F-stat testing the equality of coefficients in columns 2 and 3 is 0.998, with p value 0.318. Estimations also control for (log) firm’s size, quarter F.E, and Google’s Mobility index. Estimations are weighted using country weights. Standard errors in parentheses. Significance levels ***, **, and *, correspond to p _<_ 0.01, p _<_ 0.05, p _<_ 0.1, respectively. The amenability of remote work and internet use/penetration do not enter the regression individually because the former are absorbed in sector fixed-effects and the latter in country fixed-effects.

Notably, both exporting and non-exporting firms in sectors that are amenable to remote work enjoy a premium in countries with larger penetration of internet users (columns 2 and 3 in Table 1) although the coefficient is smaller for the latter. This is consistent with the evidence that even when remote work is feasible in a given sector, the drop in sales was 6%-7% higher for non-exporters compared with exporters (Constantinescu et al., 2022). However, the difference in this coefficient across the samples of exporters and non-exporters is not statistically significant.

> 5The amenability of remote work and internet use/penetration enter the regression individually because the former are absorbed in sector fixed-effects and the latter in country fixed-effects.

8
22 such information were available for Brazil. There was little alternative to attempting some estimate of the general equilibrium effects of food price changes or, at least, that part of the general equilibrium effects corresponding to the short-run pass-through from product prices to agricultural wages.

Using detailed information on the type of output produced by each agricultural worker in the PNAD 2006 survey, we mapped workers to their farm sectors and estimated market income effects under two different pass-through assumptions – one corresponding to a competitive labor market in the short run, and another to a market where only half of the price increase is passed on to workers. These market income effects were considered alongside the standard expenditure effect, calculated as a first-order approximation to the compensating variation for each food consumer, and a transfer income effect that captured the increase in benefit values for two social assistance programs, Bolsa Família and Benefício de Prestação Continuada.

According to our estimates, the overall effect of the price increases was to raise both extreme and moderate poverty in Brazil, despite the country‟s position as one of the main food exporters in the world. Even with full pass-through to agricultural wages, and despite increases in social assistance benefits, the incidence of extreme poverty increased from 11.0% to 12.3% as a result of higher food prices. However, these increases in poverty were much less pronounced than if the income effects had not been taken into account. Naturally, higher incomes arising from a greater value of agricultural production were particularly important in rural areas where, under the scenario of 100% pass-through, incomes rose for the bottom half of the population, reducing every measure of poverty.

In large urban areas, though, few people benefit directly from agriculture, so the standard expenditure effect (reduction in consumer surplus) dominated. And since Brazil is 80% urban, the aggregate picture for the country as a whole was one of reductions in average welfare as a result of higher food prices. Behind the average impact, however, the distributional consequences of higher food prices look rather different depending on whether or not one takes the (general equilibrium) market and transfer income effects into account. Whereas the expenditure effect PIC is essentially upward sloping over the entire distribution (pointing to a consistently regressive effect), the overall price change incidence curve that incorporates all three effects is U-shaped: the poor – particularly
Second, to the extent that women value household public goods such as children’s health and education relatively more than men do, then we would expect women’s empowerment to lead to faster economic growth in the future, as children are more educated, well-fed, and in better health.

Yet, the fact is that most households are comprised of members of more than one gender, particularly in rural areas. Cooperation within the household can lead to better household welfare outcomes for all members. Our theoretical model provides the conditions under which we expect to see greater gains from intra-household cooperation versus women’s empowerment, while highlighting that the greatest gains are achieved when both cooperation and empowerment are achieved. Improving women’s empowerment would yield the largest gains where social norms favor husbands’ influence in the allocation of resources, and where husbands place limited value on household public goods. On the other hand, promoting cooperation would be relatively more effective when social norms support wives’ control over income, and both place a relatively high value on household public goods. To test the model’s predictions, we use a unique panel data set on married couples in rural Malawi, and find that cooperation between spouses, proxied by the share of jointly controlled income in total income, is a better predictor of total income and consumption expenditures per capita than wives’ empowerment, proxied by the share of female income in total disjoint (male + female) income.

Supported also by cross-country qualitative research, our results suggest that household public goods are relatively important to both women and men in rural Malawi; that husbands’ capacity to control wives’ incomes is relatively limited; and that development programs that promote intrahousehold cooperation could lead to greater gains in income and household public goods provision compared to interventions focusing exclusively on women’s empowerment. The evidence, therefore, offers support for the integration of intra-household cooperation in government and donor agendas, policies and projects. Theoretically, we expect that focusing on both empowerment and cooperation together would lead to the greatest gains; further empirical evidence would shed better light on such gains, particularly on the impacts of intra-household cooperation. Additionally, more evidence of the impact of empowerment on both wives’ and husbands’ labor supply would be key in determining likely impacts of various interventions on household public good supply and total income earned.

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Page

# CONTENTS (Contd)

# TEXT TABLES:

1. BIends in Lending to the PHN Sector, FY89-91<sup>................................</sup> 4 2. Sectoral Composition and Funding Characteristics<sup>of PHN Portfolio, FY91</sup> .... ....... _5_ 3. Cofinancing from Official Sources in PHN Projects,<sup>FY90 and FY91 .....</sup> ........... 6

# TEXT FIGURES:

1. Lending for the Population, Health and Nutrition Sector,<sup>FY80-91 .....</sup> ............ 3 2. Sectoral Compostion of PHN Projects Approved in FY91<sup>.........................</sup> 4 3. Regional Distribution of PHN Lending, FY91 ................................. 5 4. Breakdown of Tbtal Project Costs by Expeneiture<sup>Category ........................</sup> 7

# ANNEXES:

1. Statistical Annex . 29

- TIbles: 1. FY91 PHN Portfolio by Sectoral Compostion .31 2. Primary Health Care Component of PHN Lending, FY91<sup>.32</sup> 3. PHN Lending Volume, FY80-91 .33 4. FY91-Approved PHN Projects: Funds Components<sup>.34</sup> _5._ PHN Sector Reports Completed by Region, FY82-91<sup>.35</sup> 6. PHN Sector Reports Completed in FY91 .36 7. OED Reports on PHN Sector, FY91 .38 8. Lending for Population Projects and Components by<sup>Region, FY86-91 .39</sup> 9. PHN Projects with Population Components, FY91<sup>.40</sup> 10. Nutrition Components in Structural and Agricultural<sup>Adjustment Operations,</sup> FY91 .41

# Figures:

1. PHN Projects under Supervision .43 2. Total Project Resources for Nutrition in Bank-Assisted<sup>Operations, FY81-91 .</sup> 44 3. The Bank's Nutrition Lending, FY81-91 .44 4. World Bank Lending for Population, FY80-91 .45

2. Lo3n and Credit Summaries of FY91-Approved<sup>PHN Operations .47</sup> 3. Bibliography ....... 61
households under this design, prices do not distort consumption by charging some households more than others, achieving the goal of economic efficiency. The recommended tariff design also leads to progressive prices: it generates an average gain of Rs. 4.5 to Rs. 10 for households at the lowest quintile of the income distribution and an average loss of Rs. 20 per month for households in the top quintile.

Due to the poor quality of outages information maintained by the utility, the relationship between household demand and supply quality is not considered in this paper. Although a future line of research can explore this relationship in greater detail, the proposed tariff design addresses a key feature of how supply is rationed across consumers during periods of load-shedding. Under current prices, a utility has a greater incentive to supply power to larger consumers during periods of rationing. As the per-unit price of electricity is higher for these users, during periods of scarcity, the utility earns more per unit of dispatch by supplying to these consumers rather than smaller consumers situated at lower consumption brackets. In contrast, a uniform energy price across consumers under the recommended design, takes away the incentive of dispatching power to larger consumers and rebalances the existing disparities in dispatch across users. To the extent that the utility does offer greater dispatch to high consumption households during periods of scarcity, under the recommended tariff plan, the share of power supply to rural areas will likely increase as rural consumption share is lower than urban areas. Thus, rebalancing of supply dispatches across households is an additional virtue of our proposed plan in addition to its efficiency, equity and cost-neutral properties.

Finally, the simulated results in this paper are only as good as the utility’s ability to send bills and collect revenues from households based on their actual monthly consumption. The analysis of the utility’s billing data set suggests recurring cases of “average billing”, in which the household’s bill amount was imputed based on its average half-yearly consumption rather than the cotemporaneous consumption. There could be several reasons for why these bills had to be generated, including the possibility of faulty meters or meters being inaccessible to the meter reader or, in the worst case, a potential collusion between the meter reader and the consumer to deflate the cost of electricity consumption. Designing incentives to prevent the issuance of such imputed bills and modeling the impact of their strategic position on household level demand is outside the scope of this paper. However, an increase in the incidence of average billing cases will result in greater fixed cost burden per household. As a result, households that pay their bills based on their actual contemporaneous consumption face significant welfare losses.

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**Table 3: Robustness of Impact on Management Practices to different weighting schemes**

||Overall<br>Anexo K|Principal<br>component|Lasso<br> Log Employ.|Lasso<br>Productivity|Lasso<br>WMS|
|---|---|---|---|---|---|
|**Panel A: Unbalanced Panel**||||||
|Individual Treatment*During Intervention|9.703***<br>(1.370)|6.014***<br>(0.946)|0.227***<br>(0.085)|7.065***<br>(1.238)|0.079**<br>(0.036)|
|Individual Treatment*Post Intervention|9.620***|6.012***|0.286**|8.297***|0.140***|
||(1.830)|(1.217)|(0.115)|(1.811)|(0.041)|
|Group Treatment*During Intervention|11.971***|7.266***|0.403***|9.269***|0.240***|
||(1.660)|(1.177)|(0.090)|(1.463)|(0.040)|
|Group Treatment*Post Intervention|8.544***|5.512***|0.301***|7.596***|0.225***|
||(1.894)|(1.220)|(0.106)|(1.706)|(0.040)|
|Sample Size|225|200|213|217|221|
|P‐value: Individual=Group During|0.145|0.208|0.020|0.111|0.000|
|P‐value: Individual=Group Post|0.533|0.658|0.862|0.670|0.043|
|Control Mean|55.98|5.59|2.46|43.01|0.93|
|Control SD|10.79|6.03|0.47|9.66|0.20|
|**Panel B: Balanced Panel**||||||
|Individual Treatment*During Intervention|9.861***|6.048***|0.273**|7.302***|0.100**|
||(1.756)|(1.327)|(0.119)|(1.602)|(0.049)|
|Individual Treatment*Post Intervention|9.757***|5.972***|0.309**|8.451***|0.148***|
||(2.014)|(1.402)|(0.122)|(2.003)|(0.044)|
|Group Treatment*During Intervention|12.118***|7.494***|0.445***|9.624***|0.263***|
||(2.029)|(1.525)|(0.118)|(1.781)|(0.051)|
|Group Treatment*Post Intervention|8.889***|5.736***|0.361***|8.009***|0.242***|
||(2.067)|(1.416)|(0.111)|(1.914)|(0.043)|
|Sample Size|202|178|190|194|198|
|P‐value: Individual=Group During|0.152|0.174|0.032|0.114|0.000|
|P‐value: Individual=Group Post|0.627|0.844|0.539|0.797|0.032|
|Control Mean|55.98|5.59|2.46|43.01|0.93|
|Control SD|10.79|6.03|0.47|9.66|0.20|

# Notes:

Panel A is for the 124 firms for which Anexo K management practices are measured post‐baseline, panel B for the 101 firms for which practices are measured both during and after intervention.

Robust standard errors in parentheses, clustered at the firm level. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively.

Anexo K management practices are 141 management practices divided into five sub‐areas.

Ancova estimation controls for baseline (December 2013) mean, time and triplet fixed effects. Principal Component takes the first principal component of the 141 practices.

Remaining columns using Lasso to choose the subset of practices that best predict log baseline employment, log labor productivity, and the WMS baseline management score respectively, with post‐Lasso coefficients then providing the weightings on the different practices used.

37
mix of exported goods, the top category for both countries is textiles and apparel, which account for 14.1% (17.5%) of China’s (India’s) exports. Metals including steel, are another important category of exports, representing 10.1% (11.8%) of China’s (India’s) exports. In terms of growth rates, average annual real growth of exports between 1993 and 2001 was 15.8% for China and 11.0% for India. In our product-level data set, which excludes exports by each country to the U.S., EU and China or India, average annual growth of the volume of exports (across all markets) was 16.2% for China and 11.9% for India. Given the similarities of trade structure by destination markets and by products, the similar high rates of trade growth, and the similar frequencies of antidumping investigations (that we discuss more in the next section), India is the best country to use as a control for China in such a difference-in-difference framework.

On the other hand, when we estimate equation (3), we define the dependent variable as the difference between Chinese export growth of product _h_ in year _t_ to Japan and Korea. We choose Japan and Korea as the export destinations _i_ and _k_ for the following reasons: (1) Japan and Korea are at similar stages of development with similar industrial structures, (2) the two countries have similar aggregate rates of import growth from China, and (3) both countries frequently face U.S. and EU antidumping measures during this time period with some overlap of products that China exports, making them potentially good targets for identifying trade depression. While Japan and Korea were not required by WTO rules to report changes in trade policy, including antidumping, against China during the 1992-2001 period and, thus, any reporting may be incomplete, some information is available. Japan reported one antidumping case against China (initiated in 1991) and Korea reported eight investigations between 1992 and 2001. While the information reported may be incomplete, it is supportive of our assumption that Japan and Korea’s trade policies against China did not involve using antidumping to enact high frequency tariff changes during this period.

# **3.2 U.S. and EU antidumping policy variables**

The main explanatory variables of interest are the changes to U.S. and EU import policy facing a commodity _h_ exported from China or from another country. Our estimates use the level of duties imposed by the U.S. and by the EU. For EU cases that result in price undertakings, we use reported dumping margins to proxy for the magnitude of the policy change.<sup>18</sup> The information on U.S. and EU measures imposed at the product level derives from the Global Antidumping Database (Bown, 2010a). For the case of the U.S. (EU) antidumping, the information

> 18In unreported results, we have also separated antidumping cases that end in duties versus those that end in price undertakings, and this does not affect our results.

13
# **Table A2. Migration Selection Equation**

||**Coef/z-stat**|
|---|---|
|Age|0.011|
||(0.78)|
|Age squared|-0.000|
||(0.41)|
|Father's education level|0.036<br>(2.60)**|
|Father's employment in non-farm sector|0.344<br>(3.61)**|
|High caste dummy|0.253|
||(3.78)**|
|Education|0.033|
||(0.87)|
|Constant|-1.532|
||(4.58)**|
|Observations|2762|

The dependent variable is 1 if head was born outside district of residence Robust z statistics in parentheses * significant at 5%; ** significant at 1%
**_Energy demand models for policy_** **_<u>formulation</u>_**

influenced by data availability; degree of industrialization and the environmental setting, but higher levels of disaggregation are preferred to an aggregated analysis.

Table 2: Usual disaggregation of the industrial sector

|Firstlevel|Secondlevel|Thirdlevel|
|---|---|---|
|Mining|||
|Manufacturing|Food|Sugar, edible oil, etc.|
||Textile||
||Wood||
||Paper||
||Chemicals|Fertilizers,|
||Non-metallicminerals|Cement,|
||Primary metals|Steel, Aluminium.|
||Engineering|Electronics, Cars, etc.|
||Miscellaneous||
|Construction|||

Source: UN (1991).

Following the basic principle of this approach, energy demand from various end-uses is considered next. These generally include motive power, heat, cooling, chemical energy, lighting, etc. Depending on the nature of the industry, these end-use demands could be analyzed for industrial processes and for buildings. Normally, the process energy demand would be significant for energy intensive industries while the building-related energy demand could be important for labor intensive industries. The method tries to capture the essential features of the production system through a detailed description of the technologies and practices prevalent in a region or country.

Various determinants of the end-use demand are then identified: the level of industrial activity (expressed as value added) is considered to be the main factor. However, for energy intensive industries, physical level of output can also be considered. For forecasting purposes a mechanism for determining the output of each industry and the changes in the industrial output composition would have to be developed. Finally, energy demand is estimated by linking the output from the industry to specific consumption or energy intensity. Figure 2 presents this scheme is a diagrammatic form.

55