rafmacalaba/datause-encoder-data huggingface dataset:
prevention. Although the private willingness to pay may be an underestimate of the social benefits of improved service delivery,<sup>2</sup> we believe that there will be many cases where the level of public service delivery is inadequate to meet even the _private_ valuation of the benefits of improvements. Households may be willing to pay for improvements, but for a variety of reasons (coordination failure on both the demand and supply sides, institutional constraints), neither the public nor private sectors have risen to meet the demand.

# **_To Whom?_**

It is well accepted that the distribution of public services is inequitable. The 2004 World Development Report reports that the poorest fifth of the population generally receives less than a fifth of public service expenditures, while the richest fifth receives more than one fifth of expenditure (World Bank 2004). In this view, the distribution of public services is inequitable because the value of public services provided to the wealthy is greater than the value of services provided to the poor. This claim is unarguable – if a public goal is to provide education, it is difficult to claim _a priori_ that educating the wealthy is more expensive than educating the poor, to the same standard. But this view of distribution conflicts with a slightly different conception, in which distribution is defined relative to a particular index. This leads to the measurement of the _concentration_ of the value of services at various points of (e.g.) the income distribution; and distribution of a good or service is considered progressive if it is more equitable than the prevailing distribution of income.

In this paper, we recover household-specific contribution of public services and other characteristics to the self-assessed rental price of a dwelling unit. We can then

> 2 Availability of these services are likely to generate significant positive externalities.

3
corresponding to Finland; the green line plots the values corresponding to Italy, and the red line plots the values corresponding to Serbia.

Figure 1 also demonstrates that different countries implemented their reopening policies at a different pace. We define the start of reopening as the date when the stringency index began declining after reaching the maximum in March-May 2020. Some specific country examples are illustrative of the differences in the reopening process. For instance, Italy and Serbia had similar maximum levels of stringency - Serbia even reaching the stringency index of 100 - but their reopening trajectories were very different. Italy halved the stringency of restrictions in the course of a month and later remained at roughly the same stringency level. Serbia reduced its stringency by three-quarters of its peak value in a month and a half. At the end of June, Serbia had to reimplement restrictions, and its stringency index exceed that of Italy during part of the summer. On the other hand, in Finland, the stringency of restrictions during April-May were lower than those in Italy and Serbia. Furthermore, while Finland started lifting restrictions in spring, the stringency index never dropped below half its maximum value.

Reopening processes are multi-faceted, but in this paper, we focus on three dimensions that we think are most relevant for the economic recovery: i) sequencing, ii) timing, and iii) speed. Differences along these three dimensions are expected to be relevant from an economic point of view.

The first important dimension of the reopening process is the sequence in which different social and economic activities restart their normal operation. For example, school reopening has been and is still hotly debated, given the uncertainty about contagion risk among children (Milani et al., 2020). Countries adopted different school opening policies, even when facing a similar epidemiological situation (World Bank 2020). Denmark was the first country to reopen schools, starting with primary school grades on April 15; Italy and Spain did not reopen schools until the fall term. In between, countries opted for different strategies – some reopened schools only for grades which would sit high stakes exams (for example, Albania, Austria, and Germany), while others canceled those exams (e.g., France and the United Kingdom).

Beyond the importance for education, schools occupy a central node in production networks as the providers of childcare services to allow parents of young children to be more productive. Estimates from Germany indicate that a scenario of full economic reopening with schools closed would 5
**Table 5a: Unweighted Average Tariffs for Agriculture Tariff Lines under AGOA and GSP, and for** **<u>those excluded from Preferences in 2003</u>**

||**Non-LDCs**|**LDCs**|
|---|---|---|
|**Total GSP**|3.5%|5.2%|
|**GSP**|3.5%|3.5%|
|**GSP LDC**|...|6.8%|
|**Total AGOA**|6.7%|7.7%|
|**Dutiable Lines – Products excluded from AGOA**|30.7%|31.0%|

For specific tariffs data on 2002 calculated duties and customs value (based on 2002 US GSP Group) are used to derive ad valorem equivalents. When there are zero duties for the GSP group of countries we use data for total imports to calculate the ad valorem equivalent. Data source: USITC dataweb These tables also show that the products excluded from preferences are high-duty products. For agriculture the average duty on excluded products is over 30 percent, these include the out of quota duty rates for products affected by tariff rate quotas. This, at least in part, might explain why there may be so few exports of these products from SubSaharan African countries to the US, as shown in Table 2. Similarly for manufactures the average duty on products not eligible for preferences is high relative to the overall average duty and high relative to the products covered by the GSP. Hence, in terms of the average margin of preference, AGOA significantly enhances the preferences available to Sub-Saharan African countries, particularly for those countries eligible to receive benefits for clothing products. Nevertheless, a range of high average duty products, particularly in agriculture, remain excluded from preferences.

**Table 5b: Unweighted Average Tariffs for Manufactured Products Under AGOA and GSP, and for** **<u>those excluded from Preferences in 2003</u>**

||**Without appa**|**rel benefits**|**With appar**|**el benefits**|
|---|---|---|---|---|
||**Non-LDCs**|**LDCs**|**Non-LDCs**|**LDCs**|
|**Total GSP**|3.8%|4.0%|3.8%|4.0%|
|**GSP**|3.8%|3.8%|3.8%|3.8%|
|**GSP LDC**|...|4.8%|...|4.8%|
|**Total AGOA**|6.1%|12.7%|7.7%|12.3%|
|**AGOA (excluding GSP)**|6.1%|12.7%|6.1%|12.7%|
|**AGOA apparel**|...|...|12.1%|12.1%|
|**Dutiable Lines – Products excluded**<br>**from AGOA**|9.9%|10.2%|8.7%|9.1%|

17
U

# Table 4. SUMMARY OF EVIDENCE ON USE OF ORS DURING LAST DIARRHEAL EPISODE BY RECALL PERIOD, AGE OF CHILD AND SEASON OF SURVEY

||||||RECALL|PERIOD|||
|---|---|---|---|---|---|---|---|---|
|||||LAST WEEK||L|AST 2 WEE|KS|
|SURVEY|SEASON|YEAR|< 1 YEAR|< 2 YEARS|< 5 YEARS|<1YEAR|<2 YEARS|< 5 YEARS ||
|EDHS|WINTER|<br>1988||l||l|<br>l|_<br>_|
|EGYPT<br>|||29.9|32.6|28.7||||
|LOWER<br>EGYPT|||_||36.0|||l_!|
|NATIONAL KAP|WINTER|1988||14.3|||16.1||
|MENOUFIA KAP|WINTER|1988||_|||45.0||
|MENOUFIA C.S.|ALL YEAR|1990||_||54.1|55.2|45.1|
|l_________________|JULY/AUG||_|||||40.1|
|l___________|SEPT/OCT||_|||||45.9|
||NOV/DEC|_||_||||48.4|

Source: The data are drawn from the first country report of Egypt Demographic Health Survey 2, a draft report21 on the base line round of a Menoufia child survival study underway under Johns Hopkins / AUC auspices, and recent national and Menoufia KAP impact studies2' 23 conducted by SPAAC in Egypt.

; !

12
Table 3: Average level of technology measures

||_ANUMf,j_|_NUMf,j_|_MAXf,j_|_MOSTf,j_|_Nf_|
|---|---|---|---|---|---|
|ABFs|2.1|2.1|2.7|2.0|4.8|
|GBFs|2.1|2.0|2.6|2.0|5.3|
|SSBFs|2.1|2.1|2.7|2.0|4.8|

Notes : See Section 3.1 for definitions of variables. The table reports the average across the specific class of business functions, after averaging across establishments using sampling weights.

Table 4: Relationship between technology measures

||_MOSTf,j_|_MAXf,j_|_MOSTf,j_|
|---|---|---|---|
|_MAXf,j_|0.55<sup>_∗∗∗_</sup><br>(0.01)|||
|_NUMf,j_||0.84<sup>_∗∗∗_</sup><br>(0.01)|0.25<sup>_∗∗∗_</sup><br>(0.01)|
|N|187497|187497|187497|
|R-squared|0.66|0.75|0.50|
|BF FE|Y|Y|Y|
|Firm FE|Y|Y|Y|
|Variation Explained|0.34|0.47|0.05|

Notes : This table reports the regression estimates of specifications 5, 6, and 4. To compute the last row, we first residualize the dependent and independent variables by regressing them on the fixed effects, and then we regress the residuals of the dependent on those of the independent. The reported figure is the corresponding _R_<sup>2</sup> . Regressions are estimated using establishment-level sampling weights. Standard errors are clustered at the establishment level. *, ** and *** denote 10%, 5% and 1% significance respectively. *, ** and *** denote 10%, 5% and 1% significance respectively.

50
**Figure 6. Modern vs. Traditional Exports and Level of Development, 2011–13**

<!-- Start of picture text --> 20 20<br>10 10<br>IND<br>ZAF<br>INDIDN CHN RUS<br>RUS<br>0 CHN BRA 0 IDN ZAFBRA<br>6 8 10 12 6 8 10 12<br>log GDP per capita PPP log GDP per capita PPP<br>Modern Services Exports as % of GDP<br>Traditional Services Exports as % of GDP<br><!-- End of picture text --> Source: World Development Indicators.

Notes: GDP = Gross Domestic Product; PPP = Purchasing Power Parity; BRA = Brazil, CHN = China, IND = India, RUS = Russia, ZAF = South Africa.

30
**Table 7: Networks, firm location and export participation**

|Dependent variable:|all fir|ms|low prod|export pa<br>uctivity|rticipation<br>medium pr|oductivity|high productivity|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)<br>(8)|
||Coeff.|F-stat.<br>(P-value)|Coeff.|F-stat.<br>(P-value)|<br>Coeff.<br>|F-stat.<br>(P-value)|<br>Coeff. F-stat.<br>(P-value)|
|common language * North|0.0488**||0.0463*||0.0487**||0.0524**|
||(0.0238)|8.49|(0.0236)|8.62|(0.0247)|9.22|(0.0231)<br>7.26|
|common language * Other|0.1083**|(0.00)|0.1008**|(0.00)|0.1112**|(0.00)|0.1150**<br>(0.01)|
||(0.0431)||(0.0408)||(0.0443)||(0.0450)|
|ln emigrants * North|0.0020**||0.0015**||0.0021**||0.0025***|
||(0.0008)|5.56|(0.0006)|5.55|(0.0009)|4.83|(0.0010)<br>6.34|
|ln emigrants * Other|0.0010**<br>(0.0005)|(0.02)|0.0008*<br>(0.0004)|(0.02)|0.0011**<br>(0.0005)|(0.03)|0.0013**<br>(0.0005)<br>(0.01)|
|Firm fixed-effects|Ye|s|Ye|s|Ye|s|Yes|
|Observations|2852|926|1107|627|929|874|815425|
|Firms|147|82|57|39|48|18|4225|
|Destinations|19|3|19|3|19|3|193|
|R2|0.1|1|0.0|9|0.1|2|0.13|

<u>Notes: The estimation method is OLS. Columns (1), (3), (5) and (7) report estimated coefficients from regressions that</u> additionally include interactions between the other gravity variables and region dummies. Robust standard errors clustered by importing country are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Columns (2), (4), (6) and (8) report F-tests of equality of coefficients across regions.

6
<!-- Start of picture text --> :<br>Seeds | —_ hIa | BHM posteriorteri<br>|<br>|<br>|<br>Pesticides a<br>|<br>|<br>“1: | ———m<br>Fertilizers a—<br>|<br>|<br>index3) =<br>|<br>|<br>Cultivated. |! —_ l—_Ily—_—<br>|<br>-.1 0 l 2 3 A<br>Effect size (in SD)<br><!-- End of picture text -->
# **Figure 6 Top Foreign Asset and Liability Holders, 2004**

**Top Reserve Asset Holders**

# **Top Non-Reserve Asset Holders**

<!-- Start of picture text --> 1 Japan 21.7% 1 US 18.5%<br>2 UK 14.3%<br>2 China 16.0%<br>3 Germany 8.6%<br>3 Taiwan (China) 6.0% 4 France<br>4 Korea 5 Japan<br>5 India 3.3% 6 Luxembourg<br>6 Hong Kong (China) 7 Netherlands<br>7 Russia 8 Switzerland<br>9 Italy<br>8 Singapore<br>10 Ireland<br>9 US<br>23 China 0.6%<br>10 Malaysia 49 India 0.1%<br>Others 34.9% Others 22.7%<br>0% 10% 20% 30% 40% 0% 5% 10% 15% 20% 25%<br>Top Portfolio Equity Liability Holders Top FDI Liablity Holders<br>1 US 20.6% 1 US 22.0%<br>2 Luxembourg 14.3% 2 Luxembourg 7.8%<br>3 UK 3 France<br>4 Japan 4 UK<br>5 Ireland<br>5 Germany<br>6 France<br>6 Netherlands<br>7 Switzerland<br>7 China 4.1%<br>8 Germany<br>9 Netherlands 8 Belgium<br>10 Canada 9 Hong Kong (China)<br>22 India 0.6% 10 Spain<br>24 China 0.6% 36 India 0.4%<br>Others 20.1% Others 31.7%<br>0% 5% 10% 15% 20% 25% 0% 10% 20% 30%<br>Top Debt Liability Holders Share of World GDP<br>1 US 21.5% 1 United States 28.3%<br>2 UK 17.0%<br>2 Japan 11.2%<br>3 Germany<br>3 Germany<br>4 France<br>5 Italy 4 United Kingdom<br>6 Japan 5 France<br>7 Netherlands 6 China 4.7%<br>8 Spain 7 Italy<br>9 Ireland<br>8 Spain<br>10 Belgium<br>9 Canada<br>22 China 0.6%<br>32 India 0.3% 23.7% 10 India 1.7%<br>Others Others 28.5%<br>0% 5% 10% 15% 20% 25% 0% 5% 10% 15% 20% 25% 30%<br><!-- End of picture text --> The figures show the holdings of foreign assets and liabilities, by type of asset and liability, of the ten largest holders, China, India, and the sum of all the other countries, as a percentage of total holdings of that type of asset or liability. It also shows the share of world GDP of the ten largest economies and India. Holdings are expressed as a percentage of the sum of the holdings of all the countries in the dataset. Numbers next to holdings show position in world ranking. Source: Authors' calculations drawing on the dataset constructed by Lane and Milesi-Ferretti (2006).
**-10**

Figure 4. Adjusted RPOs for Africa's major export crops <!-- Start of picture text -->
1.3<br>1.1<br>0.9  /<br>0.7-<br>0.3<br>1970  1972  1974  1976  1978  1980  1982  1984  1986<br>- CFA countries  - non-CFA  countries<br><!-- End of picture text -->
Table 7: Program Exit and Cost Proxies Dependent Variable: Indicator for Closing a Specific Program

||Privat|e HEIs|Publi|c HEIs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Lagged Enrollment Change|-0.0108***|-0.0105***|-0.0001|-0.0001|
||(0.0023)|(0.0023)|(0.0002)|(0.0002)|
|Two-Lagged Enrollment Change|-0.0039**|-0.0039**|-0.0001|-0.0001|
||(0.0013)|(0.0013)|(0.0002)|(0.0002)|
|Lagged Field Share in HEI||-0.0340**||-0.0543*|
|||(0.0125)||(0.0216)|
|Lagged HEI Share in Dept||-0.2058*<br>(0.0839)||-0.0918<br>(0.0880)|
|Lagged GDP by Field||0.0004||-0.0000|
|||(0.0002)||(0.0005)|
|Constant|0.0295|0.0570*|0.0417|0.0606*|
||(0.0215)|(0.0231)|(0.0248)|(0.0268)|
|N. of Observations|8,581|8,578|2,861|2,861|
|Mean of Dep. Variable|0.097|0.097|0.058|0.058|

Note: In this table, the unit of observation is an incumbent program. Each column presents the coefficients from an OLS regression of an indicator for whether incumbent program _i_ in department _d_ and field _k_ is closed in year _t_ , on lagged proportional enrollment change ( _t −_ 1 minus _t −_ 2) and two-lagged proportional enrollment change ( _t −_ 2 minus _t −_ 3), for the 2004-2018 period. Columns (2) and (4) also include cost proxies (the HEI’s enrollment share in the department and the field’s enrollment share in the HEI, both between zero and one) and GDP-by-field (in billions of COP), all lagged one year. All regressions include department, year, and field-category fixed effects. *,**, and *** denotes significance at the 10, 5 and 1% level. Standard errors are clustered at the institution-year level.

28
# **III. Empirical strategy and data**

Following Alesina, _et al_ ., (2015) we conduct analysis on country level data. The contribution of this paper is to exploit the greater economic and regulatory variation across a much larger, and more diverse sample of countries, including middle and low-income countries. In addition, we ‘unpack’ the composite measures of labor regulation used widely in the current literature into separate components that we argue are likely to shape firms’ decisions differently. Although Alesina, _et al_ , (2015) analyze regulatory instruments such as statutory minimum wages, employment protection and the power of unions separately, as in previous papers by others, they use the OECD’s composite indicator of employment protection legislation (EPL).

Rather than a single, composite measure of employment protection, we construct four measures that separately capture: (i) rigidity of working hours; (ii) restrictions on the use of temporary, fixed-term contracts and outsourced labor; (iii) procedural difficulty of dismissing workers; and (iv) mandated payments to workers upon dismissal (severance). We also add variables to capture other statutorily mandated non-labor cost, specifically contributions required of employers and employees for social insurance (old age pensions, disability, survivor, unemployment benefits and health coverage).

# **3.a. Data**

The data are drawn from published data sets of country-level indicators, per Table 1 (full citations of sources are listed in the references section).

**Table 1. Data sources**

|Measures of digital<br>technology use|International Telecommunications Union (ITU)<br>Quality of Government (QoG) Basic Data, University of Gothemburg<br>World Economic Forum(WEF)|
|---|---|
|Level of development<br>and other key economic<br>parameters|World Development Indicators (WDI): World Bank Group|
|Labor market policy<br>indicators|EPLex: International Labor Organization<br>Inter-American Development Bank and Organization of Economic<br>Cooperation and Development<br>Doing Business Labor Market Regulation (LMR): World Bank Group,<br>observation years 2010-2014<br>Jobs Database: World Development Indicators<br>Social Security Programs Around the World: US Social Security<br>Administration|

Given the wealth of options for measuring the use of digital technology provided by the ITU, QoG and WEF data sets, we elected to construct two new, composite measures to separately capture the extent of non-business (i.e. general household) and business usage. Rather than assign weights to component variables, we apply principal components analysis, or PCA.

PCA is a way to identify patterns in data with high dimension, which is otherwise hard to simplify. It is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables—called principal components (Johnson and Wichern, 2008). The main advantage of PCA is that it can compress the data by reducing the number of dimensions, without much loss of information. For it 6
the MVP sites. But the explanation in the above paragraph heightens rather than alleviates concerns that the MVP sites differed systematically from other potential sites, in particular in that they were apparently seen to have greater levels of political buy-in and community ownership, which might well be important factors for the intervention’s success. There is no indication that the project insisted on equal levels of political buy-in and community ownership in the comparison villages. This makes differences in outcomes between the treated and comparison villages harder to attribute to the effects of the intervention. It is also an important reason why even the differences-in-differences estimates we report above could greatly overstate the effects of a scaled-up version of the project.

A second weakness of the protocol concerns the process of selecting the comparison villages. The comparison village selection process is described as follows: The choice of candidate villages for comparison was at random, and informed by both scientific and operational issues. Scientifically, a detailed matching process is undertaken to adequately characterize village attributes. DHS data are not sufficiently disaggregated to the degree of granularity such that potential local variations in levels child mortality between MV and comparison candidates are not known in advance. The matching process therefore involves collecting data on village-level parameters with the potential to influence child mortality and related MDG outcomes including: agro-ecological zone, accessibility (distance to tar roads and markets), principal livelihood strategies, crop types, levels of electrification, numbers of clinics and schools, and the presence of other development projects in the area. A full inventory of village matching criteria is detailed in Appendix 2. This inventory is collected both for the intervention and comparison villages and updated in parallel with each round of surveys. Operationally, comparison villages had to be logistically accessible to evaluation field teams, yet far enough away from the intervention site that ‘spillover’ from the MVP would be unlikely. The same field teams will conduct assessments in both intervention and comparison sites to ensure a standardize approach to the evaluation, as well as to minimize and evenly distribute potential bias.

This paragraph describes a process of selecting candidate comparison villages that permits considerable subjectivity. The “matching criteria” comprise a handful of village traits that will be considered during matching—such as size, distance to a paved road, and agro-ecological zone. But the protocol does not indicate how closely villages will need to match, what weights the different traits will have, or how it can be known that comparison villages were not chosen using additional criteria not that do not appear on the list. Other factors that could shape the effects of the intervention in the treatment villages include the level of community organization and 20
7 Figure 1: Modern input use in SSA is not uniformly low <!-- Start of picture text --> % %<br>100 Share of cultivating households using 100 Share of cultivating households<br>inorganic fertilizer in main season using agro‐chemicals<br>90 90<br>77<br>80 80<br>70 70<br>56<br>60 60<br>50 41 50<br>40 35 40 31 33<br>30 30<br>17 17 16<br>20 20 13 11<br>8<br>10 3 10 3<br>0 0<br><!-- End of picture text --> <mark>Note: Agro‐chemicals include pesticides, herbicides and fungicides</mark> <mark>Source: Sheahan and Barrett, 2017.</mark> One in six farmers also uses agro‐chemicals, rising to one in three in Nigeria and Ethiopia—related to the more widespread use of herbicides in these countries.<sup>9</sup> These rates are substantially higher than the available numbers in the literature, and come somewhat as a surprise. It prompted follow up work, also using the LSMS‐ISA data, which suggests a strong correlation of pesticide use with increased value of the harvest, but also with increased health expenditures and time lost from work due to sickness. This draws attention to the health implications of increased agro‐chemical use in SSA and the need for effective regulatory policies as areas for future attention (Sheahan, Barrett, and Goldvale, 2016). There are also signs of improved seed use, especially for maize, with 24 to 41 percent of maize growers reporting seed purchases, though these are likely lower bounds.<sup>10</sup> Input use appears also no longer confined to traditional cash crops, with input intensification (fertilizer, pesticides, improved varieties) now equally (and at times even more) common for the maize staple.

> 9 See Tamru et al. (2016) for a recent analysis of the patterns, drivers, and labor productivity implications of the rapid expansion of herbicide use in smallholder cereal agriculture in Ethiopia over the past decade.

> 10 Comparing crop variety assessments by farmers with DNA fingerprints Ilukor et al. (2017) show that improved and hybrid variety cultivation is more widespread than commonly assumed by farmers in Uganda, even though the improved cultivated varieties are no longer pure.