rafmacalaba/datause-encoder-data huggingface dataset:
|Population estimates|Household census, global dataset,<br>global raster|2015 Zambia census, Open<br>Street Map , Nightime light<br>brightness|
|---|---|---|
|Household size|Household surveys||
|Night-time light brightness and<br>Digital Elevation Model|Global dataset Resolution|NASA (2015)‘ASTER Level 1<br>Precision Terrain<br>Corrected<br>Registered At-<br>Sensor Radiance<br>Version 3’. Sioux<br>Falls, South Dakota:<br>NASA EOSDIS Land<br>Processes DAAC,<br>USGS Earth<br>Resources<br>Observation and<br>Science (EROS)<br>Center.|
|Groundwater vulnerability to|Raster|KfW|
|contaminants|||
|Covariate grids were generated<br>features|based on GIS data describing relevant W|ASH infrastructure or natural|
|Distance from water network|Shapefile|LWSC|
|Distance from sewer network|Shapefile|LWSC|
|Distance from stream/river|Raster file (based on hydrological<br>analysis of Digital Elevation Model<br>data)|NASA(2015) – as per Digital<br>Elevation Model data source|
|Distance from cemetery/burial|Global Raster file|Open Street Map|
|ground<br>WASH covariate grids were gene<br>household or other survey data|rated by implementing a Bayesian geos<br>describing water and sanitation facilities|tatistical model applied to<br>, water quality, and flood risk|
|% HHs with soap in toilet|GPS point data from household|World Bank Lusaka|
|% HHs not treatingwater<br>% households with<br>unimproved sanitation<br>Density of population with<br>unimproved sanitation|surveys|Sanitation Assessment<br>survey (2015) and the<br>Lusaka Sanitation Program<br>(LSP) baseline survey<br>undertaken by Vision RI<br>|
|Composite risk index for water<br>source||(2016).|
|Composite risk index for toilet<br>facility|||
|Prevalence of_E. coli_in drinking<br>water sources|290 randomly selected water source<br>locations (taps, boreholes, tanks etc)<br>(CDC)|NWASCO and CDC|
|Risk of flooding (poor<br>drainage)|Raster file|Millennium Challenge<br>Corporation(MCC)|
|Frequency of complaints<br>regarding sewer network,<br>water supply/quality|Complaints were aggregated by<br>Township (the administrative level<br>below the District MeteringArea|LWSC|
# **References**

Agence Nationale de la Statistique et de la Démographie (ANSD) (2007) _Enquête de suivi de la pauvreté au Sénégal, ESPS 2005‐2006_ , rapport national, août Agence Nationale de la Statistique et de la Démographie (ANSD) (2013), _Rapport définitif de la deuxième Enquête de Suivi de la Pauvreté au Sénégal (ESPS II),_ rapport national _._ <mark>Araar A. and J.-Y. Duclos (2007), "</mark> _<mark>DASP: Distributive Analysis Stata Package</mark>_ <mark>", PEP, World Bank, UNDP and Université Laval.</mark> Baland, J.-M., C. Guirkinger, and C. Mali (2011),. "Pretending to Be Poor: Borrowing to Escape Forced Solidarity in Cameroon." _Economic Development and Cultural Change_ 60, no. 1 (2011): 1- 16.

Baland, J.-M. and R. Ziparo (2018), « Intra-household bargaining in poor countries”, in _Towards Gender Equity in Development,_ edited by Anderson S., L. Beaman and J.-P. Platteau, Oxford University Press Bargain O., G. Lacroix, and L. Tiberti (2018), “Validating the Collective Model of Household Consumption Using Direct Evidence on Sharing” IZA Discussion Paper No. 11653: Beck, S. _et al_ (2015), « Child fostering in Senegal », _The Journal of Comparative Family Studies,_ vol 46: 57-73 Beegle K. _et al._ (2012), “Methods of household consumption measurement through surveys: Experimental results from Tanzania”, _Journal of Development Economics,_ vol. 98(1), May 2012, pp 3-18.

Beegle, K. _et al._ (2016). _Poverty in a Rising Africa_ . Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22575 License: CC BY 3.0 IGO.

Biewen, M. (2002), “Bootstrap inference for inequality, mobility and poverty measurement”, _Journal of Econometrics_ , vol. 108, pp.317-342.

<mark>Boltz M., K. Marazyan and P. Villar (2019), « In</mark> come hiding and informal redistribution: A lab-in-the-field experiment in Senegal », _Journal of Development Economics,_ vol. 137, pp 7892 Bound J., C. Brown and N. Mathiowetz (2001), “Measurement Error in Survey Data” in J.J. Heckman and E. Leamer eds., _Handbook of Econometrics_ , Volume 5, Chapter 59, pp.3705-3843 Brown, C., R. Calvi and J. Penglase (2019), “Sharing the Pie: Undernutrition, Intra-Household Allocation, and Poverty”, available at SSRN: https://ssrn.com/abstract=3199129 or <u>http://dx.doi.org/10.2139/ssrn.3199129</u> 32
where they avoid sunk costs associated with accessing an external market, despite their potential to be competitive abroad.

Despite their wide presence across the world, until the early 2000s very little was known about the characteristics of EPAs worldwide. Between 2005 and 2010 the World Bank, in collaboration with the ITC, conducted two worldwide surveys on EPAs. This paper describes the characteristics of EPAs around the world using the most recent survey, conducted in 2010. We complement this analysis by discussing a wide variety of studies evaluating the impact of EPAs around the world, looking into questions related to different margins of international trade, at both a macro and micro perspectives.

This paper is organized as follows: Section 2 describes the new database on EPAs and provides some descriptive statistics on the characteristics of the agencies by region. Section 3 provides a literature review on the impact of EPAs on exports and analyzes how these findings may support EPAs’ initiatives. Section 4 concludes.

# **2 Export promotion agencies: How do they work?**

Between 2005 and 2010 the World Bank, in collaboration with the ITC, conducted two worldwide surveys on EPAs. The survey conducted in 2005 is well documented in Lederman et al. [2010]. A follow-up survey was conducted in 2010. From 149 countries that were contacted for this survey, 116 had and EPA in place. A 19-question survey was sent out by e-mail and replies were collected over a 4-month period (July-October 2010), with responses verified via phone calls. Regarding the perceived importance of the EPAs in the sample, the large majority of EPAs that replied to the 2010 survey identified themselves as the only or main agency responsible for export support and promotion in their country.<sup>4</sup> The 2010 survey gathered data on 96 EPAs, 93 of which provided information on their budget expenditures.<sup>5</sup> In this survey, export support and promotion encompasses all services designed to assist and encourage domestic firms to export goods and services and increase their foreign sales. As such, this definition of export support and promotion includes five types of activities: i) country image building; ii) export support services (e.g training); iii) marketing; iv) market research and publications, and v) policy advocacy.<sup>6</sup> This section provides a summary of the

> 4The exception is Asia, where about 30% of agencies perceived themselves as a major agency, but not the most important one.

> 5Table 9 in the appendix identifies the countries that participated in the survey.

> 6The definition of export support and promotion in this survey does not include export finance, export insurance or fiscal incentives to exporters.

3
# **Figure 1A: Household credit share with GDP per capita, 1994-2013**<sup>*****</sup>

<!-- Start of picture text --> Household credit and GDP per capita Household credit and GDP per capita<br>1994-2005 2006-2013<br>CHE<br>DNK<br>DNK<br>NLD NLDAUS CHE<br>JPNAUSUSA IRL<br>KOR PRTHKG NZL GBRDEU IRLCAN NOR KORPRT ESPNZL GBR CANUSASWE NOR<br>THA ISRESP SGP FRAFINBELAUTSWE LUX THAMYS GRC HKG FRADEUJPNBELSGPAUTFIN LUX<br>ZAF ISR ITA<br>CHL GRC ITA CHN POLCHLHUN CZE<br>IDN RUSTURARGMEXPOLBRAHUN CZESAU INDIDN MEXARGTURRUSBRA SAU<br>0 20000 40000 60000 80000 0 20000 40000 60000 80000 100000<br>GDP per capita, constant 2010 USD GDP per capita, constant 2010 USD<br>hh1994-2005 Fitted values hh2006-2013 Fitted values<br>Data source: WDI, BIS Data source: WDI, BIS<br>*: Estimated slopes of the regression lines are 0.00093 for the period of 1994-2005, and 0.00091 for the period of<br>2006-2013. The difference between the two slopes is not statistically significant.<br>Figure 1B: Nonfinancial corporation credit share with GDP per capita, 1994-2013 **<br>Nonfinancial corporation credit and GDP per capita Nonfinancial corporation credit and GDP per capita<br>1994-2005 2006-2013<br>LUX<br>LUX<br>JPN NLD<br>HKG<br>IDNTHARUSTURARGMEXPOLBRACHLHUN KORCZESAU PRTGRCISRESPNZLSGPGBRITADEUFRAFINBELIRLAUTCANSWEAUSUSA DNK CHE NOR INDCHNTHAZAFMYSTURRUSBRAPOLCHLHUN SAUCZEKORPRT GRCISRESPHKGNZLITAGBRFRADEUJPNBELSGPAUTFINCANUSAIRLNLDAUSSWE DNK CHE NOR<br>IDN MEXARG<br>0 20000 40000 60000 80000 0 20000 40000 60000 80000 100000<br>GDP per capita, constant 2010 USD GDP per capita, constant 2010 USD<br>nfc1994-2005 Fitted values nfc2006-2013 Fitted values<br>Data source: WDI, BIS Data source: WDI, BIS<br>150<br>100<br>80<br>100<br>60<br>40<br>50<br>Household credit as a share of GDP 20 Household credit as a share of GDP<br>0 0<br>400<br>200<br>300<br>150<br>200<br>100<br>50 100<br>Nonfinancial corporation credit as a share of GDP 0 Nonfinancial corporation credit as a share of GDP 0<br><!-- End of picture text -->

- *: Estimated slopes of the regression lines are 0.00093 for the period of 1994-2005, and 0.00091 for the period of 2006-2013. The difference between the two slopes is not statistically significant.

# **Figure 1B: Nonfinancial corporation credit share with GDP per capita, 1994-2013**<sup>******</sup>

- **: Estimated slopes of the regression lines are 0.00133 for the period of 1994-2005, and 0.00172 for the period of 2006-2013. The difference between the two slopes is statistically significant at the 10% level.

# **Model and Results**

The relationship between borrower type and output type and external balances is examined using the following equation:

- (1) Export share to GDP = a + b ln (initial GDP p.c) + c ln (household credit/GDP) + d ln (firm credit/GDP) + e DVFC + f (financial openness) + g (DVCX) + h (foreign direct investment) + iZ

where Z encompasses additional control variables, including the terms of trade, the exchange rate, an interaction term with initial GDP per capita and the log of private credit, institutions, and 10
shipment. The fact that nowhere in the world did the exporter get a better price than in Canada did not matter: antidumping could be used against what the Canadian government perceived to be a major economic problem -- generally low prices.

But business conditions do not have to be as severe as they were during the depression of the 1930s to activate the below-cost pricing provision. Gary Banks points out in his paper that the provision was the basis for the expansion of Australian antidumping in the 1980s and that the inquiry commissioned by the Australian government recommended repeal of the provision in order to bring antidumping under control.

# _Power politics: national_

Action against below-cost imports came into U.S. antidumping practice through the back door. The 1921 U.S. law provided that _If_ the administrator was unable to determine the exporter's home-market price (because there were no home-market sales, or for other reasons) _and if_ he could not determine the exporter's price in a third market, _then_ he could base an antidumping case on an estimate of the exporter's cost. But, as the U.S. Tariff Commission study of 1919 revealed, U.S. business was more anxious to have protection against below-cost sales than against dumping, narrowly defined. The U.S. Congress in the 1920s and 1930s was generout, with tariff Drotection. An imt rptn Qauqnd protection did not press for extension of the scope of antidumping. But after the position of the United States in the world economy had changed and the U.S. tariff had been negotiated downward, pressure increased for antidumping action. This pressure eventually brought the administering agency to add below-cost imports to the circumstances under which antidumping action would be taken.

The antidumping administrators found the necessary legal cover in the U.S. law in the phrase "in the ordinary course of trade": "The foreign market value of imported merchandise ... shall be the price ... at which such or similar merchandise is sold ... in ... the home country ... in the ordinary 29 -

-
Heterogeneous impacts on enrollment by parental education are evaluated across two groups: those children whose parents have a completed secondary education degree or less and those whose parents have more than a secondary education degree.<sup>5</sup> In the two groups of children the decrease in enrollment (both gross and net) is similar, substantial, and statistically significant. Enrollment in private education is hereby an important exception: for this outcome, the relative decrease is stronger among the children of loweducated parents.

# **4. Estimates of education losses**

In this section we follow the framework of Neidhöfer, Lustig and Tommasi (2021) (NLT thereafter) to approximate the educational loss associated with school closures. NLT nowcast the instructional losses associated to the pandemic using country level data on days of school closure, educational mitigation strategies, number of COVID-19 cases and deaths, and survey data on individual characteristics. The basic assumption is that the school closures in reaction to the pandemic affected the human capital accumulation through lost days of school. This loss could be compensated partly by parent’s investments and actions, especially in more educated households, and partly by educational mitigation strategies enacted by countries.

In this section we extend the calculations of NLT based on 2020 by adding information from administrative data for 2021. More important, whereas the estimates in NLT are based on Latinobarometro, our calculations are based on microdata from national household surveys, which allows us (i) to have large samples, (ii) to include data at the household level on some key factors for the educational impact of COVID-19, such as parental education and access to

> 5 In principle, the socioeconomic status of the family could also be approximated by household income. However, since this outcome is directly affected by the pandemic, the position of the household in the income distribution might not be a correct way of comparison over time. Hence, in this part of the analysis, we only evaluate differences by parental educational background, assuming that this measure is less likely to be affected by the pandemic (it would be so for younger parents, who are rather unlikely to have children in the age range from 6 to 24).

10
analysis shows that since the upper-middle-income group of countries is mostly responsible for the recent global CO2 growth, more efforts should be paid to reduce their CO2 emissions. This study does not mean that these countries have to take on most of the financial burden to reduce their CO2 emissions. How to share the global burden of CO2 reduction is a different question and it is beyond the scope of this study. Improving energy efficiency has contributed historically to slow down the CO2 growth and this effect is basically driven through various policies (e.g., incentives for energy efficiency improvements) and technological innovation. Although the world has witnessed a rapid expansion of renewable energy more recently, the expansion is still too small to make a noticeable impact in limiting the growth of global CO2 emissions. Further expansion of clean and renewable energy would be needed for a noticeable impact on slowing down the global CO2 growth.

# **Acknowledgments**

We acknowledge the financial support from Knowledge for Change Trust fund of the World Bank.

# **References**

[1] BP. BP Statistical Review of World Energy 2017. http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-worldenergy/downloads.html. Accessed 30 June 2017. 2017. [2] World-Bank. World Development Indicators. http://databank.worldbank.org/data/reports.aspx?source=World%20Development%20 Indicators# (Accessed 10 July 2017). 2017.

[3] Ang B-W. The structure of energy demand in East Asian developing countries: University of Cambridge; 1981.

[4] Ang BW, Zhang F. A survey of index decomposition analysis in energy and environmental studies. Energy. 2000;25:1149-76.

[5] Ang B. LMDI decomposition approach: a guide for implementation. Energy Policy. 2015;86:233-8.

[6] Ang BW. The LMDI approach to decomposition analysis: a practical guide. Energy Policy. 2005;33:867-71.

[7] Ang BW. Decomposition analysis for policymaking in energy:: which is the 25
# 3. Nominal Wage Rates

# Areas of Knowledge and Ignorance

Two series of nominal average daily wages, both on an annual basis, are readily available. One series is prepared by the Social Insurance Institute, SII, and is based upon wages for insured employees. The other series is based on the annual surveys of manufacturing industry, SMI, and appears to be prepared by the SPO. The two series show very different developments since 1960, even for manufacturing industry considered separately. I shall here describe the two series in an attempt to understand their differences and possibly make a choice between them. As we shall see both series suffer from serious deficiencies. In Section 4 an attempt is made to construct better wage series.

The aggregated SII series is an average for all insured employees. Averages for a large number of activities are published but do not seem to be computed for sub-groups such as manufacturing industry. For the sake of comparison with the SMI series, I computed averages for the following sub-groups of activities: Mining & quarrying, manufacturing, and a residual

- 39 -
- 5 -

Table **2. Social spending by the enterprise sector in Russia, in 1993-94, trln. rbl.**

||Total|1993<br>Industry|Agriculture|Total|1994<br>Industry|Agriculture|
|---|---|---|---|---|---|---|
|I.Socialspending, total|5.36|3.47|0.43|21.12|12.99|1.564|
|- health|0.99|0.64|0.086|2.79|1.93|0.126|
|- education and culture|0.58|0.41|0.047|1.78|1.29|0.159|
|- housing, only maintenance|1.96|1.15|0.169|6.48|3.97|0.486|
|- investments|n.a..|n.a.|n.a.|5.42|2.83|0.506|
|- other|1.83|1.27|0.128|4.65|2.97|0.287|
|NEEMO:|||||||
|2. Revenue from sales|184.2|107.6|9.52|607.77|357.68|25.61|
|_3._Total costs, 3/|152.6|85.9|7.65|545.28|304.8|28.02|
|4. Profit from sales|31.2|21.8|2.06|62.49|47.37|-2.5|
|_5._Total gross profit, net losses|37.5|26.73|2.8|80.44|52.71|-0.32|
|6. Total wage bill, with bonuses, l/|32.22|15.72|3.41|105.36|49.41|8.87|
|_7._Pavroll tax, 2/|9.21|3.91|0.85|33.5|16|2.3|
|S. Budget expenditure on health|5.43|||27.45|||
|9. Budget expenditure on education and cu|<br>7.96|||24.34|||
|10. Budget expenditure on housing|6.6|||28.67|||
|Social spending as:|||||||
|apart ofdistributed profit (line.5),%|14.29|12.98|15.36|26.26|24.64|488.75|
|a part of zotal labor costs (lines 6+8),%|12.94|17.68|10.09|15.21|19.86|14.00|
|**a**part**of**wage bill (line 6), %|16.64|22.07|12.61|20.05|26.29|17.63|
|**Total**labor costs with social expend.<br>dsa part total costs (line 3),%|30.66|26.89|61.34|29.34|25.72|45.45|
|Socialspendingas a part of|||||||
|**thetotal**social budget spending,**.**|||||||
|- health (line8)|18.23|||10.16|||
|- education and culture (line 9)|7.29|||7.31|||
|- housing (line 10)|29.70|||22.60|||

# Source: Goskomstat, **own** estimates

Notes: **I/** as reported in Goskomstat in the form lOf 2'! esnimate 3"' including VAT and Excises paid on inputs
new cement floors and applying a fresh coat of paint to the windows and exterior walls of the house.

Small geographical clusters of 3 to 6 eligible study households were assigned to either receive the home improvements (treatment group) or to the control group. To assess potential spillovers, some households in treated clusters were not targeted for the program (similarly to households in control clusters), thus creating exogenous variation in the distance between targeted (i.e. offered treatment) and untargeted households. Untargeted households were not told about the program, although they could have felt frustrated if they learned about it, especially if they lived in treatment clusters close to neighbors that had been offered the program.

After an initial visit where the program was announced to targeted households, the program was launched in November 2019 but had to be paused in March 2020 due to COVID-19. By March 2020, about half of the targeted houses had been upgraded and importantly, the rest of the targeted households were never told about the plans to delay the implementation. Construction eventually resumed in July 2020 and concluded in April 2021. COVID-19 lockdown measures thus created exogenous variation in the time that elapsed between the announcement of the program and its eventual implementation. In addition, we use two rounds of follow-up data, collected after the program implementation, to measure within-household variation in exposure time to the program.

We use and overall of life as our main measures housing quality quality family of SWB. These evaluative measures have been used extensively in the literature, including in Cattaneo, Galiani, Gertler, Martinez, and Titiunik (2009) and Galiani et al. (2018).

We adapt the model of adaptation developed by Kimball et al. (2015) to account for delay disappointment and find that disappointment losses, while statistically significant, only account for less than 4% of subjective measures of well-being. Consistent with Galiani et al. (2018), we also find that permanent gains are indistinguishable from zero, suggesting that in the long-run, because disappointment losses are negligible, SWB reverts back to the level prior to the program.

More importantly, however, failure to account for delay disappointment results in estimated total gains that are only 36% (43%) of the actual gains when using satisfaction with overall house quality (satisfaction with quality of family life) as a proxy for SWB. Taken together, these results suggest that failure to account for delays in program implementation may lead to a large underestimation of program impacts.

This contributes to the literature on of paper ’anticipatory’ utility consumption (see, for instance, Loewenstein, 1987; Caplin and Leahy, 2001; Brunnermeier and 3