rafmacalaba/datause-encoder-data huggingface dataset:
Gangopadhyay, S., Ramaswami, B., Wadhwa, W., 2005. Reducing subsidies on household fuels in India: how will it affect the poor? Energy Policy, 33(18): 2326-2336.

Hertel, T., 1997. Global Trade Analysis: Modeling and Applications. (Editor), Cambridge University Press.

- IMF (International Monetary Fund), 2012. Nigeria: Article IV Consultation, IMF Country Report No. 12/194, July.

- IMF (International Monetary Fund), 2013. Nigeria Staff Report for the 2012 Article IV Consultation—Debt Sustainability Analysis

- Malcolm, G., 1998. Adjusting Tax Rates in the GTAP Data Base. GTAP Technical Paper No. 12 September 1998. Accessed on January, 25, 2012.https://www.gtap.agecon.purdue.edu/resources/download/580.pdf

- Minor, P., Walmsley, T., 2013. My GTAP: A Program for Customizing and Extending the GTAP Database for Multiple Households, Split Factors, Remittances, Foreign Aid and Transfers. GTAP Working Paper Series, 79, Center for Global Trade Analysis, Purdue University, 2013.

- Narayanan, B. G., Aguiar, A., McDougall, R. Editors, 2012. Global Trade, Assistance, and Production: The GTAP 8 Data Base, Center for Global Trade Analysis, Purdue University.

- NBS, 2010. Petroleum Statistics. Nigerian National Bureau of Statistics. Accessed on January 25, 2012.http://www.nigerianstat.gov.ng/index.php/pages/sectorStatistics.

NBS, 2012. Nigeria Poverty Profile 2010. Nigerian National Bureau of Statistics (NBS), January 2012.

Nwachukwu, M. U., Chike, H., 2011. Fuel subsidy in Nigeria: Fact or fallacy. Energy, 36 (5): 2796-2801.

- Nwafor, M., Diao, X., Alpuerto, V., 2010. A 2006 Social Accounting Matrix for Nigeria. Methodology and Results. Nigeria Strategy Support Program (NSSP), Report No. NSSP007. April 2010. International Food Policy Research Institute.

- PPPRA, 2012. Petroleum Products Pricing Regulatory Agency in Nigeria. Approved prices as at December 17, 2012. http://www.pppra-nigeria.org/ accessed on December 20, 2012.

- Rao, N. D., 2012. Kerosene subsidies in India: When energy policy fails as social policy. Energy for Sustainable Development, 16(1): 35-43.

- Rice, X., 2012. Fuel gamble costs Jonathan his standing. Nigeria’s president miscalculated by scrapping subsidies without easing the blow to the poor. Financial times, January 14/15, 2012, page 3.

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given that during the time they were in operation they sometimes failed to report their records, each interpolation is based on a different number of data points—and indeed different weather stations. These problems as well as the accuracy of the data get worse as one looks at earlier years, which has a corresponding effect on our interpolations. Thus, interpolations for the year 1950 are less reliable than those for 2007.

From these weather data, we calculate the total rainfall and growing degree days (GDD) for each agricultural year (October to September), for each wet season (April to September) and for each pre- _canícula_ period (April, May, June), or the months leading to the _canícula_ , from 1951 to 2002.<sup>15</sup> Instead of maximum of minimum temperatures we use GDD, a cumulative measure of temperature based on the minimum and maximum daily temperatures. GDD measures the contribution of each day to the maturation of the crop. Each crop, depending on the specific seed type and other environmental factors, has its own heat requirements for maturity.

Different corn varieties, for example require between 2,450 and 3,000 GDDs to mature, whereas different wheat varieties only require between 1,800 and 2,000 GDDs.<sup>16</sup> Each crop has specific base and ceiling temperatures, and , respectively, which contribute to growth. The base bound sets the minimum temperature required for growth and the ceiling temperature sets the temperature above which the growth rate does not increase any further. Thus, the contribution of each day, _j_ , to the cumulative GDD is given by where<sup>andare the minimum and maximum daily temperature truncated at the base</sup> and ceiling values. In other words, any daily temperature (minimum or maximum) below the base temperature is assigned the base temperature value and any daily temperature above the ceiling temperature is assigned the ceiling temperature value.<sup>17</sup> To determine the cumulative GDD at any point in time for a specific cultivation the daily GDDs since planting are summed.

> 15 Given that the agricultural year starts runs from October to September, the first agricultural year that we use is 1951, and we only use the last three months of the 1950 calendar year.

> 16 For other important crops in Mexico the required GDDs are 2,400 for beans and 2,200 to 2,370 for sorghum.  The GDD values are taken from The Institute of Agriculture and Natural Resources Cooperative Extension, University of Nebraska-Lincoln.  Growing Degree Days & Crop Water Use. http://www.ianr.unl.edu/cropwatch/weather/gddet.html,  Accessed July 22, 2010.

> 17 We use the Modified Growing Degree Days formula where the minimum and maximum temperatures are adjusted prior to taking the average.

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We extend this approach in this paper to Tunisia and present much more detailed analysis for Egypt and Tunisia on whose status is changed by adopting a focus on household enterprises and who participates in them. Our specific research questions are (1) How do men’s and women’s employment rates change when adding enterprise-based detection questions to standard individual-level questions? (2) Was the additional market employment detected with projectbased approaches classified as subsistence work with individual measurement approaches? (3) For which women do we detect additional employment using project-based approaches?

# **2 Previous efforts to improve the measurement of female labor force participation in Egypt**

As an illustration of the challenges and innovations in measuring women’s economic participation, this section discusses efforts to improve measurement in Egypt. Efforts to improve the measurement of female labor force participation (FLFP) in Egypt date back to the early 1980s and efforts at the Central Agency of Public Mobilization and Statistics (CAPMAS) to improve the training of enumerators involved in the data collection for the Labor Force Survey (LFS). The enumerators’ manual used in 1983 clearly indicated that the definition of the labor force includes many unpaid activities done for the family, mentioning specifically family farming (Anker and Anker 1989). Other changes such as increasing the number of female interviewers and reducing the number of households per interviewer per day were also introduced in that year (Anker and Anker 1989). As shown in Figure 1, this effort paid off with a big increase in the measured FLFP rate from 7% in 1982 to 12% in 1983, with the rural rate in particular tripling from 4% to 12%.<sup>3</sup>

> 3 Given the way that CAPMAS presents the data in the LFS Bulletins from 1977 to 2007, we use a somewhat unconventional definition of the FLFP rate, which we are able to reproduce throughout the period. The numerator of the rate is the economically active female population aged 12 to 64 and the denominator is the manpower basis, which is the population age 6 to 64, excluding the permanently disabled, plus the employed population 65 and older. The indicator is calculated in the same way from 2008 to 2021 using the LFS microdata and for the special round of the LFS in October 1988 and the various waves of the ELMPS.

3
relative to shorter recall windows.<sup>4</sup> The evidence on recall windows as it applies to labor activities, however, is more mixed. In a study based in Tanzania, Arthi et al. (2018) compare reported contributions of farm labor in an agricultural plot-based module using weekly recall with those from an end of season recall window. Longer recall windows exaggerate the number of hours worked per person by a factor of four, although the reported number of active plots and individual level participation are undercounted. In our rural sample in nearby Malawi, we find reductions in reported labor contributions using the longer recall periods relative to a shorter recall window, but no overall difference in hours worked, the intensive margin on which Arthi et al. (2018) find positive effects. In urban labor markets, Heath et al. (2021) examine very short recall windows and find modest effects of switching from weekly to daily recall with higher reported self- employment spells but no impact on wage employment. However, Garlick et al. (2020) find that weekly versus monthly surveys did not influence data quality or reported microenterprise activities.

This paper also links to a second area of the methods literature centered on the use of proxy responses for data collection. Of note, Bardasi et al. (2011) find that males are more affected by proxy losses in reported agricultural labor activities than women, though this is reduced when the primary respondent is his wife or well-educated. By contrast, Serneels, Beegle, and Dillon (2017) find that proxying does not affect estimates of returns to education. Another related paper in this literature is by Kilic et al. (2022), that also intersects with the recall literature on labor measurement. They use two nationally representative surveys in Malawi conducted in the same year but following different research protocols. They find underreporting of certain labor activities in the survey using “business as usual,” that allowed for proxy reporting and presence of other household members, relative to the other survey that required own response and privacy during the interview. Contrasting their setup, with multiple differences between the “business as usual” and the more stringent protocols, our study uses experimental variation designed to isolate a single mechanism, recall windows. Our heterogeneous results, showing greater long recall loss for

> 4 Das and Sanchez-Paramo (2012) find that one-third of acute illness is unreported when comparing one month to one week recall periods. Other notable examples showing high sensitivity of consumption to recall windows include Beegle et al. (2012b), De Weerdt et al. (2016), Backiny-Yetna et al. (2017), and Di Maio and Fiala (2019). Beegle et al. (2012a) find little evidence of distortions from longer recall lags in recording agricultural inputs and production.
**Notes:** PPML HDFE estimates using annual pair-wise total trade data from COMTRADE/Baci. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Note that one must be careful with the interpretation of estimates from Table 3.7 for three reasons. First, the Multifiber Arrangement (MFA) ended in 2004, and Cambodia was one of the countries that received increased global investment and increased apparel exports when the MFA ended (Beresford 2009 and Savchenko et al. 2012). Second, the dependent variable is for Cambodia’s total exports in terms of products and countries (i.e., exports to the world). Finally, these results are unlikely to be causal because there are no provisions in the TIF that reduce trade costs.

The third dimension of Cambodia’s trade that might be relevant is the relationship with China. In terms of trade agreements, the relationship between Cambodia and China through ASEAN is already captured in the previous estimation. In addition, exports to China [from Cambodia] remain a relatively small share of Cambodia’s total exports. Figure 3.1 shows the share of total exports from Cambodia, measured as imports from Cambodia by all other countries by year in the CEPII gravity database. It shows that, even when including Hong Kong SAR, China, with China, Cambodian exports to China (and Hong Kong SAR, China) are very small relative to those to the United States, Europe, and the rest of the world.

**Figure 3.1: Share of total exports from Cambodia**

<!-- Start of picture text --> 1995 2000 2005 2010 2015 2020<br>Year<br>USA Europe<br>China Japan<br>SEAsia Rest of World<br>.6<br>.4<br>.2<br>Share of Cambodia's Exports<br>0<br><!-- End of picture text --> Source: Authors’ elaboration using data from the BACI gravity database 21 | P a g e
distribution with correlation coefficient ρ and standard deviations σ 1 and σ 2 respectively.<sup>9</sup> εε εε Quantity (2) can be estimated by where Φ 2 () . stands for the bivariate normal cumulative distribution function (cdf)) (and φ 2 () . stands for the bivariate normal probability density function (pdf)). In equality (4), the parameters β _j_ and σε _j_ are obtained from equation (1), and ρ from the following formula where the simple correlation coefficient ρ _y_ 1<sup>_y_</sup> 2 is approximated from the birth-cohort-aggregated household consumption between the two surveys. Note that in equality (4), the estimated parameters obtained from data in both survey rounds are applied to data from the second survey round (x2) (or the base year) for prediction, but we can use data from the first survey round as the base year as well. It is then straightforward to estimate quantity (3) by dividing quantity (2) function (cdf).<sup>10</sup>

# **III.2. Setting Vulnerability Lines**

Using the given poverty lines zj, quantities (2) and (3) classify the population into two groups, one is poor and the other nonpoor. But we can obtain richer analysis by further

> 9 In other words, this assumption implies that households in period 2 that have similar characteristics to those of households in period 1 would have achieved the same consumption levels in period 1 or vice versa. 10 Further asymptotic results and formulae for the standard errors are provided in Dang and Lanjouw (2013). These studies also provide validation results for the synthetic panels against the actual panel data for several countries including Bosnia-Herzegovina, Lao PDR, Peru, the United States, and Vietnam. Other studies that offer further validation and extension include Cruces _et al._ (2015), Dang and Lanjouw (2017), Dang _et al._ (2019), and Bourguignon and Dang (2019).

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<!-- Start of picture text --> N<br><!-- End of picture text --> CCSR 2100, Large Farms + 4 %Change of Ag Income IMP_farm.D100_ccsr0 I 0.0000 PI) 0.0001 -0.0500 HN 0.0501 -0.1000 HR 0.1001 -0.2000 HR 0.2001 -0.3000 HE 0.3001 -0.9900

[LSILSLS ~—s«*d‘ Decimal Degrees 0 4 8 16 24 32 NIGGOL SEO, YALE UNIV.
# 1. Background

Product market competition is often cited as a key driver of productivity and economic growth – and thus is an important piece in the puzzle of boosting South Africa’s economic performance. At present, there is significant discussion on the level of competition faced by firms across South African industries. This in part stems from concerns that product markets have remained relatively concentrated since the end of apartheid, with newer and smaller firms unable to successfully compete against larger incumbents that benefit from scale and other advantages. This has implications for the degree of rivalry in markets. Historically, there have been four main conglomerate groupings which have controlled the majority of economic activity (Roberts, 2004) - Anglo American Corporation, Sanlam, Liberty Life and Rembrandt/Remgro. Together, they held 80.2 percent of JSE market capitalization in 1985 and 59.8 percent in 2002. In addition, Roberts pointed out that the 1996 manufacturing survey suggested the largest four firms account for more than half of output in 47 percent of the 57 main product groupings. Fedderke et al. (2018) find that concentration ratios for the top 5 percent of firms across industries have increased across most industrial categories from before 2001 to 2010 and 2012.<sup>2</sup> In part, currently observed market structures result from South Africa’s legacy of exclusion (World Bank Group, 2018). However, they can also be reinforced by a lack of pro-competition principles in current government interventions in some markets, making it difficult for South Africa to overcome its historical legacy. South Africa’s overarching economic development plans have in fact advocated for competition policies in the past. The New Growth Path adopted in 2011suggests that competition is key to generating employment and investment opportunities, as well as lower prices for businesses and consumers. In a similar vein, the National Development Plan (National Planning Commission, 2012) suggests that bolstering competition in high value-added sectors and diversifying industry are key to bringing more individuals into the labor market and improving the skill base of the economy. While South Africa has made important strides in implementing its

> 2 When discussing product markets in South Africa, there are two distinct types of “concentration” which have typically been part of policy dialogue: i) concentration of economic ownership across the economy; and ii) concentration within specific markets. While the two are linked, it is crucial to distinguish between them since they have different implications. The former encompasses the concentration of economic ownership in a few parent firms across markets, while the latter refers to concentration within a specific market (or set of substitutable products). It is the latter type of concentration that is spoken of in the context of competition outcomes.

2
Figure 3: M10 and DNB over time <!-- Start of picture text --> Ajil<br>150<br>100<br>50<br>0<br>25<br>20<br>15<br>10<br>5<br>0<br>8<br>6<br>4<br>2<br>0<br>Jaffra<br>20<br>15<br>10<br>5<br>0<br>25<br>20<br>15<br>10<br>5<br>0<br>8<br>6<br>4<br>2<br>0<br>Taq Taq<br>20<br>15<br>10<br>5<br>0<br>25<br>20<br>15<br>10<br>5<br>0<br>8<br>6<br>4<br>2<br>0<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>Mar12 May12 Aug12 Nov12 Feb13 May13 Aug13 Nov13 Feb14 May14 Aug14 Nov14 Feb15 May15 Aug15 Nov15 Feb16 May16 Aug16 Nov16<br>ar12 ay12 ug12 ov12 eb13 ay13 ug13 ov13 eb14 ay14 ug14 ov14 eb15 ay15 ug15 ov15 eb16 ay16 ug16 ov16<br>/sr/um)Radiance (W/m2<br>Temperature ('00 K)<br>Spike Mean Index<br>/sr/um)Radiance (W/m2<br>Temperature ('00 K)<br>Spike Mean Index<br>/sr/um)Radiance (W/m2<br>Temperature ('00 K)<br>Spike Mean Index<br><!-- End of picture text --> Note: Time series of M10 and DNB detections from March 2012 through November 2016 for Flares in Ajil and Taq Taq in Iraq, and Jaffra in Syria Temperatures are in kilo Kelvin. Black lines demarcate period of Daesh control.

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Trako, I. and Molina, E. (2018). The Learning Crisis in Tanzania. Working Paper. World Bank, Washington, D.C.

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Wharton-McDonald, R., Pressley, M., and Hampston, J. M. (1998). Outstanding literacy instruction in first grade: Teacher practices and student achievement. _Elementary School Journal, 99_ , 101–128.

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# APPENDIX 1 - POVERTY LINES

For Hungary and Yugoslavia the social minima are In effect _subsistence minima_ that "render possible merely to satisfy very modest necessities conventionally considered to be essential to ensu-e continuous living" _(Hungarian Statistical Yearbook 1987,_ p. 328). Both Hungary and Yugoslavia also publish data on the social minimum which Is some 20 percent higher than the subsistence minimum. In the analysis for Poland we are usinig the _social minimum_ where some 10 percent of resources are supposed to have been left at the discretion of the household, i.e. to be used to satisfy non-essential needs. Polish social minimum, however. Is less, in real terms, than the subsistence minimum in Yugoslavia and Hungary (see Table 3 In the text).

Consumption baskets are based on actual consumption patterns (e.g. in Yugoslavia, the structure of consumption of the lowest Income decile) corrected with some norms reflecting "healthy" consumption. All the three minima assume that the household rents a state-owned or cooperative apartment (with rents substantially below market levels). However, since most of the population either lives in own housing (necessitating only low direct maintenance expenditures and being charged mortgages at vastly negative interest rate), or In state or cooperative apartments, underestimate of the poverty line due to housing costs Is probably small.

In Poland, social minimum Is calculated quarterly since 1980 by the Institute of Labor and Social Affairs. The basket of goods has been kept constant since 1983. In order to preserve an absolute standard of measurement we have extended back to 1978 the real value of the social minimum for 1983.

In Hungary, social and subsistence minima are calculated since 1982 by the Central Statistical Office. For the period before 1982, it was assumed that the ratio of the minimum to the average monthly per capita Income was the same as In 1982. Since real per capita income In 1978 and 1980 was about the same as In 1982, this Is tantamount to using a constant absolute level.

For Yugoslavia, social minimum Is calculated by the the Federal Secretarlat of Labor, Health and Social Policy since the