rafmacalaba/datause-encoder-data huggingface dataset:
percent of those in the short recall group report working at all, in an average of 1.19 activities in 3.6 months for 360 total hours. With the exception of total hours worked these measures are all higher for short recall compared to long recall. Ninety percent of subjects in the short recall group report working on the household farm or in other household agricultural businesses. Much smaller numbers report working in a non-agricultural family business (9.4%) or as wage employees (13.7%). Like the overall measures, these numbers are all lower in the long recall group. Overall, this sample is heavily involved in household agricultural activities, with limited participation in other activities. Non-farm activities, when reported, are more likely to be secondary activities.

# **3. Empirical Approach**

In the final interview, we randomized the order of the questions in the labor module so that some households used the long (annual) recall window first while others used the short (90-day) recall first. For those who received the short recall questions first, we use labor measures based exclusively on their quarterly survey responses, and for those who received the long recall first, we use measures based exclusively on the annual recall responses.<sup>14</sup> Our main analysis leverages this randomization to test for differences between these alternative recall windows using the following main empirical specification: L L L L L L 0 1L L L L L ii ii ii ii The coefficient of interest is 1, the difference in average reported labor measures between = ββ + ββ + XX + εε individuals using long and short recall. is a set of gender by age group by reporting status (proxy ββ ii or self) fixed effects. Age groups are defined as individuals under 25, 25-34, 35-49, and 50 and XX above, approximately quartiles of the study sample. Robust standard errors are clustered at the household level, the level of the treatment. In additional analyses we test for heterogeneity of these differences by proxy status, gender, and age. In these analyses we use fully saturated regression models to test each group’s differences against a null hypothesis of no effect and report p-values for differences across groups.

# **4. Results**

> 14 As discussed in Section 2, we have taken this approach of only using the data from the module that was administered first in the final round to avoid concerns over the potential influence of asking 90-day recall questions just before annual recall questions (or vice versa). Appendix Table 2 shows that this ordering indeed has a substantial impact, particularly on the long recall reported values.
Figure 4: Explaining the Absence of a Tax Compliance Effect

# A. Firms’ Slow Take-up of POS Subsidies

<!-- Start of picture text --> ⇐ POS Subsidies Become Available<br>2012m6 2012m12 2013m6 2013m12 2014m6 2014m12 2015m6<br>Time<br>Firms Receiving POS Subsidy (Left Axis)<br>Firms Without POS before Subsidy (Right Axis)<br>Tax Liability Response to POS Adoption<br>-4 -2 0 2 4<br>Months to First Card Report<br>Output VAT (Left Axis)<br>Net VAT Liability (Right Axis)<br>Mean Output VAT Pre-Reform: 146816<br>Mean Net VAT Liability Pre-Reform: 23970<br>20<br>10<br>8 15<br>6<br>10<br>4<br>5<br>2<br>Share of Eligible Retail Firms (%)<br>0 0 Share of Subsidy Receiving Retail Firms (%)<br>20000 7500<br>0 0<br>Point Estimate<br>-20000 -7500<br><!-- End of picture text -->

# B. Tax Liability Response to POS Adoption

# C. Share of Card Transactions in Total Sales

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of Electronic Sales in Reported Sales, E/R<br>2012 2013 2014 2015<br>N: 2012=6465, 2013=6742, 2014=6885, 2015=6549<br>4<br>3<br>2013 Median: 0.15 2013 Mean: 0.25<br>2<br>Density<br>1<br>0<br><!-- End of picture text --> Notes: Panel A plots the share of eligible retail firms receiving a subsidy for renting a POS (red dotted line), and the share of subsidy-receiving firms that did not have a POS before receiving the subsidy (blue line with triangle markers). Panel B displays event study estimates of firm behavior around the month of POS adoption. We estimate _Yit_ = _μi_ + _gt_ +<sup>�</sup><sup>_b_</sup> _k_ = _a_<sup>_δk ·D_</sup> _it_<sup>_k_+</sup><sup>_uit_, where</sup><sup>_Yit_is the outcome for firm</sup><sup>_i_in month</sup><sup>_t_,</sup><sup>_μi_and</sup><sup>_gt_are firm</sup> and month fixed effects respectively and _Dit_<sup>_k_areeventtimeindicators.Thesampleiscomposedofretailand</sup> wholesale firms that used a POS for the first time between January 2008 and December 2015 and are observed for four month before and after the event. Standard errors are clustered at the firm level and the outcome variable is winsorized at the 99th percentile. Panel C plots the distribution of electronic sales as a share of a firm’s total self-reported sales, for retail and wholesale firms that use a card machine in 2012-2015. We exclude a firm-year observation if the firm uses the card machine for less than 11 months in a particular year. This means that we exclude firms in the year in which they adopt a card machine, unless they adopt it in January or February. This Figure is discussed in Section 6.

29
# **1. Introduction**

# _1.1. Background_

African countries are among countries with less than 50% of children achieving literacy, numeracy and life skills mastery (Schaefer 2000). A large proportion of children do not have basic functional skills to read, write and enumerate after year four of their educational experience. The UNESCO (2005) report confirms these findings and shows that too many pupils are leaving school without mastering a minimum set of cognitive and non-cognitive skills.

Ghana participated in the Trends in International Mathematics and Science Study (TIMSS) in 2003 and 2007. In 2003 Ghana’s score for mathematics at 276 was higher only compared to that of South Africa among participating countries and much lower than the international average score for mathematics (467), as well as the score for other African participating countries such as Botswana (366) and Tunisia (410). In 2007, Ghana exhibited substantial improvement in the mathematics score, which increased to 309 (a 12% increase), which is still one of the lowest among the participating countries, ranking 47<sup>th</sup> among the 48 participants and lower than all the African participating countries (South Africa did not participate in 2007). Similar improvements were registered for science. The science score increased from 255 in 2003 to 303 in 2007, a 48 point increase (19%). Ghana is one of the countries in which boys perform significantly better than girls. While the score increased significantly over time for both boys and girls, the gender gap in 2007 stood at 22 points in mathematics and 28 points in science.

Table 1: Selected mathematics and science TIMSS scores

||Mathe|matics|Sci|ence|
|---|---|---|---|---|
|Countries|2003|2007|2003|3007|
|Singapore|605|593|578|567|
|Tunisia|410|420|404|445|
|Botswana|366|364|365|355|
|Ghana|276|309|255|303|
|International Average|467|500|474|500|

Source: Anamuah-Mensah et al. (2008)

# _1.2. Challenges and problems_

Ampiah (2010) outlined the problems and challenges that Ghana is faced in improving quality of education. One challenge relates to trade-offs between rapid expansion of basic education (towards universal access) and quality of education. In particular, if children attending are not enabled to improve learning outcomes, especially in literacy and numeracy, access to education is not meaningful. The authors feel that in the case of Ghana, quality of education seems to have become subordinated to quantity of education as a priority and that in many cases, educational expansion has come at the expense of quality.

The poor quality of teaching affects student learning, as most teachers lack the necessary supervision and feedback on their instructional practices. In particular, supervision is weak and 2
# **Figure 2**

Forecasted share of fossil energy in t+30 by energy project and according to year of publication Note: US scenarios are depicted as large squares

|35<br>40<br>45<br>50<br>55<br>60<br>65<br>70<br>75<br>80<br>85<br>90<br>95<br>**Fossil energy share (%x100)**|
|---|
|30|
|25|
|20|
|15|
|10|
|5|
|0|
|1970<br>1975<br>1980<br>1985<br>1990<br>1995<br>2000<br>2005<br>2010|
|Department of the Interior<br>EUOGEP<br>Energy Research and Development Agency<br>European Union / DGTREN<br>Ford Foundation<br>Greenpeace<br>Harvard Business School<br>IEA|
|IIASA<br>IPCC<br>OECD<br>PEW|
|Shell International BV<br>Union of Concerned Scientists<br>WEC<br>WEC and IIASA|

7
export promotion agencies. Many of the policies described here may appear to be untargeted towards any sector, and so one might argue they are not industrial policy per se. Yet, even if these policies can be applied to the whole economy in principle, governments with limited resources make choices about who will be the main beneficiaries, and tailor these policies to address specific constraints in those beneficiaries’ industries. In practice, selected beneficiaries of these policies are often those in sectors with international market growth, weak international competition, technological relatedness, and/or comparative or competitive advantage.

# _Different Rules for Developing Countries_

An export-led development strategy requires access to global markets, and market access requires good relations with trading partners and continued engagement in trade agreements. But while developing countries have benefited from participation in the world trading system, there is an irony. The rules of the trading system are biased against them, both de jure and de facto.

De jure, developing countries still face higher tariffs compared to wealthy countries. Calculations of actual tariff rates include preferences under bilateral and regional trade agreements (such as a free trade area or customs union) and provisions like most-favored nation status. Using the 2017 tariff data from the World Integrated Trade Solution (WITS), which are complete for almost all countries, the pattern that has emerged over time is that for nonagricultural goods (HS Chapters 25–89), value-weighted applied tariffs are 1.4 percent for low-income countries and are 2.1 percent for high-income countries, using the World Bank income classification. They are largest for lower-middle income countries at 2.5 percent. Lowincome countries retain some market access privileges given their income status, but these privileges are not available to middle-income countries. Trade agreements have allowed countries to retain higher tariffs especially in agriculture, which is the comparative advantage of many developing countries. Guatemala’s national development strategy is direct about the issue (República de Guatemala 2023, p. 472): Despite the positive results shown by exports in the last twelve years (with some important ups and downs in some periods), the country has had to put up with the high levels of tariff protection in sectors of special interest—particularly the 17
# **Figure 14 Export of Wood and Wood Products, 2001**

<!-- Start of picture text --> Sawnwood<br>45%<br>Roundwood<br>13%<br>Joinery and<br>Plywood<br>carpentery<br>9%<br>6%<br>Furniture Other wood<br>11% products<br>Packing boxes 6%<br>Firewood and pallets<br>3% Pulp of wood, 3%<br>paper and Particle board<br>paperboard<br>1%<br>3%<br>Source : Swedish National Board of Forestry, 2003<br>This export structure contrasts markedly with that of Finland. Whereas Latvia’s forestry<br>sector exports are concentrated almost entirely in labor intensive activities which lead to<br>little value added or processing in Latvia, most of Finland’s exports are in high value<br>added, technology intensive activities.<br>Figure 15 Exports of Forest Cluster in Latvia and Finland<br>Export Structure of Forest Cluster in Latvia and Finland, 2000<br>25<br>20<br>15 Latvia<br>10 Finland<br>5<br>0<br>Labor-intensive Technology-driven<br>Saw & Other Mills, Planing Containers & Boxes Furnitures & Fixtures Pulp of wood, paper & paperboard Metal & Wood working Machinery Special Industrial Machinery Electrical Industrial Machinery<br><!-- End of picture text --> This export structure contrasts markedly with that of Finland. Whereas Latvia’s forestry sector exports are concentrated almost entirely in labor intensive activities which lead to little value added or processing in Latvia, most of Finland’s exports are in high value added, technology intensive activities.

_Source_ : Swedish National Board of Forestry, 2003 What does all this imply for Latvia? Simply put, Latvia needs to focus not just on promoting high tech activities -- important though they may be. Instead, Latvia should put more effort into building an integrated forestry sector cluster. In Finland, this cluster 24
Figure 3. Distribution of Privatizations by Sector (percent) <!-- Start of picture text --> 60- _-____, ,<br>50-<br>40-<br>30 U~~~~~~~~~~~~ Sample<br>U Original List<br>20-<br>10<br>Mining Industry Services Othersl/<br><!-- End of picture text -->

> 1/ Includes land and unclassified firns.

_Source:_ Data collected by authors. Original list based on 1,500 firms. Sample reflects 308 firms.

24
the hypothesis that the presence of intermediaries with significant market power who do not provide concomitant distribution-cost-reducing services ends up raising consumer prices. The model predicts the reform should have lowered the intercept and increased the pass-through rate of the crude oil price to wholesale prices, while the precise opposite was observed.

An extension of the basic double marginalization model to allow the intermediaries to provide cost-reducing services (such as financing wholesale traders at below-market interest rates) is also incapable of explaining the lower pass-through of imported crude prices observed following the reform. This suggests the marketing intermediaries (DOTs) played a more fundamental role in the supply chain. One possible role is alleviating credit limits that wholesalers are subjected to when borrowing from alternative sources. Such credit rationing would have severely disrupted the volume of oil in the supply chain, resulting in higher average wholesale prices and lower pass-through rates from imported crude prices, consistent with the evidence found from a difference-in-difference analysis. Note that this does not deny the possibility that the DOTs might exercise significant market power. The point is that the consequences of such market power were outweighed by their financing role within the supply chain.

# **References**

Adachi, T and T, Ebina (2014), “Cost Pass-Through and Inverse Demand Curvature in Vertical Relationships with Upstream and Downstream Competition”, Economics Letters, Vol.124, No.3 (September 2014), pp.465-468.

Aristotle, S., (1988). The Politics. Cambridge University Press, Cambridge, MA.

Atkin, D and D. Donaldson (2014), Whos Getting Globalized? The Size and Implications of Intra-national Trade Costs, Working paper.

Banerji, A., Meenakshi, J.V. (2004), Buyer Collusion and Efficiency of Government Intervention in Wheat Markets in Northern India: An Asymmetrical Structural Auction Analysis, American Journal of Agricultural Economics, 86(1): 236-253.

Bardhan, Pranab K. (1984). _Land, Labor, and Rural Poverty: Essays in Development Economics._ Delhi: Oxford University Press/New York: Columbia University Press.

Bardhan, Pranab K.(1980). “Interlocking Factor Markets and Agrarian Development: A Review 35
and in other low-income countries, the effect of greater openness is not associated with heightened skills but rather with a greater specialization in low skilled intensive goods. The latter is explained by these countries having a comparative advantage in low skilled intensive goods (due to a relative abundance of unskilled labor) and with higher international integration leading to a higher specialization in the products intensive in this factor. In sum, the findings in the paper support the idea that international integration and technological innovation in the middle-income countries has been a key determinant of the increasing demand for skills in East Asia. These results have clear implications for policy. If international integration continues to intensify and technology continues to be skilled biased, the design of policies aimed at mitigating the persistent skills shortages in the region will only be successful if they continuously foster skills rather than producing temporary increases in skills.

The paper is structured as follows. Section 2 presents the data set used and reports within- and cross-country descriptive statistics for the main variables. Section 3 proposes an empirical methodology. Section 4 presents the main empirical findings and section 5 discusses several robustness checks and the heterogeneity of the results by income group. Section 6 concludes the paper

# **2. Data**

The main data set we use is a large firm level data collected by the World Bank, _Enterprise Surveys_ , covering eight developing countries in East Asia namely, Cambodia, China, Indonesia, Malaysia, the Philippines, Republic of Korea and Vietnam. The surveys used in this paper were conducted between 2002 and 2005, and the samples were designed to be representative of the population of firms according to their industry and location within each country.<sup>4</sup> We will explore one cross-section of data for each country,

> 4 _Enterprise Surveys_ have been used extensively to study the link between technology adoption and firm openness (Almeida, 2008, and Almeida and Fernandes, 2007), the investment in job training (Pierre and Scarpetta 2004; Almeida and Aterido 2008) as well as other topics as informality (Svensson 2003, Gatti and Honorati, 2008), effect of labor regulations (Almeida and Carneiro 2009) and business environment and firm size (Aterido and others, 2007).  The main disadvantage of using the _Enterprise Surveys_ is that, for most countries, there is only one cross section available. For the sake of comparability, we only include one wave of data for each country (the most recent wave). The surveys in Thailand, Malaysia and Mongolia include a worker module which could provide some additional measures of skills (e.g., type of education, core skills used in the workplace) at the worker level.

6
|Table 4 - Influen|<br>Dependent Variable: Valu<br>GMM based on th<br>ce of Components of the Do<br>Dev|<br>e of FDI Inflows (<br>e Arellano-Bond<br>ing Business Rank<br>eloping Countries|<br>100s of million U<br>methodology<br>ings on FDI Inflo|<br> <br>SD)<br>ws into Develope|<br>d and|
|---|---|---|---|---|---|
||Column A<br>|Column B<br>|Column C<br>|Column D<br>Col<br>|umn E<br>|
|SBT|1.94|1.33|1.33|1.5|1.62|
||_1.71_|_1.72_|_1.83_|_1.52_|_1.85_|
|SBC|0.02|-0.01|0.07|0.05|-0.04|
||_0.09_|_0.09_|_0.06_|_0.08_|_0.08_|
|RPT|0.16|0.15|0.03|0|0.22|
||_0.24_|_0.23_|_0.23_|_0.22_|_0.25_|
|RPC|5.45|3.48|1.41|1.74|3.31|
||_6.47_|_6.45_|_5.23_|_5.83_|_5.46_|
|IPA|45.15|54.27|30.75|39.94|48.71|
||_41.72_|_41.56_|_46.31_|_44.49_|_43.71_|
|TPN|-0.35|-0.83|**-2.57***|**-2.84****|-0.73|
||_1.41_|_1.26_|_1.38_|_1.41_|_1.2_|
|XT|2.59|3.59|-7.77|-9.45|3.9|
||_6.48_|_6.7_|_5.38_|_5.94_|_5.56_|
|XC|0.02|-0.02|**0.17****|**0.18****|-0.02|
||_0.07_|_0.08_|_0.07_|_0.08_|_0.08_|
|IM|-5.2|**-8.12***|-3.77|-4.64|**-8.36***|
||_4.06_|_4.76_|_4.25_|_4.25_|_4.6_|
|IC|0|0.04|-0.05|-0.05|0.06|
||_0.06_|_0.08_|_0.06_|_0.06_|_0.07_|
|ECT|-0.29|**-0.29***|**-0.40****|**-0.42****|**-0.26***|
||_0.18_|_0.17_|_0.19_|_0.21_|_0.14_|
|ECC|-1.55|**-2.06***|**-1.89***|**-2.32***|-1.44|
||_1.17_|_1.19_|_1.02_|_1.24_|_0.95_|
|GDPGR|7.84|**8.26***|**11.48****|**11.75****|6.38|
||_4.97_|_4.72_|_4.22_|_4.81_|_3.93_|
|GDPCAP|**0.01***|**0.01***|||**0.01***|
||_0.01_|_0.01_|||_0.01_|
|REER|1.7|0.99|1.81|1.33|0.62|
||_2.78_|_2.85_|_2.15_|_2.43_|_2.65_|
|INF|1.89|1.84|0.62|1.81|2.5|
||_4.61_|_4.64_|_4.19_|_3.96_|_4.86_|
|OPEN|-5.03|-4.91|-3.04|-4.07|-3.98|
||_3.89_|_3.74_|_4.43_|_4.15_|_3.84_|
|GOV||-4.49||||
|||_3.42_||||
|VA|||-0.84||-4.71|
||||_4.6_||_4.41_|
|PV|||-1.44||-0.62|
||||_3.36_||_3.1_|
|CC|||2.88||0.85|
||||_2.36_||_2.62_|
|Hansen J test|0.23|0.40|0.13|0.14|0.39|
|No. of observation|327|327|327|327|327|
|No. of groups|86|86|86|86|86|
|No. of instruments|54|55|49|46|57|

Standard errors corrected for robustness in italics.

*** Significance at the 1 percent level, ** Significance at the 5 percent level, * Significance at the 10 percent level.

A negative coefficient for the first 11 variables suggests on average, an increase in that variable will increase FDI Inflows all else constant (i.e. higher rankings are a lower numerical value). For the remaining variables, the opposite is true; an increase in that variable suggests, on average, an increase in FDI Inflows all else constant.

12
example child mortality in Bonilla-Chacin and Hammer (1999), child survival in Uganda in Stecklov, Bommier, and Boenna (1999), child anthropometric outcomes in Wagstaff and Watanbe (1999), and to document inequalities in a variety of health outcomes and behaviors in Gwatkin and others (2000). Sahn and Stifel (2000) use a similar approach to analyze poverty directly.

**Table 3. Percentage of children under 3 who are reported to have had an episode of fever (and fever** without a cough) in the past two weeks

||||_Anyfever_||_Fev_|_erwithouta c_|_ough_|
|---|---|---|---|---|---|---|---|
|_Country_|_Year_|_Total_|_Rural_|_Urban_|_Total_|_Rural_|_Urban_|
|_WesternandC _|_entralAfrica_|||||||
|Benin|1996|54.5|57.8|47.3|26.1|27.9|22.4|
|BurkinaFaso|1999|41.0|41.8|34.1|14.4|14.5|13.2|
|C.A.R.|1994-95|35.7|36.3|34.8|5.0|6.0|3.4|
|C6ted'Ivoire|1994|44.2|46.3|39.8|19.5|20.8|16.8|
|Cameroon|1998|30.8|31.1|30.2|11.7|12.1|10.5|
|Ghana|1998|29.0|29.8|26.4|14.9|15.4|13.5|
|Mali|1995-96|39.3|41.2|34.1|19.8|20.8|17.0|
|Niger|1997|49.4|51.1|41.2|28.9|30.1|22.9|
|Nigeria|1999|31.2|32.6|27.3|17.6|18.5|15.3|
|Senegal|1992-93|45.7|48.3|40.8|17.8|18.4|16.8|
|Chad|1996|36.9|36.9|36.6|19.9|20.0|19.5|
|Togo|1998|37.7|37.7|37.8|15.9|16.5|14.0|
|All||35.3|36.8|31.3|17.8|18.7|15.5|
|_EasternandSo_|_uthern_|||||||
|Comoros|1996|48.7|48.8|48.4|17.9|18.5|16.0|
|Kenya|1998|42.9|42.9|43.2|16.8|16.6|17.5|
|Madagascar|1997|32.7|33.1|31.0|9.8|10.2|8.1|
|Mozambique|1997|44.7|43.2|50.4|19.2|21.2|11.8|
|Malawi|1998|47.6|48.8|37.5|12.0|12.1|11.2|
|Rwanda|1992|48.6|49.0|39.2|11.8|11.7|14.2|
|Tanzania|1996|35.8|35.7|36.1|12.8|13.0|12.0|
|Uganda|1995|50.3|51.5|40.6|14.6|14.8|13.0|
|Zambia|1996-97|46.4|47.6|44.5|17.2|19.1|14.2|
|Zimbabwe|1999|31.1|32.5|28.2|8.3|7.4|10.2|
|All||41.9|42.4|39.3|14.1|14.4|12.7|

_Source:_ Author's calculations from DHS data. Notes: Data are weighted. See next section for discussion of weights.

6. DHS surveys frequently use a weighting scheme to adjust sample to population averages. These weights are used in this analysis. See discussion below on cross-country weights.

7. C.A.R. has a slightly different questionnaire design. If treatment was sought then respondents were asked how much was spent on treatment. It is unlikely though that this would have affected the percentage reporting no cough with a fever.

_7_