rafmacalaba/datause-encoder-data huggingface dataset:
increases to 35 percent for the second period and to 42 percent for the final period. In addition, the population is also substantially better educated. Women with no education constituted almost sixty percent in the first period but less than thirty percent in the last period. Correspondingly, women with more than eight years of education were just over twenty percent in the first period but almost half in the last period. Part of the increase in education is correlated with the increase in urbanization but clearly the proportion of better-educated women has increased substantially in the rural areas as well. Among the high education group almost seventy percent lived in urban areas during the first period but this had fallen to less than sixty percent in the last period.

The increase in urbanization and education is likely to exert downward pressure on fertility and the high censoring rates for the later periods are evidence of this. Figure 3 provides an indication of just how strong the decline in fertility has been. It shows the average number of children born to women before they turned 35 by their year of birth. Women born in the early 1940s had on average close to five children when they reached 35, but women born in the early 1970s had only just over three children. The low number of children is especially remarkable because it combines all education levels and all areas of residence. Hence, fertility in cities is likely substantially lower.

<!-- Start of picture text --> 1950 1960 1970<br>Respondent’s year of birth<br>5.5<br>5<br>4.5<br>4<br>3.5<br>3<br><!-- End of picture text --> Figure 3: Children ever born at age 35

# **7 Results**

Results are presented using graphs of predictions of the percentage boys born by quarter, the associated 95 percent confidence interval, and the survival curve for a “representative woman” using 24
**<u>Table 4: Descriptive statistics for smallholders at different distances to large farms</u>**

|||**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Maize**|||||||||||||
|< 25|%>0|0.100|0.114|0.138|0.167|0.259|0.325|0.329|0.341|0.363|0.366|0.363|
||Area|0.495|0.482|0.599|0.666|2.195|2.374|2.545|3.353|3.298|3.546|4.169|
|25 50|%>0|0.184|0.303|0.328|0.354|0.500|0.582|0.599|0.605|0.634|0.632|0.629|
||Area|1.451|1.811|1.961|2.109|6.729|8.282|9.296|10.012|10.369|10.753|11.736|
|50-100|%>0|0.559|0.757|0.772|0.774|0.819|0.879|0.882|0.875|0.886|0.886|0.885|
||Area|8.501|9.597|10.124|10.161|24.612|31.889|37.662|38.779|39.865|42.391|47.091|
|km to lg|, farm|89|77|77|73|60|52|51|54|51|51|52|
|Kebeles|(%)|0.536|0.670|0.649|0.655|0.709|0.772|0.720|0.705|0.771|0.760|0.714|
|**Sorghu**|**m**||||||||||||
|< 25|%>0|0.075|0.067|0.072|0.158|0.264|0.319|0.328|0.330|0.341|0.359|0.366|
||Area|2.322|2.065|1.895|1.665|1.204|1.591|2.388|1.981|2.008|2.176|2.936|
|25 50|%>0|0.162|0.178|0.196|0.258|0.294|0.322|0.355|0.369|0.403|0.424|0.413|
||Area|2.795|2.741|1.716|1.718|5.132|6.384|9.807|6.175|6.326|6.681|8.117|
|50-100|%>0|0.412|0.444|0.454|0.525|0.572|0.627|0.703|0.697|0.772|0.775|0.769|
||Area|7.644|8.197|8.141|7.022|25.550|28.348|39.008|26.066|26.804|28.612|34.261|
|km to lg|, farm|107|105|107|104|101|97|92|89|71|70|70|
|Kebeles<br>**Teff**|(%)|0.164|0.181|0.197|0.234|0.486|0.509|0.498|0.520|0.516|0.518|0.513|
|< 25|%>0|0.078|0.099|0.128|0.160|0.276|0.337|0.328|0.394|0.408|0.412|0.405|
||Area|0.507|0.395|0.263|0.283|0.416|0.589|0.566|0.702|0.746|0.786|0.768|
|25 50|%>0|0.234|0.317|0.297|0.340|0.398|0.458|0.481|0.524|0.547|0.565|0.568|
||Area|1.092|0.958|0.963|0.995|2.193|2.478|2.826|3.214|3.336|3.431|3.391|
|50-100|%>0|0.390|0.475|0.547|0.612|0.739|0.794|0.816|0.809|0.845|0.857|0.861|
||Area|9.019|6.307|5.046|4.602|5.265|7.761|10.274|10.165|10.521|11.319|11.634|
|km to lg|, farm|112|113|107|98|96|78|75|76|75|68|68|
|Kebeles|(%)|0.158|0.203|0.349|0.366|0.563|0.566|0.545|0.562|0.561|0.546|0.555|
|**Wheat**|||||||||||||
|< 25|%>0|0.020|0.017|0.017|0.108|0.242|0.315|0.320|0.364|0.392|0.380|0.365|
||Area|0.585|0.757|0.512|1.298|2.693|2.890|2.864|3.380|3.607|3.665|3.678|
|25 50|%>0|0.176|0.237|0.267|0.351|0.379|0.448|0.448|0.474|0.505|0.509|0.504|
||Area|4.577|5.375|4.051|6.728|6.229|7.670|7.696|8.247|8.989|9.167|9.229|
|50-100|%>0|0.706|0.678|0.733|0.757|0.757|0.841|0.829|0.853|0.859|0.881|0.876|
||Area|20.897|23.383|23.138|26.074|24.414|26.736|27.708|26.391|27.940|27.170|28.078|
|km to lg|, farm|80|83|82|75|88|79|80|79|76|64|65|
|Kebeles|(%)|0.105|0.119|0.122|0.144|0.373|0.390|0.405|0.408|0.389|0.409|0.399|

_Source:_ Own computation from 2003/4-2013/14 CSA smallholder farm surveys.

_Note:_ Area is in 100 ha. Statistics are calculated using the sub-sample of kebeles that are included in the analysis for each of the crops. The criteria for inclusion in the sub-sample are that the kebele is within 150 km of a commercial farm growing the specified crop in 2014 and that more than 10% of households in the kebele grow the crop.

20
10 The microeconomic evidence at the individual worker level is also consistent with the firm-level evidence. Survey work performed in the summer of 1994 indicates that about 70 percent of an urban sampling of current or ex-state enterprise employees were engaged in unofficial activities. The sample participants as a whole derived more than one-half of their total income from these nonofficial activities; formal wage income represented less than one-half of their total income. For those working at lower-than-average official wages, the share of unofficial incomes was about two-thirds.'<sup>7</sup> To complement the microevidence presented here, and to provide for a comparative methodology for FSU and CEE countries, we turn now to a top-down approach to measuring the dynamics of the unofficial economy.

# **The Macroelectric Approach to Measurement of the Unofficial Economy and Its Application to Ukraine**

In most of the FSU and CEE states, official GDP figures measure the official economy. Almost by definition, and due to the slow (and very partial at best) adaptation of statistical methodology to more market-based systems, the unofficial economy has been minimally captured in official statistics. Official GDP figures can therefore be considered a good approximation of the measurement of official activities. But contrary to the prevalent use by analysts and decision makers, they cannot provide a reliable measure of overall economic activity in an economy. For a valid measurement of overall economic activity, a different indicator is needed.

# _Unitary Elasticity of Electricity Consumption: Sensible Assumption?_

To measure overall economic activity in an economy, electric-power consumption is regarded as the single best physical indicator of economic activity. Overall economic activity and electricity consumption have been empirically observed throughout the world to move in lockstep--with an electricity/GDP elasticity usually close to one."<sup>8</sup> By having a proxy measurement for the overall economy and subtracting it from estimates of official GDP, an estimate of unofficial GDP can be derived. Before proceeding further, however, we need to address the question of likely biases in utilizing electricity consumption as a proxy for overall GDP.

We need to consider the special features of economies in transition in order to determine possible biases in utilizing electricity consumption as a proxy for overall GDP. From simple arithmetic it follows that an upward bias in the overall economy growth estimate (proxied by electricity consumption) will result in an upward bias in the unofficial economy growth
Chan-Lau, J., and T. Gravelle, 2005. “The END: A New Indicator of Financial and Nonfinancial Corporate Sector Vulnerability”. IMF Working Paper 05/231. International Monetary Fund, Washington DC, December.

Committee on the Global Financial System, 2014. “EME Banking Systems and Regional Financial Integration,” Report submitted by a Study Group Established by the Committee on the Global Financial System, Basel, March.

Cortina, J. J., T. Didier, and S. Schmukler, 2016. “How Long Do Corporates Borrow? Evidence from Capital Raising Activity.” Working Paper, World Bank, Washington, DC.

Dehn, J. and J. Arthur, 2015. “The Emerging Markets Investment Universe,” The Emerging View, Ashmore, August.

Didier, T., R. Levine, and S. Schmukler, 2015. “Capital Market Financing, Firm Growth, and Firm Size Distribution”. Policy Research Working Paper 7353. World Bank, Washington DC, July.

Feyen, E., S. Ghosh, K. Kibuuka, and S. Farazi, 2015. “Global Liquidity and External Bond Issuance in Emerging Markets and Developing Economies.” World Bank Policy Research Working Paper 7363, World Bank, Washington.

Financial Stability Board, 2015. _Corporate Funding Structures and Incentives – Final Report_ . Basel, Switzerland, 28 August.

Gabaix, X., 2011. “The Granular Origins of Aggregate Fluctuations”. _Econometrica_ 79, pp. 733-72.

Geneva Report, 2014. _Deleveraging? What Deleveraging?_ 16th CEPR-ICMB Geneva Report on the World Economy. CERP-International Center for Monetary and Banking Studies. Geneva, Switzerland, September Gonzalez-Miranda, M., 2012. “Nonfinancial Firms in Latin America: A Source of Vulnerability?”. IMF Working Paper 12/279. International Monetary Fund, Washington DC, November.

Goretti, M., and M. Souto, 2013. “Macro-Financial Implications of Corporate (De)Leveraging in the Euro Area Periphery”. IMF Working Paper 13/154. International Monetary Fund, Washington DC, June.

Gray, D., 1999. “Assessment of Corporate Sector Value and Vulnerability”. WB Working Paper Series WTP 455. World Bank Group. Washington DC, November.

Gray, D., R. Merton, and Z. Bodie, 2006. “A New Framework for Analyzing and Managing Macrofinancial Risks of an Economy”. Working Paper #07-026. Harvard Business School. Boston, MA. October.

International Monetary Fund, 2015. “Chapter 3: Corporate Leverage in Emerging Markets – A Concern?” _Global Financial Stability Report—Vulnerabilities, Legacies, and Policy Challenges: Risks Rotating to Emerging Markets_ . Washington DC. October.

29
WTO (2019) disentangles the factors that explain trade costs. Although the focus of the analysis is trade costs associated with cross-border trade (mode 1), consumption abroad (mode 2) and temporary movement of people (mode 4) transactions, it confirms that the trade facilitation related matters identified in table 2 are a significant share of total trade costs. The categories identified by the report are: i- transport costs; ii- infrastructure and border costs; iii- information and transaction costs; iv- trade barriers and regulatory convergence; and v- governance quality (see figure 1).

**Figure 1. Trade Costs: Goods and Services Trade**

<!-- Start of picture text --> 100%<br>4%<br>10%<br>90% 9%<br>80% 21%<br>Governance quality<br>70% 32%<br>60% Trade policy barriers and regulatory<br>25%<br>convergence<br>50%<br>Information and transaction cost<br>26%<br>40%<br>20% Infrastructure and border cost<br>30%<br>20% Transport cost<br>29%<br>25%<br>10%<br>0%<br>Services Goods<br><!-- End of picture text -->

## Source: based on WTO (2019)

The WTO analysis confirms that the composition of trade costs is different for goods and services trade. In the case of goods, trade costs are highly determined by transport costs, infrastructure and border costs, and information and transaction costs. For services, governance quality; trade barriers and regulatory convergence; and transport costs are the most important components of trade costs.

The WTO study also illustrates how important the trade facilitation agenda can be to reduce trade costs for services. While the study only addresses trade costs for three modes of supply, the data suggest that following a narrow definition, which would include governance quality and information and transaction costs, then trade facilitation matters account for at least 35 percent of total trade costs. A wider definition which includes regulatory heterogeneity and border costs, would increase the share of trade facilitation issues in total trade costs.

# V. Trade Facilitation in Services by Modes of Supply

## a) **Trade facilitation and trade costs**

Marel and Shepherd (2019) examine the concept of TFiS from the perspective of the recent literature on the determinants of services trade. Due to data constraints their empirical analysis focuses mainly on cross-border transactions (mode 1). According to these authors, the measures that fall within the area of TFiS relate, broadly, to notions such as “transparency”, “regulatory cooperation”, “regulatory coherence”, and “good regulatory practice”. Taken together, therefore, measures that would fall under the scope of a potential TFiS framework might include: (i) processes and procedures used in 8
definition of entrepreneurship or nascent entrepreneurs (details below) with the additional requirement that they expect to create ten jobs or more within the next five years, a definition also used by Estrin and Mickiewicz (2011) to identify high aspiration entrepreneurs. The cut-off point of ten jobs or more was selected as it is consistent with the standard distinction between small and micro enterprises.

The standard definition of nascent entrepreneurship follows Reynolds et al. (2005) and is already available in the GEM data set. An individual is considered a nascent entrepreneur if he or she is between the ages of 18 and 64 and has taken some action towards starting a business in the last year, and expects to own or share the business they are starting, which must not have paid any wages of salaries for more than 3 months. We use the available data from the GEM data which are consistent and comparable across time. Combined with the government spending variables the sample spans from 2001 to 2009 covering 50 individual countries for a total of around 650,000 observations.

We use individual controls that have been established in the literature as significant determinants of entrepreneurial activity. We control for personal characteristics such as education, age, gender, employment status, experience, and networks (Parker 2009). Education has been found to have a positive relationship with entrepreneurship (Robinson and Sexton, 1994; Davidson and Honig, 2003). The choice of entrepreneurship activity may also be affected by whether an individual is employed or not. Studies have indicated that networks, start-up knowledge, and fear of failure have been important determinants of entrepreneurship (Estrin and Mickiewicz, 2011; Wennekers et al., 2005; Aidis et al., 2010). The basic idea is better information from networks or having previous experience may lower the cost and uncertainty associated with entrepreneurial activity. We thus control for whether the individual knows other entrepreneurs, owns or manages an existing business, whether they have previously acted as a business angel, or if they have a “fear of failure.” 10
Policy Research Working Paper 6550

# **Abstract**

Land area measurement is a fundamental component of agricultural statistics and analysis. Yet, commonly employed self-reported land area measures used in most analysis are not only potentially measured with error, but these errors may be correlated with agricultural outcomes. Measures employing Global Positioning Systems, on the other hand, while not perfect especially on smaller plots, are likely to provide more precise measures and errors less correlated with agricultural outcomes. This paper uses data from four African countries to compare the use of self-reported and Global Positioning Systems land measures to (1) examine the differences between the measures, (2) identify the sources of the differences, and (3) assess the implications of the different measures on agricultural analysis focusing on the inverse productivity relationship. The results indicate that self-reported land areas systematically differ from Global Positioning Systems land measures and that this difference leads to potentially biased estimates of the relationship between land and productivity.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_A background paper for the World Development Report 2012 on Gender Equality and Development_ Energy, Gender and Development What are the Linkages? Where is the Evidence?

Gunnar Köhlin<sup>1</sup> Erin O. Sills<sup>2</sup> Subhrendu K. Pattanayak<sup>3</sup> Christopher Wilfong<sup>4</sup>

**JEL codes: Q23, Q42, Q56, J16.**

> 1 University of Gothenburg 2 North Carolina State University 3 Duke University

> 4 Duke University
# **4. Changes in inequality of opportunity**

Has inequality in educational achievement changed over time in these countries? To address this question we complement our data using data from the 2009 round of PISA, which provides us with information on the exact same circumstances we have been using so far. There are 52 countries that can be found in both rounds of PISA.<sup>16</sup> Figure 5, panel a, shows that there have been changes in the percentage of students at or above level 2 proficiency. For mathematics, half of the countries show a higher percentage of students scoring at or above level 2 proficiency compared to 2009; for reading and science (not shown here but in the Figure A3 in the Appendix) this positive trend occurs for 65% of countries. The changes, however, have been small in most cases.

Figure 5, panel b, shows that changes in inequality of opportunity have slightly more variation than changes in coverage rates (similarly for reading and science). For mathematics, around half of the countries report a fall in inequality of opportunity between 2009 and 2012; for reading and science this percentage is around 62%. The question we are interested in is the extent to which reduction in inequality of opportunity is attributable to changes in group specific coverage rates, as opposed to changes in distribution of the circumstances themselves.

# **<u>Figure 5. Coverage and Inequality of opportunity in Math, 2012 vs. 2009</u> a. Percentage of students at or above b. Inequality of opportunity level 2 proficiency**

<!-- Start of picture text --> 20 40 60 80 100 0 10 20 30 40<br>% of students at/above level 2 proficiency in math (2009) Inequality index for math (2009)<br>100 40<br>80 30<br>60 20<br>40 10<br>Inequality index for math (2012)<br>20 0<br>% of students at/above level 2 proficiency in math (2012)<br><!-- End of picture text --> Source: Authors’ calculations based on data from the OECD, PISA 2012, and UNESCO Institute for Statistics.

The distribution of observable circumstances in all countries is likely to have changed between 2009 and 2012 due to sampling “noise” as well as real changes in the characteristics of the underlying population. This needs to be accounted for in order to understand how an observed

> 16 Rounds previous to 2009 do not survey students on preschool attendance. PISA 2009 assesses 65 countries and economies. Nonetheless, of these 65 countries and economies, Azerbaijan, Kyrgyzstan, Panama and Trinidad and Tobago were not surveyed for PISA 2012. In contrast, of the 65 countries and economies surveyed in 2012, Croatia, Uruguay, United Arab Emirates, Costa Rica and Vietnam were not surveyed in the 2009 round. For 2009, economies and missing values are dropped from the sample, comprising 10% of the full sample. We also drop Albania and Israel due to a high share of missing values in the circumstances in consideration.

15
emissions shares by sectors. We focus our analysis on economies that can be matched to EDGAR data via country codes from the _World Development Indicators._ Two variables generally considered important predictors of emissions, and that are indeed correlated with emissions across countries, are population size and economic output. We use the harmonized long run estimates of GDP per capita and population in the _Maddison Database_ , which utilize a similar methodology and are published alongside the Penn World Tables (PWT).<sup>7</sup> All GDP figures are in real $2011 dollars.

Finally, to obtain a good fit and improve the credibility of the synthetic control estimates, we use fuel consumption of coal, natural gas, and petroleum products from the Energy Information Administration (EIA) _International Energy Statistics_ .<sup>8</sup> We construct per-capita measures, scaling the EIA data by the population figures from the Maddison database. A full list of the relevant covariates appears in Table 1.

# **3.2 Methods: Synthetic Control and Extensions**

In the basic setup for the case considered in the paper, we observe units _j_ = 1 _, ..., J_ + 1 for time periods _t_ = 1 _, ..., T_ . Without loss of generality let _j_ = 1 be the treated unit, and consider an intervention that occurs at time 0 . Denote observed outcomes (i.e. emissions per capita) by j and potential outcomes j j (0) with and without treatment, respectively. The TT + 1 < TT treatment effect for unit _j_ at time _t_ is defined as YY YY (1), YY Sometimes called the fundamental problem of causal inference, the object of interest 0 cannot be calculated because we only observe one of its two components. Synthetic control seeks to 1jj ττ , tt> TT use a weighted sum of control units 2 _, ..., J_ + 1 to impute the missing counterfactual outcome of the first unit (i.e. annual emissions per capita in Australia had the tax not been implemented). The weights are chosen subject to positivity and adding-up constraints ∗ and ∑ = 1 . JJ+1 jj jj=2 jj ww ≥ 0 ∀ jj ww

> 7 Available at https://www.rug.nl/ggdc/historicaldevelopment/maddison.

> 8 Available at https://www.eia.gov/international/data/world.

10
9 _Rgure J.1: 7end in Awerage Electricdy Tariffs of Developing Countries and OECD Countries, 1980-8_ <!-- Start of picture text --> 7 69<br>6<br>2<br>0<br>198D 1981 19Z 198 1964 196 196 1967 196<br>I I Develaplig OE Co_tia -_ AvOg<br>CcuM kw= Cco<br><!-- End of picture text --> Alhough domestic petroleum product pricing policies vaied across products and coutie, dzation was not as w _despred as for deecicity. Diesel subsidies wero used in oil exporting contie Kersene subsies oocurred in oil expot g ad pording counries. Gasoline, onthe contay, ws tae in anumber ofcounes In geera level of subsidies wer much higer durmg toh early 196s tha _afier_ 1985. Toward the end of he 19830s most of t oil exporting counties bad subdies on petrlum produc. Figure 1.2 shows the avep diffece (as a pecentage of domestic pnce) betwom domestic and border prices for kerosene for a selected group of coims Two _ma ftos_ contied towards the fall in subsidy leves in dte latter part of 1980s: a fall in th inenational ofl prices (Le., a fal in the opporauity costs of petroleum) am it d cm in domestic prices For most of the countries, te fall in internationa ol pnces during th **1930t** led to dnges im subs* levels. although administed piincrases designed to bring domesic prices n ine wit the border prices were also prsued in a few countries.5

**' So 1.1 for dimel ndl psob. pr distodiam. S g -** 1.2 **_to_ 1.4 fw wdupwkt** in pirm pmdmt pr. **uinutbcea ctu.**