rafmacalaba/datause-encoder-data huggingface dataset:
characteristics to the previous model. Both models control for country fixed effects and use firms’ weights.

# **3.2 Correlating Urban Infrastructure and Services with Formality-Employment Type**

We begin by estimating the correlation between worker employment types and access to urban infrastructure and services. We adjust Equation (1) so that it takes the following form: The dependent variables ( i ) are the same as in equation (1), namely indicators of gg employment type _g_ (g=unpaid worker, low-educated self-employment, informal wage employee, formal wage employee) for worker YY _i_ in country _c._ The new term _Uic_ includes whether the household has access to electricity, to a computer and to improved water, as well as a poverty proxy based on whether a household is in the bottom 40 percent of the national per capita consumption distribution.<sup>34</sup> The estimated value of the parameters will tell us how the urban infrastructure and services variables relate to the probability of θθ<sup>gg</sup> being an informal worker of type _g_ with respect to being a worker of any other type. We first estimate Equation (3) without the _Xic_ and _Jic_ terms to assess the correlation between urban variables and type of employment. We then add the socio-demographic and job characteristics (the _Xic_ and _Jic_ terms).

Similarly, to understand how urban infrastructure and services variables correlate with firm type, we re-estimate Equation (2) but add the term _Ufc_ . The dependent variables ( ) are the same as in Equation (2), being indicators of informal firm type _g_ (g= LE-SE, HE- gg SE, and employers) for firm _f_ in country _c._ The vector **_Ufc_** includes indicators of whether a ffii YY firm needs and has access to water and electricity, and computer access.<sup>35</sup> The urban explanatory variables are available for firms in two countries. We estimate Equation (4) without the _Xfc_ and _Jfc_ terms and then add the firm owner and firm characteristic as additional regressors. We include a proxy for productivity in the _Jfc_ vector to control for

> 34 <mark>The GMD includes data on the aggregate consumption at the household level, derived from household surveys. We use the distribution at the country level and designate all households in the lower 40 percent as “vulnerable.”</mark>

> 35 <mark>We use a smaller set of infrastructure and services variables in comparison to the descriptive analysis due high correlations between them.</mark>

12
**Table 12:** Sectoral Employment Predictors

||(1)|(2)|(3)|(4)<br>Any Emplo|(5)<br>yment in|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Farm|Non-Farm<br>Informal|Non-Farm<br>Formal|Labour|Small<br>Business|Business|Self<br>Employed|White<br>Collar|
|Female|.068<sup>_∗∗∗_</sup><br>(.011)|-.094<sup>_∗∗∗_</sup><br>(.010)|.017<sup>_∗∗_</sup><br>(.007)|-.112<sup>_∗∗∗_</sup><br>(.009)|.004<br>(.003)|-.013<sup>_∗∗∗_</sup><br>(.003)|-.013<sup>_∗∗∗_</sup><br>(.003)|.057<sup>_∗∗∗_</sup><br>(.006)|
|SC/ST|-.047<sup>_∗∗∗_</sup>|.092<sup>_∗∗∗_</sup>|-.041<sup>_∗∗∗_</sup>|.099<sup>_∗∗∗_</sup>|-.011<sup>_∗∗∗_</sup>|-.022<sup>_∗∗∗_</sup>|-.014<sup>_∗∗∗_</sup>|-.001|
||(.010)|(.010)|(.005)|(.009)|(.003)|(.003)|(.003)|(.003)|
|Muslim|-.097<sup>_∗∗∗_</sup>|.087<sup>_∗∗∗_</sup>|.008|.064<sup>_∗∗∗_</sup>|.023<sup>_∗∗∗_</sup>|.006|.006|-.004|
||(.017)|(.017)|(.015)|(.018)|(.008)|(.010)|(.009)|(.005)|
|Age|.008<sup>_∗∗∗_</sup>|-.010<sup>_∗∗∗_</sup>|.005<sup>_∗∗∗_</sup>|-.014<sup>_∗∗∗_</sup>|.000|.003<sup>_∗∗∗_</sup>|.002<sup>_∗∗∗_</sup>|.003<sup>_∗∗∗_</sup>|
||(.001)|(.001)|(.001)|(.001)|(.000)|(.000)|(.000)|(.001)|
|Age Sq.|-.000|.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|.000<sup>_∗∗∗_</sup>|-.000|-.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|
||(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|
|Literate|-.004|-.036<sup>_∗∗∗_</sup>|.038<sup>_∗∗∗_</sup>|-.041<sup>_∗∗∗_</sup>|.007<sup>_∗∗∗_</sup>|.016<sup>_∗∗∗_</sup>|.011<sup>_∗∗∗_</sup>|.009<sup>_∗∗∗_</sup>|
||(.005)|(.006)|(.005)|(.006)|(.002)|(.002)|(.002)|(.002)|
|Educated|-.058<sup>_∗∗∗_</sup>|-.064<sup>_∗∗∗_</sup>|.130<sup>_∗∗∗_</sup>|-.046<sup>_∗∗∗_</sup>|-.001|.018<sup>_∗∗∗_</sup>|.011<sup>_∗∗∗_</sup>|.083<sup>_∗∗∗_</sup>|
||(.006)|(.005)|(.006)|(.005)|(.002)|(.003)|(.002)|(.005)|
|On Medication|.002|.007|-.019|-.014|.020<sup>_∗_</sup>|.012|-.031<sup>_∗∗_</sup>|.001|
||(.023)|(.025)|(.021)|(.024)|(.012)|(.013)|(.012)|(.012)|
|Bank Account|.017<sup>_∗∗_</sup>|-.037<sup>_∗∗∗_</sup>|.048<sup>_∗∗∗_</sup>|-.017<sup>_∗_</sup>|.002|.008<sup>_∗∗∗_</sup>|.004|.015<sup>_∗∗∗_</sup>|
||(.008)|(.009)|(.005)|(.009)|(.003)|(.003)|(.003)|(.003)|
|Cell Phone|-.009|.011|.018<sup>_∗∗∗_</sup>|.005|.004|-.003|.004<sup>_∗∗_</sup>|.019<sup>_∗∗∗_</sup>|
||(.006)|(.007)|(.005)|(.007)|(.003)|(.002)|(.002)|(.003)|
|Observations|167419|167419|167419|167419|167419|167419|167419|167419|
|R<sup>2</sup>|.43|.43|.29|.43|.10|.15|.11|.22|
|Dep Var Mean|.53|.30|.16|.31|.02|.04|.04|.05|

his table presents the impact of individual characteristics on individuals’ sectoral employment choices using Consumer Pyramids data from 2015. The unit of observation is the individual. The sample is restricted to rural individuals aged between 15 and 65 and participating in the labour force. Educated refers to individuals who have completed secondary or higher education. Informal activities include being employed as wage labourer or in a small business; formal activities include employment as an industrial worker, operating a business, being a qualified self-employed professional, or a white collar worker; labour includes wage labour and industrial workers. Standard errors in parentheses, clustered by district.

53
not received through banks. In the case of households that receive their remittances through banks, the potential to learn about and demand other bank products is even larger.

On the other hand, because remittances can help relax individuals’ financing constraints, they might lead to a lower demand for credit and have a dampening effect on credit market development. Also, a rise in remittances might not translate itself into an increase in credit to the private sector if these flows are instead channeled to finance the government or if banks are reluctant to lend and prefer to hold liquid assets. Finally, remittances might not increase bank deposits if they are immediately consumed or if remittance recipients distrust financial institutions and prefer other ways to save these funds.

An important complication in empirically studying the impact of remittances on financial development is the potential for endogeneity biases as a result of measurement error, reverse causation, and omitted variables. Officially recorded remittances are known to be measured with error.<sup>5</sup> Estimates of unrecorded remittances range from 20 to 200 percent of official statistics on remittances (Freund and Spatafora, 2005). Reverse causality is also a concern since better financial development might lead to larger measured remittances either because financial development enables remittance flows or because a larger percentage of remittances are measured when those remittances are channeled through formal financial institutions. In addition, financial development might lower the cost of transmitting remittances, leading to an increase in such flows. Finally, omitted factors can explain both the evolution of remittances and of financial development, also leading to biases in the estimated impact of remittances on financial development.

> 5 De Luna Martinez (2005) reports that balance of payment statistics produced by developing countries often neglect remittances received via money transfer operators and almost always exclude those transferred via informal means such as hawala operators, friends, and family members.

3
# **6. References**

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Agnolucci, P. _et al._ (2020) ‘Impacts of rising temperatures and farm management practices on global yields of 18 crops’, _Nature Food_ , 1(9), pp. 562–571. doi:10.1038/s43016-020-00148-x.

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Assefa, B.T. _et al._ (2020) ‘Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia’, _Food Security_ , 12(1), pp. 83–103. doi:10.1007/s12571-019-00981-4.

Asseng, S. _et al._ (2013) ‘Uncertainty in simulating wheat yields under climate change’, _Nature Climate Change_ , 3(9), pp. 827–832. doi:10.1038/nclimate1916.

Barnett, B.J., Barrett, C.B. and Skees, J.R. (2008) ‘Poverty Traps and Index-Based Risk Transfer Products’, _Special Section (pp. 2045-2102). The Volatility of Overseas Aid_ , 36(10), pp. 1766–1785. doi:10.1016/j.worlddev.2007.10.016.

Beza, E. _et al._ (2017) ‘Review of yield gap explaining factors and opportunities for alternative data collection approaches’, _European Journal of Agronomy_ , 82, pp. 206–222. doi:10.1016/j.eja.2016.06.016.

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Burke, M. and Lobell, D.B. (2017) ‘Satellite-based assessment of yield variation and its determinants in smallholder African systems’, _Proceedings of the National Academy of Sciences_ , 114(9), pp. 2189–2194. doi:10.1073/pnas.1616919114.

van Bussel, L.G.J. _et al._ (2015) ‘From field to atlas: Upscaling of location-specific yield gap estimates’, _Field Crops Research_ , 177, pp. 98–108. doi:10.1016/j.fcr.2015.03.005.

Carletto, C. and Gourlay, S. (2019) ‘A thing of the past? Household surveys in a rapidly evolving ‐ (agricultural) data landscape: Insights from the LSMS ISA’, _Agricultural Economics_ , 50(S1), pp. 51–62. doi:10.1111/agec.12532.

Carletto, C., Gourlay, S. and Winters, P. (2015) ‘From Guesstimates to GPStimates: Land Area Measurement and Implications for Agricultural Analysis’, _Journal of African Economies_ , 24(5), pp. 593–628. doi:10.1093/jae/ejv011.

Carletto, C., Savastano, S. and Zezza, A. (2013) ‘Fact or artifact: The impact of measurement errors on the farm size–productivity relationship’, _Journal of Development Economics_ , 103, pp. 254–261. doi:10.1016/j.jdeveco.2013.03.004.

45
countries between 2000 and 2018. The countries were selected based on data availability and to maximize sectoral coverage. They include the following:

- East Asia and Pacific: Indonesia, Solomon Islands

- Europe and Central Asia: Albania, Bulgaria, Croatia, Georgia, Kosovo, Romania, Ukraine

- Latin America and Caribbean: Argentina, Brazil, Peru, Uruguay

- South Asia: Bhutan

- Sub-Saharan Africa: Burundi, Ethiopia, Ghana, Kenya, South Africa.

The database classifies an enterprise as an SOE if the state directly or indirectly owns more than 50 percent of its shares or is the ultimate controlling entity, through majority ownership of common stock or any other mechanisms of control. This definition is in line with the European Union’s definition of public undertakings in Commission Directive 2006/111/EC. It allows SOEs to be separated into two categories: fully owned SOEs (SOEs in which at least 99.5 percent of the shares are owned by the government or government entities) and partially privatized SOEs (all others).

The database provides panel data on the finances of SOEs in the power and transport sectors at the SOE/year level that are consistent over time and comparable across SOEs regardless of where they operate. To ensure consistency and reliability, researchers collected the data using a standardized accounting data template that was populated using the information on financial statements. To ensure that quantities like <mark>earnings before interest, taxes, depreciation, and amortization</mark> (EBITDA) and operations subsidies in the database are comparable across SOEs and years, they identified each item as defined by the template using the notes to the financial statements rather than relying on the way such items are presented in the SOEs’ main financial tables. Data reliability was further ensured through quality assurance checks by alternate analysts and accounting experts.

The database provides a standardized representation of the income statement, the balance sheet, and the cash flow statement of each SOE; it includes a set of selected items from the statements while maintaining consistency across different schedules. It also includes supplementary items, including currency risk, analysis of debt and loans, maturity profiles of assets and liabilities, and SOE ownership structure.

For this paper, we constructed the following variables to measure the size of SOEs: total assets to GDP, total liabilities to GDP, and total SOE expenses to GDP. To measure SOE performance, we created the following variables: employee costs (defined as the share of employment costs to total costs); EBITDA as a share of revenues; and average assets and return on average assets (ROAA), defined as total comprehensive profit or loss over the average of assets in the current 5
35

# **IV. SUMMARY AND CONCLUSION**

Our analysis of the literature and case studies on reforming the investment climate leads us to conclude with a summary of key lessons and issues that deserve greater analytical and practical attention.

## **MAIN LESSONS**

There is no standard process for reform. There is and can be no “how to” manual. Different reforms involve different stakeholders and different mixes of technical, political, and institutional issues. Reform is also shaped by a country’s politics and capacity. The best we can do is to highlight common insights and lessons which emerge:

1. _A growing set of diagnostic tools and proven good practices is making it easier to identify priorities for reform_ . Benchmarking indicators, country rankings, business surveys, and industry-specific analyses are now available to help reformers more easily identify and target the key constraints in their country’s investment climate and therefore the priorities for reform. And as the case of China shows, reformers can also determine priorities through policy experimentation and learning from their own experiences and the successes of other countries.

2. _Exposing the economy to international competition through trade and product market reforms is a good place to start_ **.** Exports and competition not only are among the most important drivers of growth; they also help create domestic pressures for tackling the other important investment climate reforms—in land, labor, and capital markets. By increasing competition, trade liberalization in Mexico compelled business associations to lobby the government for reforms aimed at reducing regulatory red tape. In Colombia the greater competitiveness resulting from global integration led employers to become vocal supporters of labor market reforms. In Central and Eastern Europe reformers deliberately used the process of accession to the European Union (EU) to push through regulatory reform, building support for the reform by linking it to the broader goal of joining the EU. That an economy’s openness is significantly associated with institutional change is among the main findings of the International Monetary Fund’s _World Economic Outlook, September 2005_ (IMF 2005). Conversely, the regions that have done the least on the investment climate front (such as South Asia and the Middle East and North Africa) also tend to be those with the highest barriers to trade and foreign direct investment.

3. _New information plays a powerful role in exposing a policy problem, fostering competition between jurisdictions, and creating demand for change._ Generating new and specific information about a policy problem can be an especially important catalyst for contentious reforms involving multiple stakeholders. In land market reforms in China (Shenzhen), Mozambique, Peru, the Russian Federation (Veliky Novgorod), and South Africa (Cape Town), reformers used detailed diagnostic studies to identify problems of access to land and, for the first time, to measure the costs and benefits of tackling it through reform. These studies helped foster debate and overcome interest group
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15
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# **Data**

Argentina does not have a comprehensive household survey that covers both rural and urban areas. Therefore, analyses in this paper are based on available data: urban households survey (EPH) from 1990 to 2003; Censuses (1991 and 2001). The Agricultural Census was sparsely used in this paper, as we could not get access to the micro dataset but only tabulations that were severely inconsistent. Additionally, this paper applies information from a special rural household survey (RHS) undertaken by the World Bank in 2003 in dispersed rural areas. The survey was undertaken in four provinces: Chaco, Santa Fe, Santiago del Estero, and Mendoza and it covers a third of the rural population in Argentina. The RHS includes 441 households.<sup>4</sup> Data provided by RHS is critical for making informed decision on alleviating rural poverty in Argentina. It is the first time in Argentina’s history that a survey of this magnitude has been conducted.<sup>5</sup> Consumption data in the RHS is measured in broad sense, i.e. it includes selfconsumption and any kind of consumption including clothes, food, rent, gas, etc. The consumption series are developed using the “Guidelines for constructing consumption aggregates for welfare analysis” or LSM135.<sup>6</sup> The reason for analyzing consumption in this way is that people tend to easier recall what they consume than what they earn. The income measure includes all income sources such as transfers, remittances, selfconsumption, labor income, and production income.

The RHS also includes information on demographics, employment, education, and health for all household members. Furthermore, a special module with agricultural production questions was applied to farming households. The survey was conducted with the aim of assessing the impact of Argentina’s 2001 crisis. Fieldwork for the RHS was conducted in the end of 2002 and beginning of 2003.<sup>7</sup> The survey was collected in the middle of a crisis and, therefore, data reflect the specific and peculiar situation among the rural population at that time. Hence, we do not make predictions or extrapolates the future or the past from the series.

# **Methodology**

> 4 To design the sample, a database with the fractions and radius of each department in each province was considered. In each fraction, a random weighted raffle of 8 to 10 sample points, depending on the number of rural people in the province, was conducted. Once the fraction and points sampled were identified the final sample points were defined considering the number of rural inhabitants in each radius.

> 5 Previous studies on livelihoods in rural areas used small samples of data, and they, therefore, take more the form of case studies, for example the study of citrus workers or of a geographic area.

> 6 Another resent study using this approach is “Panama Poverty Assessment: Priorities and Strategies for Poverty Reduction" (SKU 14716).

> 7 In Mendoza information was gathered between the 5th and 30th of December, in Santiago del Estero between the 7th and 19th of December, in Chaco between December 27th and January 15th and in Santa Fe between the 7th and 30th of December.

6