rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Descriptive statistics across size classes at the country level**

|**Country**<br>**Survey year**|**Size**<br>**class**|**Employment**|**Share of**<br>**firms that**<br>**invested**|**Age**|**Annual relative**<br>**employment**<br>**growth(median)**|**Birch**<br>**indicator**<br>**(median)**|
|---|---|---|---|---|---|---|
|_Afghanistan_<br>_2010_|Micro|3|24%|6|0%|0.0|
||Small|8|44%|6|0%|0.0|
||Medium|22|63%|6|5%|2.7|
||Large|120|65%|7|11%|38.6|
|_Bangladesh_<br>_2010_|Micro|3|50%|6|0%|0.0|
||Small|10|32%|8|2%|1.1|
||Medium|30|41%|12|0%|0.0|
||Large|155|49%|13|2%|10.8|
|_Bhutan_<br>_2009_|Micro*||||||
||Small|7|33%|13|6%|1.2|
||Medium|30|59%|16|8%|7.6|
||Large|147|82%|11|27%|108.0|
|_India_<br>_2010_|Micro|3|38%|10|0%|0.0|
||Small|9.5|31%|9|6%|1.2|
||Medium|35|49%|11|6%|6.3|
||Large|230|67%|25|6%|54.5|
|_Nepal_<br>_2009_|Micro*||||||
||Small|7|30%|10|0%|0.0|
||Medium|30|50%|15|0%|0.0|
||Large|212.5|73%|20|2%|16.7|
|_Pakistan_<br>_2010_|Micro|3|10%|15|0%|0.0|
||Small|8|23%|16|0%|0.0|
||Medium|35|43%|20|0%|0.0|
||Large|250|72%|25|2%|21.5|
|_Sri Lanka_<br>_2010_|Micro|3|9%|15|0%|0.0|
||Small|8|20%|15|0%|0.0|
||Medium|35|33%|20|0%|0.0|
||Large|250|71%|26|3%|16.7|

_Source: INTERIM ES, 2009/10; Own calculations_ _* The size class of 0-4 employees of Bhutan and Nepal had fewer than 30 observations._ 12
gross market-based capital flows to developing countries have grown by more than twofold to reach US$385 billion in 2005 [World Bank (2006)]. These developments reflect the growing interest of international investors in local-currency bond markets, and reduction of political risks and macroeconomic instability in an increasingly large number of developing countries, particularly in Europe and Central Asia, East Asia and Latin America, the regions which are benefiting the most from the global surge in capital flows and where yields and potential gains from currency appreciation have been the highest.

In the midst of this surge in net private capital flows, the heavily skewed global distribution of net private capital flows to developing countries has persisted. The Europe and Central Asia (ECA) region, East Asia and Pacific (EAP) region and Latin America and the Caribbean (LAC) region continue to enjoy the lion’s share of global capital flows to developing countries (Table A1 and Figure 1). While Sub-Saharan Africa leads all developing regions in official flows such as ODA, it continues to trail in terms of private flows. Naturally, this increasingly skewed distribution of global capital flows further marginalizes SubSaharan Africa, a phenomenon compounded by the delayed financial deepening in the sub-region.

SSA’s share of global net private capital flows to developing countries declined in the midst of financial globalization and now accounts for just 10%, against 41% for the EAP region, and about 35% for the LAC (Table A1 and Figure 1). Moreover, market-based inflows to SSA were even lower (less than 3% of total) and the majority of it went to South Africa, which accounts for over 50 percent of gross marketbased capital flows to the region [World Bank (2006)].

**Figure 1: Share of net private capital flows by region (% of total flows to developing countries), 2007***

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15 10<br>10<br>4<br>5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 23<br>17<br><!-- End of picture text --> <!-- Start of picture text --> 35<br><!-- End of picture text --> <!-- Start of picture text --> 29<br>23<br>2<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>5<br><!-- End of picture text --> <!-- Start of picture text --> 8<br><!-- End of picture text --> <!-- Start of picture text --> 9 8<br><!-- End of picture text --> <!-- Start of picture text --> ODA<br>East Asia & Pacific<br><!-- End of picture text --> <!-- Start of picture text --> Net private<br>(2005)<br>Middle East & North Africa<br><!-- End of picture text --> <!-- Start of picture text --> Sub-Saharan Africa<br><!-- End of picture text --> Source: Global Development Finance 2008 * Note: 2005 for net private flows.

The continued strong growth of market-based financial flows reflects the changing pattern of global finance. Equity flows, market-based capital flows and official flows constitute the main forms of capital

**5**
We calculate input tariffs using India’s Input-Output Transactions Table, following the method suggested by Amiti and Konings (2007). For example, if the footwear industry derives 80% of its inputs from the leather industry and 20% from the textile industry, then the input tariff for the footwear industry is 0.8 times the final goods tariff for the leather industry plus 0.2 times the final goods tariff for the textile industry. In our baseline measure of input tariffs, we use both traded and non-traded inputs, assigning tariff rates of zero to non-traded inputs.<sup>2</sup> One potential concern with including input tariffs in our empirical analysis is that final goods and input tariffs may be highly correlated, thus leading to multicollinearity problems in estimation. Panel (d) of Figure 1 shows the relationship between the change in final goods tariffs and the change in input tariffs for a given industry. Though the two measures are related, there are a number of industries that received relatively large reductions in final goods tariffs but relatively small reductions in input tariffs, and vice versa. Moreover, the overall correlation coefficient between final goods and input tariffs (across years and industries) is 0.7. Within years, the correlation coefficient is even lower (less than 0.5 in all years), which suggests that multicollinearity is not likely to be a significant problem.

We also consider two other policy changes that occurred during this period: the removal of industrial licensing requirements and the allowance of FDI into most industries without case-by-case approval. Until the 1980s, India’s “license raj” required every firm with more than 50 employees (100 employees without power) and a certain amount of assets to obtain an operating license. The license specified, among other things, the amount of output a firm could produce, the types of goods it could make, and its location. In 1985, approximately one-third of industries were “delicensed” (the requirement for a license was dropped); in 1991, most industries were delicensed as part of the broader reforms package (Aghion et al. 2008). The restrictions on foreign investment were also liberalized during the 1990’s. Prior to 1991, FDI was capped at 40% for most industries; beginning in 1991, FDI inflows of up to 51% were allowed in selected industries with “automatic” approval (Sivadasan 2009).

> 2In Appendix A, we present results from an alternative measure of input tariffs that considers only manufacturing sector inputs.

7
migration flows disaggregated by education level,<sup>18</sup> the magnitude of the change in enrollment that follows this increase in p should give some indication as to the feasibility of the brain gain hypothesis that migration may increase the stock of human capital.

To capture investment in education I use the Gross enrollment Ratio; the number of pupils enrolled in tertiary education, regardless of age, expressed as a percentage of the population in the theoretical<sup>19</sup> age group for tertiary education. The GER is calculated according to the following formula: Where:

-  _GER_<sup>_t_</sup> is the Gross enrollment Ratio at time t,

-  _E_<sup>_t_</sup> is the enrollment at time t,<sup>20</sup>

-  _Pa_<sup>_t_isthepopulationinage-groupawhichcorrespondstotertiaryeduca-</sup> tion in school-year _t_<sup>21</sup> .

It should be noted at this point that, if it is the case that enrollment in tertiary education increases with accession to the EU, this is unlikely to be solely a result of students wishing to use their new rights to migrate. Accession to the EU will almost certainly also effect domestic employment opportunities in order to migrate will do so. The time lag and uncertainty about the future ensure that whilst migration is a right it remains that _p <_ 1. This is because there are many other factors involved in the translation of intention to migrate into reality, for example changing personal/economic circumstances may have a substantial impact given the extended time period required to gain a tertiary qualification. Indeed, micro-level surveys on intentions to migrate consistently exceed actual migration figures. Note further that the indiscriminate nature of the freedom of movement of people implies that of the two reasons a potential migrant may wish to invest in education; firstly as a vehicle for exit, and secondly to benefit from the increased wage advantage abroad, only the second is relevent here. Thus the findings are likely to represent a lower bound on the estimated incentive effect of increased migrational opportunities.

> 18As mentioned above the recent dataset by Docquier and Marfouk has begun to relax this constraint.

> 19For the tertiary level, the population used is the five-year age group following on from the secondary school leaving age.

> 20Many thanks to Lucy Mei Hong (UIS UNESCO) for obtaining the historical data not publicly available on the internet.

- 21See `http://esa.unorg/unpp/forUNDPdataonline` .

14
sales and then relying on the aided recall measure of profits. This “light” version of the survey further highlights the adaptability of the AAA methodology.

# **4. CONCLUSION**

This paper presents a new business survey methodology designed to address the challenges that often plague measurement of microenterprise financials, including underreporting, implausible figures, high variance, and low autocorrelation over time. The survey tool combines automatic consistency checks of electronic data collection with real-time adjustments in reported outcomes to generate more precise estimates for business financial outcomes. This new methodological approach and survey tool represent an innovative and cost-effective way to gather accurate performance data on micro and small businesses in countries and settings where administrative data are unavailable.

This survey methodology is valuable for policy makers, researchers, and businesses alike. A key challenge to advancing private enterprise development lies in accurately being able to measure which policies and programs work (or not) to enhance business outcomes. The AAA methodology offers a reliable mechanism in this regard. The tool also provides a way for researchers to better understand small firms. As de Mel, McKenzie, and Woodruff note, “we need a much more nuanced and detailed understanding of [micro and small entrepreneurs] before appropriate policies can be devised” (2010, p.25). And one of the building blocks to developing this understanding is accurate measurement of business outcomes, which can help identify and rank growth constraints across firms.

Finally, improvements in entrepreneurial outcomes would provide a way of “helping people help themselves” (Nopo 2007, p.2). Once shown to be effective as a research tool, this technology could be provided directly to individual entrepreneurs as a way to improve their record keeping and to access business intelligence (e.g., by converting it into a simple smartphone app). Furthermore, this methodology could be leveraged to create user-friendly tools designed to increase a firm owner’s ability to track, access, and take action on business intelligence.

26
# **Table 7.2. OLS results**

_Dependent variable: Share of stages with unofficial payments in total (procedures No. 1, 2A, 2B,_ _<u>3A and 3B)</u>_

||Model 7.2.1|Model 7.2.2|Model 7.2.3|Model 7.2.4|
|---|---|---|---|---|
|Constant|-0.308*<br>(0.165)|-0.552**<br>(0.257)|-0.552**<br>(0.257)|-0.390<br>(0.248)|
|Number of stages by law|0.007<br>(0.006)||||
|Ln official payments for all documents|0.024**<br>(0.010)|0.022**<br>(0.010)|||
|Ln number of stages by law||0.138*<br>(0.082)|0.160**<br>(0.082)|0.141*<br>(0.082)|
|Ln official cost per official (by template) stage|||0.022**<br>(0.010)||
|Ln of total cost per square meter of the land plot||||0.005<br>(0.007)|
|Procedure 2A|-0.128**<br>(0.062)|-0.117*<br>(0.062)|-0.117*<br>(0.062)|-0.054<br>(0.067)|
|Procedure 2B|0.062<br>(0.074)|0.083<br>(0.074)|0.083<br>(0.074)|0.067<br>(0.075)|
|Procedure 3A|0.057<br>(0.083)|0.078<br>(0.077)|0.078<br>(0.077)|0.103<br>(0.076)|
|Procedure 3B|0.053<br>(0.080)|0.074<br>(0.074)|0.074<br>(0.074)|0.082<br>(0.074)|
|Kaliningrad oblast|0.136<br>(0.094)|0.122<br>(0.094)|0.122<br>(0.094)|0.148<br>(0.091)|
|Saint-Petersburg|0.489***<br>(0.139)|0.467***<br>(0.140)|0.467***<br>(0.14)|0.500***<br>(0.138)|
|Leningrad oblast|0.502***<br>(0.126)|0.481***<br>(0.127)|0.481***<br>(0.127)|0.507***<br>(0.125)|
|Moscow|0.302***<br>(0.107)|0.293***<br>(0.107)|0.293***<br>(0.107)|0.408***<br>(0.106)|
|Moscow oblast|0.223*<br>(0.115)|0.232**<br>(0.115)|0.232**<br>(0.115)|0.268**<br>(0.112)|
|Sverdlovsk oblast|0.017<br>(0.119)|0.007<br>(0.119)|0.007<br>(0.119)|0.031<br>(0.117)|
|Tomsk oblast|0.061<br>(0.088)|0.057<br>(0.088)|0.057<br>(0.088)|0.044<br>(0.089)|
|Khabarovsk krai|0.007<br>(0.094)|-0.010<br>(0.095)|-0.010<br>(0.095)|0.018<br>(0.093)|
|Irkutsk oblast|0.341***<br>(0.098)|0.319***<br>(0.099)|0.319***<br>(0.099)|0.354***<br>(0.100)|
|Rostov oblast|0.413***<br>(0.097)|0.393***<br>(0.098)|0.393***<br>(0.098)|0.412***<br>(0.104)|
|Perm oblast|-0.008<br>(0.104)|-0.026<br>(0.104)|-0.026<br>(0.104)|-0.016<br>(0.103)|
|Novosibirsk oblast|-0.004<br>(0.090)|-0.024<br>(0.091)|-0.024<br>(0.091)|0.013<br>(0.090)|
|Nizhny Novgorod oblast|0.108<br>(0.095)|0.093<br>(0.095)|0.093<br>(0.095)|0.119<br>(0.093)|
|Sakhalinsk oblast|0.089<br>(0.138)|0.070<br>(0.138)|0.070<br>(0.138)|0.099<br>(0.136)|
|Adj. R|0.391|0.396|0.396|0.398|
|N|203|203|203|188|
|Mean of the dependent variable|0.19|0.19|0.19|0.18|

***, **, *** Significance level of 10%, 5%, and 1%, respectively**

29
# **2. Data and Methodology**

## **2a. Data Description**

This paper draws data from the Employment and Unemployment Schedule, administered by the National Sample Survey Organization (NSSO), Government of India. Data from the 38<sup>th</sup> , 43<sup>rd</sup> , 50<sup>th</sup> , 55<sup>th</sup> , and 61<sup>st</sup> rounds – conducted in 1983, 1987-88, 1993-94, 1999-00, and 2004-05, respectively – are used (referred to as 1983, 1987, 1993, 1999, and 2004 in this paper).<sup>4</sup> NSS data provide information about current attendance at different levels of education. In addition, data from the Education Schedule conducted by NSSO in 1995-96 are also used. Each employment survey covers around 120,000 households, and around 600,000 individuals. The samples are based on stratified random sampling and all the analysis in this paper uses survey weights.

In 2000, the states of Jharkhand, Chhattisgarh, and Uttaranchal were carved from Bihar, Madhya Pradesh, and Uttar Pradesh, respectively. For the state level analysis, these states are included with the parent states to maintain comparability across time.<sup>5</sup> An individual, who has already completed a higher secondary degree, and is attending diploma (such as polytechnic courses) or degree courses, is considered attending tertiary education.<sup>6</sup>

## **2b. Methodology**

Three indicators measure attainment and participation. Each measure is described below.

## **_2b.1. Educational Attainment Rate_**

Educational Attainment Rate (EAR) measures the percentage of the population that attains a particular educational level. In this paper, EAR for tertiary education is defined as the ratio of number of persons in age group 25-34 years who have completed a tertiary education degree to total population in the same age group.<sup>7</sup> It measures completion of tertiary education prior to the age of 34, regardless of

> 4 NSSO conducts thick round survey at five-year intervals (called ‘Quinquennial Round’). Data before 1983 is not available. 5 Information for all states is given in the Annex.

> 6 Similar approach is adopted by “Working Group on Higher Education, GOI (2006b).” Before 1993 data do not distinguish between lower secondary and higher secondary. In this case secondary completion is taken as criterion. It may increase the tertiary attendance marginally as some students attending diploma courses at higher secondary level are counted as attending tertiary education.

> 7 Approximately 92-94 percent of tertiary attending students (graduate and postgraduate degree) are less than 25 years.

4
control for household coverage of each type. The results are shown in Figure 5.

The graph illustrates that there is no significant benefit (or detriment) in terms of reduction of diarrhea for any unimproved sanitation, as classified by the JMP. In other words, in a context where externalities are included as a source of benefit, the categories of unimproved/private, unimproved/shared, and improved/shared are no better than open defecation. The DLHS-3 data do not provide details on the number of households that share the latrines reported, whether the shared latrine belongs to the household or another household. These details could help us understand the intensive margin of the effect. This evidence is relevant for the Total Sanitation Campaign, where the goal is eliminating open defecation, where it is evident that which fixed-point defecation solution is offered matters. If the way to achieve the goal is building a fixed-open defecation infrastructure different from improved sanitation, this investment would have a negative rate of return because it is not producing any visible impact.

# **7.2 Externalities**

Taking into consideration the effect of externalities estimated through Equation (7), we find there is a positive and significant relationship between the level of access to sanitation in a village and the prevalence of diarrhea (Table 5). Moreover, we corroborate our hypothesis that this relation follows a concave function, where at low levels of village access there are limited positive externalities, but as access to sanitation increases, these externalities begin to accrue at a faster rate. Again, the findings are similar for fixed-point defecation and for improved sanitation. An interesting finding is the difference between the children that already have access and the ones who do not. We observe that the change in diarrhea prevalence caused by the external effect is larger for children living in households without access. But the rate of change of this effect is larger on children living in households with access.

In Figures 6(a) and 6(b) we illustrate the predicted probability of diarrhea in the past two weeks from Equations (7), (8), (9), (10), and (11) conditional on a household’s provate 14
# VII.2 The Trade Balance and the Fiscal Deficit

The deficit of the public sector, as measured by the Public Sector Borrowing Requirements, is the result of the difference between government spending and government tax revenues. It is therefore imperative in describing the effects of a given deficit to separate the effects of the financing of the deficit from those derived from the given levels of government spending or taxation. In order to do so we have to design a conceptual experiment. In our case we shall assume that there is available a neutral tax, e.g. a value added tax or a consumption tax such that changes in the level of this tax do not affect the relative structure of demand for goods or assets. The deficit is then generated by reducing this neutral tax and increasing accordingly the level of debt financing, either external or internal. From this perspective, what we will be analyzing is the effects of tax vs. debt financing in the context of an open economy. In the case of internal debt financing the government may resort to issuing interest bearing debt (bon,ds) or noninterest bearing debt (money).

The issue of tax vs. debt financinq has received a lot of attention in the literature in reference to the well known Ricardian equivalence proposition. The general thrust of the Ricardian proposition is that a tax reduction financed with debt will have no real effects on the economy if the public discounts the future taxes to service the debt and therefore increases savings by the exact amount of taxes reduced. The empirical validity of the Ricardian equivalence is, however, quite inconclusive.\l/

-----------------------------------

1/For a survey on issues related to the Ricardian Equivalence see Leiderman and Blejer(1988).

In the context of an open econon.y, the real exchange rate is a crucial relative price for the allocation of resources in the external sector. This relative price will certainly be affected by the composition of government spending and may also be affected, depending on the validity of the Ricardian equivalence proposition, by the way of financing of such spending through its effects of the Trade Balance. Next we shall discuss the general issues involved in the analysis of the Ricardian equivalence proposition in relation to the external effects of debt vs. tax financing. After establishing the theoretical aspects of the process of determination of the Trade Balance we shall present empirical estimates of the actual relationships for the Argentine data.

64
## **3.2. Afrobarometer Surveys**

The Afrobarometer is a pan-African, independent, non-partisan research network started in 1999, which measures public attitudes on economic, political, and social matters in Africa. Seven survey rounds have been conducted as of 2020 and more than 35 African countries have been surveyed since Round 5 in 2011.

The recent Round 5 to Round 7 surveys contain some transport questions, regarding the ownership of vehicles, road surface in the start point and the last 5 kilometers in the primary sampling unit, satisfaction with local government for maintaining roads, and whether the paid transport is available in the primary sampling unit or within the easy working distance.

**Table 7. Transport questions in Afrobarometer surveys (Round 5, 6, and 7)**

|**Transportquestion**|**Available round**|
|---|---|
|Own motor vehicle/car/motorcyclepersonally?|5,6,and 7|
|The road at the start point in the PSU/EA is paved/ tarred/<br>concrete?|5, 6, and 7|
|The road at the startpoint in the PSU/EA is impassable?|7|
|How well or badly would you say your local government is<br>handlingmaintaininglocal roads?|5, 6, and 7|
|How well or badly would you say your local government is<br>handlingmaintainingroads and bridges?|5, 6, and 7|
|Is paid transport present in the PSU/EA or in easy walking<br>distance?|6 and 7|
|Road surface in the last 5 km in the PSU/EA|7|
|Road condition in the last 5 km in the PSU/EA|7|

# **4. Using Harmonized Household Surveys to Produce Stylized Facts**

The diverse design of transport questions, especially the fragmented evidence on household ownership and expenditure of transport vehicles and services across countries, adds complexity and challenge to data harmonization and cross-country analysis. The World Bank’s Global Consumption Database can be taken as an example of synthesizing the evidence on household expenditure, and SSAPOV, a database of harmonized nationally representative household surveys in Sub-Saharan Africa, contains the harmonized information on ownership of vehicles.

## **4.1. Transport Expenditure: Global Consumption Database**

The Global Consumption Database is a one-stop source of data on household consumption patterns in developing countries, provided by the World Bank’s Development Data Group. It 17