rafmacalaba/datause-encoder-data huggingface dataset:
Appendix Table 1: ASI financial loan data fields Items Years

|Section: Working capital and loans||
|---|---|
|Loan value||
|- Outstanding loans|1989, 1994, 2000, 2005, 2009|
|- Overdraft, cash credit, other short-<br>term loans from banks|1989, 1994, 2000, 2005, 2009|
|Section: Other expenses||
|- Interest paid|1989, 1994, 2000, 2005, 2009|

Note: ASI fields providing financial loan information.

# Appendix Table 2: NSS financial loan data fields

|Items||Years|
|---|---|---|
|Section: Assets, indebtedness and inve|stment||
|Value of loans outstanding by source|||
|- Friends and family|1989,|1994, 2000, 2005|
|- Moneylender|1989,|1994, 2000, 2005|
|- Institutional|1989, 19|94, 2000, 2005, 2009|
|- Other|1989,|1994, 2000, 2005|
|Interest expense by source|||
|- Friends and family|1989,|1994, 2000, 2005|
|- Moneylender|1989,|1994, 2000, 2005|
|- Institutional|1989, 19|94, 2000, 2005, 2009|
|- Other|1989,|1994, 2000, 2005|
|Total interest amount||2000|

Note: NSS fields providing financial loan information. Loan amounts from and interest to friends and family, moneylender, and other are non-missing for about 1% of observations in 2009. Total interest amount is populated for about 10% or less of observations in all years except 2000.
School Effects and Costs for Private and Public Schools In the Dominican Republic by Emmanuel Jimenez Marlaine E. Lockheed Eduardo Luna Vincente Paqueo

# Table of Contents

Introduction ........................................................... 1 The Basic Conceptual Model .. 3 The Data ............................................................... 7 The Sample ....................................................... 7 Dependent Variables .............................................. 8 Independent Variables ............................................ 8 Fixed Student Characteristics .............................. 8 Changing Student Characteristics ........................... 9 School Characteristics ..................................... 9 Teacher and Classroom Characteristics ...................... 9 Peer Characteristics ....................................... 10 Comparison of School Types ....................................... 10 The Effect of Background on Achievement in Public and Private Schools.. 13 What Determines the Choice of School Type? ....................... 15 What is the Effect of Background on Achievement? ................. 17 With Background Held Constant, What is the Private School Effect? 20 The Nature of the Public-Private Differential .. 21 School, Classroom, Teacher and Teaching Practice Variables . . 22 Peer Group Effects ............................................... 25 Relative Cost of Private and Public Schools .. 29 0-type Private Schools ..................................... 31 F-type Private Schools ..................................... 31 F-type Versus 0-type Private Schools ....................... 32 Conclusions ............................................................ 34 References ............................................................. 37 Appendix ...... .... 38 Emmanuel Jimenez (Public Economics Division), Marlaine E. Lockheed and Vicente Paqueo (Education and Employment Division) are with the World Bank. Eduardo Luna is with the Pontificia Universidad Catolica Madre y Maestra.

This study was partially funded by World Bank Research Projects RPO 673-96 and RPO 674-43. The views contained in the paper are the authors' and should not be attributed to the World Bank or its affiliated organizations. The order of authorship is alphabetical. The authors acknowledge the assistance of Messrs. A. Komenan, L. Bianchi and J. Ryoo in the computational analysis.
COVID-19 shock. We rely on BPS for Afghanistan, Bangladesh, India, Nepal, Pakistan, and Sri Lanka, which includes information that spans from the onset of the crisis and the implementation of lockdown measures to the recovery phase, where mostly all firms were fully open.<sup>1</sup> _Second_ , we construct a composite measure of informality that accounts for its multidimensionality through the continuous index based on a Tetrachoric Principal Component Analysis (PCA). This approach allows us to measure the degree of informality on a continuum. _Third_ , we distinguish between low-productivity informal firms born out of “necessity” versus those that are comparable to formal firms but they still choose to remain informal to evade costs associated with formality (“parasite” firms).<sup>2</sup> _Fourth_ , we separate the role of external attributes and internal characteristics in correlating the effect of the crisis on informal firms.

The remainder of the paper is structured as follows: Section 2 summarizes the literature on the definition and measurement of the informal sector focused on firm informality. In Section 3, we explain the methodological aspects of the analysis, including the unique characteristics of the Business Pulse Survey dataset, the structure of the informality composite index that we propose, and our analysis of measures that differentiate across different views of informality such as necessity, De Soto, and “parasite” firms (See Ulyssea, 2018, 2020). We also highlight the identification strategy. Section 4 presents descriptive analyses of the variables of interest in the sample. Section 5 presents the main results of our analysis of the heterogeneous effects of the crisis according to informality, while Section 6 concludes.

# **2. Related Literature**

# **2.1 Definition and Measurement of Informality**

Informality may include definitions from the perspective of the workers, firms, and government. These may be inter-related but have different implications. On the workers’ side, a job that does not provide standard labor protection falls under informality (Perry and Maloney, 2007), while on the firm’s side, informality ranges from firms that do not exist in the records of the authorities (entirely under the radar) to much larger firms complying with many regulations, hiring formal workers, but failing to declare some of their revenues and workers to partially avoid some tax payments (La Porta and Shleifer, 2014) which makes them informal from the perspective of government. A wide array of combinations with different degrees of informality exist in between adhering to all regulatory requirements of

> 1These data are obtained over three rounds of data collection, except for the case of Afghanistan and Sri Lanka, where political risks prevented us from implementing a third round of surveys. 2See Ulyssea (2018, 2020).

4
On greater depth and activity of financial markets, consider the following outcome indicators. In the case of banking, good measures of depth and activity are given by the ratios of a broad monetary aggregate that includes saving and demand deposits, such as M2 or quasi-liquid liabilities, to the size of the economy, represented possibly by GDP. In the case of stock markets, the ratio of stock-market capitalization to GDP is a good measure of depth, and the ratio of value traded to stock-market capitalization, a good measure of activity.

On larger participation of the private sector and stronger competition, we can consider some variants of the indicators on depth and activity. For instance in the case of banking, a good indicator of financial depth that focuses on the private sector is domestic credit allocated by private commercial banks to the private sector, divided by GDP. In addition, as indicators of stronger private competition, we can consider reductions in concentration ratios within banking and stock markets (for instance the share of the largest 4 banks in total banking assets), in the average spread between deposit and lending rates (controlling, if possible, for financial transaction taxes), and in the average profit margins in banking and stock-market trading. The database developed by Beck, Levine, and Demirguc-Kunt (1999) provides comprehensive, cross-country and timeseries information on depth, competition, and private participation in financial markets.

When considering the measures of depth and activity of financial markets, it is important to focus on their general trend and abstract from temporary movements, such as those produced by short-lived credit expansions. Indeed, it would be informative to consider as a sign of improvement not only an increase in the trend but also a reduction of the volatility around it.<sup>7</sup> Although it should be expected that a certain fraction of financial institutions fail from time to time (as in any other line of business), systemic financial crises are an indication that either the regulatory framework is not appropriate or monetary policy is inadequate. Systemic financial crises can be detected in a number of different ways, such as large fluctuations in domestic credit, bank runs in otherwise healthy institutions, and massive accumulation of poor-performing loans. There is a large literature on systemic

> 7 The long-run trend and cyclical volatility of a given series can be obtained by applying filters such as the Hodrick-Prescott, Baxter-King, and other procedures that separate permanent from transitory changes.

9
9

# 4. CHANGES OVER THE LAST DECADE

In 1983, Peter Heller and Alan Tait produced an international comparison of government employment and wages covering a sample of about 45-50 countries. Table 3 and Figure 5 compare some of their findings with our data for the same countries (hence different data from our larger sample). Furthermore, for comparability, it is necessary to limit the sample to central government and wages only. Nevertheless, the trends are clear. The following points emerge:

- A large contraction in both central government employment (relative to population) and the relative wage bill is evident in all regions, with the relative size of central govermnent shrinking by about one-third when measured by employment and onefourth when measured by the wage bill;

- Over the last 10-15 years, central government employment decreased in relative terms across the board, from about 2.5% of population to 1.5%;

Table 3. Government Employment and Wages, early 1980s and early 1990s

|||_Central Go_<br>_percent of_|_vernmenf as_<br>_population_||_Central Gov_<br>_bill as perc_|_ernment wage_<br>_ent of GDP_||_Central G_<br>_average wag_<br>_of per ca_|_overnment_<br>_es as multiple_<br>_pita GDP_|
|---|---|---|---|---|---|---|---|---|---|
||_N_|_Early 1980s_|_Early 1990s_|_4'_|_Early 1980s_|_Early 1990s_||_Early 1980s_|_Early 1990s_|
|Africa||1.8|1.1|g|10.0|7.9|X|6.1|4.8|
|Asia||2.6|1.1||7.5<br>|4.9||2.9|3.8|
|Latin America<br>Developmng<br>counity sampleg|,<br>jI,<br>V|2.4<br>2.2|1.5<br>1.2|**7**<br>2|<br>7.3<br>8.5|4.7<br>6.1|,|2.7<br>4.7|2.3<br>4.1|
|**OECD countries**|**25**|<br>2.9|1.9|i,|5.5|4 4|1|.71.6||
|Total sample|.|**2.5**|**1.5**||7.3|54||3.7|3.2|

_Source: Forearly 1980s Hellerand Tait, 1983;forearly 1990's Annex A-3, Annex A4._ _*Note that the datafor the early 1990's are limited to the Heller and Tait sample.They are therefore differentfrom those for the largersample in this stufdy. N represents the number of countries in the sample._

- Among developing regions, the reduction in relative government employment took place in Africa and Asia. In Asia, this reduction was partly offset by growth in local government. In LAC, an equally impressive reduction in central personnel is almost entirely explained by a vast shift from central to local government. In Africa, total government employment fell in the 1980s both relative to population and slightly in absolute terms;
Total syndicated lending to borrowers in the 23 focus countries, excluding South Africa<sup>**20**</sup> , for infrastructure development purposes grew from $138 million in 2000 to $1.18 billion in 2006. Lending rose more than 173 percent in 2005–06. The increase in infrastructure lending as a share of total lending increased less significantly, however, rising from 16 percent to 27 percent in the same period. It must also be noted that the $1.18 billion loaned in 2006 went to borrowers in only 3 of the 23 countries: Kenya, Nigeria, and Zambia. Nearly half of the syndicated lending transacted in 2006 for borrowers in South Africa went to infrastructure. In Chile, three-quarters (just under $5 billion) of the total syndicated lending went to infrastructure development (table 3.2).

**Table 3.2   Syndicated loans for infrastructure development in Chile and Malaysia**

Amount in US$ millions <u>unless otherwise specified; number of loans</u>

||2006|No. of loans|
|---|---|---|
|Total syndicated loans for infrastructural development to Chile:|4,978.4|16|
|_of which:_|||
|Telecommunications, wireless/mobile|369.6|2|
|Telecommunications-telephone, internet and other services|725|1|
|Transportation, ship|900.0|2|
|Transportation, rail|817.6|4|
|Transportation, services|77.4|1|
|Utilities, electric power|1710.0|5|
|Utilities, water supply and treatment|378.8|1|
|Total Dealogic loans for all purposes to Chile|6,729.8|48|
|Share of total toward infrastructural development: Chile (%)|74||
|Total syndicated loans for infrastructural development to Malaysia:|2414.7|7|
|_of which:_|||
|Telecommunications, wireless/mobile|880|1|
|Telecommunications-telephone, internet and other services|343.5|1|
|Transportation, ship|246.25|2|
|Transportation, ar|230|1|
|Utilities, electric power|715|2|
|Utilities, water supply and treatment|0|0|
|Total Dealogic loans for all purposes to Malaysia|7,668.9|28|
|Share of total toward infrastructural development: Malaysia (%)|31||

Source: Dealogic Loanware.

The transport-shipping sector received the majority of lending for infrastructure development in the focus countries (excluding South Africa), followed by telecoms in all three years covered in table 3.1. In each of these years, however, a single large loan constituted the entire amount going to transport and shipping. Although telecommunications ranked second as a borrowing destination for infrastructure development lending to the 23 countries, at $270 million the amount loaned was considerably smaller than the amount borrowed by South African telecoms firms.

> 20 The remainder of this analysis, except where specified, will focus on lending to the low- and lower-middle-income AICD countries (that is, excluding South Africa).

22
Figure A16: Specification Charts for Deviations in GDD <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .2<br>.05 .1<br>0<br>* Weather Product * 0 * Weather Product * -.1<br>MERRA-2ERA5 -.05 MERRA-2ERA5 -.2<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.04 .04<br>.02 .02<br>0 0<br>-.02<br>* Weather Product * -.04 * Weather Product * -.02<br>MERRA-2 -.06 MERRA-2 -.04<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2 .2<br>.1 0<br>-.2<br>0<br>* Weather Product * * Weather Product * -.4<br>MERRA-2 -.1 MERRA-2 -.6<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

64
contingent consumption is c,1=1.306 and C22B=1.847, when q2A=0 (see table<sup>1 below). These consumption</sup> levels lead to expected utility _of:_ .2U(1.306) + .8U(1.847), or .2[110 306] .8[110 - l I - **51.3** > **50. 1.306 1.347** The investors prefer this more liquid asset.

# 5.1 A **bank can provide this liquidity through demand deposits.**

Suppose that each investor invests exclusively though that bank at date<sup>0. The bank pays 1.306 to</sup> those who withdraw at date 1, and pays 1.847 to those who withdraw at date<sup>2. The bank receives $100 in</sup> deposits as of date 0, and invests a fraction close to .26 in short-term assets(26<sup>short-term), .74 in long-</sup> term assets (74 long term). At date 1, the bank's maturing assets are worth<sup>26, 20 type I depositors</sup> withdraw, allowing each to get 26/20=1.306. There remain 74 long-terrn assets at date 2,<sup>when they will</sup> be worth X=2 each. If on that date, there remain 80 depositors, each will receive:<sup>( 74 * 2) / 80 = 1.847.</sup> The bank's cross subsidization of investors leads to a more liquid deposit. The bank invests in<sup>a portfolio</sup> with 74% long-termn physical assets, and issues 100% short-term liabilities that can be withdrawn at date 1. Intermediaries create liquidity by offering short-term financial assets which allow the tradeoff<sup>of liquidity</sup> against expected return, implicitly allowing insurance, where those who turn out to be type 2B investors subsidize type 1 investors.

**There is no role for markets in this liquidity creation.** The bank need not use the<sup>secondary</sup> market to get liquidity, because the individual uncertainty about how much liquidity<sup>is needed is diversified</sup> away by dealing with many depositors. In addition, if _a perfectly liquid_ secondary market<sup>exists, then there</sup> is no scope for creating liquidity by asset management (the cross-subsidy just described),<sup>as shown by</sup> Jacklin [1987]. If banks tried to cross-subsidize, all investors would prefer to<sup>hold assets directly.</sup>

To see the effect of a competing perfectly liquid market, suppose that all of<sup>the</sup><sup>**80**type 2 investors</sup> participate in a competitive secondary market at date 1; all are of type 2A and<sup>there are no type 2B</sup> 20
latter will reach a first best allocation. However implementation prevents volumetric pricing to achieve a first best allocation. Thus, depending on the case specific factors, non-volumetric methods may produce higher welfare compared to volumetric methods.

There is a vast of literature analyzing the effects of implementing water pricing methods to ration water. For most countries these policies have failed to perform well, mostly due to faulty approaches and inappropriate institutions that have their roots in complicated political and economic environments (Dinar and Saleth, 2005). Ray (2002) got evidence from a study of the Mula Canal in India that farmers do respond to price induced water scarcity but water price policy and/or a system of tradable water rights are not the most effective ways to increase irrigation efficiencies. Maskey and Weber (1998) form a field study in Nepal on the possibility of introducing cost recovery irrigation fees. They concluded that farmers under both government- and self-run irrigation systems are in a position to pay the Operation and Maintenance (O&M) cost. However, charging capital as well as O&M costs is found to be difficult to justify. These conclusions were made from both a comparative analysis and the marginal value product (MVP) approach.

Ortega et al (1998) simulated the behaviors of farmers and their response to different water pricing scenarios on water demand, farmers' income and the revenue collected by the government agency. This was done for irrigation districts in Spain. Results showed that the effects of alternative pricing policies for irrigation water are strongly dependent on regional, structural and institutional conditions and that changing policies produce distinct consequences within the same region and water district. Thus, equivalent water charges would create widespread effects on water savings, farm income and collected government revenue across regions and districts.

Latinopoulos (2005) considers different case studies of irrigated agriculture in Greece as they relate to water management. The study applied a Contingent Valuation Method and a Hedonic pricing method in an effort to find the value of irrigation water. The study draws the conclusions that the value of irrigation water as estimated by the water users is low because it relates to its use component. From the case studies it has also been concluded that the application of the principle of full cost recovery produces negative social impacts, it is also not an efficient management tool when implemented on its own.

9
FCS. Latin American countries also have more FCS from the OECD and the U.S. relative to their size than China and Hong Kong (China) put together. The last panel shows that FCS in India are even more irrelevant once we control for country size.

In brief, the general view of China and India as major destinations for foreign capital can be deceptive. When looking at stocks, we observe that India is still far from the major Latin American economies. China on the other hand, has a larger level of capital stocks than these economies, although this is not the case when we restrict the source countries to the OECD or the U.S. In fact, Latin American countries have large FCS than China and India relative to their size. Finally, it is important to notice that stocks in China are particularly large in the manufacturing sector, whereas stocks in India are relatively smaller in the manufacturing sector. However, the snapshot of the relative FCS position in 2003 hides important over-time trends, as discussed in the following section.

# **4. Evolution of the FCS between 1990 and 2003**

This section analyzes the evolution of FCS in Latin America relative to those in China, China and Hong Kong (China), and India. More specifically, we calculated these relative stocks as: where i stands for the Latin American countries, and j stands for China, China plus Hong Kong (China), or India. When this ratio is less than one, the relative position of host 9