rafmacalaba/datause-encoder-data huggingface dataset:
6 , “core” set of regressors selected on the basis of analytical relevance (as well as data availability); however, we also examine the empirical role of a number of less-standard saving determinants.'° Following previous literature, our core regressors include a standard group of income-related variables, namely the (log) level and the rate of growth of real per capita disposable income, and the terms of trade. To ensure cross-country comparability of real income figures, we convert the local-currency constant-price GNDI and GPDI data using World Bank Atlas exchange rates averaged over 1965-94.

In addition, our basic regressors include both price and quantity financial variables. The latter are the ratio of M2 to GNP, as standard indicator of financial depth, and the domestic (in national saving regressions) or private (in private saving regressions) credit flow relative to income, to capture consumers’ access to borrowing.'' The price variable is the real interest rate, defined as Inf(1+i)/(I1+7n)/. It is calculated using two alternative measures of inflation: the current rate and the average of current and one-period-ahead inflation. This yields two alternative real interest rate measures, of which our preferred one is that using the averaged forward-backward inflation just described; however, we also present empirical experiments using instead current inflation.

As conventional, we attempt to capture Ricardian effects in private saving equations by including as regressor the public saving ratio, measured in a way consistent with the definition of private saving under consideration; however, we also report some experiments adding the public investment / income ratio. In turn, demographic factors are represented by the old and young-age dependency ratios as well as the proportion of urban population in the total. Finally, we attempt to capture precautionary saving effects related to macroeconomic uncertainty adding the inflation rate /n(]+7) among the regressors. In this regard, we follow a rather voluminous literature in which the inflation rate has been used as a proxy for price uncertainty (Deaton 1977) and, more generally, macroeconomic instability (e.g., Fischer 1993).

We perform additional empirical experiments using measures of trend and temporary income and the terms of trade, as well as measures of income uncertainty, constructed from our data. For this purpose, we use the time-series procedure introduced by Maravall and Planas (1999). This procedure yields separate series for the trend and temporary components of real income and the terms of trade. Combining the respective trend and temporary components, we can construct one-step ahead forecasts of the original variables. The dispersion of the corresponding one-step ahead forecast errors provides a measure of the volatility of the respective innovations and hence the desired measure of “uncertainty”. As measure of dispersion we use the square of the forecast error.

Table 3 presents basic descriptive statistics and pairwise correlations (full-sample and cross-section) on the private (PA) saving ratio and the core explanatory variables.
7 seerl fl in intentionl oil prices, left domesfic diesel and kerosene pricks above borde prices. lu only exception was Indonesia, whor domestic prices for diesel and kerosene reman below border prices for mos of the 1980s Dospite the price Increases, domestic prices for electricity rmained blow the avoep incrmtal cost of power systom expansion in most of these countries durg th Peiod analyzed.

For tr countrie, gowth rafts (GDP Change 1I were higher during the time of nrg price increases as compared to the preceding two years (GDP change 1), but not for Mayia, _Turkey_ And Zimbabwe. For Maysi Turkey and Zimbabwe the fall in growth rates was temporay, and during th period that followed the price reforms they quickly saw a recovery in output (GDP chan _M)._ **As** mentioned eaier, is was a period of economic policy reforms in most of these countries and recovery in aggregate output was a result of aff these combined policy changes. To the exent tht onergy price ires led to afal in subsidies and thereby lower taxe and distordons in prices of ohr input these complementary changes contributed to the generd economic recovely in theo couni.
made such a transition, the client must generally go to the registry bureau (which is often decentralized) in person, and conduct “manual” research on site. To complement official channels, private vendors also distribute registry information in many countries (see Box 1).

# **Box 1** : **Private sector initiatives**

Many private sector initiatives have developed in response to businesses’ demand for information. In countries that have neither a central business registry nor electronic information available to the public, the private sector has attempted to fill the gap by creating databases containing the information that would normally be found in the business registry, such as contact information, a description of business activity, and the names of owners and managers. Some private vendors have specialized in building databases to evaluate the financial risk of businesses. Using financial statements, bankruptcy and insolvency notifications, and other court records, they produce reports that measure the credit and financial risk of businesses. The sources used for gathering this information vary greatly across countries and businesses, but commonly include public data at chambers of commerce, business registries, professional associations, and in some cases, telephone listings. Nevertheless, these databases, while widely used in some countries, may also be heavily monitored because of the possible conflicts with privacy rights. Some examples are D&B (www.dnb.com) and Bureau Van Dijk <u>(www.bvdep.com)</u> **<u>.</u>**

# **_3.5 The Role of Business Registries in Business Development_**

An effective business registry has a direct impact on the way business is conducted in a country, as well as on investors’ confidence. This section examines three specific roles a properly developed business registry could play: _a) Disseminator of information:_ If a country implements a mechanism to facilitate the transmission of business information (i.e. through the Internet), then the business registry becomes an important vehicle in improving the way business in that country is done. This available information facilitates trade, enhances confidence among the business community, and fosters national and international business transactions. Moreover, registries serve as a store of data that facilitate the identification of potential clients and business opportunities, as well as a means to identify underdeveloped sectors.

In the case that the registry requires businesses to post financial statements and indicate bankruptcy procedures, it then becomes an important tool for gauging the integrity and financial risk of private businesses. This can facilitate access to commercial credit from banks, and help 9
Policy Research Working Paper 7412

# **Abstract**

The recent decline in India’s rural female labor force participation is generally attributed to higher rural incomes in a patriarchal society. Together with the growing share of the urban population, where female participation rates are lower, this alleged income effect does not bode well for the empowerment of women as India develops. This paper argues that a traditional supply-side interpretation is insufficient to account for the decline in female participation rates, and the transformation of the demand for labor at local levels needs to be taken into account as well. A salient trait of this period is the collapse in the number of farming jobs without a parallel emergence of other employment opportunities considered suitable for women. The paper develops a novel approach to capture the structure of employment at the village or town level, and allow for differences along six ranks in the rural-urban gradation. It also considers the possible misclassification of urban areas as rural, as a result of household surveys lagging behind India’s rapid urbanization process. The results show that the place of residence along the rural-urban gradation loses relevance as an explanation of female labor force participation once local job opportunities are taken into account. Robustness checks confirm that the main findings hold even when taking into account the possibility of spurious correlation and endogeneity. They also hold under alternative definitions of labor force participation and when sub-samples of women are considered. Simulations suggest that for India to reverse the decline in female labor force participation rates it needs to boost job creation.

This paper is a product of the Poverty Global Practice Group, and the Office of the Chief Economist, South Asia Region. 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 uchatterjee@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
# CONTENTS

|I.|Intr|oduction<br>...|..........|<br>1|
|---|---|---|---|---|
||i.|Issues<br>....<br>............|1||
||ii.|Approaches to Inflation-Adjustment<br>..|<br>4||
|II.|The|Indexation Approach ................|.........|... 6|
||i.|<br>Income Statement Adjustment ........<br>a. Depreciation<br>b. Interest<br>c. Inventory<br>d. Capital Gain<br>e. Foreign Exchange Loss/Gain|<br>7||
||ii.|Balance Sheet Adjustment ..........<br>a. Net Worth Method<br>b. Equivalent Methods<br>c. Methods in various countries|<br>24||
||iii|.The Effects of Partial Indexation<br>....<br>a. Issues<br>b. METR Effects|39||
||iv.|Periodic Adjustment ..... ........|.. 50||
||v.|Administrative<br>Issues ..... .......|. 51||
|III|. Ca|sh Flow Taxation<br>.....................|.........|.. 52|
||i.|<br>Tax Base Definition ...............|<br>52||
||ii.|Inflation-Neutrality<br>..............|<br>53||
||iii|. Other Features ......<br>.<br>............|<br>56||
|IV.|Co|nclusion<br>......................|.........|.........<br>58|
|||Appendices|||
|A.|Equ|ivalence of Balance Sheet Based Adjustme|nts....|61|
|B.<br>C.|A S<br>Bib|urvey of Indexation Provisions in 8 Cou<br>liography|ntries..|66<br>69|

I am indebted to Jesus Seade for suggesting the topic of this paper and for comments and criticism which helped structure the paper. Detailed comments from Charles McLure and Zmarak Shalizi on drafts of this paper helped improve the exposition considerably. I owe.thanks to Chad Leechor, Pradeep Mitra, Javad Khalilzadeh-Shirazi, and Wayne Thirsk for discussions and comments.
reform in Kenya. Second, private sector firms interviewed in mid-2006 invariably pointed to the liberalization which picked up steam in 1993/94 as a positive turning point.

# **_4. Government Debt Dynamics_**

A key requirement for macro stabilization noted in section 2 is balance in the government’s intertemporal budget constraint, for which a sufficient condition is that public debt be on a sustainable trajectory, i.e., there is no need for a drastic change in fiscal policies to rein in indebtedness. If intertemporal balance is not assured, there will be a tendency for macroeconomic uncertainty to increase, engendering expectations of rising inflation and interest rates, which will slow down private investment and growth. Governments borrow when they run fiscal deficits, which can be decomposed into the primary fiscal deficit and interest payments. The path of the debt-to-GDP ratio is then determined by the primary deficit and a term proportional to the difference between the real interest rate and the real growth rate. If the primary deficit is positive and large, and real interest rates exceed growth rates, then the debt-toGDP ratio will grow until corrective action is taken or a crisis—typically involving a burst of inflation and/or a disruptive debt default—forces such action. Debt can also increase when the government issues recapitalization bonds to bail out banks or takes over the guaranteed loans of loss-making parastatals, while the opposite happens when government assets are privatized.

Box 1 summarizes difficulties encountered in assembling the debt data as well as data on macro variables more generally, while Figure 2 shows the path of Kenya’s debt-to-GDP ratio and its currency composition over the past 11 years. The government debt-to-GDP ratio has fallen by some 27 percentage points over the past 11 years to a level of 46 percent by the end of 2006/07, while the share of foreign-currency denominated debt has gone down from about 80 percent of total debt in 1996/97 to 50 percent at the end of 2006/07. This shift towards domestic debt was occasioned by the need to pay off maturing external debt and compensate for the reduction in external aid after 1993; but at the same time, there has been an impressive reduction in indebtedness as shown in Figure 2.

11
comparable with the answers to the other constraint questions, it is not possible to create a ranking of constraints that includes finance. Finance is therefore not reported in Table 15.

For each country, the country mean score for each constraint is reported and the constraints are ordered from the most to the least costly.

- In every country except Bhutan and the Maldives, **electricity** is one of the two highest ranked constraints.

- In five of the eight countries, **corruption** is among the four highest ranked constraints.

- **Political instability** is in the top three except in Bhutan– but was not included in the survey instrument for India, Sri Lanka and the Maldives.

# _Nepal_

Political instability is the top-ranked constraint in Nepal. This is followed by electricity, transport and corruption. Only in Bhutan is transport also one of the topranked constraints. Least problematic are access to land, business licensing and the courts.

Nepal was surveyed in 2000 and 2009. Electricity and transport were included in the two surveys and reported constraints went up in both cases.

# _Bangladesh_

In Bangladesh, the ranking is electricity, political instability, corruption, and then access to land. Least problematic to firms are transport, the courts, and labour regulation.

Surveys were conducted in Bangladesh in 2003 and 2007. Most aspects of the business environment were included in each survey. There were few substantial changes over time: although concern with customs, transport, tax administration and anti-competitive practices went down.

28
**Table 19.  Percentage** **<u>of direct beneficiaries of microcredit lending, by decile, 2004-05.</u>**

||Urban Areas|Rural Areas|All Egypt|
|---|---|---|---|
|**Deciles**||||
|Poorest|23.8|25.2|24.9|
|2|9.9|18.1|16.2|
|3|8.9|15.6|14.1|
|4|7.8|11.8|10.8|
|5|6.1|9.1|8.4|
|6|7.8|6.5|6.8|
|7|11.3|5.8|7.1|
|8|8.0|3.7|4.7|
|9|8.9|3.0|4.4|
|Richest|7.7|1.3|2.8|
|**_Memo:_**||||
|Poverty rate, beneficiaries|33.1|49.4|45.6|
|Povertyrate, population|10.1|26.8|19.6|

_Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

We have sought to measure the impact of six separate SFD programs; in every case there are several measurable outcomes, each estimated in at least three distinct ways for three types of comparisons. This gives well over 200 measures of impact. Inevitably, with such a large body of estimates, some of the results do not appear to be entirely consistent, and a few are either surprising or implausible. That said, the general thrust of the estimates is that SFD programs have had clear and measurable effects, in the expected direction, for all of the programs considered here.

Educational interventions have reduced illiteracy, but have had no clear effect on employment; health programs have brought down household spending on health by almost a fifth, and markedly reduced under-5 mortality; potable water provision has lowered household spending both on water and health, and has also reduced under-5 mortality; sanitation interventions have cut household spending on sanitation and lowered poverty; and road projects are associated with a 20 percent drop in household transportation costs, and a higher employment rate. As a general rule, SFD interventions have had the strongest identifiable impact when they have complemented other projects, rather than operating alone.

The SFD supports microcredit, but does not lend directly to households. Our analysis of the impact of microlending in general finds that it is associated with higher levels of non-farm income per capita. In the metropolitan areas and in urban Upper Egypt, microcredit goes together with higher household expenditures and less poverty, although this association is not evident elsewhere.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 34 of 36_
# Trade Fraud and Non-Tari¤ Measures<sup>�</sup>

Hiau Looi Kee<sup>y</sup> Alessandro Nicita<sup>z</sup> World Bank United Nations Key Words: trade discrepancies, non-tari¤ measures, ad valorem equivalent of NTMs, tari¤ evasion, trade fraud

## JEL Classi...cation Numbers: F10, F14

�We thank Pol Antras, Erhan Artuc, Chad Bown, Dave Donaldson, Davin Chor, Ana Fernandes, Michael Ferrantino, Penny Goldberg, Russell Hillberry, Aaditya Mattoo, Fernando Perro, Bob Rijkers, and the seminar participants of the IMF/WB/WTO Joint Trade Workshop, National University of Singapore, SAIS and George Washington University for their comments. Research for this paper has in part been supported by the World Bank’s Multidonor Trust Fund for Trade and Development. The authors declare that they have no relevant or material ...nancial interests that relate to the research described in this paper. The results and opinions presented in this paper do not represent the views of our institutions, the Executive Directors, or the countries they represent.

> yDevelopment Research Group, The World Bank, Washington, DC 20433, USA; Tel.: (202) 473-4155; Fax: (202)522-1159; e-mail: hlkee@worldbank.org.

> zUnited Nations Conference on Trade and Development. Email: Alessandro.Nicita@un.org.
|||||**Table 1:**|**HIV/AIDS**|**and Social**|**Capital O**|**LS Estima**|**tes**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|11|11a|11b|
|HIV|-0.734***|-0.771***|-0.851***|-0.766***|-0.760**|-0.986*|-0.483**|-0.605**|-0.424*|-0.507**|-0.575**|-0.610**|-0.373**|
||(0.200)|(0.199)|0.200|0.193|0.241|0.357|0.250|0.311|0.229|0.254|0.272|0.272|0.165|
|Control||||||||||||||
|corruption||1.977|4.665**|||||||||||
|||2.171|1.624|||||||||||
|Rule of law||||5.248**|5.216***|5.740***|4.650***|7.010**|3.963|5.402**|7.424***|6.486***|6.928***|
|||||1.625|1.467|1.596|1.567|2.168|3.909|1.642|1.870|1.729|1.683|
|Ethnic fraction|||||-0.394|||||||||
||||||6.889|||||||||
|Linguistic fraction||||||9.247||||||||
|||||||6.823||||||||
|GINI|||||||-11.148*|-12.953**|-12.981***|-10.253|-17.999***|-15.045***|-15.277***|
||||||||6.485|6.170|6.689|6.553|7.177|6.761|5.921|
|Education||||||||-5.788||||||
|||||||||4.255||||||
|Initial income|||||||||0.812|||||
||||||||||3.608|||||
|Population dens||||||||||-2.565***|-3.017***|-2.646***|-2.434***|
|||||||||||0.671|0.621|0.594|0.674|
|Age|||||||||||-0.629**|-0.550**|-0.334|
||||||||||||0.324|0.312|0.284|
|Scandinavian|29.027***|27.737***|27.443***|27.301***|27.255***|28.254***|25.467***|23.237***|24.840***|23.989***|22.246***|||
||3.445|4.009|4.011|3.753|4.063|4.026|4.182|4.630|4.361|4.299|4.324|||
|Nordic||||||||||||25.428***|24.468***|
|||||||||||||3.715|3.420|
|LDC<br>|-10.853***|-7.068**||||||||||||
||3.163|3.635||||||||||||
|Constant|34.341|31.365|25.865|25.212|25.361|22.302|64.474|80.201|64.660|61.622|106.433|93.566|86.719|
||2.613|3.376|1.655|1.696|2.605|1.988|22.838|23.057|39.414|23.101|30.656|28.663|25.927|
|N|79|78|78|78|78|76|75|65|72|73|72|72|70|
|R-squared|0.381|0.385|0.358|0.37|0.390|0.412|0.425|0.466|0.435|0.444|0.466|0.508|0.643|
|RESET|0.6535|0.039|0.0382|0.087|0.081|0.472|0.044|0.3271|0.115|0.020|0.019|0.111|0.080|

The dependent variable is trust. White-corrected standard errors in parentheses. *** , ** and * denote statistical significance at the 1%, 5% and 10% level. N is the number of observations. RESET refers to p-values for the Ramsey’s RESET (Ho is that the model is linear). 11b excludes Iran and Indonesia .

20
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