rafmacalaba/datause-encoder-data huggingface dataset:
This system reads: ``` Drgpspc = + 0.338*Drgpspc_1 + 0.0297*Dm2gdpratio_2 - 0.0325*CIa_1 (SE) (0.0682) (0.0109) (0.0138)
- 0.00608*CSeasonal - 0.017*DDrealPercapitaGDP - 0.224*DInflation
(0.00169) (0.0101) (0.0379) + 0.00591*DRealInterest + 0.00713*DDExchangeRate_1 (0.00274) (0.00353)
- 0.0028*DUSTBills_1 + 0.023*dumm2003q1 - 0.0215*dumm2002q3
(0.00147) (0.0064) (0.00642) + 0.0407*dumm2002q4 + 0.0346*dumm2004q1 + 0.0183*dumm2007q2 (0.00579) (0.00535) (0.00568)
- 0.0368*dumm2008q2
(0.00572) Dm2gdpratio = + 0.418*Drgpspc_1 + 0.114*CIa_1 - 0.135*CSeasonal (SE) (0.165) (0.0345) (0.00431)
- 0.00788*CSeasonal_2 - 0.0665*dumm2002q3 + 0.0575*dumm1999q1
(0.00397) (0.0143) (0.0134) + 0.053*dumm2002q4 + 0.0758*dumm2002q1 + 0.0711*dumm2003q1 (0.0144) (0.0136) (0.0151)
- 0.0722*dumm2007q1 + 0.046*dumm2007q2 - 0.47*DUnemployment_1
(0.0144) (0.0138) (0.255) ``` The short-term private savings function for Egypt is more aligned with the simple permanent income hypothesis. Savings growth is negatively related to contemporaneous change in income growth. The change in income growth reflects on lower growth in private savings. The change in income growth by 10% leads to a drop in private savings growth by around 0.2%. This finding contradicts the proposition of Life-Cycle Model and findings of earlier studies by Modigliani, 1970; Madison, 1992; Bosworth, 1993 and Carroll and Weil, 1994).

However, these findings are aligned with the simple permanent income that proposes that current savings could drop in case of higher future income growth. This school of thought interpret the decline in aggregate saving rate by the fact that the lifetime wealth of the young is high enough relative to that of their elders (Athukorala and Sen, 2004). This in turn reflects on lower rates of inter-temporal substitution and triggers higher preference to current consumption. Accordingly, growth in per capita income could actually lead to a decrease in saving. This finding provides significant guidance to policy on the need to robustly provide involuntary savings mechanisms. This is critical to smooth the younger cohort’s consumption across time and mitigate incidence of future poverty of the elderly.

In addition, our tests concluded that macroeconomic uncertainty negatively influences private savings. Although this contradicts with the theory of precautionary savings, it is a common finding in empirical studies on savings behavior. Loayza, Schmidt-Hebbel, and Servén (2000) found only one of the six seminal empirical studies on savings and growth in LDCs that included inflation among the explanatory variables finds a positive and significant effect on the private saving rate. This is supported by the fact that macroeconomic uncertainty in LDCs - suffering financial market 19
- 52 -

Figure 1 Consumption Distributions <!-- Start of picture text --> Cumulative percent of population<br>100<br>3 7 11 15 19 23 27 31 35 39 43 47 5* 55 59<br>Monthly consumption per person (Rp'OOO)<br>1984 + 1987<br><!-- End of picture text --> Figure 2 Consumption Densities <!-- Start of picture text --> probability (x1OOOO)<br>0.7<br>0.6<br>0.5<br>0.4-<br>0.3<br>0.2 -<br>0.1<br>0<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59<br>Monthly consumption per person (Rp'OOO)<br>-<br>1984 ' 1987<br><!-- End of picture text -->
The skilled labor demand in agriculture is a derived CES technology. The skilled labor demand depends on the distributional share of wage between unskilled and skilled labor together with the elasticity of substitution of labor in the production process and level of unskilled labor demand in the agriculture sector. The skilled labor demand in agriculture is derived from (12).

LDQag,reg = (1-  _lbag,reg /  _lbag,reg ))<sup>_lb(ag)</sup> *(WNQ reg /WQ reg)<sup>_lb(ag)</sup> *LDNQag,reg ...... (13)

**Non-agricultural activities:** The value added in non-agriculture is a CES between capital and labor in the sector concerned.

VAin,reg =  _inin,reg *(  _inin,reg *KTin,reg<sup>-_in(in)</sup> +(1-  _ini,reg )*LDi,reg<sup>(-_in(in)</sup> ........... (14) where VAin,reg is the value added in industry, KT and LD are capital and labor demand in industry, respectively.

The total labor demand in non-agriculture sectors is a derived CES function. The first-order condition of profit maximization of (14) provides (15).

LDin,reg = (1-  _inin,reg )/  _inin,reg ))<sup>_in(in)</sup> *(Rin,reg /Win,reg )<sup>_in(in)</sup> *KTin,reg ............. (15)

**Labor aggregation in non-agriculture** : The total labor in non-agriculture is a CES aggregate of unskilled and skilled labor.

LDin,reg=  _wkin,reg[  _wkin,reg LDNQin,reg<sup>-_wk(in)</sup> +(1-  _wkin,reg )LDQin,reg<sup>-_wk(in)</sup> ]<sup>(-1/_wk(in)</sup> ...(16) The skilled labor demand in non-agriculture is a derived CES technology. The skilled labor demand depends on the distributional share of wages between unskilled and skilled labor together with the elasticity of substitution of those in the production process and level of unskilled labor demand in the industry. The demand for skilled labor is derived from (16).

LDQin,reg = (1-  _wkin,reg )/  _wkin,reg ))<sup>_wk(in)</sup> *(WNQ reg )/WQ reg )**<sup>_wk(in)</sup> *LDNQin,reg ....(17) where, wq and wnq are the wage rates of skilled and unskilled labor, respectively.

# **Government sector**

Value added of the government sector is simply the wage bill. For simplicity, we have assumed that all the government employment consists of skilled labor.

VAad,reg = LDQad,reg............................................ (18) The demand in the government sector is determined by the ratio of value added at the government sector to the wage rate.

21
Castes” (FC) (a term that includes any jati that is not included in one of the officially defined categories because they are considered more privileged).

A second methodological challenge to understanding the relationship between caste, class and gender is the paucity of data with detailed information on economic indicators and women’s status. The National Family Health Survey, the District Level Household Survey and the Annual Health Surveys, for example, all collect detailed information on the status of women, but limit the list of economic indicators to a checklist which includes only household assets and occupational status of household members. Anthropologists and ethnographers have gathered in-depth qualitative information on these issues but their work has typically focussed on small samples in a few villages or communities (examples include Chen 1995; Kapadia 1995; Jeffrey and Jeffrey 1996; Seymour 1999; Srinivas 1979; and many others). Given the enormous geographic, demographic and cultural variations within India, this has limited the generalizeability of the findings.

This paper examines data with extensive information on caste and gender status from three states–Bihar, Odisha and Tamil Nadu. Our data are unique in having not only broad geographic coverage but also detailed information on jati category, consumption expenditure, and indicators of the status of women. Conducted between 2011 and 2013, these surveys served as a baseline for the evaluation of livelihoods programs that targeted the poor. The sampling strategy for all the data from all the three states, therefore, was designed to ensure that they were representative of vulnerable populations in rural areas who are currently eligible for a variety of poverty-alleviation programs in these states. This limits the data, which are not representative of the state as whole, but are more representative of poor rural populations. However, the limitations of the data can also be seen an advantage. Oversampling poor regions allows us to better understand how caste and gender are experienced in poor rural communities in these states.

We find that the relationship between caste and the status of women is more nuanced than suggested by the literature: It depends heavily on location and the definitions of caste. We examine three sets of indicators for the status of women in all three states: employment, decision-making authority on key household decisions, and physical mobility. We use a simple reduced-form regression model to explore the role of caste in determining these outcomes. We begin our analysis by using broad caste categories and then we turn to jati-level definitions of caste. In both sets of regressions, we control for numerous individual, household and community characteristics.

When we use broad caste categories that correspond to official definitions used by the Government of India, we find evidence of the inverse relationship between caste and gender status, but the relationship varies by state. For example, we see a relatively strong negative relationship in Bihar, but almost no discernible relationship in Tamil Nadu where 4
# **3. Business Registries**

The information presented in this study was collected from business registries and other government sources in 84 countries. The data were collected via a survey and follow-up phone calls.<sup>8</sup> These other sources include statistical agencies, tax and labor agencies, chambers of commerce, and private vendors (such as D&B), which were used only when business registry data were unavailable or non-existent.<sup>9</sup> While this analysis reasserts the great heterogeneity that characterizes these public entities in terms of _inter alia_ , prevailing regulations, methodologies, and implantation of digital administration, a number of common challenges and achievements have been identified and are described in this section.

# **_3.1 What are Business Registries?_**

Business registries<sup>10</sup> are public entities generally established by commercial or business code mandates and managed by the Ministries of Commerce or Justice (Labariega Villanueva, 2006). They are responsible for registering businesses, as well as noting any significant modification to the internal structure of these businesses throughout their life-span. The main purpose of business registries is to guarantee that businesses comply with current regulations and to make such information available to the public. Their composition varies greatly across countries, as is amply evidenced by the fact that they can either coexist with real estate registries (e.g. Mexico), be managed by chambers of commerce or professional associations (e.g. Syria), or be stand-alone agencies (e.g. U.K.).

# **_3.2 Who Must Register?_**

While the laws for business registrations vary greatly across countries, a common thread among all is the “legal entity” element: any business with a legal entity (or “corporate personhood”) separate from its owners must be duly registered.<sup>11</sup> Thus, the definition of _what_ constitutes a separate legal entity in a given country is key in deciding which businesses are required to register.

> 8 The complete survey data are available at: http://www.ifc.org/ifcext/sme.nsf/Content/Entrepreneurship+Database. 9 A complete list of sources is provided in Annex 1.

> 10 Also called Incorporation Offices (US), Companies Registration Offices (IR), Companies House (UK), Business Register (AU), Mercantile Registries (SP), Public Registries of Property and Commerce (MEX), Registry of Commerce (FR), etc.

> 11 The registration of businesses without legal entity (e.g. professional associations, individual merchants, etc.) can be voluntary, not compulsory, in some countries (e.g. Spain).

6
Price and Quality Effects of VERs Revisited: A Case Study of Korean Footwear Exports by Jaime de Melo and L. Alan Winters

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|VERs on Korean Footwear Exports|4|
|3.|Index Numbers for the Measurement of Export<br>Prices and Quality|14|
|4.|Multilateral Comparisons|9|
|5.|Bilateral Comparisons|15|
|6.|Conclusions|23|
|Foot|notes|25|
|Bibl|iography|27|
|Appe|ndix 1: The Korean Footwear Export Data|28|

L. Alan Winters is of the University College of North<sup>Wales. This paper</sup> is part of the research project RPO 672-40. We thank<sup>Taeho Bark for</sup> helping us collect the data. We thank Paul Brenton<sup>and Julie Stanton</sup> for many helpful suggestions and extremely valuable assistance.<sup>Helpful</sup> comments from Bela Balassa and particularly from Bee<sup>Aw and Mark Roberts</sup> on an earlier draft are also acknowledged.
<u>Win</u> ~~m~~ m— <u>Wr</u> —= (Xnm — <u>Xp</u> Jam + <u>(Skills</u> — <u>Skills¢)Bm + X</u> (am — ar) + <u>Skills’</u> (Bm — Br) a B <!-- Start of picture text --> m<br>Wm — Wy = (Xm —Xp)a + (Skills — Skillsp)B + |X m(am—@ )—X (ap —a )| +<br>[SH mnES (Bm — B ) — Skills,(By - B )|<br><!-- End of picture text -->
reinterviewed in early 1992, a total of 9,036 were still operating businesses.

Our empirical approach is to seek patterns of participation that accord with the predictions detailed in the previous section. However it should be emphasized (again) that there is a fundamental identification problem faced _both_ by our methodological approach and by other approaches that assume enforcement efforts are the sole determinants of participation. We are aware of no data set with the requisite information on both costs and benefits of participation to evaluate the relative importance of each approach."<sup>7</sup> Our limited goal in this section is to document empirically the heterogeneity and depth of participation; show the importance of firm size, age, and mode of production as correlates of participation; and (partially) establish a role for firm dynamics and life cycle considerations as key concerns for modeling participation (in both developing and industrialized countries).

# _1. Heterogeneity of Participation_

Though the data set is bounded above at five workers (fifteen in manufacturing), even within this narrow firm size range informality is clearly not an all or nothing proposition. The summary statistics in Table 1 show that there are high participation rates in societal institutions for even these small firms: 41.7 percent are registered with the federal treasury, 25.2 percent are registered with the local treasury (including Mexico City), 34.6 percent pay some taxes to one or both treasuries, 34.6 percent of firms with paid workers have them registered with IMSS (Mexico's social security administration), 22.5 percent are members of a business guild or association, 15.6 percent pay dues to a business organization, and 33.1 percent of firms that existed in 1989 were enumerated in the Census of that year.

Table 2 presents cross tabulations along several dimensions of participation and shows that participation along one dimension need not imply participation along others. For example, '' Such a data set would have to identify exogenous variation in government policy that is independent of firms' decisions over formality. This identification is extremely difficult in practice because most policies are implemented nationwide, confounding the effect of policy changes with business cycle and macroeconomic forces that also influence fmn behavior. A differences-in-differences approach that utilized between state (or province or region) variation in policies would work in principle. But the existence of multi-establishment **firns** that cross state lines would make assignment into the proper treatment groups problematic.

12
**Figure 1. Media coverage of the crisis to gauge public sentiment**

<!-- Start of picture text --> All searches, Grand Total<br>5,000<br>4,500<br>4,000<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09<br><!-- End of picture text --> The high correlations of coverage across major media outlets, as reported in the table below, are an indication of the global nature of the crisis.

**Table 1. Correlation matrix for all searches**

|**Source**|Bloomberg<br>Headlines|Financial<br>Express<br>(India)|FT|NYT|The<br>Economic<br>Times<br>(India)|The<br>Economist|The<br>Guardian|WSJ|
|---|---|---|---|---|---|---|---|---|
|Bloomberg Headlines|1.00|0.66|0.96|0.97|0.99|0.94|0.96|0.97|
|Financial Express (India)||1.00|0.65|0.78|0.70|0.74|0.76|0.70|
|FT|||1.00|0.95|0.97|0.94|0.98|0.98|
|NYT||||1.00|0.98|0.95|0.98|0.98|
|The Economic Times (India)|||||1.00|0.94|0.97|0.97|
|The Economist||||||1.00|0.97|0.95|
|The Guardian|||||||1.00|0.99|
|WSJ||||||||1.00|
|Source: World Bank staff<br>estimates|||||||||

# **III. A Framework for Measuring Investor Confidence**

Measuring changes in investor confidence is not an easy task, since it must be grounded in the psychology of investor behavior and attitudes. There are two main approaches: using surveys of investors or constructing indices based on market information. The first approach uses surveys of investors’ opinions on particular issues at particular times and often focuses on a specific segment of the investor community–for example institutional investors in the United States (Shiller, 2000) or private equity investors in the United Kingdom (Deloitte, 2009).

4
|(8)<br>Judge<br>more strict<br>(share)<br>0.059***<br>(0.017)<br>-0.001**<br>(0.000)<br>0.003***<br>(0.001)<br>0.026<br>Yes<br>Yes<br>Yes<br>0.123<br>0.151<br>0.062<br>3537<br>plication is a<br>. All models<br> fxed efects,<br>ber x hearing<br> 21 hearings<br>ore than one<br> * p_<_0.10, **|
|---|
|(7)<br>Pre-trial<br>likelihood<br>of being<br>heard<br>0.025<br>(0.026)<br>-0.001**<br>(0.001)<br>0.003<br>(0.002)<br>0.484<br>Yes<br>Yes<br>Yes<br>0.854<br>0.149<br>0.182<br>3533<br> decree ap<br> the decree<br>dar month <br> the (cham<br>ween 4 and<br>hat have m<br>as follows:|
|(6)<br>Number of<br>pretrial<br>hearings<br>-1.996***<br>(0.489)<br><br>-0.043***<br>(0.015)<br>0.075**<br>(0.030)<br>0.042<br>Yes<br>Yes<br>Yes<br>8.259<br>6.468<br>0.143<br>4795<br> Entered after<br> application of <br>ber and calen<br>, clustered at <br>efore and bet<br>nly for cases t<br> are denoted|
|(5)<br>Fast-<br>tracked<br>0.091**<br>(0.039)<br>0.003***<br>(0.001)<br>-0.004<br>(0.003)<br>0.090<br>Yes<br>Yes<br>Yes<br>0.140<br>0.347<br>0.070<br>4795<br>oceedings. <br>r after the <br>on)-, cham<br>arentheses<br> hearings b<br>g), col 7 (o<br>ance levels|
|(4)<br>Desk<br>rejection<br>0.169***<br>(0.023)<br>-0.000<br>(0.000)<br>-0.004***<br>(0.002)<br>0.022<br>Yes<br>Yes<br>Yes<br>0.000<br>0.000<br>0.078<br>4795<br>pre-trial pr<br>ed chambe<br>e (Interacti<br> errors in p<br> 38 and 4 <br> 1 (censorin<br>s). Signifc|
|(3)<br>Likelihood<br>of pre-trial<br>completion<br>in 4<br>months<br>0.231***<br>(0.046)<br>0.001<br>(0.001)<br>-0.011***<br>(0.003)<br>0.033<br>Yes<br>Yes<br>Yes<br>0.487<br>0.500<br>0.130<br>4795<br>e reform on <br>ed its assign<br>ter the decre<br>S. Standard <br>ring between <br>xcept for col <br> adjournment|
|(2)<br>Hazard<br>ratio -<br>fnishing<br>pre-trial<br>1.320***<br>(0.129)<br>1.005**<br>(0.002)<br>0.986**<br>(0.006)<br>Yes<br>Yes<br>Yes<br>4795<br>pacts of th<br> case enter<br> change af<br>ated by OL<br> cases ente<br>rvations, e<br>t have any|
|(1)<br>Duration<br>of pre-trial<br>hearings<br>(in days)<br>Entered after decree -42.929***<br>application (_β_)<br>(12.943)<br>Trend<br>-1.115***<br>(0.375)<br>Interaction<br>0.455<br>(0.811)<br>P-value 6pWCBoot<br>0.086<br>Chamber FEs<br>Yes<br>Calendar month FEs<br>Yes<br>Without adj. period<br>Yes<br>Pre-mean<br>156.941<br>Pre-sd<br>146.025<br>R-Squared<br>0.203<br>Observations<br>4568<br>Notes<br>: Estimates of case-level im<br>dummy that takes value 1 when a<br>include a linear trend - allowed to<br>and a constant. All models estim<br>of entry) level. Window includes <br>after decree application. 4795 obse<br>hearing), col 8 (only for cases tha<br>p_<_0.05, *** p_<_0.01.|

56
and New Zealand<sup>14</sup> (medium efficiency) and Iceland<sup>15</sup> (low efficiency). There are determinants of the ranking and there are some plausible explanations in the corresponding footnotes.

Table 4. Estimates of Technical Efficiency, Marginal effects, and inefficiency

|**Function**<br>**Mean 2002-**<br>**2005**|**Efficiency**|**TransLog (TL)**<br>**Marginal**<br>**Effects**|**Inefficiency**<br>**E(u/e)**|**Efficiency**|**Cobb-Douglass (**<br>**Marginal**<br>**Effects**|**CB)**<br>**Inefficiency**<br>**E(u/e)**|
|---|---|---|---|---|---|---|
|All sample|0.937|0.068|0.068|0.991|0.011|0.011|
|High-Medium<br>|0.953|-0.824|0.049|1.000|-0.872|0.000|
|Tech countries<sup>1</sup>|||||||
|Medium-Low-<br>Tech countries<sup>2</sup>|0.926|0.688|0.082|0.985|0.624|0.018|

Notes: 1 High-Medium Tech countries: R&D (% GDP) > 1 (year 2002) 2 Medium-Low-Tech countries : R&D (% GDP) < 1 (year 2002) Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

United Kingdom’s economy has a large trade deficit in manufacturing and has become a net importer of energy.

11 Canada has moved from agriculture straight to services (this industry is very diverse and employs 75% of the total million working population). Manufacturing has never been a dominant sector of the Canadian economy, but it has been an important secondary industry.

12 The main economic activity of Georgia is agriculture. Mining, construction, financial services and communication are other sector making significant contribution towards Georgia’s GDP. Georgia has a good supply of hydropower, however it imports a major part of its energy resources. 13 Mexico has one of the largest economies in the world. The industrial sector in Mexico is very heterogeneous (the industrial sector combines technologically advanced businesses and antiquated industries). The agricultural sector is also an important part of the Mexican economy. The private sector has started assuming an increasingly important role in both the agricultural and the industrial sectors.

14 Manufacturing and creative media largely constitute the New Zealand economy. Some of the major industries of New Zealand include iron and steel, natural gas processing, printing, publishing and recorded media, wood processing, cement, and fishing. Other minor industries in New Zealand include paper, tanning, transport equipments, wine making, tourism, and timber trade. Manufacturing industries in New Zealand contribute over 15% of GDP and over 44% of export receipts. Agriculture also contributes significantly to the economic growth of New Zealand. 15 Iceland has a Scandinavian-type economy. This means that the main economic activity of Iceland is the fishing industry. Iceland also exports animal products and aluminum. New businesses in Iceland are tourism, software production, financial services and biotechnology.

13