rafmacalaba/datause-encoder-data huggingface dataset:
Table 33: EARNINGS FUNCTIONS (lnW2) BY TYPE O EMPLOYEE

||Public|Sector|Private|Sector|Priv<br>Formal|ate<br>Sector|Priv<br>Informal|ate<br>Sector|
|---|---|---|---|---|---|---|---|---|
|Intercept|10.37|(55.1)|9.48|(46.8)|10.44|(30.9)|9.41|(27.4)|
|Years of Primary Schooling|.095|(2.3)|<br>.059|(1.7)|<br>.022|(0.6)|-.014|<br>(0.1)|
|Years of Secondary Schooling|.142|(6.4)|<br>.109|(3.0)|<br>.074|(1.9)|<br>.007|<br>(0.1)|
|Years of Higher Schooling|.082|(2.6)|<br>.196|(1.8)|<br>.261|(2.8)|||
|Years of Current Job Experience<br>|.047|(3.2)|<br>.084|(4.3)|<br>.102|(4.4)|-.049|<br>(1.3)|
|(Years of Current Job Experience)<br>2|-.C05|(0.9)|-.0018|<br>(2.7)|-.0023|<br>(3.1)|<br>.0010|(1.0)|
|Years of Previous Job Experience|.067|(2.9)|<br>.002|(0.0)|-.011|(0.3)|-.073|<br>(0.7)|
|(Years of Previous Job Experience)<br>2|-.0038|<br>(2.4)|<br>.001|(0.6)|<br>.0006|(0.3'|<br>.016|(1.8)|
|Years of Apprenticeship|.013|(0.3)|<br>.053|(1.8)|<br>.039|(1.3)|<br>.107|(2.3)|
|Years of Secondary<br>VTE|-.021|(0.8)|<br>.139|j<br>1)|<br>.142|(3.3)|||
|Years of Post-Secondary<br>VTE|.079|(0.8)|-.480|<br>(,.6)|<br>.012|(0.1)|<br>.104|(0.6)|
|Years of Teacher Training|.016|(0.2)|-.712|(1.4)|1||_||
|CEPE|.082|(0.3)|-.153|(0.7)|-.031|(0.1)|-.056|<br>(0.1)|
|BEPC|-.139|(0.4)|-.354|(0.9)|<br>.095|(0.3)|1.08(|*) (1.0)|
|Secondary<br>VTE Diploma|.082|(0.3)|-.253|(0.8)|<br>.033|(0.1)|||
|Post-Secondary<br>VTE Diploma|.100|(0.3)|<br>.680|(1.1)|<br>.533|(1.0)|<br>.244|(0.3)|
|Teacher Diploma|.081|(0.3)|<br>.211|(0.3)|1||||
|Other Diploma|.003|(0.0)|<br>.025|(0.1)|<br>.377|(0.8)|<br>-|_|
|Sex (1-female)|-.444|(4.9)|-.255|(1.7)|-.116|(0.7)|-.027|<br>(0.1)|
|Non-Ivorian|-.296|(0.6)|<br>.159|(0.9)|-.361|(2.7)|-.027|<br>(0.1)|
|Seasonal Job|-|_|-.617|(2.5)|<br>-.232|(0.3)|<br>-.119|(0.4)|
|Abidjan|.183|(2.0)|<br>.279|(2.1)|-.039|(0.3)|<br>.029|(0.1)|
|Rural Areas|-.099|(0.7)|-.217|(1.2)|-.139|(0.6)|-.363|<br>(1.2)|
|Lambda|-.254|(1.8)|-.919|(4.4)|-.080|(0.3)|-.669|<br>(1.7)|
|R2|.70||.54||.50||.27||
|F|19.68||13.35||8.57||1.48||
|Number of cases|213||288||202||86||

Note: Absolu.te t - values in parentheses.

(*) Includes "other diploma"
||S|hape paramet|er|
|---|---|---|---|
|MLD|0.50|0.25|0.10|
|_ρ_= 0_._05|0.36 (1.67)|0.52 (2.59)|1.12 (3.71)|
|_ρ_= 0_._25|0.37 (1.26)|0.61 (2.18)|0.47 (2.25)|

Table 5: Bias for estimates of inequality: (a) Normal-Mixture distribution (left number); (b) Normal distribution (right number)

# **5 A small empirical study**

For this small empirical application we will treat the 2010 micro census from the United States, a 1 percent sample, as a “new” country (i.e. as if the data constitutes a full population census). The data is publically available at IPUMS USA. There are a total of 1 _._ 25 million households<sup>7</sup> residing in 422 counties, which is the level at which we will be estimating poverty and inequality (i.e. the “small area level”). The census includes individual income data as well as data on a wide range of individual characteristics. We use the income data to compute household income per capita. All other individual level variables are also aggregated at the household level as this denotes the level at which we will build the income model. We defined the head of the household as the member of the household with the highest level of individual income. (In many empirical applications much of the available data is at the household level.) Table 6 provides some descriptive statistics of the data.

|Variable|Description|Mean|Std. Dev.|
|---|---|---|---|
|lnycap|Log of household income per capita|9.939|0.998|
|metro<br>~~s~~uburb|In sub-urb of major city (dummy)|0.290|0.454|
|metro<br>~~n~~one|Away from major city (dummy)|0.201|0.401|
|hh<br>~~s~~ize<br>~~1~~|Household of size one (dummy)|0.300|0.458|
|hh<br>~~s~~ize<br>~~2~~|Household of size two (dummy)|0.342|0.474|
|hh<br>~~s~~ize<br>~~3~~|Household of size three (dummy)|0.147|0.354|
|share<br>~~c~~hild15|Share of children aged younger than 15|0.108|0.199|
|hh<br>~~h~~edu2|At least one member with a masters degree (dummy)|0.366|0.482|
|hh<br>~~e~~mployed|Number of employed household members|1.104|0.936|
|hd<br>~~f~~emale|Head of household is female (dummy)|0.417|0.493|
|hd<br>~~l~~ogage|Log of age of household head|3.889|0.371|
|hd<br>~~l~~edu|Household head has lower education only (dummy)|0.048|0.214|
|hd<br>~~h~~edu1|Household head has college degree (dummy)|0.226|0.418|
|hd<br>~~h~~edu2|Household head has a masters degree (dummy)|0.312|0.463|
|hd<br>~~e~~mployed|Household head is employed (dummy)|0.660|0.474|
|hd<br>~~h~~isp|Household head is Hispanic (dummy)|0.091|0.288|
|hd<br>~~b~~lack|Household head is African-American (dummy)|0.106|0.308|

Table 6: Descriptive statistics of variables used in empirical application (derived from 2010 IPUMS USA) We draw a balanced sample of 6330 households (15 households per county). The log of

> 7The exact number of households in the census is 1 _,_ 242 _,_ 967.

21
Thirdly, netting out the effect of structural change by demeaning the distribution of capital expenditures by 2-digit industrial sectors, in addition, suggests still greater stability over time of the right tail of the distribution, consistent with the resilience of larger undertakings (Figures A10 and A11).

**Asia leads the way among developing countries in FDI concentration.** East Asian countries have accounted for a significant part of the global increase in FDI concentration, as shown in Figure 3. In particular, China has been a key driver of this trend until 2017 (see Figure A8). More recently, however, FDI concentration has accelerated within the broader East Asian region beyond China, in countries such as Malaysia, Indonesia and Viet Nam. The pattern is consistent with a “reallocation effect,”<sup>5</sup> whereby investment flows from developed (“North”) to developing (“South”) countries are shifting away from China and toward neighboring low-wage economies that are more geopolitically aligned with the United States (IMF 2023).

Figure 3: East Asia leads the way in global FDI concentration <!-- Start of picture text --> 2<br>East Asia and Pacific<br>Latin America and Caribbean<br>South Asia<br>Europe and Central Asia<br>Middle East and North Africa<br>Sub Saharan Africa<br>1.5<br>1<br>.5<br>2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>Project year<br>Concentration Ratio (5 investing MNEs)<br><!-- End of picture text --> Note: The analysis relies on greenfield FDI capital expenditure values. Each line denotes the destination region’s concentration ratio calculated as the regional average share of each country’s 5 largest investing MNEs in total capital expenditure, weighted by the latter for each year. Countries with on average less than 10 investing MNEs per year are excluded from this analysis.

**Fact 2.** _Uncertainty in destination markets is associated with greater FDI concentration_ . Among destination-specific characteristics that may reduce MNEs’ expected returns, economic and political uncertainty features prominently in recent studies (Juvenal & Monteiro 2021, Jardet et al. 2023). Resilience, or in other words the ability of firms to cope with risk and recover from shocks, is positively associated with firm

> 5A phenomenon also noted in trade literature – see Fajgelbaum et al. (2021)

10
24 experience of the former east Germany's exports suggests that among these factors low wages are crucial. Although east Oerman industry was regarded as the most technologically sophisticated within the CMEA, no redirection of exports has so far occurred despite the generous export auarantees provided by the Gennan govermment. Total exports contracted In 1991 by 53 percent and had it not been for export guarantees to the FSU, the decline would have been much more sigDificant.33 This situation can be attributed mainly to loss of competitiveness triggered by large increases in east German wages (in 1991 they were around 70 percent of the Westem level). As Dornbusch and Wolf (1992:239) observe: "The eastern German productivity level resembles Mexico's or Korea's, while the wage level matches that of the United States and is ten times greater than that of the neighboring Czech and Slovak Federal Republic. " A competitive disadvantage, the result of much lower productivity in east German industries and much higher unit wage costs, could not be ofriset by quality advantages.3 '

**Table** 9: ChOni,n Orientation of Hunarian **mid** Potish Experts of Poier **6fnlettfng Equit** Exports (SITC. **71)** frm the FSU to the EC betwsam **1985 and 1991** (mittion **USS)**

|Years|FSU|Hwu<br>EC|gary<br>Subtotal|Share of EC<br>(In percent)|FSU|Polan<br>EC|d<br>Subtotat|Share ofEC<br>(in percent)|
|---|---|---|---|---|---|---|---|---|
|1985<br>1986<br>1987<br>1988<br>1989<br>1990<br>1991|518<br>500<br>549<br>468<br>434<br>294<br>133|71<br>100<br>121<br>154<br>176<br>257<br>325|589<br>600<br>670<br>622<br>609<br>550<br>458|12<br>17<br>18<br>25<br>29<br>47<br>71|759<br>619<br>559<br>722<br>653<br>524<br>147|108<br>120<br>154<br>194<br>235<br>370<br>386|<br>864<br> <br>740<br> <br>713<br> <br>916<br> <br>888<br>894<br>533|12<br>16<br>22<br>21<br>26<br>41<br>73|

Source: The United Nations CO4TRADE data base, as reported by Hungary and Poland.

Thus, one may conclude that the troika export expansion was to some extent propelled by redirecdon of sales from the CMEA to the EC.3<sup>5</sup> The improvement in competitiveness may be precarious, however. The loss of momentum in Polish exports of electrical engineering products in 1991 may indicate that 32 One caveat should be made: without a detailed analysis at the firms level, it is also impossible to _asss_ waiether exports diverted from the FSU were profitable or represented 'distress sales," i.e., at prices below production costs.

3 See Focus Germany, Deutsche Bank Research, March 1992 34 According to the Institute for Economic Research in Halle, the unit wage cost was around 70 percent higher than in west Germany. As the authors of a report emphasize, this situation is hurting east German w"mpetitiveness because it is not '... neutralized with quality advantages' (Pocus Germanv. Deutsche Bank Research, March 1992:4).

35 Richter (1992) draws a similar conclusion from the analysis of change in Hungarian trade patterns.
**Figure 4.23. Payment incidence by quintiles in capital cities**

(% of households) <!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Central w ater District heating Electricity Natural gas<br>%<br><!-- End of picture text --> Note: average among 18 ECA countries for central water, 12 ECA countries for district heating and natural gas, 15 ECA countries for electricity.

Source: see Table 2.1

**Figure 4.24. Payment incidence by quintiles in other urban cities**

(% of households) <!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Central w ater District heating Electricity Natural gas<br>%<br><!-- End of picture text --> Note: average among 18 ECA countries for central water, 12 ECA countries for district heating and natural gas, 15 ECA countries for electricity.

Source: see Table 2.1 The prevalence of poorly targeted categorical exemptions from payments for different groups (i.e., pensioners or war veterans) may explain why so many people do not pay and why the differences between high income quintiles and low income quintiles are rather small. Box 4.2 below summarizes the development of categorical privileges in Russia, which have mushroomed in numbers and now benefit about 40 percent of the population. Categorical privileges are also found in many of the other transition countries, especially those of the FSU. They are notoriously poorly targeted and politically difficult to eliminate since they benefit large numbers of people and since much of the cost is not directly financed by the government but indirectly financed by the service providers through erosions in service quality and deterioration of the capital stock.

40
# **1.  Introduction**

This paper presents the latest update of the Worldwide Governance Indicators (WGI) research project.<sup>1</sup> The indicators measure six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. They cover 212 countries and territories for 1996, 1998, 2000, and annually for 2002-2007.<sup>2</sup> The indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 35 separate data sources constructed by 32 different organizations from around the world. We assign these individual measures to categories capturing these six dimensions of governance, and use an unobserved components model to construct six aggregate governance indicators in each period.

As in the past, we complement our estimates of governance for each country with margins of error that indicate the unavoidable uncertainty associated with measuring governance across countries. These margins of error have declined over time with the addition of new data sources to our aggregate indicators, and are substantially smaller than for any of the individual sources. We continue to encourage users of the governance indicators to take these margins of error into account when making comparisons of governance across countries, and within countries over time. In particular, a useful rule of thumb is that when confidence intervals for governance based on our reported margins of error overlap in comparisons of two countries, or a single country over time, this suggests that the data do not reveal statistically (or for that matter practically) significant differences in governance.

The margins of error we report are not unique to the WGI, nor are they unique to perceptions-based measures of governance on which we rely. Measurement error is pervasive among all indicators of governance and institutional quality, including individual indicators as well as ‘objective’ or fact-based ones -- if these are available at

> 1 This paper is the seventh in a series of estimates of governance across countries. Documentation of previous rounds can be found in Kaufmann, Kraay, and Zoido-Lobatón (1999a,b,2002), and Kaufmann, Kraay, and Mastruzzi (2004, 2005, 2006a, 2006b and 2007b).

> 2 A few of the entities covered by our indicators are not fully independent states (e.g. Puerto Rico, Hong Kong, West Bank/Gaza, Martinique, French Guyana and others). A handful of very small independent principalities (e.g. Monaco, San Marino, Lichtenstein and Andorra) are also included. For stylistic convenience all 212 entities are often referred to in this paper as “countries”.

1
institutions, but these resources are typically one-time donations, not annual funding. Current levels of official development assistance (ODA) are nowhere near robust enough to reduce large amounts of deforestation. For instance, ODA lies in the range of $500-600 million per year (OECD, 2005), and the majority of funds are already devoted to other, much needed development projects.

Current funding streams even fall short of projected expenses for implementing REDD on a global scale. In a survey of REDD financing and country activities, interim financing expenditure and commitments for REDD activities totaled almost US$2 billion from 2010 to 2012, with US$552 million dedicated to performance-based payments for emissions reductions (Synthesis Report 2010). These expenditures and commitments, however, are significantly less than what most countries estimate they will need financially to reduce deforestation. For example, the Democratic Republic of Congo alone estimates that it will spend US$2 billion from the period 2011-2015 on REDD activities. Brazil will require an estimated US$1.5 billion for implementing a REDD national strategy and supporting capacity building activities over the same period, and has yet to estimate an amount for performance-based payments which will significantly add to the costs (Synthesis Report, 2010).

Currently, there is no global agreement on greenhouse gas emissions beyond 2012, and thus no established long-run funding source, such as through a global cap and trade system with REDD offsets. Even if an agreement were to emerge, Aldy and Stavins (2010) argue that there may not be a single cap and trade system to provide financing for REDD activities. Private funding in principle could be raised through a range of programs, including voluntary initiatives, or national and regional regulatory programs that allow the purchase of carbon credits generated through REDD activities (UNEP 2011). There are already examples of private voluntary 10
# **II. Prospects of Intra-Regional Trade: a Product-Level Assessment**

The success of the regional trade agreement may depend crucially on the extent to which the core market, India, given its relative size, becomes more accessible to its periphery and vice versa. In this context, the prospects of increasing regional trade may depend more on the existence of product complementarities and export efficiencies (defined by comparative advantage) and other characteristics such as the degree of concentration and diversification of trade profiles amongst the regional partners, particularly between India and the other countries of the region. These are evaluated in detail in the following section. A useful beginning to the analysis is a review of trade structures at a broad level.

An analysis of product-level trade data, at the SITC heading level in Table 6 suggests that agriculture and primary materials have dominated intra-regional trade in South Asia for the past two decades. For example, in 1998 the percentage of regional exports that originated in food and live animals was over 60 percent for Pakistan, 41 percent for India, 35 percent for Sri Lanka, and 34 percent for Nepal. For Bangladesh, crude materials were its major regional exports, with a share of 60 percent of total exports. In contrast, South Asia’s exports to the rest of the world are dominated by manufacturing products.

The data in Table 6 also reveal that, although food and live animals constitute a major share of regional exports, there have been large fluctuations over different periods. Such fluctuations are due to the import demand for basic food products such as rice, vegetables, fruits, pulses, onions, potatoes, and sugar, which vary with domestic supply conditions and, which, in turn, strongly impact the trade policies of respective member countries. The SACs often exercise arbitrary policies to maintain stable domestic prices in these “essential commodities.” When shortfalls occur domestically imports are encouraged and when the domestic supply is stable, restrictions are re-imposed, explaining the large swings in export shares.

A further insight into product-level breakdown is necessary to highlight the nature and characteristics of regional and extra-regional trade patterns to capture the anomalies that may have had an 19
# TABLE 7

# SENSITIVITY OF OPENNESS-PERFORMANCE ESTIMATES TO FISCAL POLICY'

||Annua|l Level Data<br>1967-87|Annu|al Change Data<br>1967-87|Ann|ual Level Data<br>1981-87|_A_|_mnal_Change Data<br>1981-87|
|---|---|---|---|---|---|---|---|---|
|Measures|Openness|Gov't<br>Consumption|Openness<br>|Gov't<br>Consumption|Openness<br>|Gov't<br>Consumption|Openness<br>|Gov't<br>Consumntion|
|Trade<br>Shares|0.57***<br>(4.72)|<br>-0.88***<br>(-5.24)|<br>0.03*<br>(1.83)|-0.08***<br>(-4.41)|-0.41***<br>(3.73)|<br>0.11<br>(0.49)|0.07***<br>(3.35)|<br>-0.03<br>(-0.77)|
|Black<br>Market<br>Premium|_o.o001**<br>(-2.55)|<br>-0.63***<br>(-3.42)|_0.0O5**<br>(-2.12)|<br>-0.06**<br>(2.35)|-0.00001<br>(0.76)|<br>0.29<br>(1.17)|-0.0001<br>(-0.03)|<br>-0.03<br>(-0.70)|
|HT Index|0.03**<br>(1.93)|0.43<br>(1.43)|||-0.004<br>(-0.35|0.06<br>)(0.17)|||
|D$Index|0.13***<br>(15.82)|<br>0.96**<br>(5.49)|||-0.12**<br>(8.41)|0.09<br>(0.39)|||

Note:1/ Fiscal policy is proxied by the times Series Data on GDP share of government consumption which was available for 23 countries only. These regressions thus involve a slightly smaller sam pe than those in Tables 1 to 4, but their adjusted R-squares improve with the inclusion of this variable. T-statistics are in parenthesis.

31
Rogers, J. H., C. Scotti, and J. H. Wright. 2014. “Evaluating Asset-Market Effects of Unconventional Monetary Policy: A Multi-Country Review.” _Economic Policy_ 29 (80): 749–799.

Romer, C.D. and D.H. Romer. 1989. “Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz.” _NBER Macroeconomics Annual_ , 4, pp.121-170.

Romer, C. D., and D. H. Romer. 2004. “A New Measure of Monetary Shocks: Derivation and Implications.” _American Economic Review_ 94 (4): 1055–84.

Takáts, E., and A. Vela. 2014. “International Monetary Policy Transmission,” BIS Working Paper 78, Bank for International Settlements, Basel, Switzerland.

U.S. Census Bureau. 2017. _X-13ARIMA-SEATS Reference Manual._ Washington DC: U.S. Census Bureau.

World Bank. 2022. _Global Economic Prospects_ . January. Washington, DC: World Bank.

21
Pooled OLS: Between districts: Within districts only: where, _yid_ is the incidence of spending (or the number of applications or the percentage of applications funded) per thousand people in the ward, _Pid_ is the poverty headcount ratio at the ward level, and _X id_ and _Zid_ give a broader set of socio-economic and political controls. Equation (1) is estimated through pooled OLS, equation (2) is estimated at the district level using district averages, and equation (3) includes district-level fixed effects and hence is identified using only within-district variation across wards.

# **5. Targeting Performance at the National Level**

# _5.1. Funding (and Number of Projects) per Capita_

The typical analysis of targeting performance in Social Funds programs examines the incidence of spending among the poor relative to the population as a whole. To demonstrate that the targeting performance of TASAF II is not substantially different than those found in previous studies (World Bank 2002), Table 2 provides a first look at the incidence of TASAF II expenditures. The first two columns use the amount of money spent through TASAF II‟s NVF per person per ward as the dependent variable. When we regress this variable on the ward-level poverty headcount ratio, we see progressive targeting that is statistically precise but relatively muted in economic significance. The marginal effects indicate that a ward going from being universally non-poor to universally poor would see TASAF II spending per person increase by only $1.29 from a base of $2.63. In other words, a one standard deviation increase in the poverty rate of a ward (an approximately 20 percentage point increase in the headcount ratio) would cause an increase of only $0.25 per person or $5,000 total in a ward with average population. These results are typical of the broader literature on CDD that show mildly pro-poor benefitincidences. The results in the third and fourth columns, which analyze the number of TASAF II projects (rather than funding) per person, are very similar in interpretation and magnitude.

Using our rich set of covariates, we can then further analyze these results to understand whether TASAF II funding is progressive in a broader sense. In columns 2 and 4 of Table 2, we 12