rafmacalaba/datause-encoder-data huggingface dataset:
Table A.4: No role for perceived rank in th impact of IOp on investment behavior

|**Dep var:**Binary = 1 if subject chooses to|(1)<br>invest|(2)|(3)|
|---|---|---|---|
||All|Without rank feedback|With rank feedback|
|Perceived pre-investment earnings rank|0.024|-0.266|0.115|
||[0.833]|[0.204]|[0.390]|
|IOp treatment|7.468|7.938|6.268|
||[0.377]|[0.578]|[0.559]|
|IOp X Perceived rank|-0.033|0.171|-0.097|
||[0.807]|[0.510]|[0.543]|
|Constant|1.090|3.234**|0.399|
||[0.143]|[0.023]|[0.651]|
|Observations|499|164|335|
|Pseudo R-squared|0.0001|0.009|0.002|

**Dep var:** Portion of earnings invested, conditional on being _>_ 0

||All<br>Without rank feedback|With rank feedback|
|---|---|---|
|Perceived pre-investment earnings rank|0.333<br>0.917|1.55e-14|
||[0.743]<br>[0.612]|[1.00]|
|IOp treatment|7.468<br>7.938|6.268|
||[0.377]<br>[0.578]|[0.559]|
|IOp X Perceived rank|-0.917<br>-0.776|-0.850|
||[0.495]<br>[0.742]|[0.610]|
|Constant|19.78***<br>14.64|22.69***|
||[0.002]<br>[0.157]|[0.009]|
|Observations|385<br>130|255|
|R-squared|0.004<br>0.007|0.005|

Note: Results are from logit and OLS regressions, respectively, with robust standard errors. Columns 2 and 3 are partitioned by whether subjects receive feedback on their actual investment rank (prior to investment). IOp treatment is a dummy equal to 1 if Round 3 investment funds are determined by childhood zip code, and 0 for the control group. Pre-investment rank is subjects’ actual earnings rank based on the total value of the Round 3 investment funds. It is on a scale of 1-10, where higher values indicate higher earnings. Robust p-values are in brackets. Significance also indicated using stars, for reader convenience: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# _A.2. Robustness: Type of information_

Information on actual income rank entails receiving one of three types of news: whether each subject guessed their income rank correctly, underestimated it or overestimated it. Appendix Table A.5 shows that the type of news received is not correlated with investment behavior. It also shows that mean treatment differences described are also robust to controlling for 29
42 and though tho infbmton gleaned about Ul In developed ooonomies Is not fully transferable, itIs at least somewhat instue.

Nothing **has** been said about the possibility of constructing Ul programs In the formorly planned economIes of Eastern Europe. The program outline presented In Section C Is as useful there as In developing countries. But the discussions In Section E about dualism and the size of the modem ector are hardly apptlcable. What I appropriate Is absolutely unclear at this point, sInce the eventual nature of the more basic economi institutions that will arise Is unknown. Nonetheless, the evidence from Wetem counties can still proide guidelines for choosing program parameters; and the discussion of program goals should forco people who make policy to think about their expectations for any Ul program that is proposed.
disaggregated Most Favored Nation (MFN) applied tariffs for the two eras that we use as benchmarks: a pre-liberalization period circa 1985 (in practice, between 1985 and 1993) and a post-liberalization period circa 2000 (in practice, between 1999 and 2004).<sup>17</sup> For robustness, we consider three different tariff measures for imports of capital, intermediate inputs, and consumption. The corresponding tariff rates before and after liberalization are given in figures 3-5.

_City Populations._ The city-level population data refer to populations of agglomerations/metropolitan areas that include a central city and neighboring towns (suburbs) forming a connected region of a dense, predominately urban population that is economically and culturally linked to the central city (e.g., by commuters).<sup>18</sup> The data can be downloaded from http://www.populstat.info/, http://world-gazetteer.com/, and http://www.citypopulation.de/ for the pre-liberalization period circa 1985 (in practice, between 1980 and 1994) and the post-liberalization period circa 2000 (in practice, between 1995 and 2004).<sup>19</sup> _Instruments_ **.** To create country-specific instruments for tariff reductions under the Uruguay round of GATT, we use (i) GATT membership data of Rose (2004)<sup>20</sup> and (ii) historical GDP data of Angus Maddison covering GDP of countries (in our sample) 17ET show that tariff rates in liberalizing countries started to decline prior to the signing of the agreement and that the decline has accelerated with it. See Figure 3 in ET.

18Given the nature of an urban agglomeration, there is an unavoidable measure of arbitrariness in the determination of its boundaries in any data set.

19Country-specific details of the data set are given in the supplemental appendix, where we present the exact dates and sources of data for tariff rates and city-level populations for each country in our sample for the pre-liberalization and post-liberalization periods. In the supplemental appendix, we also include a table showing the representativeness of our country sample.

20GATT membership data from Rose (2004) has been obtained from
decline only in 2002. In Estonia falling unemployment after 2002 led to an increase in employment rates despite slightly declining participation rates.

**Figure 5.  Evolution of Labor Force, Employment, and Unemployment Rates in Latvia**

<!-- Start of picture text --> 1500<br>1400<br>1300<br>1200<br>1100 Labor force<br>Labor force new<br>1000<br>Employed<br>900 Employed new<br>1980 1990 1992 1994 1996 1998 2000 2002<br>1985 1991 1993 1995 1997 1999 2001<br>22<br>20<br>18<br>16<br>14<br>12<br>10<br>8<br>6 Unemployment<br>4 rate (LFS)<br>2 Registered<br>0 unemployment rate<br>1990 1992 1994 1996 1998 2000 2002<br>1991 1993 1995 1997 1999 2001<br>thousand population<br>percent<br><!-- End of picture text --> Notes: “Labor force new” and “Employed new” series correspond to recently updated LFS data, while “Labor force” and “Employed” are previously published by Central Statistical Bureau of Latvia data. Harmonized time series are not yet available. LFS unemployment rate is calculated by ILO standards since 1996; for 1990-1995 estimated rate is shown in the Figure. _Source: Central Statistical Bureau of Latvia_ How does the Latvian labor market look in a broader international perspective? Figure 6a shows unemployment and labor force participation rates for new EU members. The Latvian labor market compares unfavorably to Estonia, the Czech Republic, Slovenia, and Cyprus on these criteria. Only Bulgaria and Poland have both less favorable activity and unemployment rates. Lithuania and Slovakia have higher unemployment rates than Latvia. Hungary has substantially lower activity and unemployment rates than Latvia. The three Baltic countries and Romania share similar employment – population ratios.

As shown in the Figure 6b, in year 2002 all three Baltic countries had activity rates close to the EU-15 average. Employment/population ratios for the age group 15-64 were, however, lower than the EU-15 8
POLICY RESEARCH WORKING PAPER 1918

# **Summary findings**

Banking markets are becoming increasingly international through financial liberalization and general economic integration.

Using bank-level data for 80 countries for 1988-95, Claessens, Demirgii~-Kunt, and Huizinga examine the extent of foreign ownership in national banking markets. They compare net interest margins, overhead, taxes paid, . and profitability of foreign and domestic banks.

The comparative functions of foreign banks and domestic banks is very different in developing and industrial countries, possibly because of a different customer base, different bank procedures, and different regulatory and tax regimes: • In developing countries foreign banks tend to have greater profits, higher interest margins, and higher tax payments than do domestic banks.

• In industrial countries it is the domestic banks that have greater profits, higher interest margins, and higher tax payments.

It is common to read, in the literature on foreign banking; that the entry of foreign banks can make national banking markets more competitive, thereby forcing domestic banks to operate more efficiently. Claessens, Demirgii~-Kunt, and Huizinga show that increasing the foreign share of bank ownership does indeed reduce profitability and overhead expenses in domestically owned banks - so the: general effect of foreign bank entry may be positive.

Interestingly, the number of foreign entrants-matters more than their market share,'suggesting that they affect .. local bank competition more on en!ry rather tpan after gaining a substantial market share.

This paper - a joint product of the East Asia and Pacific Region and the Development Research Group- is part of a larger effort in the Bank to study the effects of increasing global integration of financial services. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room MC4-404, telephone · 202-473-3722, fax 202-522-2530, Internet address hvol@worldbank.org. The authors may be contacted at sclaessens@worldbank.org, ademirguckunt@worldbank.org, or hhuizinga@worldbank.org. May 1998. (13 pages) ~~. . . .~~ _The Policy Research Working Paper Series disseminates th(;! findings of work in progress to enco11rage the exchange of ideas about development issttes. An objective of the series is to get the findings out quickly, even if the presentations are fess than fully polished. The papers carry the names of the authors and should be cited accordingly. The fi11dings, interpretations, and conclusions expressed in this paper are entirely those of the authors. Tiley do not necessarily represellt the view of the World Ba11k, its Executive Directors,_ o~ _tbe, countries they represent._ Produced by the Policy Research Dissemination Center
highly persistent over time, implying that the pre-democracy elite’s political power can potentially also persist over time in a democracy. The following two subsections investigate two features of malapportionment that could further allow pre-democratic elites to maintain political influence over time in a democracy.

# **4.3 Malapportionment and Political Competition**

This section uses within country data for a number of Latin America countries to study the relationship between legislative malapportionment and political competition. We rely on _within_ country data since our theoretical argument suggests that malapportionment shifts the distribution of political power across regions within countries, which may also affect the degree of political competition.

Our measure of political competition is the Herfindahl-Hirschmann index of political concentration (HH index). We calculate this index using the share of votes going to different political parties (or coalitions of parties) in different regions<sup>25</sup> . An increase in this index denotes an increase in the degree of political concentration. The first two columns of Table 7 display the results of running regressions similar to Equation 3 but using the HH index as the dependent variable. Columns 1 and 2 include regressions without and with controls, respectively. The results show a positive relationship between malapportionment and the degree of political concentration. However, the coefficients in the regression with controls are only marginally statistically significant (p-value of 0.15). In terms of economic significance, a one standard deviation increase in overrepresentation leads to an increase of about 0.09 standard deviations of our measure of political concentration.

To further study this mechanism and to relate it to the results in Table 4, we examine the effect of malapportioment on an interaction term between the HH index and the vote share going to parties associated with pre-democracy regimes. The idea here is to study whether political concentration in overrepresented areas favors parties that are closer to former non–democratic

> 25 We constructed the HH index for Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guatemala, Honduras, Mexico, Panama, Paraguay, Uruguay, and Venezuela, using as many years of voting data as are available for each country.

18
**_Energy demand models for policy_** **_<u>formulation</u>_**

(EIA, 1978) and of International Institute for Applied System Analysis (IIASA) [in Haefele et al (1981)] stand out.

One of the major developments during 1973-1985 was the investigation and debate about the interaction and interdependence between energy and the economy. In a simple aggregated conceptual framework, Hogan and Manne (1979) explained the relationship through elasticity of substitution between capital and energy, which consequently affects energy demand. Berndt and Wood (1979) is another classical work in this area which suggested that capital and energy may be complimentary in the short-run but substitutable in the long-run. In contrast, Hudson-Jorgenson (1974) used a disaggregated study using the general-equilibrium framework to analyse the effects of oil price increases on the economy.

The other major development of this period is the divergence of opinion between topdown and bottom-up modellers. While the traditional top-down approach followed an aggregated view and believes in the influence of price and markets, the bottom-up models stressed on the technical characteristics of the energy sector. Despite attempts of rapprochement the difference continues until now.

The high prices of oil in the 1970s emphasised the need for co-ordinated developments of the energy systems and led to a number of modelling efforts for strategic planning. IAEA developed WASP for the electricity sector planning in 1978. This model has been used extensively and modified over the past three decades to add various features. Electricity related models often tend to rely on optimisation as the basic approach. Hobbs (1995) identifies the following as the main elements of their structure: a) an objective function where often cost minimisation is considered but financial and environmental goals can also be used; b) a set of decision variables that the modeller aims to decide through the model; c) a set of constraints that ensure the feasible range of the decision variables.

125
Policy Research Working Paper 7165

# **Abstract**

Social policy in Mexico has focused on identifying and supporting chronically poor households. Yet, Mexico has a significant number of households that are just above the poverty line who are not eligible, by definition, for antipoverty programs and are at risk of falling back into poverty in the event of an economic crisis or shocks like loss of employment and natural disasters. These shocks can have serious negative effects on welfare in the absence of social safety nets targeted to these households. This study uses household survey data to better understand these “vulnerable” households, including their profile and risk exposure and, more importantly, to document the extent to which these households are covered by public transfers and insurance mechanisms. The analysis shows that until 2010 most social programs, including the few with productive components, such as vocational training and productive investment grants, barely covered the vulnerable. The study concludes that public policies need to pay attention to the vulnerable households and find the right policy mix between targeted interventions and universal insurance schemes to serve this economic group.

This paper is a product of the Poverty Global Practice Group. 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 adelafuente@ worldbank.org and crodriguezc@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
percent in Brazzaville and 31.5 percent in Pointe-Noire, compared to an unemployment rate of only 5.8 percent in rural areas and 9.0 percent in semi-urban areas.

Unemployment affects especially the youth. Countrywide, according to the 2005 household survey, the unemployment rate was highest for the 15-19 age group (above 40 percent) and declined gradually thereafter with unemployment rates of 36.7 percent for the 20 to 24 year old and 27.7 percent for the 25 to 29 old. In urban areas, according to an employment and informal sector survey carried out in 2009, 25 percent of the population under 30 years is unemployed if the ILO definition is employed whereas this rate increases to more than 40 percent if a broader definition is used (EESIC, Republic of Congo 2010a and 2010b).

**Figure 3:** Unemployment rates (in percent)

<!-- Start of picture text --> 30.9<br>19.4<br>16.1<br>13.1<br>12.2<br>1974 1984 1994 2005 2009<br><!-- End of picture text --> _Source:_ RGPH 1974, 1984, 1994, ECOM 2005, EESIC 2009 (urban areas)

# 2.2 <u>Infrastructure – bottleneck and potential growth source</u>

Access to and cost of infrastructure represent major constraints to private sector investment. In fact, according to the Enterprise Indicator survey carried out in 2009, more than 70 percent of private firms consider the lack of electricity as the main obstacle for private sector development in Congo (World Bank 2009, Figure 4). Power cuts have reduced firm turnover by 19 percent and 82 percent of firms have to rely on a generator. The quality of the fixed line telecommunication network is poor, and the international/regional connectivity is underdeveloped. The overall dilapidated infrastructure leads to exceptionally high input costs in all areas.

Poor transport infrastructure and transport services remain a bottleneck to investment and trade. As reported in the 2012 Doing Business report (World Bank 2011), it takes 62 days to import goods and 50 days to export goods. The railway between Pointe-Noire and Brazzaville is very inefficient with high freight tariffs, and the road between the two cities is still under construction. The rail network is among the worst in Africa in terms of service quality and safety, and tariffs are among the highest. Rail traffic fell by two-thirds during the conflict and has never returned to pre-conflict levels. Tariffs, at USD 0.16 per ton/km, are up to three times as high as in southern Africa. This is due to insufficient rehabilitation and maintenance of tracks, outdated and insufficient rolling stock, weak internal supervision, management deficits and human resource limitations. Surface transportation costs are more than double the southern African average (Briceño-Garmendia and Foster 2009).

4
same sector are assumed to be imperfect substitutes. This means the removal of a 10 percent tariff on a given commodity will lead to less than 10 percent reduction in the price of the domestic substitute. Second, the model accounts for inter-industry transaction through an input-output table. This feature allows the model to track the cascading effect of an excise tax. A tax on oil for instance will spread beyond final energy consumption to affect final goods whose production uses oil at some stage. Third, the tax rates used are effective and not statutory. Finally, simulations are conducted under a revenue neutrality constraint. This means that if an indirect tax is removed, it must be replaced by an equal yield income tax that preserves government revenue. This constraint allows the authors to focus on the pure price-distorting effects of the tax system.

Empirical implementation of CGE models of the type used by Devarajan and Hossain (1998) for fiscal incidence analysis requires a data framework that clearly shows both the sources and uses of income for each agent, and the interconnections among these. A social accounting matrix (SAM) provides such a framework<sup>49</sup> . The matrix reflects _the circular flow of economic activity_ for the chosen year (known as base year). It is an analytically integrated data set showing the interdependence of various aspects of the economy, such as production, consumption, trade, accumulation, and income distribution.

Table 4.3. Structure of a SAM for an Open Economy

||_Activity_|_Commodity_|_Factor_|_Household_|_Government_|_Investment_|<br>_World_|_Total_|
|---|---|---|---|---|---|---|---|---|
|Activity||domestic<br>sales|||export<br>subsidies||exports|total sales|
|Commodity|intermediate<br>consumption|||household<br>consumption|government<br>consumption|<sup>investment</sup>||total demand|
|Factor|GDP at factor<br>cost|||||||GDP at<br>factor cost|
|Household|||GDP at<br>factor cost||transfers||foreign<br>remittances|household<br>income|
|Government|indirect taxes|tariffs||income tax||||government<br>revenue|
|Savings||||household<br>savings|government<br>savings||foreign<br>savings|total savings|
|World||imports||||||total imports|
|Total|production<br>cost|total supply|GDP at<br>factor cost|total household<br>expenditure|government<br>expenditure|total<br>investment|total foreign<br>exchange||

_Source_ : Adapted from Robinson (1989)

> 49 This account draws heavily from Essama-Nssah (2006).

54