rafmacalaba/datause-encoder-data huggingface dataset:
> economic growth because the higher taxes needed to finance the consumption expenditure reduce returns on investments and the incentive to invest. This is confirmed **by** Grier and Tullock **(1987).** Using pooled cross-section/time-series data **(115** countries including 24 **OECD** countries in the post-World War II period), they find a significantly negative relationship between the growth rate of real **GDP** and government consumption's share of **GDP. By** contrast, government investment expenditure, such as the provision of infrastructure services, is thought to provide the enabling environment for growth. Aschauer **(1989)** finds that "core infrastructure" **-** streets, highways, airports, mass transit and other public capital **--** has the most explanatory power for private sector productivity in the United States over the period **1949-85.** For other categories of public spending, there appears to be some disagreement over whether they constitute "productive" expenditure. While Kormendi and Meguire **_(1985),_** Grier and Tullock **(1987),** Summers and Heston **(1988)** classify defense and education as government consumption and hence unproductive, Barro **(1990)** models them as productive. He considers spending on public education as investing in human capital. Similarly, defense spending helps protect property rights which increases the probability that an investor will receive the marginal product of capital. Based on data on **98** countries, Barro **(1990)** finds that an increase in resources devoted to non-productive government consumption is associated with lower per capita growth<sup>6</sup>

In our analysis, we refrain from an a priori classification of public expenditures into "productive" and "unproductive". Instead, we allow the data to tell us which components 6Based on a 119-country (developed and developing) sample, Levine and Renelt **(1992)** have analyzed the relationship between a diverse collection of fiscal policy indicators and growth. They find that,<sup>whilethereare</sup> econometric specifications that yield significant coefficient estimates between specific fiscal-policy indicators and growth, the relationship is not "robust".

**9**
|Long term trend|-0.05|0.28|-0.04|
|---|---|---|---|
|Short run adjustment|-0.46***|-0.51***|-0.41***|

# **5. Summary**

The empirical research on urbanization, housing, and economic development highlights that investment in housing follows an S-shaped trajectory with housing investment taking off around national development levels of about $3,000 (2005 USD). Given rapid urbanization at lower income levels across countries in Africa and parts of Asia, urbanization will be a messy process where population density in cities may not be supported by the necessary capital investments.

The urgent priority for public policy is to strengthen the institutions for urban planning and service delivery to ensure that planning for land use integrates future investments in housing, industry, and infrastructure, and access to basic services is expanded to maintain urban livability. Urban planning early on will also help in preparing for ‘orderly’ urban development when capital investment steps up.

# **References**

- Acock, A. C.(2005): Working with Missing Values,  Journal of Marriage and Family 67 (November 2005): 1012–1028.

- Applegarth, P. A (2004): Capital Market and Financial Sector Development in Sub-Saharan Africa: A Report of the Africa Policy Advisory Panel, Center for Strategic and International Studies.

- Arellano, M. (1987): “Computing Robust Standard Errors for Within-Groups Estimators,” Oxford Bulletin of Economics and Statistics, 49(4), 431–434.

- Ballesteros, M. (2000): _Land Use Planning in Metro Manila and the Urban Fringe: Implications on the Land and Real Estate Market_ . Philippine Institute for Development Studies Discussion Paper No. 2000-20. Manila: PIDS.

- Ballesteros, M., M. (2002): The Dynamics of Housing Demand in the Philippines: Income and Lifecycle Effects, Research Paper Series No. 2002-01,  Philippine Institute for Development Studies

29
<!-- Start of picture text --> 25<br>.<br>°<br>e L<br>15<br>.<br>.<br>A.°.°°<br>- °<br>°<br>05<br>6 5 4 3 -2 -1 0 1 2 3 4 5 6<br>Months to NHIS implementation<br><!-- End of picture text --> <!-- Start of picture text --> .<br>7<br>6<br>e e<br>5 . ° :<br>- . - = .<br>A<br>3<br>.<br>6 5 4 3 2 -1 0 1 2 3 4 5 6<br>Months to NHIS implementation<br><!-- End of picture text --> <!-- Start of picture text --> 25 .<br>. 7<br>°<br>e L<br>2e6<br>e e<br>15 5 . ° :<br>. . - - = .<br>.<br>A.°.°° A<br>- °<br>° 3<br>05 .<br>6 5 4 3 -2 -1 0 1 2 3 4 5 6 6 5 4 3 2 -1 0 1 2 3 4 5 6<br>Months to NHIS implementation Months to NHIS implementation<br>335 3<br>°<br>.25<br>33<br>.7.-..2<br>~ _ . 2 °<br>325 °. —eae 18 = -<br>°.e ‘4 . | 7 = : = ae<br>32 . :<br>.05 . °<br>°<br>.<br>315 0 °<br>6 5 4 3 2 Months“1“1 to NHIS()() implementation11 2 3 4 5 6 6 5 4 3 -2 Months-1-1 to NHIS00 implementation11 2 3 4 5 6<br><!-- End of picture text -->

<!-- Start of picture text --> 335<br>33<br>~ _ .<br>325 . ae<br>e<br>32 . :<br>315<br>6 5 4 3 2 Months“1“1 to NHIS()() implementation11 2 3 4 5 6<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>°<br>.25<br>..2.22<br>2 °<br>18 = -<br>‘4 . | 7 = : = ae<br>.05 . °<br>°<br>.<br>0 °<br>6 5 4 3 -2 Months-1-1 to NHIS00 implementation11 2 3 4 5 6<br><!-- End of picture text -->
# **Al.6 Freedom House (FHFW, FHNT)**

Freedom House is a non-governmental organization dedicated to the promotion of democratic values around the world. Freedom House was established in 1941 and is headquartered in New York City. As of May 1999, Freedom House can be reached on the web at http://www.freedomhouse.ora. In 1997 and 1998, Freedom House produced two publications containing governance measures: Freedom in the World and Nations in Transit. Freedom in the World was launched in 1955, and became an annual publication in 1978 with a consistent methodological approach. In 1997-98, it covered 191 countries and 60 related territories. Nations in Transit was launched in 1995. In 1998, it covered 28 post-communist countries. In this paper we use data from the 1997-98 edition of Freedom in the World and the 1998 edition of Nations in Transit.

Freedom House develops its assessments using a team of academic advisors, in-house experts, published resources, and local correspondents including human rights activists, journalists, editors and political figures. Freedom House staff also conduct regular fact-finding missions to countries being assessed. An academic advisory board provides input to the project in general.

# Freedom in the World (FHFW)

FHFW evaluates political rights and civil liberties around the world. Freedom House defines political rights as those freedoms that enable people to participate freely in the political process, and civil liberties as the freedom to develop views, institutions and personal autonomy apart from the state. In 1997, Freedom House also published an assessment of freedom of the press, taking into consideration freedoms in law and in practice, freedom from political and economic influence over media content and actual violations of press freedom such as arrests or murders of journalists For all countries, the subjective assessments are based on checklists of rights and freedoms, reported in Table A1.6. A Freedom House team assigns a rating to each item on the checklist and produces an initial assessment for each country. The team then assess whether the checklists might have missed an important factor for a particular country. The scores are then reviewed to ensure quality and consistency across countries, and a final rating is produced.

# Nations in Transit (FHNT)

FHNT evaluates the progress in democratic and economic reform in post-communist countries. Country surveys are written by Freedom House staff or consultants and are reviewed by academics and senior Freedom House staff. Each report is divided into nine sections, ranging from the political process to progress in price liberalization. For each section, a preliminary rating is based on a checklist of issues. The academic oversight board establishes the final ratings by consensus following extensive discussions and debate, which are reviewed by the Freedom House rating committee.

40
**<mark>Figure 5. Sectoral results, % relative to the baseline in 2030 (EU-Indonesia FTA)</mark>**

<!-- Start of picture text --> Output Exports Imports<br>Social services<br>Communication and...<br>Finance and other...<br>Trade and transport<br>Construction<br>Utilities<br>Other manufacturing<br>Machinery and...<br>Electronic equipment<br>Transport equipment<br>Metals<br>Chemical, rubber,...<br>Wearing apparel and...<br>Textiles<br>Food, beverages, tobacco<br>Natural resources /...<br>Other agriculture<br>‐20 0 20 40 60 80 ‐100 0 100 200 0 10 20<br><!-- End of picture text --> At the other end of the spectrum, the small expected impact of RCEP is driven by already low levels of protection among the member countries. Due to the economic integration within the Association of Southeast Asian Nations (ASEAN) and with further countries (e.g., PTAs between ASEAN and China, Japan, Australia, New Zealand, India, Korea, as well as Japan-Indonesia CEPA), the trade-weighted average tariffs that Indonesia faces in other RCEP members range from zero in Singapore up to 5% in India. The Indonesian trade-weighted average import tariff vis-àvis other RCEP members is also very low at 0.3%. The highest average import tariffs on Indonesian products occur in food processing (6.23%), agriculture (1.41%) and transport equipment (1.03%). NTMs levels are also small. The trade-weighted average value in other RCEP countries amounts to 7.10% while the Indonesian average (ad valorem equivalent) NTM is the lowest among the agreements considered here, with a value of 3.02%. Although the trade-weighted averages are quite small, NTMs faced by Indonesia in RCEP countries are very high in certain sectors. The highest barriers occur in services such as communication (57.19%), finance and other business 14
|**Table 2b: Robustness**|**of Village Hea**<br>**_(Robu_**<br>|**lth Service Res**<br>**_st standard erro_**<br>|**ults to Inclusi**<br>**_rs in parenthe_**<br>|**on of Alternat**<br>**_ses)_**<br>|**e Political V**<br>(5)|**ariables**<br>|
|---|---|---|---|---|---|---|
||(1)<br>% children<br>with below<br>normal birth<br>weight|(2)<br>% children<br>with below<br>normal birth<br>weight|(3)<br>Number of<br>barangay<br>health<br>workers|(4)<br>Number of<br>barangay<br>health<br>workers|<br>Number<br>of<br>barangay<br>health<br>|(6)<br>Number of<br>barangay<br>health<br>projects|
|**_A: Including indicatorfor_**|<br>**_incumbent m_**<br><sup>~~*~~</sup>|<br>**_ayorfacing a ter_**|**_m limit_**||projects||
|Indicator = 1 for 3<sup>rd</sup><br>term mayor|0.019<br>(0.01)|0.01<br>(0.01)|-0.43<br>(0.82)|-0.44<br>(0.97)|-0.18<br>(0.29)|-0.05<br>(0.32)|
|Vote Buying|0.072<sup>~~***~~</sup>|0.06<sup>~~**~~</sup>|-4.93<sup>~~***~~</sup>|-4.69<sup>~~**~~</sup>|-1.14<sup>~~**~~</sup>|-1.33<sup>~~***~~</sup>|
|(% respondents|(0.027)|(0.03)|(1.81)|(1.90)|(0.58)|(0.62)|
|<br>reportingit in bgy)<br>**_B: Including average num_**<br>**_elections)_**|**_ber of candida_**|**_tes contesting i_**|**_n municipal el_**<br>|**_ections (1998,_**<br>|**_2004, and 2_**<br>|**_007_**<br>|
|Average Number of|0.003|0.01|-1.28<sup>~~*~~</sup>|-1.96<sup>~~***~~</sup>|0.51<sup>~~*~~</sup>|0.49<sup>~~*~~</sup>|
|<br>Candidates|(0.009)|(0.01)|(0.67)|(0.75)|(0.29)|(0.32)|
|Vote Buying<br>(% respondents|0.07<sup>~~**~~</sup><br>(0.03)|0.06<sup>~~**~~</sup><br>(0.03)|-4.16<sup>~~***~~</sup><br>(1.62)|-3.91<sup>~~**~~</sup><br>(1.79)|-1.25<sup>~~**~~</sup><br>(0.52)|-1.39<sup>~~**~~</sup><br>(0.59)|
|reportingit in bgy)<br>**_C: Including number of p_**|**_oliticians with_**<br>|**_whom mayor re_**<br>|**_portspolitical_**<br>|**_affiliation_**<br>|||
|Number of politicians|0.003|0.004|0.11|0.66|-0.16|-0.10|
|<br>affiliated with mayor|(0.006)|(0.008)|(0.56)|(0.76)|(0.21)|(0.23)|
|Vote Buying|0.07<sup>~~**~~</sup>|0.06<sup>~~**~~</sup>|-4.83<sup>~~***~~</sup>|-4.99<sup>~~***~~</sup>|-1.15<sup>~~*~~</sup>|-1.31<sup>~~**~~</sup>|
|(% respondents|(0.03)|(0.03)|(1.80)|(1.96)|(0.61)|(0.61)|
|reportingit in bgy)<br>**_D: Including margin of vi_**|**_ctory in 2007_**|**_municipal electi_**|**_ons(asproport_**|**_ion of total vo_**|**_tes)_**||
|Average Margin of|-0.02|-0.050<sup>~~**~~</sup>|-1.45|-0.57|-1.12<sup>~~*~~</sup>|-1.53<sup>~~**~~</sup>|
|<br>Victory|(0.02)|(0.02)|(2.18)|(1.76)|(0.68)|(0.75)|
|Vote Buying|0.057<sup>~~*~~</sup>|0.046<sup>~~*~~</sup>|-5.02<sup>~~**~~</sup>|-3.50<sup>~~*~~</sup>|-2.18<sup>~~***~~</sup>|-2.29<sup>~~***~~</sup>|
|<br>(% respondents|(0.032)|(0.03)|(2.05)|(2.17)|(0.65)|(0.77)|
|<br>reportingit in bgy)|||||||
|District Fixed Effects<br>_Each panel is a separate regress_<br>_additional political variable list_|No<br>_ion including all_<br>_ed in the panel_|Yes<br>_the variables in the_|No<br>_multivariate spec_|Yes<br>_ification listed in_|No<br>_Table 2a, plus_|Yes<br>_each_|

32
- 30 -

|Memo Items:<br>|||||<br>|
|---|---|---|---|---|---|
|Implied inflation (seemodel)<br>|||40.95%<br>|22.05%<br>|<br>5.68%<br> <br>|
|Implied real GDP growth<br>|||-2.49%<br>|-1.06%<br>|<br>2.10%<br>|
|Nominal GDP<br>|91 O0<br>||123972<br>|149711<br>|161528<br>|
|Exchange Rate (LE per USS)<br> <br>|2.Z1<br>|3.6<br>|3.6<br>|5.07<br>|6.19<br>|
|Goverrnent's<br>Good-8-Serv.<br>Expenditure|18468|22403|22403<br>|31578|38541|
|Extra Fiscal Effort<br> <br>|||5000<br>|||
|on good-&-serv.<br>expend.||*|0<br>|||
|on tax revenue<br>|||4000<br>|||
|on non-tax revenue|||1000|||

Sources: Egyptian Authorities, IMF, World Bank, and own calculations.

Notes: a/ After the EXTRA fiscal Effort b/ Before any EXTRA fiscal effort c/ Money is endogenous d/ Money exogenous, nominal subsidy pass-through endogenous
function of per capita GDP in that country-year. These regressions include country-specific intercepts, and country-specific linear, quadratic, or cubic time trends as follows: (7) Dimtc = α c + logGDPct + f c ( _t_ ) + ε _imtc_ Standard errors are adjusted for clustering at the level of the country-year observation; similar results are obtained when clustering at the level of countries or individual surveys.

We would expect the results from this one-step procedure to be very close to those from the two-step procedure that removes trends from the data, although there are some minor differences in estimation.<sup>9</sup> The first row in Table 2 shows that, reassuringly, estimates based on the one-step procedure imply that a one unit-change in log per capita GDP is associated with a - 17.22 to -44.61 change in mortality, compared to -18.03 to -40.95 for the corresponding estimates from the two-step procedure in Table 1.

# I. <u>Adjusting for changes in the composition of women giving birth</u>

In principle, there are two mechanisms that could account for a negative association between infant mortality and aggregate economic circumstances. First, it is possible that a child born to a woman of given characteristics is more likely to die if economic circumstances are unfavorable. Second, it is possible that the composition of women giving birth changes with economic circumstances. Clearly, these two causes for the countercyclical relationship between GDP and infant mortality—changes in mortality risk for a child born to a given woman or

> 9 One difference concerns the weighting of the data. In the two-step procedure in Table 2, observations are weighted by the survey weights provided in the DHS in the first step, and are then weighted by the inverse of the variance of the estimated coefficient on the year dummy in the second step. In the one-step results presented below, observations are weighted only by the survey weights provided in the DHS. Since the one-step procedure directly analyzes the unit data, it implicitly “weights” the estimates for a given country-year cell by the number of observations in this cell, N. This is closely related, but not equivalent to, the inverse of the variance. A second difference is that the two-step procedure removes separate trends from the GDP and infant mortality data, while the one-step regression only includes a single time trend for each country.

12
Table 7 Estimated Trabinng Effect on Employment - Male

# Trainees vs. Controls

|ItW-'E|g<br>.<br>e<br>,N0.<br>t,,<br>.<br>-. ...........<br>(spec, with 6duc<br>Dummse.)<br>Ud<br> <br>|.'S'.~~~~~~~~~~~re<br>.1i<br>(se~a. with continous<br>'|schoMaiale<br>|
|---|---|---|---|
||.<br>ncorrecte<br>9,1Se.<br>Corfictio,|Uncorrected|Sel. Corr,ectiodp|
|FROM UNWEIGHTED<br>PROBITS:<br>||||
|After 3 months<br>|.055'<br>.084'|.072-|.098-|
|After 6 months<br>|0<br>.055-|0|.077"|
|After 12 months|0<br>0|0|0|
|FROM WEIGHTED PROBITS':<br>||||
|After 3 months<br>|.1 15<br>.097-|.1 13*|.095"|
|After 6 months<br>|0<br>.095*|0|.10|
|After 12 months|0<br>.058|0|0|

* Difference between predicted employment probabilities due to training dummy. Estimated from probit equations shown in Tables --. probits. Column (21 is based on probits with selectivity correction.

Column (1) is based on uncorrected

> **_b_** Controls selected according to selection criteria used for trainees.

_e_ Sample weights are constructed to match proportion of trainees in total population of unemployed age 15-54 (19881. Near-Significant (.10) Significant (.05)
58 The Argentine central government currently has approximately $170 billion in debt (table 7).<sup>53</sup> This amount is up from $144 billion at the end of 2001 (MECON, 2003b). The increase of some $25 billion despite the absence of access to capital markets for new borrowing stems from interest arrears as well as from the debt burden undertaken by the government as a consequence of political decisions to shift some of the losses from the devaluation and pesification from households and firms to the government, as well as from nationalization of about $11 billion in provincial debt.

Table 7

|Composition of Argentina's Federal<br>|Governme|nt Debt|
|---|---|---|
|(billions of dollars)<br> <br>|<br>||
|Dec. 01<br> <br>|Dec. 02<br>|Jun 03 e|
|I. Performing|||
|Loans|||
|Multilateral<br>32.4|30.4|30.4|
|Bilateral<br>4.5|4.3|4.3|
|Banks<br>2.0|1.2|1.2|
|Other<br>1.5|0.3|0.3|
|Guaranteed loans (swap)<br>42.3|22.8|27.2|
|Compensation bonds|||
|Denom. in dollars|6.2|6.2|
|Denom. in pesos|7.3|8.7|
|Bonds to depositors|||
|Denom. in dollars|5.7|5.7|
|Denom. in pesos|0.1|0.2|
|Short term<br>6.7|||
|Subtotal, performing:<br>89.4|78.2|84.0|
|II. Nonperforming|||
|Bonds denom. in dollars<br>53.6<br>|60.4<br>|60.4<br>|
|Bonds denom. in pesos<br>1.5|4.9|5.9|
|Arrears:|||
|principal|4.3|4.3|
|interest|7.7|7.7|
|Provincial debt assumed|10.6|10.6|
|Subtotal, nonperforming:<br>55.1|88.0|88.9|
|III. Total<br>144.5|166.2|172.9|
|Memorandum: percent|||
|denominated in pesos:<br>23.3|21.2|27.3|
|Source: MECON (2003b); Marx (2003).|||
|Exchange rate: end 2002 = 3.36 p/$; June 20|03 = 2.82 p|/$.|

Approximately $65 billion in government bonds is in default (including interest arrears). Some $35 billion is owed to the international financial institutions ($14 billion to the IMF, about $8.5 billion each to the IDB and World Bank, and about $4 billion to bilateral creditors). The remaining $60 billion or so comprises the various blocks of 53 See Marx (2003); Broda (2003); and MacroVision Consulting (2003).
10

# **III. Country-Specific Analysis: The Role of External and Domestic Factors on Output Variability during 1992-1998**

In this section we examine, on an individual basis for the same set of eight countries, the relationship between output, external capital markets, TOT, real interest rates and real exchange rates during the period 1992-1998. The central objective is to determine the sources of output volatility during this period and to describe output's response to historical shocks in the rest of the variables. To do so, we estimated individual VAR models following the methodology proposed by Koop, Pesaran and Potter (1996). The first part of the section briefly describes the evolution of the main variables for each country. The second part summarizes the main points of the Generalized VAR methodology. The third part presents the results.

# A. **Behavior of the main variables during the 1992-1999 period**

# 1) External Factors

The terms of trade during this period showed a downward trend, aggravated by the Asian crisis that compressed commodity demand worldwide. Fig. _5_ shows for individual countries the terms of trade during the period.

Fig. 5a

**Terms of Trade**

<!-- Start of picture text --> 160<br>140<br>120<br>c 00<br>-Argentina<br>~~ 80 __________________ C hile<br>-- Brazil<br>>~ 6 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - M exico<br>40 40<br>HHHHH CU H HHHH HHH HHHH CC if HHHHH HHHHHHHHHHO - a<br>I c o I ,, I D I 5 D. I,I C I - '- G I at I C I C I '- a I Q D :<br><!-- End of picture text --> Capital flows to the region were also disrupted. The indicator we used, the sovereign spreads of Brady bonds (Figure 6 )11, shows that recovery after the Tequila shock was 1 The Brady Bond sovereign spreads are available for the whole sample period only for Argentina, Brazil and Mexico. For the other countries we used the Latin American aggregate (LATEMBI), from JP Morgan.
# **Free Riding in Loan Approvals: Evidence from SME Lending in Peru**<sup>*****</sup>

Irani Arraiz<sup>⁑</sup> , Miriam Bruhn<sup>§</sup> , Benjamin N. Roth<sup>‡</sup> , Claudia Ruiz-Ortega<sup>¶</sup> , Rodolfo Stucchi<sup>†</sup>

**JEL Classification:** D82, G21, O16

**Keywords:** Asymmetric information, credit market competition, access to credit.

> * We thank the Entrepreneurial Finance Lab and our partner bank for sharing their data and information, as well as Pierre Freundt from Equifax Peru for patiently answering our questions. We are also grateful to Sumit Agarwal, Abhijit Banerjee, Vivek Bhattacharya, Jishnu Das, Ralph de Haas, Quy-Toan Do, Xavier Gine, Daniel Green, Dean Karlan, Asim Khwaja, Bailey Klinger, Michael Kremer, Nicola Limodio, Ernest Liu, Soledad Martinez-Peria, David McKenzie, and Jonathan Zinman for helpful discussions and feedback. Financial support for this project was provided by the Strategic Research Program. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, IDB Invest, or the World Bank, their Boards of Directors, or the countries they represent.

⁑ IDB Invest § World Bank and IZA ‡ Harvard Business School ¶ World Bank † IDB Invest 1