rafmacalaba/datause-encoder-data huggingface dataset:
- Baltagi, B. H. and P. X. Wu (1999): Unequally spaced Panel Data Regressions with AR(1) Disturbances, _Econometric Theory_ , **15** , 1999, 814–823+ Printed in the United States of America.

Baltagi, Badi H. (2001): Econometric Analysis of Panel Data. Wiley, John & Sons.

- Barth, J, M. Lea and T. Li (2012): China’s Housing Market: Is a Bubble about to Burst?, Milken Institute, Oct., 2012.

- Breusch, T. S., and A. R. Pagan. (1980): "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics." _Review of Economic Studies_ , 47(1):239-253.

- Baxter, M. and M. Crucini (1993): Explaining Saving-Investment Correlations, American Economic Review, vol. 83, Issue 3, pp-416-436.

- Case, K. E, John M. Quigley and Robert J. Shiller (2011): Wealth Effects Revisited, 1978-2009, Cowles Foundation Discussion Paper No. 1784, February, 2011.

- Chen, J., Guo, F. and Zhu, A. (2011):  The housing-led growth hypothesis revisited: evidence from the Chinese provincial panel data, Urban Studies, 48(10), pp. 2049–2067.

- Collier, P and A. J. Venables (2013): Housing and Urbanization in Africa: unleashing a formal market process, Chapter 15 in Glaeser, E. and A. Joshi Ghani edited “Rethinking Cities”, World Bank, forthcoming

Collier, P. (2014) “Building African Cities that Work”. Mimeo.

- Davis, J.C. and J.V. Henderson (2003), Evidence on the political economy of the urbanization process, Journal of Urban Economics 53 (2003) 98–125.

- Dipasquale, D. (1999):  Why don’t we know more about housing supply (1999), Journal of Real Estate Finance and Economics, 18:1, 9±23 (1999)

- Drukker, D. M. (2003):  Testing for serial correlation in linear panel-data models.  Stata Journal (3)2: 168-177.

- Economist Intelligence Unit (EIU) (2011):  Building Rome in a day: the sustainability of China’s housing boom. Report, Access China Service, The Economist Intelligence Unit, London.

- Feldstein, M. s.  and Horioka, C. Y (1980): Domestic Savings and International Capital Flows, Economic Journal, vol. 90, pp314-29.

- Ghai, D.  and C. Hewitt de Alcántara (1990): The Crisis of the 1980s in Sub-Saharan Africa, Latin America and the Caribbean: Economic Impact, Social Change and Political Implications, _Development and_ Change, Vol. 21, (1990). 389-426.

- Giddings, S. W (2007): Housing Challenges and Opportunities in Sub Saharan Africa, International Housing Coalition, Washington DC.

- Hansen, C., B. (2007): Generalized Least Squares inference in Panel and Multilevel Models with Serial correlation and Fixed Effects, Journal of Econometrics, Vol. 140, Issue 2, October

30
Both surveys show similar trends in consumption at the sub‐aggregate levels, except for education and miscellaneous expenditures, but with the variations being much larger in the HBS. This can be partly explained by the changes in the survey design in the HBS. It is worth mentioning that when addressing the changes in the HBS design through imputation methods, we still observe a significant increase in the consumption of food and non‐food items.

**Table 9: Comparison of monthly non‐food consumption by sub‐group in Tanzania Mainland**

|||**HBS**||||**NPS**|||
|---|---|---|---|---|---|---|---|---|
||||||||**Variation**|**Variation**|
||**2007**|**2011/12**|**Variation**<br>**%**|**2008/09**|**2010/11**|**2012/13**|**2010/11–**<br>**2012/13**|**2008/09–**<br>**2012/13**|
|**Non‐food consumption per**<br>Non‐food consumtion|**adult equi**|**valent, mont**|<br>**hly**||||<br>**%**|<br>**%**|
|p,<br>nominal|15,707|27,831|77%|14,763|18,010|25,227|40%|71%|
|Non‐food consumption,<br>real|15,942|27,162|70%|14,755|18,016|24,974|39%|69%|
|Non‐food consumption,<br>|26,432|27,162|3%|17,661|18,016|19,861|10%|12%|
|real, inter‐year (CPI)<br>Non‐food groups, monthly pe<br>Alcohol tobacco and|<br>r adult equi|<br>valent consu|mption, nom|<br>inal|||||
|,<br>narcotics|795|305|‐62%|1,365|1,566|1,705|9%|25%|
|Clothing and footwear|3,012|4,234|41%|‐|‐|‐|||
|Housing, utilities and fuels|2,735|7,827|186%|1,819|2,665|3,711|39%|104%|
|Household maintenance|1,897|1,224|‐35%|1,019|1,118|1,437|29%|41%|
|Health|646|1,977|206%|2,022|2,131|3,223|51%|59%|
|Transport|1,629|4,804|195%|2,801|3,498|5,348|53%|91%|
|Communication|886|3,647|312%|1,896|2,242|3,370|50%|78%|
|Recreation and culture|822|1,510|84%|50|60|64|7%|29%|
|Education|688|1,105|61%|2,159|2,907|3,814|31%|77%|
|Restaurants and hotels|705|351|‐50%|‐|‐|‐|||
|Miscellaneous|1,890|846|‐55%|1,632|1,826|2,556|40%|57%|
|Non‐food groups, monthly pe<br>|r adult equi|valent consu|mption, real||||||
|Alcohol, tobacco and<br>narcotics|815|309|‐62%|1,411|1,611|1,729|7%|23%|
|Clothing and footwear|3,064|4,180|36%|‐|‐|‐|||
|Housing, utilities and fuels|2,783|7,687|176%|1,771|2,603|3,594|38%|103%|
|Household maintenance|1,932|1,202|‐38%|1,041|1,137|1,445|27%|39%|
|Health|659|1,979|200%|2,082|2,179|3,234|48%|55%|
|Transport|1,631|4,596|182%|2,765|3,479|5,272|52%|91%|
|Communication|887|3,517|297%|1,857|2,213|3,329|50%|79%|

33
et al., 2000). More generally, foreign banks in Latin American countries (Argentina, Brazil, Chile, Mexico, Peru, and Venezuela) showed more robust loan growth, a more aggressive response to asset deterioration, and greater ability to absorb losses than did domestic banks during this period (Crystal et al., 2001, 2002).

In case studies of the Asian financial crisis, foreign banks were not a major stabilizing force, but again this could be because they did not constitute a large share of the banking sectors of these countries. In Korea, foreign banks reduced their lending during the crisis while domestic banks did not (Jeon et al., 2006). A study of Malaysian banks from 1996 to 2001 comes to more nuanced conclusions (Detragiache and Gupta, 2006). Domestic and foreign banks that were mainly active in Malaysia had substantially lower profits and more non-performing loans than diversified foreign banks that were not specialized in Asia. The authors speculate that Malaysian subsidiaries of diversified banks received support from parent banks during the crisis and that their clienteles differed from those of Malaysian-focused banks. Support from parent banks did, therefore, play a small stabilizing role during the crisis in Malaysia.

More recent evidence also sheds light on how foreign banks’ lending patterns promote stability in host country banking sectors. A study of banks and firms in 13 countries in Eastern Europe from 2000 to 2005 shows that lending relationships for foreign banks tend to be more stable than those of domestic banks in that foreign banks are less likely to drop their clients, even in the aftermath of an acquisition (Giannetti and Ongena, 2009b). Over time, however, competition from foreign banks has produced changes in the lending policies of domestic banks, making their lending relationships more stable and generally improving access to credit for all firms. A study of 91 countries from 1995 to 2003 finds that foreign bank presence is associated with real growth in industrial value added and with relatively lower volatility in that measure during crisis periods (Bruno and Hauswald, 2009). In that sense, foreign banks are seen as a stabilizing force, though the authors admit that these effects are more pronounced in middle and high-income countries than in low-income countries.

And yet there are indications that foreign banks can at times transmit economic shocks from their country of origin to host countries. For example, from 1989 to 1996 the Japanese banking crisis negatively affected the supply of real estate loans in U.S. states with high levels of 14
9

# **4 Poverty Simulation Methodology**

# **4.1 Basic Approach**

This section briefly sketches the approach used in the main part of the analysis. A fuller treatment is given in Appendix 1 of Datt and Walker (2002) and in Datt et al. (2003). The general approach is to simulate changes in consumption by applying annual GDP per capita growth rates to unit-record household survey data. For this application, consumption is defined in per adult equivalent terms. Allowance can be made for varying population and GDP growth rates at the sectoral/regional level. The simplest version assumes that growth is distribution neutral, i.e. that inequality is unchanged. Alternatively, we can incorporate estimates of changes in inequality levels in the simulation.

The discussion presented here is done going forwards from the 1991/92 survey data. It is also possible to project the simulations backwards from the 2000/01 survey. Except for one case included in the appendix, the simulations conducted for this paper are done going forwards.

# _Notation_

Define the following terms for a sample of _n_ households:

- <sup>_c_</sup> _i_ , _t_<sup>is per adult equivalent consumption for sample household</sup><sup>_i_in year</sup><sup>_t_, where</sup><sup>_t=0_</sup> represents the survey year

- <sup>isthe individual weight for household</sup><sup>_i_in year</sup><sup>_t._These weights are the product of the</sup>

- <sup>_w_</sup> _i_ , _t_ household sampling weights (the inverse of the probability of selection of the household in the survey) and household size.

- the sector (or, alternatively, region) of household _i_ in the survey year is given by<sup>_s_</sup> _i_ , _t_<sup>_._This is</sup> assumed fixed over time, but we do allow for population shifts across sectors.

- <sup>_S_</sup> _i_

- •<sup>_g_</sup> _t_ is the real GDP growth rate for the sector of household _i_ in year _t._

- <sup>_S_</sup> _i_

- •<sup>η</sup> _t_ is the population growth rate for the sector of household _i_ in year _t._

# _Basic projections_

The basic form of the projections is to calculate per adult equivalent consumption recursively, while adjusting the weights for population changes year-by-year:
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ (Table 13, continued)

||FIRMS<br>|||
|---|---|---|---|
||Mean|Median|Observations|
||Botswana (2006)<br>8,124|5,165|113|
||Gambia (2006)<br>2,817|2,405|33|
||Ghana (2007)<br>2,115|1,417|313|
|**e**|Kenya (2003)<br>10,161|2,583|231|
|**hon**|Namibia (2006)<br>8,565|5,679|104|
|**lop**|Swaziland (2006)<br>6,520|4,159|69|
|**Ang**|Tanzania (2003)<br>3,328|1,262|215|
||Tanzania (2006)<br>4,373|2,727|272|
||Uganda (2003)<br>2,114|1,487|276|
||Uganda(2006)<br>4,017|2,540|307|
||Total<br>3,269|1,913|1,933|
||Benin (2004)<br>2,994|1,662|244|
||Burkina Faso (2006)<br>5,078|3,522|50|
||Burundi (2006)<br>3,131|1,924|102|
||Cameroon (2006)<br>8,691|5,390|118|
|**ne**|Cape Verde (2006)<br>6,424|3,403|47|
|**ho**|DR Congo (2006)<br>2,615|1,888|149|
|**cop**|Guinea (2006)<br>3,790|1,406|135|
|**ran**|Madagascar (2005)<br>10,991|7,845|217|
|**F**|Mali (2003)<br>2,899|1,616|135|
||Mauritania (2006)<br>5,469|4,606|80|
||Rwanda (2006)<br>4,793|2,867|59|
||Senegal(2003)<br>4,598|2,295|205|
||Total<br>4,853|2,472|1,541|

*: Annual wage for a worker is computed as follows. The worker is asked the unit of payment (hourly, daily, etc.), the amount paid by unit (in current LCU), and the number of hours worked per week. First, we compute the monthly wage on the basis of the payment unit, the amount, and the number of hours worked per week (where relevant). Then, we multiply this number by the implied months worked per year, on the basis of the hours worked per week (assuming workers work on average 48 weeks per year). For firms, we take the total annual labor cost (in current LCU) and divide it by the number of employees (permanent and temporary). All wages are then transformed into USD-PPP (PPPs are from 2005). Source: Authors' calculations from Enterprise Surveys

40
41 below). In 1991 for instance, 24% of laid off people found a new job without registering as unemployed. Moonlighting in the service sector, that existed extensively before transformation, may just be transformed into a legal or semi-legal small business, if the credit constraint is surmountable. If the new activity of a job leaver is illegal (e.g. working abroad), the worker may officially leave the labour force altogether (see discussion below).

A further factor which might explain smaller inflows into unemployment relative to other transition economies is the small share of agricultural employment in the Czech Republic at the beginning of the reforms. In transition economies unemployment rates are higher in rural than in urban areas. One explanation for this could be larger restructuring needs of the agricultural sector implying more labour shedding and larger inflows into unemployment in rural areas. Because of a relatively low share of agricultural employment the Czech Republic might then have lower inflows into unemployment than other transition economies (OECD, 1995).

# 3.3.3 The Czech labour market: outflow out of unemployment into employment

Also according to Boeri (1994) average monthly outflow rate out of unemployment in 1992 was 24.8%. That is the highest outflow rate in CEE countries. By western European standards this is also a high outflow rate.

It is generally believed that the private sector in the CR is more dynamic than in other CEE countries and that it its activity is underestimated by the official statistics (Ham et al., 1993 and Benacek, 1994). In almost all transforming economies in the region demand for services was unsatisfied or satisfied by moonlighting, so that the opportunities for small enterprises in this sector are ample. But most probably potential entrepreneurs in all CEE have considerable difficulties to acquire the capital necessary for a start. In the CR, however, real wages are relatively low, which contributes to private sector development. First it makes any kind of enterprise less costly and thus lowers the barrier to entry due to lack of capital. It also makes tourism, a sector booming tremendously in the CR, even more attractive. Secondly, it attracts foreign firms to set up new enterprises in the CR or to subcontract with Czech firms. Thirdly, low wages turn to be privatised firms into more lucrative deals, while too high real producer wages might cause the net present value of many to be privatised firms to be negative (cf. Akerlof et al., 1991). Another, more general reason for the more dynamic private sector growth in the
WPS4133

# Fungibility and the Flypaper Effect of Project Aid: Micro-Evidence for Vietnam

## Dominique van de Walle and Ren Mu<sup>*</sup>

Research Development Group, World Bank

### **Abstract**

While most economists assume that aid is fungible, most aid donors behave as if it is not. Recipient government responses to development project aid are studied in the context of a specific World Bank-financed project. We estimate the impact of a rural road rehabilitation project in Vietnam on the kilometers of roads actually rehabilitated and built. Using local-level survey data collected for this purpose, we test whether the evidence supports the standard economic argument that there will be little or no impact on rural roads rehabilitated, given fungibility. We find evidence that, although project aid impacts on rehabilitated road kilometers were less than intended, more roads were built in project areas. Our results suggest that there was fungibility within the sector, but that aid largely stuck to that sector.

JEL _classification_ : O19; O12; F35; _Key words_ : Impact evaluation, fungibility of aid, flypaper effect, rural roads, Vietnam World Bank Policy Research Working Paper 4133, February 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ. worldbank.org._

* We are grateful to Vu Tuan Anh, Christina Malmberg-Calvo, Dorothyjean Cratty, Binh Nguyen and Martin Ravallion for their encouragement, help and support.  We received useful comments on this paper from Jean-Marie Balland, Kathleen Beegle, Michael Clemens, Hai-Anh Dang, Jishnu Das, Quy-Toan Do, John Giles, Elizabeth King, Martin Ravallion, participants at the 2004 NEUDC meetings in Montreal and at the 2006 “Effectiveness of Aid to Poor Countries” conference in Namur, and the journal’s two anonymous referees and editor.  We gratefully acknowledge funding support from CIDA (TF034859), the World Bank’s East Asia and Pacific Transport Unit and the Poverty and Social Unit in the Vietnam Resident Mission Office, and the DFID trust fund for Poverty Analysis and Policy Advice. Address for correspondence: Dominique van de Walle, World Bank, 1818 H ST, NW, Washington, DC 20433. tel: 202-473-7935, <u>dvandewalle@worldbank.org.</u>
with the businessman as the “line player” to whom the first entry in each cell corresponds and the bank as the “column player” to whom the second entry in each cell corresponds.

**<u>Table 4: Game matrix</u>**

|||Ba<br>N|nk<br>C|
|---|---|---|---|
|Bi|L|21.43 / 28.57|29.14 / 20.86|
|usnessman|H|57.14 / -14.29|30.86 / 12.00|

The Nash equilibrium is given with the combination H/C, where the businessman chooses project H at an expected cash flow of 30.86 and the bank offers contract C at an expected cash flow of 12.00. Neither the businessman nor the bank will deviate from this choice as a deviation for either would lead to lower expected cash flows (29.14 and -14.29, respectively). Taking strategic considerations into account, the businessman thus chooses project H and not, as was calculated under the efficiency approach, project L.

This erroneous solution under the efficiency approach clearly results from negligence of the credit contracts, which determine the distribution of payoff between the two players. Only in cases when nothing is to be distributed does this approach make sense, such as in the case of 100 percent equity financing. However, in the case of debt financing, as in our example, the bank bears part of the costs and therefore the businessman has incentives to choose the high risk project (H). That is exactly what happened, ultimately leading to the financial crisis.

Note that statistical data are needed in both models to calculate the expected cash flows per player in each action combination. However, whereas in the efficiency approach the mere existence of statistical data is sufficient, the strategic approach goes much further by allowing each player to _actively_ make a decision. This decision considers the distribution of cash flows between the players as determined by the distribution rules. Distribution rules create incentives that can only be captured by applying strategic considerations. However, it is also important to note that the strategic approach no longer leads to an optimum from a social point of view since the project with the lower NPV is selected.

8
GFDL Model <!-- Start of picture text --> 0.06<br>0.05<br>=== Current<br>wee Future<br>> 0.04<br>a<br>4<br>@<br>s<br>£ 0.03<br>=<br>©<br>2<br>©<br>a 0.02<br>0.01<br>02<br>67 8 9 10 11 12 13<br>Log,, Damage (U.S. Dollars)<br><!-- End of picture text -->
**Figure 5 Composition of Foreign Assets and Liabilities Reserve and Non-Reserve Assets to GDP**

<!-- Start of picture text --> 140%<br>120% Non-Reserve Assets<br>100%<br>Reserve Assets<br>80%<br>60%<br>40%<br>20%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>Debt and Equity Liabilities to GDP<br>140%<br>120%<br>Equity Liabilities<br>100%<br>Debt Liabilities<br>80%<br>60%<br>40%<br>20%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br><!-- End of picture text --> East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. Source: Authors' calculations drawing on the dataset constructed by Lane and Milesi-Ferretti (2006).
_Table 6: Double-difference estimates of VHI impact on adult BMI_

|Age in<br>1992/93|Per capita<br>consumption<br>quintile in<br>1992/93|<br>Treatment<br>effects|z-stat|F-test consumption<br>quintile differences|Prob> F|
|---|---|---|---|---|---|
|18+|all|0.303|2.91|F(4, 1358) = 6.18|0.000|
|18+|1|-0.425|-2.02|||
|18+|2|0.049|0.26|||
|18+|3|0.302|2.09|||
|18+|4|0.168|1.31|||
|18+|5|0.617|6.15|||

# VI. **HEALTH SERVICE UTILIZATION**

An obvious channel through which VHI impacts on nutritional status is increased use of health care. Using the VLSS panel to investigate this possibility is not straightforward because the wording of the health utilization questions changed significantly from one wave to the next. We adopt two approaches: in the first, we use the panel and do the best with what is available; in the second, we focus on the 1998 wave and estimate VHI impacts via single-difference matching (i.e. comparing _levels_ of utilization of the insured and matched uninsured, rather than _changes_ in them).

In the 1992/93 VLSS, respondents who reported illness or injury during the previous 4 weeks were then asked whether a contact with _any_ health service provider had occurred during the same period. Examples of different types of provider were read out, but they were simply examples. Respondents were then asked who the _first_ contact was with, but they were not asked whether there were any further contacts with the same provider or with any other provider. In the second wave, _all_ respondents—whether or not they had reported any recent sickness in a preceding question—were taken through a closed list of different types of provider, and asked in each case whether a contact had occurred in the previous 4 weeks, and if so how many times the provider in question had been consulted. The different approaches in the two years make comparisons across years difficult, but what can be done is to construct a variable in both waves indicating whether someone reporting sickness made a contact with _any_ provider (World Bank et al. 2001).

The results in Table 7 suggest that VHI increased the probability of a sick person contacting a provider among the sample as a whole and for individuals aged five and over, but not for children under the age of five. These results are of course limited by the limited usefulness of the utilization variable. It does not capture the number of visits, simply the probability. It excludes preventive visits, quite possibly a route via which VHI coverage translates into better outcomes for younger children. And there is the possibility that in 1997/98 some visits were inadvertently omitted because of the closed list of provider types. The list is extensive, but it does not include explicitly any visits to a school clinic. Given the fact that VHI 14