rafmacalaba/datause-encoder-data huggingface dataset:
summarizes the research literature that analyzes either the PEFA indicators or the Enterprise Survey question on kickbacks in procurement. Section 3 explains the main hypotheses and rationale for various control variables. Sections 4-7 report empirical results, including for tests that disaggregate by firm size, and from a series of robustness tests. Section 8 builds the case for a causal interpretation of the main findings, while section 9 concludes.

# **2. Related Literature and Data Sources**

To ensure value for money, procurement systems are designed to generate competition among suppliers for contracts. Hoekman (1998: 255) cites evidence from several sources that noncompetitive procedures can increase costs by 30% or more, and asserts that “in most situations competition is the best rule of thumb” in procurement.

A larger number of bidders on a contract does not necessarily reflect stronger competition, depending on the particular auction mechanisms used, and on whether firms are colluding. In general, however, “the presence of numerous bidders is at least one indication of active competition for contracts” (Kenny and Crisman, 2016: 3). Costs generally fall with larger numbers of bidders, although the number of bidders needed to minimize costs (and the actual number of bidders on a typical contract) appears to vary by sector (Estache and Iimi, 2008). Using data on donor-financed road projects in developing countries, Estache and Iimi (2008) estimate that the average cost per kilometer of road construction is $700,000 USD when there are only two bidders but falls below $500,000 when there are six or more bidders for each contract. Rigid bidding systems that eliminate buyer discretion and mandate selection to the lowest-cost bidder reduce costs, but often at the expense of lower-quality works, goods or services (e.g. Cameron, 2000; Celentani and Ganuza, 2002; Decarolis, 2014).

Two studies exploit value thresholds to test the impact of more widespread advertising of contract tenders on bidding, using regression discontinuity designs. Using a database of World Bank financed contracts, Kenny and Crisman (2016) show that better advertising of contract tenders<sup>2</sup> increases the number of bidders. Coviello and Mariniello (2014) analyze Italian procurement auctions, where values above a certain threshold must be publicized in the Regional Office Gazette and two

> 2 Contracts above a certain threshold in value must be published in UN Development Business online, with bidding documents available in English, French or Spanish.

4
# **Macroeconomic Volatility after Trade and Capital Account Liberalization**<sup>_∗_</sup>

Cosimo Pancaro<sup>_†_</sup> European University Institute October 2010

**Abstract**

What are the equilibrium effects of trade and capital liberalization on conumption smoothing? This question is addressed by studying the response to productivity shocks in a baseline two country, two goods, incomplete market model, where foreign borrowing is secured by collateral. The paper shows that international financial integration, modeled by relaxing a borrowing constraint a la Kiyotaki in the domestic country, worsens consumption smoothing; international trade integration, modeled by a reduction of non linear iceberg transportation costs, improves it.

As a measure of consumption smoothing, the analysis uses the ratio between the simulated standard deviation of consumption growth and the simulated standard deviation of output growth. These results are qualitatively consistent with the empirical evidence provided by Kose, Prasad and Terrones (2003).

_Keywords:_ International business cycles, capital account liberalization, trade liberalization. _JEL Codes:_ E32, F41.

> _∗_ I am greatly indebted to my supervisor Giancarlo Corsetti for his guidance and advice. I would also like to thank A. Cunat, G. Duernecker, E. Faia, B. Gruss, A. Kose, H. L ̈utkepohl, K. Rabitsch, M. Ravn, as well as conference participants at the 2009 Spring Meeting of Young Economists and at the 2008 Macroeconomic and Financial Linkages: Theory and Practice Conference for their helpful comments. All errors are my own responsibility.

> _†_ Cosimo Pancaro, Department of Economics, European University Institute, Villa San Paolo, Via della Piazzuola 43, 50133 Florence, Italy. Email: cosimo.pancaro@eui.eu.

1
- Buchinsky, M., 1987a. Leads and Lags in Comodity Prices: An ADDlication of Cross-Spectral Analysis. World Bank International Commodity Markets Division Working Paper No. 987. Washington, D.C.

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- Choe, B.J., 1985b. The Outlook for Thermal Coal. World Bank Staff Comodity Working Paper No. 12, Washington, D.C.

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- Choucri, N., 1981. "Interdependence in the World Oil Market: Structure and Process." Energy Lab Working Paper, Massachusetts Institute of Technology, Cambridge, MA.

- Chung, C.H. and G. Ukpong, 1981. "The Word Tea Economy: An Econometric Model of Its Structure, Performance and Prospects," in World Bank Commodity Models, Volume I, World Bank.

- Cremer, J. and M. Weitzman, 1976. "OPEC and the Monopoly Price of World Oil." European Economic Review, 8: 155-64.

- Dammert, A. and 0. Palaniappan, 1985. Modelling Investments in the World Copper Sector. Austin: University of Texas Press.

- Dammert, A. and J. Chhabra, 1987. Long Term Prospects for the Lead and Zinc Industries. EPDCS Internal Working Paper. Washington, D.C.: World Bank.

- Farzin, Y.H., 1986. Competition in the Market for an Exhaustible Resource. Greenwich, CN: JAI Press.

- Ghosh, S., C.L. Gilbert and A.J. Hughes-Hallet, 1987. Stabilising Speculative Commodity Markets. Oxford: Oxford University Press.

- Gilbert, R., 1978. "Dominant Firm Pricing Policy in a Market for an Exhaustible Resource." Bell Journal of Economics, 9: 385-379.

47
**17**

The results in column (1) in Table 3 suggest a small positive effect of agriculture on nonagriculture, though it is imprecisely estimated. While it remains statistically insignificant when looking at the middle-income countries (column 2), it becomes larger and statistically significant for the low-income countries (column 4). Columns (3) and (5) introduce interaction terms of the GDP-variables with a SSA-indicator. For the middle-income countries the SSA sub-group shows a higher effect of agriculture (sign. at the 11%-level), while agriculture does not appear to affect nonagriculture in the SSA low-income countries (the coefficient on lagged agriculture and the interaction term basically cancel each other out).

The corresponding regression results looking at the effect of lagged non-agricultural GDP levels on agricultural GDP are presented in Table 4. As expected, rainfall shocks emerge as an important determinant of agricultural GDP, in particular in the low-income sub-sample. Yet we do not find any linkage effects from the non-agricultural sector to agriculture. Note that while the Sub-Saharan African interaction term is significant, the effect of non-agricultural growth on the agricultural sector in SSA (calculated as the sum of the basic coefficient and the interaction coefficient, and tested with an F-test) is not significantly different from zero in any of the regressions. Overall, these findings are consistent with the view that development in agriculture (Granger) causes on average development in non-agriculture in low-income countries, though not in the middle-income countries. We do not find evidence of a reverse effect.

To conclude, the micro-evidence from structural models and the cross-country regressions indicate that the indirect effects from fostering growth in agriculture are on average substantial, even though they tend to be lower in SSA than those found for Asian and Latin American countries. Second, while some recent evidence calls into question whether agricultural multipliers largely exceed the non-agricultural multipliers (Dorosh and Haggblade, 2003), virtually all studies (including the GMM analysis presented in this study) concur that the feedback effects from agriculture to non-agriculture are on average at least as large as the reverse effects. Finally, looking
|Net effect on Female 1 sd above mean|‐0.0359|‐0.107|0.0223|‐0.0453|‐0.262|‐3.337|
|---|---|---|---|---|---|---|
|P‐value|0.212|0.73571|0.995544|0.204|0.73571|0.995544|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,786|3,783|3,783|3,786|3,783|3,783|
|R‐squared|0.516|||0.516|||
|_Panel C: Children_|||||||
|Other worker ill|0.061|0.211|3.494|‐0.010|0.344|6.828|
||[0.045]|[0.324]|[3.012]|[0.097]|[0.561]|[5.585]|
|Other worker ill X Female|‐0.147**|‐0.577|‐7.910*|‐0.034|‐0.655|‐9.037|
||[0.069]|[0.467]|[4.613]|[0.109]|[0.675]|[6.798]|
|Number of children under 10 in household|0.024|0.154|0.44||||
|X Other worker ill|[0.031]|[0.208]|[1.854]||||
|Number of children under 10 in household|0.022|0.150|3.172||||
|X Other worker ill X Female|[0.037]|[0.323]|[2.612]||||
|Number of own children under 10||||0.052|0.056|‐1.270|
|X Other worker ill||||[0.043]|[0.277]|[2.759]|
|Number of own children under 10||||‐0.033|0.206|3.032|
|X Other worker ill X Female||||[0.042]|[0.329]|[3.143]|
|Net effect on Male 1 sd above mean|0.117|0.569|4.517|0.136|0.501|3.275|
|P‐value|0.030|0.101|0.142|0.013|0.226|0.452|
|Net effect on Female 1 sd above mean|0.020|0.342|3.987|0.010|0.424|2.723|
|P‐value|0.724|0.358|0.154|0.903|0.232|0.338|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,786|3,783|3,783|2,724|2,722|2,722|
|R‐squared|0.517|||0.538|||
|_Panel D: Marriage_|||||||
|Other worker ill|0.034|0.020|2.373||||
||[0.052]|[0.366]|[3.247]||||
|Other worker ill X Female|‐0.08|‐0.277|‐5.078||||
||[0.078]|[0.500]|[4.529]||||
|Other worker ill X Married|0.084|0.595|2.700||||
||[0.071]|[0.462]|[4.663]||||
|Other worker ill X Married X Female|‐0.074|‐0.228|0.864||||
||[0.102]|[0.661]|[5.987]||||
|Net effect on Married Male|0.118|0.615|5.073||||
|P‐value|0.013|0.037|0.110||||
|Net effect on Married Female|‐0.036|0.109|0.859||||
|P‐value|0.570|0.732|0.748||||
# **Annex 1 Data available for c the assessment indirect impacts**

# _Available data on the economic cost of disasters_

The emergency Events Database (EM‐DAT) maintained by the Center for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain, Belgium (http://www.emdat.be) is an important source of publicly available data on natural disasters. This database is compiled from diverse sources such as UN agencies, NGOs, insurance companies, research institutions and press agencies.

EMDAT defines a disaster as a natural situation or event which overwhelms local capacity and/or necessi‐ tates a request for external assistance. For a disaster to be listed in the EMDAT database, at least one of the following criteria should be met :

- 10 or more people are reported killed

- 100 people are reported affected

- a state of emergency is declared

- a call for international assistance is issued

Disasters can be hydro‐meteorological, including floods, wave surges, storms, droughts, landslides and avalanches; geophysical, including earthquakes, tsunamis and volcanic eruptions; and biological, covering epidemics and insect infestations (these are much more infrequent in this database).

The amount of damage reported in the database consists only of direct damages (e.g., damage to infra‐ structure, crops, and housing). The data report the number of people killed, the number of people af‐ fected, and the dollar amount of direct damages in each disaster.

Reinsurance companies surveys are also an extensive source of data but two main issues are met :

- the data are not publicly available, or only in a really aggregated fashion

- reinsurance companies are collecting data based on the losses insured, so biased towards coun‐

- tries in which insurance is well developed.

# _Data needs for econometric analysis_

For econometric analysis, long time series with sufficient numbers of events are required to have an ade‐ quate sample. Three main problems are met. First collection of data on natural disasters started to be precise and of large scope in the 90s. Earlier data collection is mainly available for large events, and data are less precise. Second, large natural disasters are, fortunately, scarce. This does not allow for a large sample necessary for econometric analysis.

Third, the socio‐economic baseline may be an important difficulty to distinguish effects of natural disas‐ ters from effects due to socio economic evolution. For instance, effects of natural disasters on economic growth may be difficult to disentangle from macroeconomic shocks, change in population, trends in ur‐ banization, development of utilities networks etc.

# _Data needs for model‐based approaches_

Model‐based approaches require large set of data (e.g., input‐output tables) on the affected economy, and detailed data on which sectors are affected by the disaster. At regional scale, it is particularly rare to 28
computers in class. Finally, the questionnaire of the school was answered by the principal or the coordinator of the school. It includes general variables at the school level.

# **C.  Statistical Models**

Because the treatment was randomly assigned, we can assess the causal effects of the program by directly comparing the average outcomes of students in the treatment group with students in the control group. To do this, we employ three statistical models estimated through ordinary least squares. First, we use a simple difference model of the following form: The variable _Yij_ in this specification is the variable whose average value is to be compared between the two groups at the child (i), school (j) level. The variable _T j_ is a dummy variable for whether or not school j was selected for treatment in the randomization process. The coefficient β 1 then provides the estimated differences in the variable _Yij_ between the treatment and control group. This specification is primarily used for two purposes: to compare the treatment and control group using information collected in the baseline survey to check for comparability and to conduct simple comparisons of outcome variables (without controlling for baseline characteristics).

Equation (1) can also be used to estimate the treatment effects, but we can improve the precision with which we estimate the treatment effects by controlling for

- 11 -
higher levels of schooling than farmers' daughters. They had about one more year of schooling. The magnitude of this effect seems to have been relatively stable across cohorts. Second, daughters of mothers with no jobs have significantly more education than farmers' daughters. However, the point estimate for the youngest cohort suggests that this difference may have narrowed, supporting the view that access to education became more equitably distributed over time.

In contrast, the coefficient estimates for 'father is a whitecollar worker," which are higher, increased significantly from the oldest cohort up to cohort 1950-54, and declined thereafter. These indicate a widening of the schooling gap, up to cohort 1950-54, between females whose fathers were white-collar workers and those whose fathers were farmero. This gap is widest for th3 cohorts born in 1945-54 who were the first potential beneficiaries of the early expansionist programs for primary education. However, as attitudes towards the education of females changed in the rural areas, the gap narrowed among the younger cohorts. Our results fcr males also indicate that those whose fathers held white-collar or blue-collar jobs had an edge over farmers' sons. That edge increased first during the expansionist programs but it eroded earlier than it had for females as well.

In general, the results from the cohort regressions on the effects of parental background indicate that the education policies of the mid-SOs and 60s had equalizing effects across broad segments of the population. However, the initial impact of relaxing the supply constraint through building more schools was to worsen inequality among different groups who had divergent 40
program—amounted to 1.9 percent of GDP. This stimulus was complemented by three packages in three consecutive months starting in December 2008, each valued at about 0.6 percent of GDP. The aggregate fiscal stimulus amounted to 3.6 percent of GDP, consisting largely of expenditure measures (2.9 percent), including additional borrowing permitted for the state governments. In addition, there was the automatic impact of the decline in government revenues by 1.9 percent of GDP in 2009 and another 0.5 percent in 2010.

Current expenditures accounted for more than 85 percent of expenditure measures in India. Only a minority of the major states also increased capital spending. In all states taken together, real capital expenditure remained unchanged in 2009 and rose by 5 percent in 2010, less rapidly than national GDP (table 6).

In Brazil, fiscal expansion at the national level was partly counteracted by fiscal contraction by subnational governments in 2009. The fiscal deficit of the central government expanded 3 percentage points of GDP, reflecting both the operation of automatic stabilizers and discretionary stimulus measures, including temporary tax reductions and spending increases.

However, in 2009 the fiscal deficit of subnational governments (states and municipalities combined) contracted 1.3 percentage points of GDP, turning into a small surplus. The net effect was a fiscal expansion of only 1.7 percentage points.

# **Table 7 Real Annual Changes in Subnational Government Expenditures in Brazil, 2003–09 (percent)**

|_Expenditure_|_2003–07_|_2008_|_2009_|
|---|---|---|---|
|Education and culture|4.2|10.2|3.6|
|Health and nutrition|8.7|8.5|9.0|
|Social programs|8.1|20.0|14.1|
|Transport|8.1|21.7|15.0|
|Housing and urban|5.8|22.2|–7.9|
|Capital investment|7.5|33.8|0.1|

Sources: National Treasury Secretariat, Government of Brazil; and authors’ calculations.

As part of its policy response to the global crisis, the federal government raised the permissible borrowing limits for those states that met agreed fiscal targets; taken together, the 22
# **Table 4. WLS Estimates of the Wage Equation** **<u>(weight= employment by industry in 2007)</u>**

|Explanatory Variables:|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Depende|nt Variable:|Change in|logwages|of stayers|
|Imports from the U.S.|0.008*<br>(0.004)|0.006<br>(0.004)|0.006<br>(0.004)||0.007**<br>(0.003)|
|Exports to the U.S.|0.010**<br>(0.004)|0.011***<br>(0.004)|0.007*<br>(0.004)|0.007**<br>(0.003)||
|One-period ahead: Imports from the U.S.||-0.002<br>(0.004)|-0.003<br>(0.003)|||
|One-period ahead: Exports to the U.S.||0.003<br>(0.003)|0.004<br>(0.004)||0.001<br>(0.004)|
|Related imports from the U.S.|||0.001<br>(0.030)||0.018<br>(0.035)|
|Related exports to the U.S.|||0.140<br>(0.091)|||
|One-period ahead: Related imports from the U.S.|||0.049*<br>(0.027)|||
|One-period ahead: Related exports to the U.S.|||-0.002<br>(0.036)||0.003<br>(0.009)|
|Crisis * Exports to the U.S.||||0.012<br>(0.009)||
|Observations|1,133|1,028|1,025|1,133|1,027|
|R-squared|0.553|0.554|0.571|0.552|0.548|
|Robust standard errors in parentheses; errors are c<br>*** p<0.01, ** p<0.05, * p<0.1<br>Time effects are included but not reported. Crisis|lustered by<br>dummy equ|industry.<br>als one duri|ng 2008q4|and 2009q|1.|

50
# **8. Economic Significance of the Estimated Effect**

To summarize the results from our regressions, this section briefly discusses the economic significance of the estimated effect of Basel III on SME financing. To this end, we gauge the marginal effects from the estimated baseline models in tables 13 and 14 (column (4)) around the mean of the independent variables as well as the dependent variables. We do so by running simple ordinary least squares on the column (4) specifications in tables 13 and 14. This approach offers a range for the effect that could be more informative about the average economic significance of our estimates.

The economic effect of Basel III implementation on SME access to financing in EMDEs can range between negative 0.244 and negative 0.593. The SME access to financing variable has a mean of 3.43 in our sample and a standard deviation of 1.31. These values imply that Basel III implementation could, on average, increase the access to finance constraints by about one-third of the standard deviation. Although this effect could be less economically important for countries such as Turkey and the Philippines, with the best financial access in our sample (values of 4.3 and 4.2 out of 5), it may be more important for countries such as Kenya and Ghana, with the worst access (2.4 out of 5). For instance, Basel III implementation could, keeping other things constant, push Peru (3.8) back to the level of Ukraine (3.4), or Thailand (3.4) back to the level of Argentina (3.0). On economic significance, we assess the short-run effect of Basel III implementation on SME financing in EMDEs as moderately negative.

# **9. Concluding Remarks**

This paper studied the effect of Basel III implementation on SME financing in EMDEs. The literature on the consequences of implementing (global) financial regulations, including Basel III, is burgeoning (Boissay et al. 2019). But it typically centers around the regulatory effect on overall bank lending or economic activity. This paper contributes to the scant and recently growing literature studying the effect of Basel III on SME financing—an effect in which policy makers are highly interested.

We used firm-level data from the World Bank’s Enterprise Surveys as the core data set and merged it with several other data sets containing bank as well as country characteristics. We analyzed 37