rafmacalaba/datause-encoder-data huggingface dataset:
appendix) shows the yearly number of (treated and untreated) exporting firms.

||Sector (CNAE 2.0)|Numbe|r of frms-|year||Share (%)||
|---|---|---|---|---|---|---|---|
|||Total|Export|Apex|Total|Export|Apex|
|10|Food|111,209|3,499|1,588|11.75|7.42|8.50|
|11|Beverages|6,946|398|254|0.73|0.84|1.36|
|12|Tobacco|514|105|0|0.05|0.22|0.00|
|13|Textiles|32,163|1,743|890|3.40|3.70|4.76|
|14|Wearing apparel|163,472|2,687|3,835|17.27|5.70|20.52|
|15|Leather|44,489|2,616|1,814|4.70|5.55|9.71|
|16|Wood and cork|54,416|2,457|343|5.75|5.21|1.84|
|17|Paper|14,158|732|182|1.50|1.55|0.97|
|18|Printing|42,627|339|188|4.50|0.72|1.01|
|19|Refned petroleum|1,566|245|61|0.17|0.52|0.33|
|20|Chemicals|26,652|3,497|816|2.82|7.42|4.37|
|21|Pharmaceutical|3,054|698|95|0.32|1.48|0.51|
|22|Rubber and plastics|48,569|3,686|1,007|5.13|7.82|5.39|
|23|Non-metallic|75,631|1,965|850|7.99|4.17|4.55|
|24|Basic metals|15,146|1,383|337|1.60|2.93|1.80|
|25|Metal products|108,889|3,079|1,043|11.50|6.53|5.58|
|26|Computer, electronic|10,952|2,133|533|1.16|4.52|2.85|
|27|Electrical equipment|14,060|1,893|278|1.49|4.01|1.49|
|28|Machinery and equip.|42,500|6,488|1,441|4.49|13.76|7.71|
|29|Motor vehicles|17,167|2,423|450|1.81|5.14|2.41|
|30|Other transport|3,185|338|56|0.34|0.72|0.30|
|31|Furniture|56,999|1,670|1,344|6.02|3.54|7.19|
|32|Other manufact.|25,667|2,651|1,181|2.71|5.62|6.32|
|33|Repair machinery|26,550|428|101|2.80|0.91|0.54|

Table 2: Sectoral distribution - Manufacturing, Exporting, Treated firms (2007-2010) Tables 1 and 2 show that the distribution of manufacturing firms according to their exporting and Apex’s treatment status are very heterogeneous among sectors and regions. This heterogeneity should be taken into account, since there are common shocks (e.g. exchange rate, labor market, interest rate, infrastructure, etc.) that might have a heterogeneous impact across firms in different sectors and location. Next, I compare firms’ characteristics that vary over time and provide some descriptive statistics for those variables that have been highlighted as important in explaining export performance in the trade literature.

Table 3 shows the number of firms’ observations (N), from 2007 to 2010, their mean, the standard deviation (sd), the median (p50) and the percentiles (p10) and (p90), for a set of variables used for the empirical analysis. The number of employees is available on RAIS as the number of contracts the firm has in a year _t_ . This variable considers the period a worker was hired by the firm over the year. In this case a worker is weighted as 1 only if she was hired over 12 months in a year. Otherwise she is weighted proportionally to the time she was hired. Firm’s age is a proxy for the age of the firm that takes into account the maximum period of employees’ experience working at firm _i_ available in RAIS over time. Employee’s age is the average age 9
of Issues”. Oxford Economic Papers 32.1 (1980): 8298.

Berman, N., P. Martin, and T. Mayer (2012): “How do different exporters react to exchange rate changes?”, Quarterly Journal of Economics, 127, pp. 437492.

Biglaiser, G (1993), “Middlemen as Experts”, Rand Journal of Economics, Summer 1993.

Bonnet, C., P. Dubois, S. Villas-Boas, and D. Klapper (2013): “Empirical evidence on the role of nonlinear wholesale pricing and vertical restraints on cost pass-through”, Review of Economics and Statistics, Vol. 95, No. 2, pp. 500515.

Braverman, A and J. Stiglitz (1982), “Sharecropping and the Interlinking of Agrarian Markets”. American Economic Review, 72, 695-715.

Burkart, M, and T. Ellingsen (2004), “In-Kind Finance: A Theory of Trade Credit”, American Economic Review, June 2004.

Burstein, A., and G. Gopinath (2013): ‘International prices and exchange rates’, Handbook of International Economics, Volume IV, Elsevier Carpenter, F., 1866. Six Months at the White House with Abraham Lincoln. Hurd, New York. Casaburi L., Rachel Glennerster and Tavneet Suri (2013), Rural Roads and Intermediated Trade: Regression Discontinuity Evidence from Sierra Leone, Working paper, Stanford University. Chau, Nancy H & Goto, Hideaki & Kanbur, Ravi, 2009. “Middlemen, Non-Profits and Poverty,” CEPR Discussion Papers 7459, C.E.P.R. Discussion Papers.

Emran, M. S, D. Mookherjee, F Shilpi, and H. Uddin (2015), Trading Margins and Contracts in a Vertical Marketing Chain: Evidence from Edible Oils Market in Bangladesh, Working Paper, World Bank.

Fafchamps, M, & Ruth Vargas Hill (2008). ”Price Transmission and Trader Entry in Domestic Commodity Markets,” Economic Development and Cultural Change, University of Chicago Press, vol. 56, pages 729-766.

Ghosh, P, D. Mookherjee, and D, Ray (2001), Credit Rationing in Developing Countries: An Overview of the Theory, in Mookherjee, D and D. Ray ed. Readings in the Theory of Economic Development, Wiley, 2001.

Goldberg, P., and R. Hellerstein (2008): A structural approach to explaining incomplete exchangerate pass-through and pricing-to-market, American Economic Review, Vol. 98, No. 2, pp.

36
As expected, the incidence of poverty in urban areas is consistently lower than that in rural areas. Even in the poorest provinces, where over 70 percent of the rural population are poor, urban poverty is below 25 percent. In contrast, the difference between rural-urban poverty headcounts is relatively small in the more prosperous provinces in the Southeast (see Table 6).

In order to determine whether the poverty estimates for any two provinces are statistically different from one another, the standard error of the difference between their poverty headcounts must be calculated. This statistic can be computed using the equations described in Section 2.3, which take into account the modeling error, the idiosyncratic error, and the sampling error associated with the 3% sample Census. We have calculated the standard errors of these differences (based on the urban-rural regressions) for the 1830 possible pairs of provinces, and their rural and urban sub-samples, together with the 61 urban-rural pairs in the samne province. Our results can be summarized as follows:

- About one-quarter (23 percent) of the provincial pairs with a 6 percentage point gap<sup>14</sup> in their poverty incidence are significantly different from each other at the 5 percent level of statistical significance. Forty-three percent of the provincial pairs with an 8 percentage point gap and 70 percent of those with a 10 percentage point gap have statistically different poverty levels. This implies that poverty headcounts are generally not statistically different from one another in provinces that are adjacent to each other in poverty rankings. Provinces that are four to five provinces away from each other in the ranking, however, will usually have statistically significant differences in their poverty headcounts.

- Poverty headcounts in 65 percent of the rural provincial pairs are significantly different from one another (at the 5 percent level), but just 33 percent of the urban

- **14** A 6 percent gap refers to a gap greater than 5.5 percent and less than or equal to 6.5 percent.

27
**Table 8.4 TANZANIA, Descriptive statistics of production function variables under different imputation mechanism**

||**under**<br>**Variable**<br>|**different imput**<br>**#Obs. (#imputed)**|**ation m**<br>**Mean**|**echanism**<br>**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|Sales|Complete case|511|14.52|2.43|7.54|20.73|
||ICA method|667 (156)|14.60|2.30|7.54|20.73|
||Random ICA meth.|667 (156)|14.85|2.25|7.54|20.73|
||EM alg. [1]|801 (290)|14.51|2.18|7.54|20.73|
||EM alg. [2]|647 (136)|14.48|2.42|7.54|20.73|
||EM alg. [3]|647 (136)|14.48|2.41|7.54|20.73|
|Materials|Complete case|539|13.76|2.58|4.78|20.07|
||ICA method|667 (128)|13.82|2.52|4.78|20.07|
||Random ICA meth.|667 (128)|14.08|2.49|4.78|20.07|
||EM alg. [1]|803 (264)|13.74|2.32|4.78|20.07|
||EM alg. [2]|646 (107)|13.67|2.58|4.78|20.07|
||EM alg. [3]|646 (107)|13.67|2.57|4.78|20.07|
|Capital|Complete case|529|13.59|2.69|6.86|19.54|
||ICA method|664 (135)|13.54|2.57|6.86|19.54|
||Random ICA meth.|664 (135)|13.91|2.51|6.86|19.54|
||EM alg. [1]|806 (277)|13.46|2.40|6.86|19.54|
||EM alg. [2]|654 (125)|13.26|2.74|6.86|19.54|
||EM alg. [3]|654 (125)|13.26|2.81|5.47|19.54|
|Employment|Complete case|730|10.92|1.37|7.50|15.23|
||ICA method|788 (58)|10.91|1.34|7.50|15.23|
||Random ICA meth.|788 (58)|10.94|1.34|7.50|15.23|
||EM alg. [1]|790 (60)|10.92|1.36|7.50|15.23|
||EM alg. [2]|758 (28)|10.92|1.36|7.50|15.23|
||EM alg. [3]|768 (38)|10.92|1.36|7.50|15.23|

Source: Authors’ estimations with ICSs data.

We find that the proportion of missing values is an important factor in the observed underlying distributions after imputing missing values. Therefore, by means of explanation it is useful to discuss the results by groups of countries. The first group, with India and South Africa, comprises those countries with the largest response rate of PF variables, 65% in India and 70% in South Africa. The second group includes Tanzania and Turkey, whose response rates are only 40 and 30% respectively.

As shown in the kernel densities, the response rate dramatically determines the shape of the densities after imputing missing values. In India (see Figure 4.1), where the response rate is reasonably high in all the variables except capital, all the methods lead to estimated densities similar to those of the complete case. However, in the case of capital where the response rate is considerably lower, we observe a dramatic change in the distribution of the imputed values by the Random ICA method. Concretely, the distribution appears to have two modes, moving a considerable proportion of density from the center of the distribution to the right. This misleading behavior is already indicated in the case of materials, although to a lesser extent.

Regarding the estimated distributions of the remaining imputation mechanism, all of them lead to results similar to those of the complete case, including the ICA method and EM 62
Policy ReseaRch WoRking PaPeR 4853

# **Abstract**

Infrastructure projects are often technically complicated and highly customized. Therefore, procurement competition tends to be limited. Competition is the single most important factor toward auction efficiency and anti-corruption. However, the degree of competition realized is closely related to bidders’ entry decision and the auctioneer’s decision on how to assess technical attributes in the bid evaluation process. This paper estimates the interactive effects among quality, entry, and competition. With data on procurement auctions for electricity projects in developing countries, it is found that large electricity works are by nature costly and can attract only a few participants. The limited competition would raise government procurement costs. In addition, high technical requirements are likely to be imposed for these large-scale projects, which will in turn add extra costs for the better quality of works and further limit bidder participation. The evidence suggests that quality is of particular importance in large infrastructure projects and auctioneers cannot easily substitute price for quality.

This paper—a product of the Economics Team, Finance, Economics and Urban Development Department—is part of a larger effort in the department to understand and enhance efficiency and effectiveness in public procurement for infrastructure. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aiimi@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
split the sample into two groups, first the observations from 1984 to 1987 and second those from 1988 to 2003. This sample split is based on an inspection of the skills data contained in the next subsection.

# _3b. Data Sources and Description_

The dependent variable in the KK model should be a measure of bilateral stocks of FDI. Unfortunately, Singapore only sporadically compiled outbound and inbound FDI data for some countries in the sample before 1994. Information from 1984-2003 is available on bilateral stocks of foreign equity investment (FEI), defined as the sum of direct equity investment and portfolio equity investment. Direct equity investment incorporates all investments made in ownership entailing a measure of management control, with a minimum ownership threshold of ten percent, while portfolio investment involves financial stakes unrelated to management control. Thus, the measure we analyze, FEI stocks, is somewhat broader than FDI. However, in Singapore portfolio investment is relatively small. Stocks of such investment amounted to no more than 9.5 percent of total FEI stocks from 1994 to 2003 and averaged less than 4.0 percent. Moreover, the correlation between FEI stocks and FDI stocks from 1994 through 2003 is 0.929. We employ data on investment stocks in all sectors, which include manufacturing and services, and in total manufacturing. The sources of the FEI data are the _Statistical Yearbook of Singapore, Foreign Equity Investment in Singapore_ and _Singapore’s Investment Abroad,_ published by the Department of Statistics, GOS. They are converted into millions of 1990 US dollars using contemporaneous exchange rates and the US GDP deflator.

Data on the right-hand side variables come from sources detailed in CMM (2001), updated through 2003. Real GDP is measured in billions of 1990 US dollars for each country.

15
<!-- Start of picture text --> A. B. Cc D=A*C E=A-D<br>. .<br>Line of business Retained Premium] Non-prop XLRI | Non-prop XLRI | XLRI premium Retained.<br>(prop RI) 2012 2012 Rate on GNPI 2012 Premium (prop &<br>XL} 2012<br>Motor vehicle liability insurance 20,000 9,500 xs 500 2.3% 460 19,540<br>Other motor insurance 5,000 9,500 xs 500 4.9% 247 4,753<br>Marine, aviation and transport insurance 2,500 24,500 xs 500 9.6% 240 2,260<br>Fire and other damage to property insurance 10,500 8,248<br>Fire non natural catastrophe per risk 20,500 xs 500 14.6% 1,530<br>Fire natural catastrophe per event (EQ) 69,500 xs 500 6.9% 722<br>General liability insurance 5,000 4,500 xs 500 AAM 222 4,778<br><!-- End of picture text -->

|.<br>.<br>Line of business|A.<br>Retained Premium]<br>(prop RI) 2012|B.<br>Non-propXLRI <br>2012|Cc<br>| Non-propXLRI <br>Rate on GNPI|D=A*C<br>|XLRI premium<br>2012|E=A-D<br>Retained<br>.<br>Premium (prop &<br>XL} 2012|
|---|---|---|---|---|---|
|Motor vehicle liability insurance|20,000|9,500xs 500|2.3%|460|19,540|
|Other motor insurance|5,000|9,500xs 500|4.9%|247|4,753|
|Marine, aviation and transport insurance|2,500|24,500xs 500|9.6%|240|2,260|
|Fire and otherdamage to property insurance|10,500||||8,248|
|Fire non natural catastrophe per risk||20,500xs 500|14.6%|1,530||
|Fire natural catastrophe per event (EQ)||69,500xs 500|6.9%|722||
|General liability insurance<br><br>|5,000<br><br>|4,500xs 500<br>|AAM<br>|222<br> <br>|4,778<br> <br><br>|
|[total<br>z|o<br>—_|P||<br>|e<br>|<br>39,579|
22 And fifth, the mean WRI and TRI in the earlier period were half as large again for high-income countries as for the 41 developing countries (and 2.5 times larger for the European Union), and that gap became even wider by the more-recent period. This is reflected too in the faster increase in these indexes during the early period and their slower decline (especially for non-EU countries) in the later period.

# **Economy-Wide Effects of Reforms since 1984 and of Remaining Policies**

It is clear from the above that there has been a great deal of change over the past quarter of a century in policy distortions to agricultural incentives throughout the world, and considerable diversity in the rates and types of change. In addition to the anti-agricultural and anti-trade biases of policies of many developing countries being reduced since the K/S/V era, export subsidies of high-income countries have been cut and some re-instrumentation toward less inefficient and less trade-distorting forms of support, particularly in Western Europe, has begun. However, protection from agricultural import competition has continued to be on an upward trend in developing countries, if one ignores the latter 1980s when the limited transmission of the slump in international food prices to domestic markets led to NRAs spiking upwards.

What, then, have been the net economic effects of agricultural price and trade policy changes around the world since the early 1980s? Also, how do the effects on farm incomes and economic welfare in developing countries compare with the effects of those price distortions still in place as of 2004? Valenzuela, van der Mensbrugghe and Anderson use a global economy-wide model (the World Bank’s Linkage model – see van der Mensbrugghe) to provide a combined retrospective and prospective analysis that seeks to assess how far the world has come, and how far it still has to go, in removing the disarray in world agriculture. It quantifies the impacts both of past reforms and current policies by comparing the effects of the above NRA and CTE distortion estimates for the period 1980-84 with those of 2004.

Several key findings from that economy-wide modeling study, summarized in table 8, are worth emphasizing. First, the policy reforms from the early 1980s to the mid-2000s is estimated to have improved global economic welfare by $233 billion per year, and removing the distortions remaining as of 2004 would add another $168 billion per year. This suggests
_Figure 7: Net Interest rate sensitivity without derivatives, 2002 to mid 2006_ <!-- Start of picture text --> DKK bn<br>80<br>60<br>40<br>2002<br>20<br>2005<br>2004<br>0<br>-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5<br>2003<br>2006 (1st half)<br>2006 (1st half) -20 2003<br>2004<br>-40<br>2002<br>-60<br>2005<br>-80<br><!-- End of picture text --> _Table 12: Interest Rate Sensitivity Without Derivatives_

|||InterestRa<br>|te Change<br>||10 Year government bond<br>|
|---|---|---|---|---|---|
||- 1%|-0.7%|+0.7%|+ 1%|yield(end ofperiod)|
|||_DK_|_Kbn_||Percent|
|2002|-39.9|-23.4|3.3|20.8|4.46|
|2003|-11.8|-3.7|-11.9|-20.2|4.47|
|2004|-35.4|-20.5|3.3|0.84|3.85|
|2005<br>|-65.8|-39.9|17.1|19.3|3.28|
|2006(1<sup>st</sup>half)|-17.5|-8.4|-7.6|-14.3|4.12|

Source: Danish FSA. Bloomberg (yields) The net interest rate sensitivity of the industry without taking into account positions in derivatives shows for most years a net loss when interest rates fall and a gain when they rise (Figure 7 and Table 12). In other words the liability side effects dominate those of the asset side. In addition, the gains from a 1 percentage point increase in rates are smaller than the losses from a similar reduction in rates, illustrating the compound effect of the asymmetric impact of interest rate changes on both the asset and liability side. In 2003 the pension sector was in a very peculiar situation where interest rate changes in any direction would have resulted in a deterioration of the balance sheet., The net interest rate sensitivity (without derivatives) of the industry as a whole has increased over time as the asset interest rate sensitivity has been unable to keep up with the effect of increased interest rate sensitivities on the liability side increasing the duration gap in the balance sheets.

19
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83
|(1)<br>(2)<br>(3)<br>(4)<br>Dependent Variable<br>M2/GDP<br>Stock Market<br>Value/GDP<br>Stock Market<br>Turnover/GDP<br>Net Interest Margin|EFNX<br>1.904***<br>0.735***<br>0.020***<br>-0.0015**<br>(0.680)<br>(0.266)<br>(0.006)<br>(0.0007)<br>Controls<br>Log(Trade/GDP), Log(Income), Legal Origin Dummies<br>Estimation:<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>Observations<br>115<br>79<br>79<br>104<br>**_Panel A: Cross-Sectional Estimates_**|EFNX<br>0.549**<br>1.067<br>0.068<br>(0.278)<br>(1.197)<br>(0.085)<br>Controls<br>Log(Trade/GDP), Log(Income)<br>Estimation:<br>2SLS<br>2SLS<br>2SLS<br>Observations<br>537<br>285<br>274<br>Countries<br>115<br>79<br>79<br>Country FE<br>yes<br>yes<br>yes<br>Time FE<br>yes<br>yes<br>yes<br>**_Panel B: Panel Estimates_**<br>Notes: Panel A: robust standard errors in parentheses; Panel B: standard errors clustered at country level are in<br>parentheses; ** significant at 5%; *** significant at 1%; Dependent variable,_Private Credit/GDP,_is credit to the<br>private sector by banks and other financial institutions as a share of GDP;_EFNX_is the external finance need of exports;<br>_Log(Trade/GDP)_is log of exports and imports as a share of GDP;_Log(Income)_is log of PPP-adjusted real per capita<br>income from Penn World Tables;_French, German, Scandinavian, and Socialist Legal Origin_dummies are as defined<br>originally by La Porta et al. (1998)._Predicted EFNX_is the predicted external finance need of exports constructed by<br>first estimating sector-level gravity regressions, and then generating predicted export shares for each country and sector.<br>Panel A: all of the variables are average values over 1970-99. Panel B: all of the variables are five-year averages 1970-<br>74; 1975-97, ... 1995-99. Variable definitions and sources described in detail in the text.|
|---|---|---|

33