rafmacalaba/datause-encoder-data huggingface dataset:
years of study; and (iv) entry into municipalities within the same urban area is likely to be highly correlated. Given these problems, we decided instead to use the concept of “urban agglomerations,” defined in a comprehensive urban study developed by IPEA, IBGE and Unicamp (2002), and used by Da Mata, Deichmann, Henderson, Lall and Wang (2005a and 2005b).

Even though we had RAIS micro data available from 1986 to 2003, we found the data from 1993 to 2001 to be more reliable and therefore use only these years for the analysis. For each year, we identified new establishments with at least 10 employees, and recorded the urban agglomeration in which they entered. If the entering establishment belonged to a multi-establishment firm, we also identified the agglomeration of the parent.

As can be noted from Table 1, most parent establishments are located in the Southeast (58.15% in 1993). Note, however, that the percentage of entering parent firms over the period 1993-2001 (i.e., the CF years) falls to 42.7% in the Southeast, but rises to 21.01% in the Northeast (from 12.49% of the parents in 1993). Thus, considering only the spatial distribution of parents (which are footloose by definition), there is some evidence that the CF’s were effective. There is also evidence in this table, however, that if branches are closely tied to parents, the CF’s have a lot to overcome in vertically integrated industries, given the initial distribution of parent firms in 1993.

How empirically relevant is the “pull” of the parent firm? The answer depends upon the sector being considered. Table 2 shows percentage of entrants over years 19932001 that have headquarters. We would expect proximity to the parent to be particularly important for Metallurgy, Communcations and Electronics, Transportation, Chemicals, and two situations from the data, the econometric results presented in the following sections show evidence of the importance of the “0001” headquarters location, which suggests the validity of our assumptions.

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**Figure 10:** Distribution of the means of the ML (blue) in the test sample (2018) for 100 simulations. The red vertical line shows the mean CC and the red horizontal line shows the 95% confidence interval in the 2018 sample

<!-- Start of picture text --> Millet Sorghum<br>850 900 950 1000 1000 1050 1100 1150 1200<br>x x<br>Rice Maize<br>3000 3500 4000 1700 1800 1900 2000<br>x x<br>0.20<br>0.20<br>0.10<br>0.10<br>0.00 0.00<br>0.008 0.12<br>0.08<br>0.004<br>0.04<br>0.000 0.00<br><!-- End of picture text --> <!-- Start of picture text --> Cowpea<br>400 450 500 550 600<br>x<br>0.12<br>0.08<br>0.04<br>0.00<br><!-- End of picture text --> <!-- Start of picture text --> Groundnut<br>1850 1900 1950 2000 2050 2100 2150 2200<br>x<br>0.06<br>0.04<br>0.02<br>0.00<br><!-- End of picture text --> 35
# **I. INTRODUCTION**

Competition is the single most important factor to contain pubic procurement costs and perhaps discourage collusive bidding behavior and corrupt practices. However, many infrastructure projects seem to continue faced with considerable difficulties to intensify competition in the procurement process (Estache and Iimi, 2008a). This may be partly unavoidable because infrastructure projects are normally extremely valuable and highly customized.<sup>1</sup> However, there are still several simple measures that could potentially extend market competition but sometimes have not been exploited carefully.

The current paper revisits the problem of endogenous participation in auctions and the division into procurement lots in the water supply and sewage industry. In this sector, it is always of particular concern that the observed degree of competition has been limited. In the public-private partnership (PPP) context, less than five multinational water enterprises may have been dominant over the world (Foster, 2005).<sup>2</sup> Even in the traditional public procurement context, the degree of competition is in general limited (Estache and Iimi, 2008a).

There is a tradeoff between competition in auctions and size of contracts. Not many firms can apply for highly valuable contracts, because of financial and experiential constraints. Highly specific works (which are normally valuable as well) would also limit competition. On the other hand, infrastructure projects are normally expected to exhibit economies of scale and possibly economies of scope in preparation and implementation. Therefore, larger lot works could be procured at relatively low costs per unit, though fewer applicants may participate.

> 1 Estache and Iimi (2008d) discusses the interactive effects among entry, quality and competition with data from public procurement auctions for electricity projects. These elements are closely related with one another.

> 2 According to the Private Participation in Infrastructure (PPI) database, the top 10 percent largest firms— defined by the number of transactions that each company obtained—were awarded about half of total infrastructure PPP contracts. In the water industry, especially, a multinational French water service operator, Veolia Environnement (former Vivendi Environnement, or Compagnie Générale des Eaux), was alone awarded 51 contracts, and another French company, SUEZ (former Lyonnaise des Eaux), won 50 transactions in the developing world.
are located will be coded as conflict squares. The 14 countries in the dataset cover 8 million square kilometers or just over 100,000 squares.

# **Log population in square**

High resolution population data is available through the UN geodata portal. We use the fourth version of population data from a joint UNDP and CIESIN project. The database population count assessments are from a compilation of existing data sources. The sources of error for population counts and distribution are admittedly substantial, particularly in developing areas. In this version, population figures are transformed into a distributive surface to use in spatial analysis. A rasterized (gridded) format is based on interpolating `accessibility weights' by administrative units and assigning population totals based on the assumption that population distribution and densities in Africa are strongly correlated with accessibility. The accessibility index is sum of population totals of the towns in the vicinity weighted by distance. Each raster unit is assigned a population based on interpolating population densities from the accessibility weight and a distance decay function for surrounding areas. Adjustments to the accessibility grid were made for inland water bodies, elevation and protected areas. Although by no means a perfect account of local population, the data remains the most comprehensive and sophisticated spatially referenced population data available. We use estimated total population figures, observed once for every decade since 1960.<sup>4</sup> In original form, the population count has a resolution of 1km x 1km. For this paper, the population counts were aggregated to the 8.6 x 8.6 km grid square. For observations in the period 1.1.1960–31.12.1964 we used the population figure for 1960. For observations in the period 1.1.1965–31.12.1974 we used the population figure for 1970, etc.

Since the area of each square is identical, the variable also indicates the local population density. The variable was log-transformed in the analysis reported below. 0.5 were added to all observations to avoid non-defined transformations.

> 4The UNDP/CIESIN data are available by continent at http://grid2.cr.usgs.gov/globalpop/Africa/part2.html.

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China’s WTO accession commitments.<sup>54</sup> This is important since all post-WTO reform options are evaluated on the background of the implemented WTO commitments. For comparison purposes we use the same set of elasticities and tax distortions as in Ianchovichina and Martin (2004) (Table 4A). These originally come from GTAP version 5 database (Dimaranan and McDougall 2002).

The macro closure for the empirical model is one of full employment, perfect mobility of skilled and unskilled workers between nonagricultural sectors, and perfect mobility of unskilled workers within agriculture. However, there are barriers to labor mobility between rural and urban employment (see Ianchovichina and Martin, 2004). To capture the effects of these barriers to mobility, the model allows for both imperfect transformation between unskilled workers in agricultural and unskilled nonagricultural employment, and an implicit tax on nonagricultural employment. The imperfect transformation reflects the substantial differences in farm and nonfarm unskilled workers’ characteristics, and the ability to transform at a cost farm into nonfarm workers through training, experience, and the creation of nonfarm jobs in rural areas.<sup>55</sup> The tax is designed to reflect the pure policy-induced barriers between rural and urban workers, such as the cost of a residence permit and the inability to sell farm land.<sup>56</sup>

> (2004), available also as a World Bank Policy Research Working Paper at http://econ.worldbank.org/files/26864_wps3053.pdf.

> 54 China’s WTO accession commitments include reduction in protection to post-accession (2007) tariff levels, liberalization of the services sectors, restructuring of the automobile sector, removal of quotas on China’s clothing and textile exports, and China’s agricultural export subsidies.

> 55 We set the elasticity of transformation at 1.32. This is the estimate by Sicular and Zhao (2002) used in Ianchovichina and Martin (2004).

> 56 The tax reflecting pure policy-induced barriers between rural and urban wages is set at 34 percent. This is the estimate by Shi (2001) used in Ianchovichina and Martin (2004). Even though the government recently removed the requirement for a residence permit in urban areas, in practice migrant workers in urban areas still need to incur the costs of health care and schooling for children in urban areas.

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<!-- Start of picture text --> 1<br>8<br>2<br>=8<br>a3 6<br>34<br>E<br>oO<br>2<br>0<br>1 2 3 4 5<br>ABF EXT<br>——— Brazil -~——— Vietnam ——— Senegal<br><!-- End of picture text --> <!-- Start of picture text -->
-<br>co}<br>is‘b<br>we<br>a<br>w<br>N<br>° .<br>1 2 3 4 5<br>ABF EXT<br>——— Senegal ———— Brazil === Reject Ranges (5% FWER)<br><!-- End of picture text -->

<!-- Start of picture text --> 2<br>Se<br>iva]Woomw<br>xt<br>Ke)<br>at<br>S)<br>N<br>° ——— |<br>1 2 3 4 5<br>ABF EXT<br>—— Vietnam ——— Brazil == Reject Ranges (5% FWER)<br><!-- End of picture text -->

<!-- Start of picture text --> 2<br>Se<br>Wemwiva]<br>xt<br>Ke)<br>at<br>S)<br>N<br>o :<br>1 2 3 4 5<br>ABF EXT<br>—— Senegal ——— Vietnam === Reject Ranges (5% FWER)<br><!-- End of picture text -->
mean age of the labor force rises 2.8 years on average for the set of countries included in the study. That is, the older profile of NSE workers reflects the older profile of the overall labor force.

From the gender point of view (Figure 7 and 8), the picture seems to be more heterogeneous, particularly in the case of part-time employment, where there are several countries in which the gender profile of non-standard employees remains almost constant in the last two decades (Argentina, Brazil, Bolivia, Mexico), while other countries show a growing female participation (Uruguay, Chile, Peru, Dominican Republic and El Salvador). As in the case of the age profile, the growing female participation is a phenomenon consistent with the trend in the overall employment. Indeed, in the considered period, there is an observed a rise in female participation in the total employment of 3.9 percentage points on average. Statistics among the age and gender profile for total employment are included in the Annex II of the paper.

Figure 5: Part-time employment by age group. (Mid-1990s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s<br>Argentina Uruguay Brazil Chile Peru Bolivia Dominican Mexico El Salvador<br>Republic<br>>64 years old 50‐64 40‐49 25‐39 15‐24<br><!-- End of picture text --> Source: Own calculations based on Household surveys Page 11 of 51
of the case of Silicon Valey as a good example, but it can be negative such as a big player that transfers their production to another region). I am trying to capture these dynamics through the variable “Export-spillovers.”<sup>31</sup> This variable accounts for the number of other exporting firms in the same micro-region (a geographic definition from IBGE that divides Brazil in 555 regions) and sector (CNAE 2 digits) excluding the own firm.<sup>32</sup> It is noticeable that among exporting and treated firms the number of other exporters in the same micro-region is larger than for non-exporters.

The variable multi comes from CEB-BCB (Brazilian acronym for National Census of Foreigner capital in Brazil elaborated by the Brazilian Central Bank) and it is a register of foreigners’ share in Brazilian firms from 1995 to 2000. It is a dummy variable that takes the value of 1 if firms have more than 50% of their assets held by foreigners. Since this information is not updated it should be interpreted cautiously. Nonetheless, it is useful to identify well established multinational firms, those which are more likely to have a wider network, be more productive and export.<sup>33</sup> Finally, trade literature emphasizes that importing is very common among exporting firms. Table 3 shows that almost 60% of exporting firms in Brazil also import. Apex’s treated firms have a lower share (17%) than exporters,<sup>34</sup> but this share is much larger than the average among manufacturing firms (6%).

# **4 Identification Strategy**

A critical question in any program evaluation is how to identify its effect on the variable of interest. In the case of firms that received support from Apex-Brasil, since we can only observe their actual outcome we have no counterfactual. Therefore, we do not know what the performance of these firms would be if they had not received this support, which is a typical problem of missing information.<sup>35</sup> An alternative solution is to create a control group with firms that have not participated in the program and use them as counterfactual. However, this procedure also leads to another missing

> 31Bernard and Jensen (2004) also use a similar variable as a control and call it Spillovers: Exporter(stateindustry), which accounts for the share of exporting plants in the state and industry.

> 32It means that if a firm _i_ is an exporting firm in time _t_ and it is located in the micro-region _k_ with _n_ number of exporters, this variable considers _(n-1)_ exporters _i_ in region _k_ .

> 33There are approximately 1,700 firms identified as multinational according to this criteria. Among them, 80% export and less than 6% received Apex support (almost all of them under non-Peiex program), which would be expected.

> 34This share becomes very similar if Peiex’s treated firms are excluded.

> 35Indeed, if we were able to answer this question we could identify the difference between both outcomes (with and without treatment) as the impact of the program. For a review about recent development in program evaluation literature see Imbens and Wooldridge (2009).

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|Cluster|N|Mean|Std Dev.|Min.|Max.|Country of Max.|
|---|---|---|---|---|---|---|
|2004|||||||
|L|5|5.46|085|408|631|BiH|
|V|2|456|043°|4425|486|Hungary|
|G|4|907|156|7.15|10.47|Armenia|
|H|12|881|183|664|12.08|Ukraine|
|2008|||||||
|L|3.|207|129|0.66|320|Kazakhstan|
|V|6|0.05|320|-455|276|Lithuania|
|G|12|6.92|194|480|10.80|Azerbaijan|
and U.S. firms. In other words, while the role of the stock market as a source of finance is broadly similar for Indian and U.S. firms, internal finance is less important for Indian firms than U.S. firms. The fundamental difference between Indian and U.S. firms stems from the role that external debt plays as a source of finance; it is much more important for Indian firms than U.S. firms. It is in this sense that the Indian financial system can be termed as a bank-oriented one. Also, the figures for total borrowings in tables 2, 3, and 4 bring out some distinct features of Indian corporate finance. Total borrowings of Indian firms have three components: (i) term-loans from development banks; (ii) debentures; and (iii) working capital loans from commercial banks. For the U.S. firms however (table 6), borrowings consist of debentures and long-term borrowings from other bond issues.<sup>3</sup> Therefore, the fundamental difference between Indian and U.S. firms with regard to borrowings relates to the role and nature of development banks in the Indian context.

To summarize, these patterns in the sources and uses of funds do suggest that Indian firms are dependent on internal finance to a far smaller degree than their U.S. counterparts. Consequently, Indian firms are far more dependent on external finance than U.S. firms. These results are therefore consistent with the prediction of internal finance being less important for Indian firms than U.S. firms and external finance being more important for Indian firms than U.S. firms, given that information and agency problems are less severe for Indian firms compared to U.S. firms, since the Indian financial system is predominantly a bank-oriented one.

' Unfortunately, COMPUSTAT does not provide the details of working capital loans from commercial banks for U.S. firms.

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# **3.3 Estimating Impacts**

We estimate the intention-to-treat effect of being offered one of the four treatments through the following pre-specified ANCOVA specification for firm _i_ in follow-up survey round _t_ : 0 1I I I I I 2<sup>OOIIOOI</sup> 3<sup>TTIITTI</sup> 4<sup>CCI+</sup> 8 ii,tt + ∑ ii<sup>+ ββ</sup> ii<sup>+ ββ</sup> ii<sup>+ ββ</sup> ii<sup>+ ππYY</sup> ii,0 (1) YY = ββ + ββ<sup>IIIIIIICCOOIII</sup> ii,0 ss=1 ss ii,tt γγMM θθ 1(IIiiII) + εε Where _Yi,t_ is the outcome variable of interest; is the baseline value (where available); is a dummy variable indicating whether or not this baseline variable is missing for a particular firm if ii,0 ii,0 YY MM it is measured for some firms; _Insourcing, Outsourcing, Training,_ and _Consulting_ are dummy variables for being offered one of these respective treatments; are randomization strata dummies (the induction batches); and is the error term. Since random assignment is at the 1(IIiiII) individual firm level, robust (Eicker-White) standard errors are used. ii,tt 1, 2, 3 and 4 then provide εε intent-to-treat effects, which are the effect of being offered a specific treatment relative to the ββ ββ ββ ββ control. Testing = 0 for a given treatment _j_ provides a test of whether a particular treatment has affected firm outcomes or not. In addition, we also carry out the joint test that none of the jj ββ treatments had any effect ( 1 = 2 = 3 = 4 = 0), and that all four treatments had equal effects ( 1 = 2 = 3 = 4). ββ ββ ββ ββ ββ ββ ββ ββ As noted, our sample size is smaller than originally planned, which reduces our statistical power. This is less of an issue for binary outcomes like use of business practices, but is more of a challenge for outcomes like sales, profits and employment, which are heterogeneous and heavily skewed. We therefore use two approaches to help potentially improve power. The first is to employ the post-double selection lasso approach of Belloni et al. (2014). This uses a disciplined way of selecting baseline control variables to add to equation (1) to the extent that they are strong predictors of future outcomes. Moreover, this method also selects variables that predict treatment status, which can help account for chance imbalances and any imbalances caused by selective attrition. Second, while our main interest lies in examining the trajectory of treatment impacts in order to determine whether impacts persist well after the interventions end, we also attempt to boost power by pooling both rounds of data on firm growth (sales, profits, employees) and examining average impacts over the two years (McKenzie 2012).

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<mark>intra-household heterogeneity in the response to – and the impacts of – climate change based on individual characteristics (including gender, age, and education) and endowments. In the case of panel surveys, such as many of those implemented by the LSMS team, this also provides a unique opportunity to study intergenerational relationships that may influence climate-induced migration.</mark> <mark>Another key feature of LSMS surveys is the georeferencing of community, household, and plot locations which facilitates integration with high-resolution third-party geospatial data products which have greatly increased the scope of microeconomic research on climate change adaptation and impacts. This specific feature is a central element for enabling data integration and therefore instrumental to increasing the relevance of household survey data for climate-migration analysis, given the current data landscape characterized by the spreading availability of novel data products such as big data.</mark> <mark>Lately, the LSMS team has been testing and validating new modes for collecting national household survey data with the goal of increasing the adaptability of survey systems in the context of unforeseen shocks so they are better positioned to provide rapid responses to shifting data collection needs, such as those that emerged during the COVID-19 pandemic crisis (Zezza et al., 2022; Gourlay et al., 2021).</mark><sup>27</sup> <mark>This kind of innovative solution can play a critical role in increasing the availability of timely and high-frequency data to monitor and assess the socioeconomic impacts of different types of shocks, including those triggered by slow- and rapid-onset changes in climate.</mark> <mark>Leveraging these comparative advantages (over other traditional migration data sources), LSMS has</mark> great potential for increasing its fitness for purpose in climate migration research. To achieve this objective, there are methodological shortcomings that need to be resolved. The next subsection identifies the priority areas of a potential methodological agenda for unleashing the potential of LSMS-type surveys in the climate migration research domain over the short- and medium-term.

# **3.4 Priority areas and actions going forward: A tentative methodological agenda**

This proposed agenda focuses on improvements and innovations that can help tackle key research questions of the climate-migration nexus, as suggested by the conceptual framework presented in subsection 3.1 and for which multi-topic household surveys in general – and LSMS in particular – if properly tweaked, hold a significant comparative advantage (as discussed in the previous subsection).

> 27 Specifically, the LSMS has been supporting High-Frequency Phone Surveys (HFPS) in a number of low-income countries to track responses to and socioeconomic impacts of COVID-19. For more information about the LSMS-supported HFPS see <u>https://www.worldbank.org/en/programs/lsms/brief/lsms-launches-high-frequency-phone-surveys.</u>

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