rafmacalaba/datause-encoder-data huggingface dataset:
Census, and need to be defined similarly and have similar mean values. The set of potential variables to be used in the model has been defined in the first stage of the procedure outline earlier. If we linearise (1), we model the household’s logarithmic per capita expenditure as The vector of disturbances **_u_** is distributed _F_ (,0 Σ ) . The model (2) is estimated by Generalised Least Square. To estimate this model we need first to estimate the error variancecovariance matrix Σ in order to take into account possible spatial autocorrelation (expenditure from households within a same cluster tend to be correlated) and heteroskedasticity. To do so we first specify the error terms as: where η _c_ is the location effect and ε _ch_ is the individual component of the error term. In practice we first estimate equation (2) by simple OLS and use the residuals as estimate of the ˆ overall disturbances, given by μ _ch_ . We then decompose those residuals between uncorrelated household and location components: The location term ( η ˆ _c_ ) is estimated as cluster means of the overall residuals and therefore the household component ( _ech_ ) is simply deducted. The heteroskedasticity in the 2 latest error component is modelled by the regressing its squared ( _ech_ ) on a long list of independent variables from model (2), their squared and interactions as well as the imputed welfare. A logistic model is used for this. Both error computations are used to produce two matrices which are them summed to ∑<sup>ˆ</sup> , the estimated variance-covariance matrix of the original model (2). That matrix is used for the estimation of the final set of coefficients for the main model (2).

> 2 We also deleted or redefined dichotomic variables being less that 0.03 or larger than 0.97 to avoid serious

7
# **Regional Convergence in Brazil**<sup>∗</sup>

Kaleb Abreha Rafael Ornelas Gabriel Zaourak

**JEL Classification:** O11, O47, O54, R11, R12

**Keywords:** Regional inequality, regional convergence, labor productivity, Brazil

> ∗ The World Bank, 1818 H Street, NW Washington, DC 20433, USA. Corresponding email addresses: gzaourak@worldbank.org (G. Zaourak), kaleb.abreha@gmail.com (K. Abreha), and ramaralornelas@worldbankgroup.org (R. Ornelas). The views expressed herein are solely our own and should not be attributed to the World Bank, its executive directors, the countries they represent, the Mosbacher Institute, the Bush School, or Texas A&M University. All errors and omissions are ours.
|||**Table 8.**<br>|**OLS esti**|**mation of effici**<br>|**ency in c**|**ontract awardi**<br>|**ng**||||
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.<br>Err.|Coef.|Std.<br>Err.|Coef.|Std.<br>Err.|Coef.|<br>Std.<br>Err.|Coef.|Std.<br>Err.|
|ln_num_|-0.110|(0.145)|-0.060|(0.118)|||0.591|(0.116) ***|0.575|(0.140) ***|
|ln_securitybefore_|0.065|(0.112)|||0.071|(0.113)|||-0.044|(0.126)|
|ln_cost_|0.534|(0.164) ***|0.549|(0.114) ***|0.509|(0.162) ***|||||
|ln_length_|-0.123|(0.132)|-0.249|(0.107) **|-0.125|(0.130)|||||
|ln_lane_|-0.131|(0.375)|-0.098|(0.393)|-0.133|(0.368)|||||
|ln_thickness_|-0.065|(0.122)|-0.129|(0.090)|-0.067|(0.125)|||||
|ln_gravel_|0.065|(0.059)|0.015|(0.045)|0.065|(0.059)|||||
|ln_bitumen_|0.020|(0.052)|0.012|(0.044)|0.022|(0.054)|||||
|ln_earth_|-0.052|(0.058)|0.007|(0.046)|-0.060|(0.059)|||||
|ln_brick_|0.002|(0.054)|0.073|(0.061)|0.003|(0.054)|||||
|ln_gabion_|0.053|(0.039)|-0.003|(0.029)|0.048|(0.037)|||||
|ln_excavate_|0.022|(0.025)|0.031|(0.019)|0.019|(0.024)|||||
|ln_cement_|-0.138|(0.042) ***|-0.114|(0.031) ***|-0.129|(0.038) ***|||||
|D(_postqualify_)|-0.145|(0.411)|0.369|(0.324)|-0.065|(0.409)|1.510|(0.295) ***|1.149|(0.311) ***|
|Constant|-4.064|(2.084)**|-4.626|(1.427)***|-3.871|(2.076)*|1.026|(0.402)**|1.453|(0.662)**|
|Obs.|119||154||119||155||120||
|R-squared|0.710||0.719||0.708||0.463||0.496||
|_F_-statistics|19.80||24.44||20.34||13.40||23.20||
|No. of dummy variables:|||||||||||
|Project districts|18||18||18||18||18||

Note: The dependent variable is the number of days required to award a contract after the bid openning, _dayscontracting_ . Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.
cost and increase the skills of employees, they also agreed on the provision of training for workers (Glassner et al., 2010). Other innovative procedural changes in the agreements included allowing a stepwise increase in wages in accordance with the company’s economic situation and a temporary deviation from pay increases set in the sectoral agreement (Glassner et al., 2010).

Figure 3: In Sweden real wages responded more quickly to the crisis than in the majority of OECD countries and productivity recovered rapidly <!-- Start of picture text --> Growth rate of real Growth rate of nominal unit Growth rate of labor<br>hourly wages labor cost productivity<br>Finland<br>Finland Germany<br>Denmark<br>Denmark OECD average<br>OECD average<br>OECD average G7 average<br>EU15 average EU15 average France<br>France<br>Sweden EU15 average<br>Germany United Kingdom Finland<br>G7 average G7 average United Kingdom<br>United<br>France Sweden<br>Kingdom<br>Sweden Germany Denmark<br>-1.5 0.0 1.5 3.0 -3 0 3 6 9 -1.5 0.0 1.5 3.0<br>2007 2008 2009 2010<br><!-- End of picture text --> Source: OECD (2014b).

Note: Countries and country groups in each chart are in descending order by the indicator value in 2008. All growth rates are annual.

8
_YGAP_ shocks explain together more than 8 percent of the _CA_ variance over any time horizon considered. The exchange rate and the interest rates account only for 2.5 and 2 percent, respectively, of the variance of _CA_ either in the short or long run.

**Table 1: Forecast error GVD for Eurozone countries [1975-2011]:** percent of variation in _CA_ explained by column variable

||Ba<br>|seline VA<br>|R<br>|||
|---|---|---|---|---|---|
|Years ahead|_R_|_REER_|_GR_|_YGAP_|_CA_|
|2|2.02|2.57|6.08|2.39|86.93|
|4|2.03|2.58|6.07|2.41|86.9|
|Altern|ative VAR|_(replacin_|_g REER b_|_y ULC)_||
|Years ahead|_R_|_ULC_|_GR_|_YGAP_|_CA_|
|2|2.13|1.27|5.70|2.77|88.13|
|4|2.15|1.28|5.70|2.81|88.06|

Our results imply that real exchange rate movements have played only a minor role in the external balance dynamics in the Eurozone countries as a whole. Moreover, economic growth changes seem to have been a key determinant of external current account fluctuations. One reason for the relatively limited role of _REER_ shocks in the external balance could be that the loss of competitiveness depresses internal demand (via slower GDP growth), which leads to a reduction in imports, thus, partially compensating the negative effect of an exchange rate appreciation on the external balance.

Replacing the _REER_ variable with the unit labor cost ( _ULC_ ), as an alternative measure of competitiveness in the VAR specification, provides a robustness exercise.<sup>15</sup> As is shown in Table 1, the qualitative results of the alternative VAR (where _REER_ is replaced by _ULC_ ) remain unchanged.

The finding of limited contribution of real exchange rate (or unit labor cost) changes to the external imbalances of Eurozone countries may, to some extent, reflect the merging in a single group of periphery and core countries with large and opposite external imbalances especially in recent years. Dynamics of the current account balance in core and periphery countries have been mostly asymmetric in recent years while patterns of real exchange movements have been less markedly divergent (see Figures 1 and 5b). It is then suitable to split the sample in two groups (core and periphery) to explore if any significant differences exist in the dynamics of the external balance between these two groups.

> 15 The _ULC_ series are taken from the OECD database.

16
Table 3b: Decompositions of over-time change in Science score at percentiles: Ghana, 2003-2007

|Percentile|Total change|Characteristics|Median<br>Coefficients|Residuals|
|---|---|---|---|---|
|P10|62.36|11.56|46.50|4.30|
||(2.48)|(1.85)|(2.68)|(2.54)|
|P20|59.79|11.57|43.89|4.33|
||(2.73)|(1.66)|(2.55)|(1.90)|
|P30|56.81|10.90|42.31|3.59|
||(2.77)|(1.68)|(2.25)|(1.72)|
|P40|53.67|10.65|40.85|2.17|
||(2.81)|(1.76)|(2.23)|(1.67)|
|P50|49.98|10.39|39.49|0.10|
||(2.90)|(1.95)|(2.19)|(1.87|
|P60|46.49|10.10|38.03|-1.64|
||(2.83)|(2.00)|(2.35)|(2.31)|
|P70|43.16|10.12|35.94|-2.90|
||(1.78)|(2.17)|(2.53)|(2.87)|
|P80|39.98|9.74|33.34|-3.11|
||(2.59)|(2.50)|(2.89)|(3.49)|
|P90|38.61|9.84|29.80|-1.03|
||(2.18)|(3.03)|(3.53)|(3.95)|

Standard errors in parentheses.

The effect of included characteristics on test scores (median coefficients effect) is the largest component of the over-time increase in tests scores in Ghana. This component contributed more to the improvement in the performance of students at lower parts of the score distribution for mathematics, while it generally had a uniformly positive effect on the improvement of science scores. It accounted for more than 100% of the gross mathematics score improvement at the 10<sup>th</sup> percentile and 68% at the 90<sup>th</sup> percentile. Similarly, it accounted for 75% and 77% of the gross science score improvement at the 10<sup>th</sup> and 90<sup>th</sup> percentiles, respectively.

The second component of the increase in scores (due to improvement in characteristics) is generally small for mathematics, but increases to about one-quarter of the total change at higher points in the distribution of scores. In science the characteristics effect is larger, accounting for 20-25% of the total. The third component (residuals) had a small and generally not statistically significant effect, especially around the middle of the distribution. Below the 20<sup>th</sup> percentile it contributed negatively to the over-time change in the mathematics test scores, while it accounted for a small part of the increase in science scores, only below the 30<sup>th</sup> percentile.

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Table 11: Agenda Seting Power, by Gender and Position

||||_Depe _|_ndent variabl_|_e:_|
|---|---|---|---|---|---|
||Next|Same|% Next|5 Same|Length Topic|
||(1)|(2)|(3)|(4)|(5)<br>(6)|
|Female|0.07|0.06|0.02|0.02|0.13<br>0.10|
||(0.05)|(0.04)|(0.04)|(0.03)|(0.17)<br>(0.14)|
|Citizen|0.11<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|0.32<sup>_∗∗∗_</sup><br>0.24<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.07)<br>(0.08)|
|Female x Citizen|_−_0.11<sup>_∗_</sup>|_−_0.10<sup>_∗_</sup>|_−_0.05|_−_0.04|_−_0.33<br>_−_0.31|
||(0.06)|(0.05)|(0.05)|(0.04)|(0.23)<br>(0.20)|
|District FE|✓|✓|✓|✓|✓<br>✓|
|Backwardness Score Control|✓|✓|✓|✓|✓<br>✓|
|Topic FE||✓||✓|✓|
|Female President Control|✓|✓|✓|✓|✓<br>✓|
|Observations|1,651|1,651|1,456|1,456|1,605<br>1,605|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the district, in parenthesis. Te Backwarndess Score is an measure of village level development, calculated using demographic and infrastructal variables, including the share of population belonging to the Scheduled Castes and Tribes, as well as indicators for the presence of a primary or secondary school, hospital or medical clinic, and bank.

of gender, we plot the interaction between gender and position in Figure 3. Among politicians, women are slightly more likely to shape the agenda than men (Figure 3a); by contrast, among citizens, women are consistently _less_ likely than men to drive the agenda, and for the length of the topic discussed, these differences are statistically significant at the 0.05 level (Figure 3b).

Finally, to ensure that these results are robust to alternative specifications of the topic model itself, we re-run the analysis with varying number of topics ( _K_ = _∈{_ 20 _,_ 30 _}_ ) and find largely consistent results (presented in Appendix 2B).

To be fair, disparities in agenda-seting power may be inconsequential from a development perspective if men and women tend to discuss the same issues; however, if there are issues that are disproportionately addressed by women, who are also more likely to get ignored, then we may be particularly worried about development outcomes. To examine whether men and women do in fact overlap or differ in the issues they discuss, we plot the expected difference in topic proportions between male and female citizens, along with the 95 percent confidence interval, for all non-proforma topics (Figure 4). While we see no significant differences between men and women for the bulk of issues (sanitation, employment, service failures, housing, etc.), we do see stark differences on particularly _gendered_ issues, including self-help groups (women) and the 30
In the following sections, we first discuss the data used to assess biomass availability and household behavior. Section 3 presents the methods used. Section 4 is the results section that includes a description of biomass availability and fuelwood use; an analysis of the determinants of household welfare and the role played by fuelwood scarcity; and coping strategies and women’s labor allocation. Section 5 presents conclusions.

# **2. DATA**

Our main interest in this paper is to understand household dependence on biomass for energy and see how scarcity may affect household welfare. In order to understand the relationship between poverty and fuelwood use, we estimate biomass availability in Malawi based on satellite data. We combine these estimations with household level data from a recent household survey.

## <u>Remote Sensing and Biomass Data</u>

Several sets of remotely sensed data were used to estimate biomass distribution in Malawi. The first is 8 days composite data recorded by the Moderate Resolution Imaging Spectroradiometer ( MODIS) that are atmospherically corrected and cloud cleared.<sup>4</sup> These data, which provide average surface reflectance over 8 days with a spatial resolution of 463.3 meters, are used to map the amount and spatial distribution of standing biomass at the year 2004. The second is a set of Orthorectified Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data for the year circa 1990 and circa 2000 respectively.<sup>5</sup> These data provide surface reflectance at the resolution of 30 meters and are used to accurately identify training samples for the estimated biomass model. The MODIS Continuous Field of View (MOD44B) product provides information on vegetation density and is used to separate forested land from areas of non-forest. Elevation information for Malawi is extracted from the Global Digital Elevation Model (GTOPO30) with a horizontal grid spacing of 30 arc seconds (about 1 km).<sup>6</sup> In order to estimate biomass at the year 2004, we need to identify the relationship between remotely sensed data and biomass. For this purpose, we use a study undertaken by the Ministry of Forests and Natural Resources, Government of Malawi in 1993: _Forest Resources Mapping and Biomass Assessment for Malawi_ . The report provides detailed land cover maps for Malawi

> 4 MODIS data are available at http://delenn.gsfc.nasa.gov/~imswww/pub/imswelcome/

> 5 Landsat images are available at http://www.landsat.org/

> 6 GTOPO30 was developed at the U.S. Geological Survey's Center for Earth Resources Observation and Science (EROS).

6
_m_<sup>_DD_</sup> _zt_ from April to September <u>(</u> _<u>n</u>_ <u>)</u> and the monthly average of _m_<sup>_DD_</sup> _zt_ from October to December ( _<u>n</u>_ <u>).</u> The anticipatory slump is quantified by _ε_<sup>_S_</sup> and hence its positive value implies that imports drop in the months before an upcoming tariff reduction relative to a reference period in the year. We include product fixed effects _Dz_ to account for product seasonalities that are not eliminated by the double difference of the trade flows.<sup>10</sup> The estimation equation (6) captures the response of trade flows to the (annual) change in tariffs in the narrow time window between _<u>n</u>_ in year _t −_ 1 and _<u>n</u>_ in year _t_ .<sup>11</sup> For example ∆1:4 _−_ 11:12 _m_<sup>_DD_</sup> _zt_ takes the log difference between the monthly average of _m_<sup>_DD_</sup> _z,t−_ 1 from November to December of the previous year <u>(</u> _<u>n</u>_ <u>)</u> and the monthly average of _m_<sup>_DD_</sup> _zt_ from January to April ( _<u>n</u>_ <u>).</u> Again, to control for seasonal effects we include product fixed effects. A large negative estimate of _ε_<sup>_B_</sup> would indicate firms anticipating the upcoming tariff reduction by delaying their orders until the tariff reduction is implemented.

# **2.2 NAFTA’s Phaseouts**

NAFTA was signed by the three executives of the United States, Canada and Mexico in December 1992 after a lengthy negotiation. It took another year to be approved by legislators in the three countries. The US Congress ratified it on November 20, 1993, and US President Clinton signed it into law on December 8. NAFTA finally came into force on January 1, 1994. The agreement incorporated Mexico to the preferential FTA. Canada and the United States signed in 1988, and brought a major elimination of tariffs for trade with Mexico. By then, Canadian-US tariffs had mostly been removed. As a result of these tariff reductions, Mexico’s share over total US imports almost doubled between 1994 and 1999. Because NAFTA was one of the first major preferential FTA and its tariffs reductions undercut the Most-Favored-Nation (MFN) rates, NAFTA has been widely studied to evaluate the gains from trade and, hence, to estimate the trade elasticity.<sup>12</sup>

> 10 Results are robust to using more aggregate (less restrictive) seasonal fixed effects.

> 11Only for this specification we have used ln(1 + _Micztn_<sup>),sothatwemaintainallobservationsfor</sup> which we estimate the anticipatory decline.

> 12See for example Kruegger (1999), Head and Ries (2001), Fukao et al. (2003), Romalis (2007), Kehoe and Ruhl (2013), Caliendo and Parro (2015).

7
and company websites (FactSet, 2021).<sup>6</sup> FactSet analysts map the resulting information in consistent records of normalized relationship types, taking into account links detailed in a firm’s own records and the records of its partners (“reverse” links). By monitoring additional sources and “reverse” matching links, the database provides an exceptionally comprehensive view of supply chain linkages relative to other databases such as Compustat Segment (Huang et al., 2023).

As of March 2024, the relationship record of more than 54,000 “source” or reporting companies has been actively monitored, yielding a dataset of more than 2 million relationship links that involve approximately 360,000 entities. Table A.3 presents an overview of the list of variables obtained from FactSet. For each link, information on the start and end date is included, with historical data available from 2003. Global coverage starts from 2014, with the latest addition of Latin America in 2016.<sup>7</sup> Furthermore, the nature of each relationship is specified. For our study, we focus on customer and supplier relationships, which we supplement for some of the analysis with information on ownership ties and other firm characteristics. At each point in time, we observe the parent and ultimate parent firm of a company, and if applicable, any subsidiaries.<sup>8</sup> Additionally, the dataset provides information on firm name, industry affiliation, headquarter country, and entity type which we leverage throughout our analysis. Information on the firm’s annual sales, converted to constant 2020 U.S. dollars, is added whenever available.

We construct the agribusiness supply chain network by keeping all direct customer or supplier links that involve at least one agribusiness company. We identify this latter based on their primary NAICS 6-digit code, including relevant industries from the agriculture,

> 6In financial reporting, a 10-Q is a company’s quarterly report, containing unaudited financial statements, while the 10-K is an extensive annual report with audited financials, executive details, and structure. An 8-K is filed by firms to swiftly update shareholders on unscheduled, important events like management shifts, investigations, deals, closures, layoffs, or bankruptcy, often leading to multiple filings within a quarter for prompt communication. U.S. listed firms must for instance detail any customer that accounts for more than 10% of its revenue (Gofman and Wu, 2022), with some firms voluntarily disclosing additional customers (Huang et al., 2023).

> 7Note that we already observe many records of Latin American source companies in 2014, suggesting a backward updating of relationship for Latin American firms.

> 8The parent or ultimate parent will be identical to the company itself for self-owned firms.

8
tried to compensate for the lower interest rate by charging higher fees. Higher fees were also observed by an analysis of individual loan contracts, which also revealed that transparency suffered as banks stopped providing the total effective cost of the loan as an APR (World Bank, 2014). The increase in fees was not always explicitly mentioned and non‐interest fees were sometimes expressed as a ratio, making it difficult for customers with limited financial literacy to understand the full cost of the loan.

**Figure 11: Effect of interest rate caps on in Zambia**

<!-- Start of picture text --> Net loan growth (% yoy) Fee to interest income ratio ‐ MFIs<br>80% 0.2<br>60%<br>0.15<br>40%<br>0.1<br>20%<br>0.05<br>0%<br>‐20% 0<br>Bank loans MFI loans<br>Jun‐12 Dec‐12 Jun‐13 Dec‐13 Jun‐14 Dec‐14 Jun‐15 Dec‐15 Jun‐16 Dec‐16 Jun‐17 Mar‐11 Sep‐11 Mar‐12 Sep‐12 Mar‐13 Sep‐13 Mar‐14 Sep‐14 Mar‐15 Sep‐15 Mar‐16 Sep‐16 Mar‐17<br><!-- End of picture text --> _Source:_ Bank of Zambia Data in Figure 12 suggest that following the implementation of the cap MFIs also tried to reduce overhead costs by increasing the average loan size ‐ large loans are cheaper to administer ‐ and thinning out their branch networks. The MFI sector experienced some consolidation and several institutions, especially some consumer lending MFIs that had charged very high rates prior to the cap, exited the market.

In November 2015 the Bank of Zambia removed all caps and issued a circular on the disclosure of fees to promote transparency in pricing. Assessing the direct impact of the repeal is difficult as it coincided with a period of significant economic distress in Zambia, which strongly affected the financial sector and impacted interest rates. Still, data suggest that following the repeal lending rates started to increase, for SME loans the median spread over the policy rate rose from the capped 9% in June 2015 to 19.5% in June 2017, and the price differentiation across banks widened. Parallel to rising interest rates banking fees seemed to 26