rafmacalaba/datause-encoder-data huggingface dataset:
municipalities to provide a far more disaggregated look at convergence over a longer period, while also addressing distributional concerns. The data set includes variables on income, poverty, and inequality, spans 22 years (five waves of data over 1992–2014), and covers 2,361 municipalities. To the best of our knowledge, this is the first study that implements Ravallion’s (2012) method using municipal-level data.

The rest of the paper is organized as follows. Section 2 reviews the relevant empirical and theoretical literature on convergence, highlighting the scarcity of work exploring within-country poverty convergence at a high level of geographical disaggregation. Section 3 presents the methodology used to construct the dataset, which is a novel exercise applying small area estimation techniques to household surveys and population censuses to compute comparable measures of mean household per capita income, poverty headcount ratios, and the extent of inequality at the municipality level. Sections 4 and 5, respectively, test for convergence in mean per capita incomes and poverty headcount ratios across municipalities. The analysis emphasizes comparisons among subgroups of municipalities that exhibit sizable disparities (for example, urban versus rural, and municipalities located in states along the U.S. border versus the rest) as well as across subperiods to contextualize the various changes Mexico has experienced over the years (that is, economic crises, ups and downs in overall poverty rates, and the expansion of public expenditure). Both analyses explore the role of redistributive programs, such as targeted cash transfers to the poor and fiscal redistribution to subnational governments, in driving these changes. Section 6 digs deeper to explore the role of the initial distribution of poverty and inequality in determining the speed of convergence and decomposes the estimated magnitude of poverty convergence. Section 7 brings together the main messages of each section to conclude. Taken together, the findings suggest that income and poverty convergence have taken place at the municipality level in Mexico during 1992–2014 and that redistribution has played an important role.

# **2 Literature review and past evidence in Mexico**

The theoretical and empirical literature on economic growth offers stylized facts on which the analysis of economic development paths at the municipality level can be anchored. A first widely studied stylized fact stems from the influential works of Baumol (1986) and Barro and Sala-i Martin (1991, 1992, 1995) on the convergence hypothesis, often labeled the catch-up effect or the advantage of backwardness, whereby poorer countries tend to experience more rapid economic growth rates than richer countries, in effect, catching up to the latter.

3
occurrence of climate extremes in Tanzania and her key trading partners. In order to better understand those potential responses, we use a suite of climate model experiments to project 21<sup>st</sup> century changes in (a) the occurrence of extreme dry years in Tanzania and her key trading partners; (b) how often Tanzania experiences a dry year in the same year that her key trading partners do not experience dry years; and (c) how often each of Tanzania’s key trading partners experiences a dry year in the same year that Tanzania does not experience a dry year.

# **4.2.1 – MODELS, SCENARIOS, AND CLIMATE ANALYSES**

We analyze climate model results from the Coupled Model Intercomparison Project (CMIP3) (Meehl et al., 2007). CMIP3 has archived results from multiple general circulation models (GCMs) developed at climate modeling centers around the world. The archive has been extensively analyzed by the international community, and the multi-model output served as the backbone of much of the Working Group I contribution to the Fourth Assessment Report (“AR4”) of Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2007).

The CMIP3 archive contains GCM simulations of the pre-industrial period, the 20<sup>th</sup> century, and various scenarios of the 21<sup>st</sup> century, including a number of the 21<sup>st</sup> century “SRES scenarios” produced by the IPCC in its Special Report on Emissions Scenarios (Nakicenovic and Swart, 2000). Varying numbers of models have archived varying subsets of results for these different simulations. Together, these various climate model simulations create an “ensemble of opportunity” that has been used to analyze a wide variety of phenomena in the climate system, including detection and attribution of historical climate change (e.g. Santer et al., 2009), the sensitivity of global mean temperature to elevated greenhouse forcing (e.g. Knutti et al., 2008), the response of extreme events to global warming (e.g., Tebaldi et al., 2006; Knutson et al., 2008; Diffenbaugh and Scherer, 2011), and the potential impacts of climate change on natural and human systems (e.g. Williams et al., 2007; Ahmed et al., 2009; Battisti and Naylor, 2009; Loarie et al., 2009).

In the present study, we focus on the CMIP3 simulations that have been forced by the IPCC A1B scenario ( Nakicenovic and Swart , 2000). The cumulative CO2 emissions and global mean temperature change are very similar in the IPCC illustrative scenario suite over the first half of the 21<sup>st</sup> century (Meehl et al., 2007). The A1B scenario falls near the middle of the illustrative suite in the second half of the 21<sup>st</sup> century, with global atmospheric carbon dioxide concentrations reaching between 600 and 800 parts 22
14 Table 9: Two Stage Least Squares for Barro Type regression and Inequality variables using Barro regressors, Log of per capita ODP, Squared Log of per capita GDP, Africa dunmmy, Latin America dummy and Socialist Government dummy as instruments.

|Dep. Var|(2)<br>LGPC7088|(3)<br>LOPC7088|(4)<br>L()PC7088|(5)<br>LOPC7088|
|---|---|---|---|---|
|# of Obs|72|72|72|79|
|Constant|0.08266**<br>(2.96)|0.0526*<br>(3.11)|0.0721**<br>(3.23)|0.0350*5<br>_(2.53)_|
|SODPPC70<br>____________|-0.0032*<br>___<br>(-2.00)|-0.0032**<br>(-2.02)|-0.0033**<br>(-2.14)|-0.0030*<br>(-2.20)|
|REVC7085|-0.0084<br>(-0.82)|-0.0080<br>(-0.81)|-0.0088<br>(-0.89)|-0.0047<br>(-0.47)|
|ASSP7085<br>-|-0.0542**<br><br>(-2.31)|_-0.0566_*<br>(-2.45)|-0.0574*0<br>(-2.53)|4.0436**<br>(-2.26)|
|PP170DVN|-0.0060*<br>(-2.23)|-0.0064<br>(2.42)|-0.0065**<br>(-2.51)|-0.0061 s<br>(-2.20)|
|CPRIM60|0.0165<br>(1.38)|0.0172<br>(1.46)|0.0160<br>(1.40)|0.0220*<br>(1.87)|
|CSEC60|0.0113<br>(0.46)|0.0082<br>(0.33)|0.0059<br>(0.24)|0.0124<br>(0.60)|
|SOV7088|-0.0725*<br>(-2.27)|40.0714**<br>(-2.28)|-0.0710 *<br>(-2.32)|-0.0556*<br>(-1.90)|
|_GINI_|-0.1265**<br>(-2.47)||||
|THEEL||-0.0707*<br>(-2.59)|||
|COBFPVAR|||-4.0454**<br>(-2.70)||
|RTP40||||-0.0041 e<br>(-2.21)|
|R-Squared|0.32|0.34|0.35|0.28|
# **Exhibit 2.6 – Investment indicators by industry, 2007** _<u>Medians</u>_

||**Travel and**<br>**Leisure**|**Construction**<br>**and Materials**|**Consumer**<br>**Staples**|**Industrial**<br>**Cyclical**|
|---|---|---|---|---|
|**Yearly net fixed asset growth, %**|||||
|Peer group|-3.6|-4.6|-3.4|-4.5|
|Egypt - Top tier|12.5|-2.7|-6.3|-3.2|
|**Retained earnings to Equity, %**|||||
|Peer group|19.6|17.2|23.0|14.7|
|Egypt - Top tier|1.2|28.4|2.1|22.2|
|**Price to book ratio**|||||
|Peer group|1.6|1.7|1.5|1.6|
|Egypt-Top tier|6.5|1.9|1.2|3.5|

_Source: Worldscope; World Bank staff analysis_

# **Exhibit 2.7 – Statistical differences between the median Egyptian firm and the median benchmark group firm** _<u>Quantile regressions for period 2003-2007</u>_

||_Difference_<br>**Median diffe**<br>**countries, (pp**|_Statistically_<br>_different?_<br>**rence with LMI**<br>**)**|_Differen_<br>**Media**|_ce_<br>_Statistically_<br>_different?_<br>**n difference with peer**<br>**group, (pp)**|
|---|---|---|---|---|
|**Net fixed asset growth, %**|2.9|Yes|-1.7|No|
|**Price/Book Value Ratio**|0.8|Yes|0.5|Yes|
|**Retained Earnings to Equity, %**|-3.2|No|-0.4|No|

_Note: Differences are expressed in percentage points. Statistically different refers to a p-value less than 0.1._

# _2.1.3 Leverage: Higher debt levels with an emphasis on short term liabilities and a reliance on trade credit_

Firms can finance their activities in several ways. The research literature suggests that companies can raise money easier in more developed markets. In that respect, Egyptian firms are doing particularly well. <mark>Exhibit 2.8</mark> shows that relative to both the LMI group and the peer group, they exhibit significantly higher debt to equity levels (+9.5 pp). In addition, a superior total liabilities to equity level (+31.3 pp) further indicates Egyptian firms use either more trade credit and/or obtain funds from other creditors. To finance their activities Egyptian firms seem to rely much more on short term liabilities (+ 9 pp), which confirms that trade finance is important. Lastly, relative to the peer group, because of the high profitability, Egyptian firms are in a good position to take on more debt, as evidenced by a higher interest coverage ratio.

On the sector level, we observe higher debt to equity ratios for all sectors except for Consumer Staples which has also been deleveraging with 8.6 percent per year (Exhibit 2.9). Deleveraging has also been taking place in Industrial Cyclical, but it still carries more total debt. The importance of trade finance for all sectors is again confirmed by higher total liabilities to equity level across the board. Higher short term debt levels for all sectors (except Consumer Staples) points to the relative importance of shorter maturities in Egypt. Interestingly, Industrial Cyclical seem to have been substituting long term debt for short term debt, whereas Travel and Leisure is reducing both short term and long term liabilities, likely driven by its extreme profitability.

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road projects applies to violence against civilians (Tables 2 and 3).

Table 2: Roads and One-Sided Violence (UCDP)

||(1)<br>events|(2)<br>events|(3)<br>events|(4)<br>fatalities|(5)<br>fatalities|(6)<br>fatalities|
|---|---|---|---|---|---|---|
|project ongoing|-0.031**|||-0.044**|||
||(0.012)|||(0.022)|||
|project completed||-0.045***|||-0.081***||
|||(0.015)|||(0.028)||
|road project stock|||-0.030***|||-0.067***|
||||(0.011)|||(0.019)|
|Observations|18722|18722|18722|18722|18722|18722|
|R2|.349|.349|.35|.295|.295|.297|

Note: * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001. Standard errors are clustered at the cell level. Cell and year-fixed effects are always included. Events and fatalities are in logs (+1).

Table 3: Roads and Violence against Civilians (ACLED)

||(1)<br>events|(2)<br>events|(3)<br>events|(4)<br>fatalities|(5)<br>fatalities|(6)<br>fatalities|
|---|---|---|---|---|---|---|
|project ongoing|-0.006|||-0.028|||
||(0.017)|||(0.025)|||
|project completed||-0.062**|||-0.080**||
|||(0.027)|||(0.039)||
|road project stock|||-0.026|||-0.048*|
||||(0.018)|||(0.027)|
|Observations|18722|18722|18722|18722|18722|18722|
|R2|.496|.496|.497|.36|.36|.36|

Note: * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001. Standard errors are clustered at the cell level. Cell and year-fixed effects are always included. Events and fatalities are in logs (+1).

Second, we also get a strong negative relationship between road projects and battles using ACLED data. Battles relate to events linked to fights for territory and armed clash. The coefficients remain negative and significant for the number of events but are not significant any more for the number of fatalities. Last, the results for non-state violence and state-based violence using UCDP are insignificant (Table A3).

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reduction in value added taxes was captured by the top of the two deciles of the distribution. However, since everyone is now paying less taxes, the marginal contribution to inequality is now smaller, but this change is only minor, a decline in the impact from 0.0051 to 0.0048 Gini points.

In contrast, the reduction in the tax rate for personal income taxes had a much larger impact on inequality. Since personal income taxes continue to have a flat tax, the level of progressivity in relative terms, as measured by the Kakwani index, has not changed. However, when considering the absolute amount of tax relief, since the top of the distribution have higher incomes, most of the gains of the changes in policy were concentrated at the top of the distribution. In fact, 55 percent of the PIT tax relief benefited the top two deciles. As a result, the redistributive impact of personal income taxes has been substantially reduced, as their marginal contribution to the redistributive effort fell from 0.012 to 0.007 Gini points.

<!-- Start of picture text --> Figure 15. Impact of changes in the PIT and VAT by decile<br>(percent of market income + pensions)<br>A. Value Added Taxes B. Personal Income Taxes<br>20% 12%<br>Baseline Simulation Baseline Simulation<br>10%<br>15%<br>8%<br>10% 6%<br>4%<br>5%<br>2%<br>0% 0%<br>Source: World Bank estimates based on Romanian HBS (2016).<br><!-- End of picture text --> Thus, the result of the decline in PIT and the VAT rates was an increase in inequality. The Gini coefficient for Disposable Income increased with a lower PIT flat rate, while there is nearly no impact from the change in VAT (Figure 16A).<sup>22</sup> An increase in inequality is likely not what the authorities were hoping to achieve with these reforms.

However, when it comes to poverty, the impact of lower VAT and PIT rates is expected to be poverty reducing. Poverty after taxes and transfers, measured by consumable income and using the US$5.50 PPPa-day poverty line is expected to have declined from 15.4 to 15.0 percent due to the lower PIT rate (Figure 16B), which is still higher than the poverty rate prior to taxes and transfers. If the VAT rate were to decline to 18 percent, this would further reduce the poverty rate, but only to 14.8 percent, which would still be insufficient to mitigate the impact of the burden of taxes in the system.

> 22 Note that the reduction in the redistributive effort of the PIT might be even larger if the household survey were able to accurately account for top incomes, which are typically underreported in household surveys.

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quadratic term respectively, 0 is an intercept, 1 are the respective slope estimates, refers to a normally distributed error term. nn bb bb . . . bb εε <u>Dependent variable</u> For the dependent variable, we use the logarithm of the average monthly wage within the past year from a person’s primary job (variable in the RLMS data set). If a person had an additional job, the maximum wage value among the two (variables and ) was selected for the analysis. In the waves from 1994 to 1996, the question mentioned above was absent; for those JJ13.2 waves, we exploited a variable about the average amount of money earned by a respondent within JJ13.2 JJ40 the past 30 days (variable ) as a reasonable approximation. <u>Independent variables</u> JJ10

# <u>Independent variables</u>

The present research uses both metric (measured in years) and categorical education variables. The metric version was created by assigning the average expected number of years corresponding to each attained education level. For the categorical version (EDUC), we distinguished three categories: _(1) secondary, (2) vocational_ and _(3) higher_ . Incomplete levels were incorporated into the respective upper categories (e.g., incomplete higher into higher). Vocational education here includes the International Standard Classification of Education (ISCED) levels for vocational education: 35, 45 and 55.<sup>2</sup> We are interested in exploring returns to education in general, and vocational and higher education. Estimations of premiums to primary and secondary schooling levels are technically not possible since there is a minuscule proportion of people with only primary education or lower. The experience variable was calculated as a potential experience, subtracting from the current age the years of education minus 6 (the typical school starting age). Regression (1) was estimated separately for each year for the entire sample and separately for males and females. The Appendix (Tables A1 to A23) presents the results for each year.

We are particularly interested in the returns to specific levels of education, estimated through a series of dummy variables. Using Secondary Education completed as the base or omitted dummy for purposes of interpretation, we use dummy variables for vocational and higher education. The specification is presented in equation (2): L 0 1 V 2 H 3 ⋅E 4 ⋅E (2) VV HHhe LL(W LLWW) = WW + WW ⋅DD + WW ⋅DD + WW pp+ WW pp<sup>2</sup> + εε

# **4. Results**

Results of equation (1) for the whole sample are shown in Figure 2 with an adjoining graph showing the increase in the mean years of education over the period 1994 to 2018. Returns by each year in the Russian Federation need to be considered carefully because of the high educational attainment of the population. There are hardly any individuals in the sample who have less than a high school education (precisely 35 of 1,000 as shown in Figure 1), and only a handful of

> 2 The ISCED classification as it is applied to the Russian Federation is graphically explained in the OECD online publication accessible at https://gpseducation.oecd.org/CountryProfile?primaryCountry=RUS.

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these were the first major long-distance divided highway networks to be developed in India. The construction of the GQ changed national supply networks and led to a substantial reallocation of manufacturing firms into the GQ corridor (Datta, 2012; Khanna, 2016; Ghani, Goswami and Kerr, 2016). The economic impact of the NS-EW corridor has so far been little studied due to its later completion date.

# **III Data**

To estimate effects of new roads on forest cover, we combine five different national data sources. We use a validated high resolution satellite-based measure of forest cover. Data on rural roads come from the administrative implementation data generated by the rural road construction program, and geographic data on new major highway networks come from national highway maps. While these datasets form the basis of our core specifications, we also use data from the 1991, 2001 and 2011 Population Censuses and 3rd through 6th rounds of the Economic Census to control for location characteristics and explore mechanisms of treatment effects. All of these are census datasets that describe the entire population of India and are geocoded to the village, town and subdistrict levels. This section describes the details of how we prepare and combine all of these datasets. Table 1 shows summary statistics for all variables used.

# **III.A Forest Cover**

Detailed and reliable administrative records on forest cover and deforestation rarely exist, especially in developing countries. Instead, we obtain high resolution time series estimates of forest cover using a standardized publicly-available satellite-based dataset. Vegetation Continuous Fields (VCF) is available at 250m resolution and provides annual tree cover from 2000-2014 in the form of the percentage of each pixel under forest cover (Townshend et al., 2011). For our primary specification, we define forest cover as the total log pixel value plus one in a given geographic area.<sup>5</sup> Results are robust to using the average percentage of

> 5 Results are robust to using the inverse hyperbolic sine transformation instead of log plus one.

8
TradeLens (container tracking data) TradeLens, started in 2018 and discontinued in 2023, was a data and document sharing data platform bringing together shipping lines and other participants in container logistics. The project was a collaboration between IBM and GTD Solution, a division of shipping conglomerate Maersk. TradeLens brought together over 1,000 entities involved in the global supply chain, including over 200 ports and terminals, over 15 custom authorities and, by mid-2022, was facilitating the information exchange of about 65% of containerized trade.

# <u>TradeLens data model</u>

TradeLens used a data model with three related classes: Consignments, Transport Equipment and Shipments. It tracked consignments, transport equipment (containers) and shipments, while managing the identifiers and relationships between them. The TradeLens data structure allowed a consignment to be sequentially in multiple transport equipment (ship, rail, barges, truck), along with other consignments. It also allowed transport equipment to be part of multiple consignments.

The LPI 2023 dataset retrieval for this report was defined by the data provider for a period of 6 months between May 1, 2022, and October 31, 2022. The sample contained timestamps for 11 events for four transport modes (Ocean, Road, Barge, and Rail) and two load statuses (Full or Empty), associated with over 3 million unique tracked consignments (container voyages) and over 30 million observations in total. The dataset covers over 11,000 UN/LOCODEs<sup>14</sup> (including destinations, origins, and live locations (locations of specific event’s timestamps).

# <u>Process framework and definitions of phases</u>

The conceptual framework for processing the data is presented in Figure 4 and with more detail in Appendix 1. The dataset contained a sequence of events and their timestamps for each consignment. On average, there were 9.8 events associated with each consignment. Additional static information on each consignment includes UN/LOCODEs for origin and for destination.

Compared to aviation and postal tracking data, the container data is more complex. Events do not happen in a strict sequential order, as for UPU or Cargo iQ. Shipping transshipments or land corridors at origin and destination increase the number of possible combinations.

The sequence of a container is as follows (possible but not compulsory legs are in italics):

1. An empty container is sent from a depot to the exporter’s premise where it is stuffed (sending from the depot is a registered event, not the stuffing)

_2. The full container arrives at an inland facility_ _3. The container leaves the inland facility (by rail, barge, or by default truck)_ _4. Processes 2-3 are repeated at intermediate facility_ 5. The full container arrives at the port of export 6. The container is loaded on vessel 7. The vessel departs

> 14 <u>https://unece.org/trade/cefact/unlocode-code-list-country-and-territory</u>

9