rafmacalaba/datause-encoder-data huggingface dataset:
(comparison 3), producing districts display higher socioeconomic outcomes than non-producing districts, but also display higher inequality. However, when we only select sub-samples of these districts for the comparison, small differences emerge. In columns (1) and (2), we compare producers with nonproducers in non-producing provinces and in the same province, respectively. In both cases results weaken for poverty and extreme poverty. Moreover, when we compare producers with non-producers in the same province (comparison 2), the inequality differences are no longer significant. Finally, in column (4) we restrict the analysis to non-producing districts only. In accordance with the OLS results, no differences subsist between districts in producing and those in non-producing provinces _except_ for higher inequality.

In Table 6, we go beyond average mining effects and study whether the _magnitude_ of mining activity affects socioeconomic outcomes. We measure the magnitude of mining activity in a given district as (the log of one plus) the value of mining production in the district between 2002 and 2006; similarly, we measure mining activity in a province as (the log of one plus) the value of mining production in the province between 2002 and 2006.<sup>10</sup> We then replace the dummies in Equation (1) with these two variables.

Results in Table 6 look almost identical than under average effects. However, their interpretation remains more nuanced, since the _magnitude_ of mining activity now matters. In regressions (2) and (3), with department dummies, an increase in district-level mining activity leads to better socioeconomic outcomes, even when correcting for other government transfers per capita. At the same time, higher mining activity also leads to higher inequality. On the other hand, similarly to the results reported in Table 4, for a non-producing district the value of production in other districts in the province does _not_ affect any socioeconomic outcome apart from raising inequality. Such a result may come as a surprise since higher production in a province is associated with higher _Canon_ transferred to all districts in that province, and suggests a rather weak effect of the _Canon_ – something that we explore below in greater details. In regressions (4) and (5) we compare districts within provinces. The results are similar but somewhat weaker than in the regressions with average effects of Table 4. While higher values of production affect positively socioeconomic outcomes (and, again, also lead to higher inequality), the estimates for poverty and extreme poverty, while keeping the correct sign, are now statistically insignificant.

> 10 To avoid double counting, for each district we subtract from this last variable the value of production in the district itself.

15
|**Table 3a: Correl**|**ation of Vote buy**<br>**_(Robust std. er_**<br>(1)|**ing with Municipa**<br>**_rors in parenthese_**<br>(2)|**l Health Expenditu**<br>**_s)_**<br>(3)|**res**<br>(4)|
|---|---|---|---|---|
||<br>Share of health<br>in 2008 total<br>spending|<br>Share of health<br>in 2008 total<br>spending|Average share of<br>health in total<br>spending (2007-<br>|Average share of<br>health in total<br>spending (2007-<br>|
|**_Bivariate specification, without_**|<br>**_and with district f_**<br><sup>~~**~~</sup>|<br>**_ixed effects:_**<br><sup>~~**~~</sup>|2008)<br><sup>~~*~~</sup>|2008)<br><sup>~~**~~</sup>|
|Vote Buying|-0.035|-0.046|-0.03|-0.043|
||(0.01)|(0.02)|(0.02)|(0.02)|
|**_Multivariate specification:_**|<sup>~~***~~</sup>|<sup>~~*~~</sup>|<sup>~~**~~</sup>|<sup>~~*~~</sup>|
|Vote Buying|-0.047<br>(0.02)|-0.054<br>(0.027)|-0.045<br>(0.02)|-0.05<br>(0.03)|
|Mayor’s family reported as<br>major employer or landowner|-0.01<br>(0.02)|-0.01<br>(0.015)|-0.015<br>(0.014)|-0.01<br>(0.01)|
|Indicator variable = 1 if no clan<br>dominates municipal politics|-0.006<br>(0.01)|-0.007<br>(0.01)|-0.008<br>(0.009)|0.007<br>(0.01)|
|Municipal dependency on<br>national revenue share|-0.09<br>(0.09)|-0.09<br>(0.09)|-0.13<br>(0.085)|-0.13<br>(0.09)|
|Municipal poverty estimate|0.00047<br>(0.0004)|0.00045<br>(0.0004)|0.0007<sup>~~**~~</sup><br>(0.0003)|0.00066<sup>~~*~~</sup><br>(0.0004)|
|Municipal population (log)|-0.009<br>(0.01)|-0.0085<br>(0.008)|-0.015<sup>~~*~~</sup><br>(0.008)|-0.014<br>(0.01)|
|District Fixed Effects:|No|Yes|No|Yes|
|District 1||-0.0015<br>(0.015)||0.005<br>(0.01)|
|District 2||-0.0057<br>(0.02)||-0.003<br>(0.02)|
|District 3||-0.008<br>(0.02)||-0.004<br>(0.02)|
|N, R-sq|30<br>0.197|30<br>0.204|30<br>0.269|30<br>0.2824|

33
using different methodologies such as setting negative values to 0 or using absolute values with different thresholds for counting a value as 0.

To provide some further details into the noise and distribution of the VIIRS data, Figures 2 and 3 show the distribution of VIIRS values below 2<sup>5</sup> for all countries and their populated areas and the number of cloud free days per month. Looking at Figure 2 first, one sees a clustering on both sides of 0, with the bin containing 0 and very small values constituting more than 15 percent. Furthermore, more than 13 percent of the total points have a negative value and 46 percent more have a value between 0 and 0.3, meaning that almost 60 percent of all points have negative or very small values. It is also worth noting again that these are only points that have been identified as being populated,<sup>6</sup> meaning that they should be relatively free of disturbance from non‐human sources. Despite this, there are still very small deviations from 0.

Even when limiting the light to cells with population over 50 (approximately 30 percent of populated cells), the distribution stays similar with 85 percent of light values being below 2, 75 percent below 1, 46 percent below 0.3 and 5 percent being negative. This distribution pattern follows through to high population numbers. When looking at population numbers above 1,000 (less than 12 percent of the total) 17 percent of the points still have nightlight values below 0.3 albeit only 0.3 of points have values below 0. The question will then be if VIIRS is only useful for very urbanized areas, if at all.

> 5 Values below 2 constitute approximately 95 percent of the total. This was done due to the maximum VIIRS value being 17,699 and hence a density graph would be meaningless going from ‐1.5 to 17,699. Also, any bin above 2 is very small and would not contribute to the graph.

> 6 See the next section for details on population layers.

8
- Lane, Philip R. 2003. "Business Cycles and Macroeconomic Policy in Emerging Market Economies." _International Finance_ 89-108.

- Lloyd, Simon, Ed Manuel, and Konstantin Panchev. 2024. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk." _IMF Economic Review, Palgrave Macmillan;International Monetary Fund_ vol. 72(1), pages 335-392.

- Mendoza, Enrique G., and P. Marcelo Oviedo. 2006. "Fiscal Policy and Macroeconomic Uncertainty in Developing Countries: The Tale of the Tormented Insurer." _NATIONAL BUREAU OF ECONOMIC RESEARCH._

- Miningou, Elise Wendlassida. 2019. _Quality Education and the Efficiency of Public Expenditure: A Cross-Country Comparative Analysis._ Policy Research Working Paper;No. 9077, Washington, DC: World Bank.

- Ostrom, Elinor. 2015. _Governing the Commons - The Evolution of Institutions for Collective Action._ Cambridge: Cambridge University Press.

- Riascos, Alvaro, and Carlos A. Vegh. 2003. "Procyclical Government Spending in Developing Countries: The Role of Capital Market Imperfections."

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- Tornall, Aaron, and Philip R. Lane. 1999. "The Voracity Effect." _American Economic Review_ 2246.

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- Velasco, Andrés. 1999. "2. A Model of Endogenous Fiscal Deficits and Delayed Fiscal Reforms." In _Fiscal Institutions and Fiscal Performance_ , by edited by James M. Poterba and Jurgen von Hagen, 37-58. Chicago: University of Chicago Press.

- Wagner, Adolph. 1958. "Three extracts on public finance." In _Classics in the theory of public finance_ , by Richard A. Musgrave and Alan T. Peacock, 1-15. London: Palgrave Macmillan UK.

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Table 5: Unconditional estimations for services sector

|||DV: Fem|ale-owned ent|rant share||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log total incumbent employment<br>in district-industry|0.002<br>(0.002)|-0.003<br>(0.002)|0.001<br>(0.002)|-0.003<br>(0.002)|-0.010+++<br>(0.003)|
|Log female-owned incumbent<br>businesses in district||0.013+++<br>(0.002)||0.012+++<br>(0.002)|0.009++<br>(0.004)|
|Log female-owned incumbent<br>businesses in district-industry|||0.011+++<br>(0.004)|0.006<br>(0.004)|0.012+++<br>(0.002)|
|District Traits:||||||
|Female literacy rate|0.012+<br>(0.006)|0.010<br>(0.006)|0.012+<br>(0.006)|0.010<br>(0.006)|0.013++<br>(0.006)|
|Sex ratio|0.001<br>(0.004)|0.000<br>(0.004)|0.001<br>(0.004)|0.000<br>(0.004)|-0.002<br>(0.004)|
|Population density|-0.016+++<br>(0.005)|-0.015+++<br>(0.006)|-0.016+++<br>(0.005)|-0.014+++<br>(0.005)|-0.011++<br>(0.005)|
|Education level|0.011++<br>(0.005)|0.008+<br>(0.005)|0.008+<br>(0.005)|0.007<br>(0.005)|0.004<br>(0.005)|
|Age profile|0.017+<br>(0.009)|0.013<br>(0.010)|0.016+<br>(0.009)|0.013<br>(0.009)|0.018+<br>(0.010)|
|Infrastructure level|0.019+++<br>(0.006)|0.017+++<br>(0.006)|0.016+++<br>(0.006)|0.015+++<br>(0.006)|0.013++<br>(0.005)|
|Labor regulations stringency|0.011++<br>(0.004)|0.010++<br>(0.005)|0.011++<br>(0.005)|0.011++<br>(0.005)|0.011++<br>(0.005)|
|Sector|Informal|Informal|Informal|Informal|All|
|Observations|4,292|4,292|4,292|4,292|4,458|
|Adjusted R-squared|0.224|0.232|0.225|0.233|0.220|

Notes: Regressions consider relative rates of female entrepreneurship across services district-industries in India. Regressions include industry-year fixed effects. Regressions include unreported controls for log district population and its square, total fertility rate, and travel time to one of the ten biggest cities in India. None of these variables are economically or statistically significant. Regressions weight observations by an interaction of log district size and log industry size. Regressions cluster standard errors by district.
# INFLATION. MONETARY BALANCES AND THE AGGREGATE PRODUCTION FUNCTION

# THE CASE OF COLOMBIA

## _I. ITRMQD-UCTION_

1.01 The role of monetary balances in economic growth has long been a topic of macroeconomic research, see, among others, Levhari and Patinkin (1968). Following the work of Sinai and Stokes (1972), a number of studies have given this perspective empirical content. By demonstrating the significance of the role of monetary balances in an aggregate production function, this work has shown that money and financial policy need to be carefully considered in studies of growth. Nore recently this type of empirical analysis has been extended to the production function of a developing country, Pakistan, by Khan and Ahmad (1985). Their results are consistent with the findings for the U.S. and Japan --that is, real money balances are an important factor of production.

1.02 This paper also examines the role of real money balances in an aggregate production function of a developing economy, Colombia. In addition to being a developing country, Colombia is also interesting to analyze in this way for a number of other reasons. First, although it has experienced high and variable rates of inflation, it has also introduced a competitive system of quasi-monetary bulances that have been indexed for inflation. In fact, the success of this system appears to have played a significant role in the continued expansion of the Colombian financial system. See the World Bank (1987), and Barro (1975).

1.03 Over the 1974 to 1984 period, for example, Colombia was one of the few Latin American countries not to experience any significant reduction in broadly defined monetary assets as a share of GDP.<sup>11</sup> In contrast to most other Latin American countries, Colombian monetary balances continued to grow more rapidly than did the economy without any significant interruptions. It would be interesting to determine whether this more competitive and buoyant financial system reduced the costs of this factor of production by such an extent that it affected the level of economic growth.

I/ Data on M2\GDP for Argentina, Brazil, Chile, Colombia, Mexico, and Peru for the 1974-84 period indicate that all of these countries except Colombia at least once experienced a substantial reduction in broadly-defined monetary balances as a share of GDP. The Colombian ratio was much less volatile, as well as almost monotonically increasing. In one year, 1979, there was a less than one percent decrease in M2/GDP. A log linear regression of M2/GDP-Aeut, where t-time, for each of the six countries for the 1974-84 period (Mexico only 1977-84) indicates that in Colombia the coefficient on time was positive and significant at the one percent level, and the R<sup>2</sup> was equal to .68. The results for the other countries did not yield a significant time trend except for Brazil, but in Brazilian case, the coefficient was negative. Source for the data is International Financial statistics.
the spot market rates—the fact which has not been explored in the trade literature. In practice, importers closely watch annual contract rates as they determine their trade volumes for the entire year.<sup>31</sup> Third, if annual-contract markets dominate containerized shipping, the within-route price dispersion should be detectable in the transaction-level datasets, conditional on them reporting freight rates. Two papers have recently conducted this type of analysis: Holmes and Singer (2018) and Ardelean and Lugovskyy (2021).

**Stylized Fact 20.** _There are two types of containerized shipping markets: long-term contract market and spot market. The rates may differ significantly between the two._

Both document rate and link it to the and papers pronounced freight dispersion firm shipment sizes: larger firms and larger shipments face lower freight rates. The papers use different datasets and provide different interpretations of these findings. Holmes and Singer (2018) use Bills of lading filed with the U.S. Department of Customs and Border Protection (CBP). They attribute the lower freight rates faced by larger trading firms to the container indivisibility and the ability of larger firms to pack shipments fuller than smaller companies.<sup>32</sup> Ardelean and Lugovskyy (2021) use Chilean Imports reported by Panjiva. They explicitly model price-discriminating carriers with trading firms getting quotes from carriers, where each quote is subject to a fixed cost of processing. Larger firms have greater bargaining power in this setting as they have greater incentives to obtain more quotes. This advantage disappears if the number of carriers is limited so that all firms, large and small, obtain the same number of quotes.

Empirically, they find that the 90<sup>_th_</sup> percentile importing firms pay up to 23% lower freight rates than the 10<sup>_th_</sup> percentile firms. In line with their theoretical predictions, they also find that price discrimination disappears in the thin markets—i.e., in markets with three carriers or fewer. These results indicate that reducing market power distortion in the upstream industry (containerized shipping) may amplify the informational friction distortion in the downstream industry (freight rate

> 31See https://www.freightwaves.com/news/importers-face-massive-hike-in-ocean-contract-costs

> 32The main advantage of their dataset is in observing container types and sizes and specifying whether a container is filled to capacity. The disadvantage is in not observing important variety-specific characteristics, such as price and weight per unit.

31
successive surveys conducted during 2002-2006. Here, “distinct” means that if an SME uses 2 products of the same type (e.g., 2 checking accounts) with the same bank or uses the same product with 2 different banks, the product will only be counted once. The figure shows that the number of banking products used by SMEs from all the banks they deal with ranges from 4.5 in Venezuela to 8.5 in Argentina. The average number of products and services across the 7 economies in the sample is 6.8. This evidence is consistent with the evidence collected by the IFC and shown in Figure 11, which describes separately the number of deposit and credit products used by SMEs, but not the number of other products and services used.<sup>45</sup> Figure 16 provides information on the number of products used by SMEs disaggregated by product type for 2006. The top panel shows the average number of deposits and savings products used by SMEs. The middle panel shows the average number of financing products used by SMEs, while the bottom panel shows the average number of services and other products used by SMEs. On average, SMEs use 1.7 deposit and savings products, 1.9 financing products, and 4 services and other products. The numbers are relatively similar across countries, with Venezuela ranking lower in all cases. Interestingly, the number of services and other products is typically larger than the number of deposits, saving, and financing products combined.

Tables 3, 4, and 5 provide more detailed information on the type of products and services used by SMEs in 2006. The tables report the fraction of surveyed SMEs that claim to be using a given bank product or service. Table 3 focuses on the deposit and

> 45 The World Bank Investment Climate Assessments also show evidence consistent with this finding. According to these surveys of firms across countries, most SMEs have a bank deposit account, but a much smaller share use bank credit. The difference shrinks with firm size; the number of companies having bank credit rises as firms are larger. The combined evidence thus suggests that non-lending products are quite common in the relation of banks with SME clients.

36
**Table 5. Capacity utilization factors of electricity generation technologies (%)**

|Technology|IEA (2015)|Lazard (2019)|IRENA (2018)|NREL (2019)|
|---|---|---|---|---|
|PV (Utility)<br>|11-21|21-34|12-27|15-27|
|CSP<sup>#</sup>|34-60|39-68|32-60|50-64|
|Wind-Onshore|20-49|38-55|25-54|23-48|
|Wind-Offshore|30-48|45-55|26-50|28-45|
|Gas-CC|85|55-70|n.a.|87|
|Gas-GT|85|10|n.a.|30|
|Geothermal|80-92|n.a.|68-94|80-90|
|Hydro|40-63|n.a|23-74|60-66|
|Coal|85|66-83|n.a.|85|
|Nuclear|85|90-91|n.a.|92|
|Biomass|48-80|n.a.|44-94|56|

# Higher values (> 40%) correspond to CSP with storage facilities.

**Figure 9. Trends of LCOEs along with capacity availability factors**

<!-- Start of picture text --> 200<br>LCOE vs Capacity Factor PV CSP<br>180 Wind Onshore Wind offshore<br>Gas CC Gas GT<br>Geothermal Hydro<br>160<br>Coal Nuclear<br>Biomass<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Capacity Availability Factor (%)<br>LCOE ($/MWh)<br><!-- End of picture text -->

# **5. Historical Trends of Electricity Generation Costs**

How do the costs of electricity generation technologies evolve over time? Costs of renewable energy technologies are rapidly declining; what about electricity generation costs with fossil fuel-based technologies and nuclear technology? In this section, we try to answer these questions.

29
34 discriminant analysis incorporates too many variables (12) for purposes of classification. More specifically, it is possible to think that by incorporating the political indexes for the period of 3 years following the devaluation we are providing "too much" information. In order to investigate how the results were affected by these considerations we also undertook discriminant analyses using separately the historical political variables, on the one hand, and the post-devaluation indexes on the other. When historical variables only were used the results were still very supportive of the theory: only five of the episodes were misclassified. One of the episodes classified as successful on economic grounds (Chile) is classified as "limited success" on political grounds; another 3 economically successful (Ecuador 1979, Colombia 1967, and Egypt 1970) devaluations are misclassified as unsuccessful when the political criterion is used; finally one of the episodes classified as unsuccessful on economic grounds (Israel, 1971) is misclassified as having had limited success when the political criteria is used. All in all, however, the<sup>results arestill</sup> remarkable with only 12.8% of the episodes being misclassified.26 DISCRIMINANT ANALYSIS 2: In this exercise we used indicators on frequency of government change, overall violence, unsuccessful transfer attempts and political executions (variable (7) through (10) in our list). In the initial analysis we used these indicators for all three time periods: historical, for one year after and three years after the<sup>devaluation.</sup> When this was done, all 39 episodes were classified correctlyl In order to check for the robustness of these results, we also performed a discriminan **t** est

> 26When instead of using the hs.tgriAlc politLcal variables only, we restricted the analysis to the post (3 years) devaluation indicators, the results were similar: only 5 (different) episodes were misclassified.
provided by the state). The decision required the schools to admit a random selection of applicants from poor households and to integrate the scholarship students into the classes of the non-scholarship students. Beginning in 2007, rich students (typically from above the 95<sup>th</sup> percentile of the consumption distribution in Delhi) shared classes with students who were on average, at the 25<sup>th</sup> percentile of the distribution.

In Delhi, most children enter school at the age of 4 and remain in the same school through the higher grades. The court order applied only to new admissions of children. Beginning in either 2007 or 2008, depending on the school, cohorts of students in the affected schools were made up of 20 percent poor students. By using controls for fixed differences among the schools to which the court order applied, Rao identifies the impact of desegregation (the “treatment”).

A sequence of activities and games permitted Rao to compare the behavior of treated and untreated students. Students at the elite schools were invited to participate in weekend activities to help fundraise for a charity serving disadvantaged children. Treated students were 10 percent more likely to volunteer than non-treated students.

Rao had the children play dictator games. This is a game that economists use to measure generosity towards anonymous others. In a dictator game, there are two players—a dictator and a recipient. The dictator is given an endowment (in this case, it was $0.20, which is 10 Indian rupees). The dictator has to decide how to split the endowment between himself and the recipient. Treated students shared 45 percent more of their endowment than non-treated students.

30