rafmacalaba/datause-encoder-data huggingface dataset:
# **Decentralization and Fiscal Management in Colombia**

William Dillinger and Steven B. Webb

## World Bank

Since 1986, Colombia has been decentralizing its democracy and public finances, joining Argentina and Brazil in the ranks of Latin American countries with highly decentralized public sectors. In Colombia, political decentralization, in the sense of elected mayors in 1986 and then governors in 1991, came after spending had been substantially delegated to the local levels, starting in 1968. In contrast to Argentina and Brazil, Colombia implemented decentralization long after the transition from military to civilian rule and without the complication of hyperinflation. Indeed, on the one hand, Colombia has enjoyed a tradition and reputation for sound macroeconomic management, which was associated in some minds with central fiscal control or with centrally controlled fiscal deconcentration in the early 1980s. In the 1990s, on the other hand, the expansion of political and fiscal decentralization coincided with the expansion of fiscal deficits and a failure to share in the general increase in macroeconomic stability in most other parts of Latin America.

This paper analyzes the institutional features of fiscal decentralization in Colombia and its effects on macroeconomic stability. The paper identifies a number of weaknesses in Colombia's decentralization but finds that decentralization has not been the main cause of the nation's macroeconomic problems. The institutions for decentralization and overall macroeconomic management have prevented Colombia's serious political problems-guerrilla war and corruption from drug money-from causing even greater problems for economic policy and have mitigated the fiscal problems that have arisen in other countries from the decentralization process. Of course, fiscal stability is not the only measure of success for decentralization, and the extent or form of decentralization to date has not been adequate to solve Colombia's political problems.

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the 2015 flood, and to the annual expenditure level (see Figure 10), but these differences are not statistically significant. However, the findings call for further investigation: do impacted business-owning households face a trade-off between housing repairs and investing more productively in their business?

_Figure 10: Fraction of households who made investment in enterprise in the last year by different subgroups_ <!-- Start of picture text --> 35%<br>29% 29%<br>30%<br>26%<br>25%<br>25%<br>21%<br>20%<br>20% 18% 17%<br>15%<br>10%<br>5%<br>0%<br><!-- End of picture text -->

# **8.2. Prioritization of investments**

Results suggest that business-owning households that were affected by the flood are more likely to prioritize investments in their house over investments in their enterprise. In Table 11, we include results from business-owning households only, which show that affected households are significantly more likely to invest in their house and significantly less likely to invest in their business. This clearly supports the case that due to the flood, business-owning households allocate more resources to their house in order to improve and/or repair it than pursuing more productive enterprise investments. These results still hold when controlling for per capita expenditure and for flood risk perception as proven through a multinomial logit regression. The results of this are displayed in Table 37 in Appendix 10.

This finding suggests that impacts of floods on investment behaviors in income generating activities may have long term welfare implications on the household, and more generally on poverty reduction and even macroeconomic growth.

_Table 11: Choice between housing or enterprise investment by exposure_

||Affected<br>|<br>|||
|---|---|---|---|---|
||No|Yes|Differenc|e|
|House/Enterprise investment|||||
|No investment|65%|57%|-8%||
|Investment in house but not in enterprise|10%|25%|14%|***|
|Investment in enterprise but not in house|17%|8%|-10%|***|
|Investment in both house and enterprise|8%|11%|3%||
|Observations|375|212|587||

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forcing the parameters to be the same across the entire distribution of individuals may be too restrictive and may hide important information.

A simple solution and yet a powerful tool is to make use of quantile regression techniques. The method of quantile regression amounts to minimizing the absolute sum of errors rather than, as in least squares, minimizing the sum of their squares.

Formally the method, first developed by Koenker and Basset (1978), can be formulated as<sup>9</sup> yi = xi ′ βθ + uθi = Quantθ(yi | xi) = xi ′ βθ (2)

where Quantθ(yi | xi) denotes the θ<sup>th</sup> conditional quantile of y given x, and _i_ denotes an index over all individuals, _i_ = 1,...,n.

In general, the θ<sup>th</sup> sample quantile (0 < θ < 1) of y solves Buchinsky (1995) examines various estimators for the asymptotic covariance matrix and concludes that the _design matrix bootstrap_ performs the best. In this paper, the standard errors are obtained by bootstrapping using 200 repetitions, in line with the literature.

# **4. Data description**

# _4.1. The Surveys_

The data used in this study are drawn from surveys of manufacturing firms in Côte d’Ivoire conducted in 1995 and 1996. The surveys are part of the RPED, a multi-year study of the manufacturing sector in several African countries (Cameroon, Côte d’Ivoire, Ghana, Kenya, Rwanda, Burundi, Tanzania, Zambia, and Zimbabwe). The RPED was organized by the Africa Region Technical Department of the World Bank.

The RPED is designed to provide an overview of the performance of manufacturing firms in the post-structural adjustment period, and focuses on a wide variety of aspects of firm behavior. The survey instruments include several modules covering creation of the enterprise; the enterprise in general; interior competition; labor markets; financial markets; solutions to

> 9 See Buchinsky (1998).

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on the amount and quality of education acquired and negatively on the cost of education. One dimension of the quality of education is related to the enrollment decisions of peers. Higher enrollment by others affects both class size and the average quality of peers in a given school, which allows for (either positive or negative) enrollment spillovers in a given schooling market _m_ . Households choose the schooling option that yields the highest utility.

Household _i_ ’s utility from enrolling in schooling option _j_ in market _m_ is then given by: where utility is modeled as a function of: (1) observable school quality, _Xj,t_<sup>_m_;(2)theshareof</sup> ’marginal’ children enrolling in the same school type, _σj,t_<sup>_m_; and (3) a set of individual socio-economic</sup> characteristics _Z_<sup>_m_whichagainreflecthousehold’spreferencesforeducationoption</sup><sup>_j_inmarket</sup> _i,t_<sup>,</sup> _m_ as well as their ability to afford it. Before detailing the estimation procedure, we briefly outline how the relevant school-level characteristics are measured.

A large literature examining school choice finds that test scores are a good summary of the dimensions parents care about when enrolling in a particular school (Sandra E Black 1999, Mark Schneider & Jack Buckley 2002, Alejandra Mizala & Miguel Urquiola 2013). We therefore measure school quality, _Xj,t_<sup>_m_, by exam performance in public and private schools in the district.Specifically,</sup> we use performance on the Kenya Primary Certificate of Education (KCPE), a nationwide exam taken at the end of 8 years of primary school.<sup>11</sup> Since the exam scores are based on test-takers in grade 8, they are pre-determined with respect to the enrollment decisions modeled here. In addition, the vector _Xj,t_<sup>_m_also contains dummies for public and private school before and after FPE.</sup>

> 11KCPE results for those not enrolled are set to zero. This seems sensible if we think of the way in which students would benefit from their KCPE results: signaling their education level to future employers, gaining access to secondary school etc.. All of these avenues are not open to students who do not enrol in school. We also experimented with setting KCPE of the non-enrolled to 50 and 100, the latter of which is close to the lowest KCPE in school observed in the sample, and obtain identical results. Ideally, we would of course like to be able to link each household to a particular school and KCPE. The intra-class correlation within a district for KCPE varies between 15%-25% (across sectors and before and after FPE) and we would therefore argue that the collapsed scores contain some relevant information.

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Table 7. Estimated Fertility and Mortality Rates by World Bank Region and Country Department, 1985-2025 0

|Region and|||1|985-90|||||20|00|||||20|25|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country<br>department|CBR|CDR|TFR|NRR|<br>e0|IR|CUR|CDR|TFR|NRR|eO|IhR|<br>CBR|CDR|TFR|NRR|<br>eO|INR|
|World total|27.1|9.8|3.435|t.450|64.6|67|23.2|8.6|2.964|1.300|67.3|51|17.3|8.3|2.266|1.059|71.9|28|
|Borrowers and rno-borrowers|||||||||||||||||||
|Sub-Saharan Africa|47.0|15.3|6.497|2.396|51.5|105|40.0|11.2|5.489|2.196|57.1|80|24.1|6.5|2.747|1.251|66.8|42|
|Asia and Oceania|26.1|8.9|3.232|1.367|64.0|64|21.5|7.9|2.617|1.157|67.3|47|16.0|8.5|2.149|1.008|72.2|25|
|Europe, Middle East,|||||||||||||||||||
|and North Africa|23.5|10.7|3.116|1.365|68.1|60|21.0|9.6|2.900|1.304|69.7|47|16.5|9.2|2.246|1.052|72.9|27|
|America|23.2|7.9|2.827|1.315|7C.1|<br>44|19.3|7.3|2.462|1.164|72.2|34|15.1|8.5|2.084|1.000|75.1|21|
|Borrowers only|||||||||||||||||||
|Africa Region|48.0|15.7|6.669|2.442|50.9|106|40.8|11.5|5.649|2.249|56.6|81|24.4|6.5|2.776|1.263|66.7|42|
|1 OccidentalandCentral|47.3|15.4|6.586|2.453|52.0|103|40.0|11.3|5.644|2.279|57.5|77|23.2|6.4|2.618|1.200|67.5|40|
|2 Eastern|48.3|16.1|6.736|2.435|50.1|108|41.1|11.9|5.698|2.240|55.6|84|24.7|6.8|2.820|1.270|65.9|45|
|3 South-Centralard|||||||||||||||||||
|Indian ocean|46.1|14.8|6.407|2.394|52.0|101|39.5|10.8|5.443|2.203|57.8|77|24.0|6.2|2.759|1.271|67.7|**38**|
|4Westerm|48.9|15.3|6.806|2.513|51.2|104|41.0|11.0|5.676|2.280|57.2|78|24.2|6.2|2.736|1.252|67.1|40|
|5 Sahelian|47.3|18.8|6.477|2.217|46.8|127|42.3|14.3|5.875|2.198|52.3|99|26.2|7.9|3.031|1.326|63.1|55|
|6 Southern|48.0|14.8|6.689|2.482|52.2|99|40.6|10.7|5.700|2.254|57.9|76|24.3|6.1|2.764|1.271|67.7|39|
|Asia Region|26.9|9.1|3.323|1.392|63.0|66|21.9|8.0|2.653|1.168|66.6|49|16.2|8.3|2.155|1.008|71.9|25|
|1 Other South|38.4|14.3|5.194|1.867|52.4|114|32.6|11.1|4.065|1.598|56.9|90|20.2|7.7|2.432|1.087|65.9|47|
|2 Southeast|29.0|7.8|3.598|1.56|6 64.2|56|23.5|6.7|2.826|1.290|67.9|40|16.6|7.4|2.123|1.008|72.5|22|
|3 China|21.1|6.7|2.400|1.090|69.5|33|17.9|6.9|2.161|1.001|72.0|25|14.5|8.9|2.099|1.00|075.2|15|
|4 India|31.6|11.4|4.261|1.67|7 57.1|84|24.0|9.1|2.958|1.256|62.0|62|17.0|8.2|2.159|1.00|0 69.5|31|
|5 Irdonesi**a**nd Pacific|29.0|10.7|3.637|1.475|57.3|84|23.9|8.5|2.760|1.200|63.0|58|16.7|8.1|2.145|1.00|2 69.8|30|
|EMN Region|34.7|11.1|4.747|1.94|7 60.9|83|29.0|8.8|3.866|1.659|64.4|62|19.3|7.3|2.362|1.08|7 70.1|34|
|1 Pakistan and Turkey|41.0|12.3|5.559|2.178|56.3|96|31.7|9.3|4.232|1.738|60.5|75|21.1|6.9|2.553|1.16|6 68.1|39|
|2 North Africa|35.7|9.1|5.056|2.121|61.7|72|29.7|6.8|3.740|1.678|66.0|46|18.1|5.9|2.114|1.01|6 72.0|23|
|3 Middle East|40.4|11.3|5.740|2.287|58.4|87|32.9|8.4|4.429|1.887|63.1|65|20.0|6.6|2.337|1.06|6 69.5|38|
|4Europe|15.0|10.0|2.040|0.964|71.6|<br>25|14.3|10.2|1.993|0.945|73.6|18|13.1|11.6|2.074|0.994|75.9|12|
when using employment, or the combination of employment and exports, to construct the weights for the exposure to local automation measure.

# **5 Results**

## **5.1 Baseline Results for Exports and Imports**

Table 2 shows the OLS results for exports. The dependent variable is the change in total exports per worker (in log points) from each Mexican LLM to the US from 2005 to 2014.<sup>3</sup> As explained above, the exposure to US robots is the change in the number of robots per thousand workers by sector in the US, weighted by the initial export share of each sector within LLMs. Column 1 displays the most parsimonious specification, where we only control for State fixed effects. The coefficient associated with exposure to US robots indicates that an increase in one robot per thousand workers in the US is associated with a 9.5 log points (9 percent) decline in exports per worker growth from Mexico to the US.<sup>4</sup> This effect represents 8.5 percent of the overall increase in exports to the US during this period.

Column 2 (our preferred specification) includes baseline LLMs characteristics as control variables. These are in levels to capture different trends in exports across locations. While the value of the coefficient of interest declines, it changes only slightly to -7.28. Column 3 drops LLMs at the top 1 percent of exposure to US automation. The impact of US automation is still negative and significantly higher in absolute value, which suggests that potential outliers are not driving the results. To test if the results are affected by the secular decline in manufacturing or routine jobs, column 4 controls for the initial shares of employment in manufacturing, while column 5 controls for the share of workers in routine and non-routine manual occupations. Neither of these control variables seems to affect the

> 3 We use log points (i.e. ln(x) x 100) to facilitate the interpretation of the coefficients.

> 4The change in the dependent variable as a result of an increase in one robot per thousand workers is equal to _△ln_ � _Empexportsm,_ 2000 _m,_ 1 � - _△ ln_ � _Empexportsm,_ 2000 _m,_ 0 � _×_ 100 = 9 _._ 5, which implies that the growth percent change is equal to _e_<sup>-0</sup><sup>_._095</sup> - _≈_ -0 _._ 09, i.e. around 9 percent.

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* 11 -

advantage of the longer sample is that it captures several important turning points, including the debt crisis.

As noted, the sensitivity of capital flows to external factors such as international interest rates, the business cycle in industrial countries, and rates of returns on a variety of foreign assets, has been examined in a number of recent papers (see Frankel (1994) for a summary of this literature). The dependent variables potentially affected by the foreign variables have included international reserves and real exchange rates (Calvo, Leiderman, and Reinhart (1993)), bond and equity flows (Chuhan, Claessens, and Mamingi (1993), and total portfolio flows (Fernandez Arias (1994)). The main conclusion that emerges from these papers is that external variables play a significant role, at least for the larger countries. The measure of capital flows used in this study, the balance on the capital account including errors and omissions, is more comprehensive than in previous studies. The capital account balance is expressed as a percent of GDP; the real international interest rate used refers to a United States 3-month Treasury bill rate adjusted by the rate of inflation as measured by the consumer price index--an "ex-post" measure of real returns.

# 2. International interest rates, contagion, and capital flows

Given that the available capital account data is annual, thus limiting the number of observations per country to 24, there are considerable efficiency gains from pooling the time series data of the 11 countries in question. We thus focus on a panel of countries where the dependent variable is the balance on the capital account as a percent of GDP, while in this simplest of settings, the independent variable is a real short-term U.S. interest rate.

To further examine the role of common shocks, and as an intermediate step in analyzing whether there are spillover effects from the larger countries to the smaller ones, we use (as before) factor analysis. This is the same approach as in Calvo, Leiderman, and Reinhart (1993) and is
Using bank-level data, we first investigate the impact that larger exposure to the energy sector had on banks’ balance sheet outcomes and risk dynamics after the shock. We then use the loan-level data to examine separately the lending dynamics of firms in energy-related sectors and all other sectors. Focusing first on borrowers in energy-related sectors, we examine changes in the value and terms of their loans across banks after the shock. Our outcomes of interest include the total amount of credit borrowed by energy firms, as well as the amount borrowed to finance working capital and investment projects. Other outcomes we analyze include interest rates, collateral rates, and loan maturities. To investigate whether banks transmit this sector-specific shock to other sectors, we compare lending to non-energy firms by banks with different degrees of exposure to the energy sector before and after the energy price shock.

Since firms may switch to other financing sources to smooth bank credit shocks, we complement our loan-level data with firms’ yearly balance sheet information. The firm-level data allow us to identify whether shocks that affect the credit conditions of firms also affect their real outcomes. We construct a measure of exposure to the energy price shock at the municipality level. In addition, we investigate whether the states with more exposed banks had a sharper slowdown. We use quarterly gross domestic product (GDP) data for Mexican states and construct a state-level measure of exposure to the energy sector of the banks operating in the state.

We find that banks with greater ex-ante exposure to the energy sector significantly expanded their ex post lending to firms in the sector. This expansion took the form of larger loans for working capital at lower interest rates, suggesting that banks attempted to keep stressed firms afloat and their own capital ratios intact. For instance, an increase of one standard deviation in exante exposure to the energy sector (relative to the bank’s tier 1 capital) is associated with an increase of 1.2 percentage points in ex post exposure to the sector, an 18 percent increase in the size of loans to firms in the energy sector, and a 0.8 percentage point (roughly 6 percent) decrease in the interest rate. Consistent with a hold-up problem, these economically important magnitudes are concentrated among larger energy firms with which banks have greater exposures. This strategy was associated with a substantial increase in the risk taken by more exposed banks. An increase of one standard deviation in exposure to the energy sector results in 10.1 percent higher 4
**Table A4. First stage for baseline regressions, export rank 1-25**

||lo|g avg. dest. inco|me|expor|t share|log avg.<br>dest. distance|
|---|---|---|---|---|---|---|
|Instrument|(1)|(2)|(3)|(4)|(5)|(6)|
|export share of sales|0.51***||||||
|log avg. distance of destination market|-0.00***|0.02***||0.04***|||
|log sales|0.01***|0.01***|0.01***|0.00|0.01***|0.11***|
|United Kingdom|-0.01|-0.06*|-0.07*|-0.10***|-0.11***|-0.39|
|United States|0.00|0.01|0.01|0.03|0.02|-0.18|
|Sweden|0.15***|0.22***|0.24***|0.14***|0.20***|1.31***|
|Switzerland|0.12**|0.11*|0.12*|-0.01|0.01|0.55|
|Brazil|-0.04***|-0.05***|-0.05***|-0.02**|-0.01|0.43***|
|Norway|-0.07*|-0.05|-0.04|0.04|0.06|0.52|
|Japan|0.04**|0.05**|0.05**|0.02|0.02|0.11|
|Angola|-0.02***|-0.03***|-0.02***|-0.01**|0.00|0.31***|
|Singapore|0.00|-0.07|-0.07|-0.14***|-0.14***|-0.21|
|Israel|0.01|-0.00|-0.00|-0.02|-0.02|0.07|
|Turkey|0.05***|0.07***|0.07***|0.03*|0.04*|0.15|
|Australia|0.00|-0.02|-0.02|-0.04|-0.04|-0.05|
|Canada|-0.01|-0.01|-0.01|-0.00|-0.02|-0.29|
|Morocco|0.02|-0.01|0.01|-0.06|-0.02|0.92*|
|Hungary|0.06|0.04|0.03|-0.05|-0.06|-0.08|
|Russia|-0.04**|-0.03*|-0.03*|0.02*|0.02|-0.04|
|South Africa|0.00|0.00|0.00|-0.00|0.00|0.14|
|Chile|-0.01|-0.01|-0.00|0.01|0.03|0.36|
|Hong Kong|0.05*|0.09***|0.10***|0.08***|0.10***|0.40**|
|Poland|0.05|0.06|0.06|0.02|0.04|0.42|
|Cape Verde|-0.01|-0.16**|-0.15**|-0.30***|-0.28***|0.37|
|China|0.07|0.08|0.08*|0.02|0.04|0.32|
|Saudi Arabia|-0.04|-0.03|-0.04|0.01|-0.00|-0.24|
|Argentina|0.02***|0.02**|0.02**|-0.00|0.00|0.10|
|Tunisia|0.01|-0.08|-0.09|-0.17***|-0.19***|-0.51|
|initial source interactions|Y|Y|Y|Y|Y|Y|
|frm efects|Y|Y|Y|Y|Y|Y|
|year efects|Y|Y|Y|Y|Y|Y|
|N|45659|45659|45659|45659|45659|45659|
|R2|0.95|0.94|0.94|0.97|0.96|0.93|

Notes: Covariate corresponding to fourth row is (indicator for positive 1997 export revenues from UK)*(relative price level in UK, current year). Covariates in subsequent rows defined similarly. Table continued on following 3 pages. Robust standard errors in parentheses. *10% level, **5% level, ***1% level.
activity with other jobs. Beneficiaries engaged in textile activities have significantly lower income gains relative to those engaged in services or industrial activities.

Moreover, even though income gains are not significant for the average program participant they are concentrated in specific groups, possibly initially better positioned to take advantage of the program. In particular, individual income gains are larger (and significant) for those with more education, those beneficiaries 30-40 years old and those for whom MEP activities were related to an ongoing activity.

Finally, we briefly analyzed the profitability of the projects as a function of the beneficiaries’ characteristics. Kremenchutzky and Massad (2006) used a subsample of the 113 projects covered in our sample for which detailed cost and sales information was collected and compute their profitability.<sup>40</sup> Table A4 in the annex reports the averages of some variables of interest for three groups: projects with negative profitability (9), those with medium or low profitability (24) and projects with high profitability (32). The findings show that there is no clear systematic correlation between profitability and gender or with education. Projects with higher returns tend to have younger beneficiaries (around 38 years old), fewer household heads, less children in the household and a slightly smaller household size. Beneficiaries are also less likely to have an outside job and to work more hours (average of 32 hours) when the project has a higher rate of return. Finally, and one would expect, these beneficiaries also receive higher labor incomes.

> 40 They can obtain these estimates for a total of 65 projects. They compute the project’s profitability subtracting from total revenues (from sales) the cost of inputs and machinery and the estimated “minimum return” per each beneficiary involved (equivalent to 150 pesos, which is the _Jefes_ transfer). A project has a negative return if it does not raise enough revenues to pay for the inputs and the implied remuneration for each beneficiary.

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