rafmacalaba/datause-encoder-data huggingface dataset:
PUBLIC INVESTMENT AND ECONOMIC GROWTH IN MEXICO Ulrich Lachler David Alan Aschauer World Bank Bates College Mexico's growth rate began to plummet roughly at the same time that its public investment level declined. That decline also coincided with a slowdown in the growth of infrastructure capital in the electric, transport and communications sectors; Feltenstein and Ha (1995). In view of these parallel developments, many economists have attributed at least part of the blame for Mexico's growth decline on the lack of adequate public investment.

The notion that public investment and economic growth are positively related is often taken for granted. It underlies, for example, the Mexican Government's official economic strategy for promoting growth in 1997 and 1998.1 There are several reasons, however, for suspecting that such a relationship may not be stable or may only hold in certain circumstances. Identifying those circumstances is important from a policy viewpoint in order to ensure that public spending has the expected impact and does not lead to resource misallocation. The next section lists the main reasons why public investment can either have a positive or a negative impact on overall economic growth. This is followed by an empirical analysis of the impact of public investment in Mexico, using both a time-series and cross-country approach.

# A. **Supply-Side Links Between Public Investment and Growth**

If public and private capital are perfect substitutes, an increase in public investment would have the same effect on growth as an increase in private investment. That is, both contribute to the accumulation of physical capital, which increases the capacity to sustain a higher level of output. An extra Peso spent on public investment, however, may not necessarily lead to an additional Peso in physical capital because public investment may displace or crowd out private investment. Such crowding out could happen in one of two ways: financially, if the public sector finances the increase in public investment with higher taxes or by borrowing in the domestic capital market, and physically, to the extent that the public sector investment exhausts market opportunities that could have been filled by the private sector. The first occurs when capital is not perfectly mobile internationally. In that case, public borrowing in the domestic capital market drives up domestic interest rates, which reduces private investment demand. The amount of physical crowding out, on the other hand, depends on whether the public investment involves "public" or private goods and, thus, substitutes for or is

> lEconomic Policy Guidelines (Criterios Generales de Politica Econ6mica) for 1997 (pg. 3) and 1998 (pg. 17). The latter states, for example, that the strategy for promoting economic growth rests on two fundamental pillars: the maintenance of favorable conditions to support higher private investment levels and continued impulses to public investment.
_COTE D’IVOIRE: Volatility, Shocks and Growth_

# **Figure 18: Cycles in De-Trended Output per Worker, Terms of Trade, and Real Exchange Rates**

<!-- Start of picture text --> Terms of Trade, Real Exchange Rate, & Per Worker Output<br>0.5 Cycles in Deviations from Trend<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>Terms of Trade Real Exchange Rate Output/Worker<br>Log of Ratio of Actual to Trend<br>Year 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001<br><!-- End of picture text -->
|(2)<br>ln(light)<br>-0.272<br>[0.283]<br>-0.671**<br>[0.276]<br>-1.889**<br>[0.820]<br>0.116<br>[1.031]|4,879<br>263<br>c linear time trends.<br>tries over the period<br>e city, and averaged<br>work distance to the<br>tively. Distances are<br> Spot Price FOB) in<br>ute to the primate is<br>mate) indicates that<br>rs, clustered by city,|
|---|---|
|(1)<br>ln(light)<br>-0.693***<br>[0.247]<br>-0.663***<br>[0.255]<br> poptop20% not primate)<br> poptop20% not primate)|4,879<br>263<br>and city fxed efects, and city-specif<br> 287 cities in 15 coastal primate coun<br>mber, summed across all pixels in th<br>nd dist(poptop20%) are the road net<br> in the top population quintile, respec<br>ally the annual average Europe Brent <br> a dummy indicating that a city’s ro<br>primate. 1(nearest poptop20% not pri<br>of is DN=5.5. Robust standard erro<br> percent level, respectively.|
|edian)<br>edian)<br> median)*1(nearest <br> median)*1(nearest|ncludes country*year<br>ced annual panel of <br> the lights digital nu<br>ble. Dist(Primate) a<br>n the same country) <br> price of oil (specifc<br>paving _<_ median) is <br>that country to the <br>imate. The tobit cut<br>he ten, fve, and one|
|m<br> m<br> _<_<br> _>_|i<br>n<br>of<br>a<br>(i<br>e <br> <br>n<br>r<br> t|
|te)*_Poil_*1(primate route paving _<_ <br>te)*_Poil_ *1(primate route paving _>_ <br>p20%)*_Poil_*1(primate route paving <br>p20%)*_Poil_*1(primate route paving|ons<br>ed cases<br>n is a separate tobit regression that<br> analysis is the city-year, for a bala<br> The dependent variable is the log <br>lite-years within a year when applic<br> in the country and the nearest city <br>n thousands of kilometers. _Poil_ is th<br>f dollars per barrel. 1(primate route<br>than the route of the median city i<br> city in the top quintile is not the p<br>kets. *, **, *** mean signifcance at|
|a<br>a<br>o<br>o|i<br>r<br>m<br>of<br>. <br>el<br>y <br> i<br>o<br>d<br>t <br>c|
|dist(prim<br>dist(prim<br>dist(popt<br>dist(popt|Observat<br>left censo<br>Each colu<br>The unit <br>1992–2008<br>across sat<br>largest cit<br>measured <br>hundreds <br>more pave<br>the neares<br>are in bra|

36
# **_Figure 2_**

_Maize Production in Tanzania_ <!-- Start of picture text --> Thousand of metric tons<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012<br><!-- End of picture text -->

**Source** : U.S. Department of Agriculture

**_Figure 3_**

_Maize Price in Dar es Salaam and Export Bans_ <!-- Start of picture text --> TZ Shillings/kg (real 2010, CPI-deflated)<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14<br><!-- End of picture text -->

**Source** : FAO GIEWS, newspaper articles, and interviews with industry representatives

34
**Table 4: Closing SAR’s Infrastructure Gap Will Require Investing a Higher Share of GDP** _Investment Requirements 2011-2020 (% of GDP, per year)_

||Ind|ia|Bangl|adesh|Paki|stan|N|epal|Sri L|anka|SAR|(5)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Low**|**High**|**Low**|**High**|**Low**|**High**|**Low**|**High**|**Low**|**High**|**Low**|**High**|
|**Transport**|1.97|3.44|3.60|4.50|0.98|1.23|2.32|3.49|2.17|3.64|1.97|3.31|
|**Power**|2.17|2.71|1.10|1.65|3.66|5.49|3.34|4.46|0.97|1.82|2.22|2.89|
|**WSS**|0.55|0.94|1.20|1.80|0.53|0.80|1.08|1.62|0.12|0.37|0.57|0.96|
|**Solid**<br>**Waste**|0.19|0.38|0.21|0.42|0.19|0.38|0.24|0.30|0.04|0.27|0.19|0.38|
|**Telecomm**|0.87|1.30|0.50|0.50|0.71|0.71|0.27|0.40|0.40|0.50|0.82|1.19|
|**Irrigation**|0.81|1.21|0.77|1.15|0.55|0.83|0.99|1.48|0.50|0.63|0.78|1.17|
|**Total**|**6.55**|**9.98**|**7.38**|**10.02**|**6.63**|**9.44**|**8.24**|**11.75**|**4.21**|**7.23**|**6.55**|**9.89**|

_Source:_ Authors’ calculations. _Note:_ Water Supply and Sanitation (WSS)

**The bottom line is that South Asia region needs to invest US$ 1.7 trillion to US$ 2.5 trillion (in current prices) in infrastructure until 2020 – equivalent to US$ 1.4 trillion to US$ 2.1 trillion at 2010 prices** (Table 3) **.** Going forward, a mix of investing in infrastructure stock and implementing supportive reforms will allow SAR to close its infrastructure gap. In GDP terms, if investments are spread evenly over the years until 2020, SAR needs to invest between 6.6 and 9.9 percent of 2010 GDP per year – an increase of up to 3 percentage points compared with the 6.9 percent of GDP invested in infrastructure by SAR countries in 2009 (Table 4).<sup>13</sup>

# **5. Infrastructure Investment Trends**

**What are the odds that SAR can put together enough funds to meet these investment targets?** A look at past infrastructure investment trends should shed some light on this question.

## **5.1 Total Sector Investment**<sup>**14**</sup>

> 13 These percentages are based on the investment requirements at 2010 prices.

> 14 While the main source of the data for this sub-section are multi-year development plans prepared by the National Planning Commissions, annual reports from ministries, state banks, and other related government agencies have also been used in order to form as much as a complete picture as possible. The data used is a mixture of estimated and actual expenditure, as not all plans state actual expenditure from the previous plan or fiscal year. Furthermore, these plans do not distinguish between Capital and Operational Expenditures (CAPEX and OPEX). For public sector investment, South Asia is defined as: Bangladesh, Bhutan, India, Nepal, and Pakistan. Afghanistan, the Maldives and Sri Lanka are not included in this definition due to data limitations.

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# **For Online Publication: Appendix**

## **A Comparisons with Contemporaneous Work**

There are at least two possible reasons why the conclusion of our paper differs from that of Jaravel (2018), a contemporaneous work which concludes that higher SNAP take-up rates among eligible households reduce inflation rates. At a high level, we differ in (1) data series and time period; (2) explanatory variation; and (3) methodology used to address endogeneity. Whereas we examine both Nielsen Retail Scanner and Nielsen Consumer Panel prices over 2006-2015, Jaravel (2018) examine Nielsen Consumer Panel prices over 2004-2008. Whereas we focus on the intensive-margin variation in SNAP benefits using an IV approach, Jaravel (2018) focuses on the SNAP take-up rates by regressing 2004-2008 prices on 2001-2007 SNAP take-up rates among eligible households using a long-difference approach.

One possible reason for the discrepancy is that both conclusions are valid and limited only by external validity: that is, mechanisms specific to each time period and/or each explanatory variation (intensive versus extensive) explain the difference.

Another possible reason may be biases due to endogeneity or measurement error in using take-up rates to proxy for benefits. Jaravel (2018) writes that take-up rate changes were mainly affected by state-level policy changes and that “these policies had a large impact on the take-up rate for food stamps and their implementation across states was not correlated with economic fundamentals such as unemployment,” mentioning Ganong and Liebman (2018). However, the results of Ganong and Liebman (2018) say that not all of the take-up rate changes during this period stemmed from state policy changes, and that much of the variation could have been endogenous. Indeed, Ganong and Liebman’s (2018) estimates that in some years between 2001 and 2007, 20% to 30% of the take-up variation could be attributed to changes in unemployment, suggesting that simply controlling for a long difference in unemployment rates between years 2001 and 2007 may not be sufficient to partial out unobservable economic trends spanning this period. Ganong and Liebman (2018) conclude that perhaps 25% to 50% of the variation could be explained by policy changes, with the rest of the variation left unaccounted for.

## **B Formalizing the Actual SNAP Formula and Instrumentation Strategy**

We first formalize the SNAP formula illustrated mathematically in Section 2.2. Comprehensively, let _Xi_ = _{Ni, Si, Ii, Ri, Ui, UIi, EIi, Ci, Di, Mi, Li}_ represent the set of a potential SNAP recipient _i_ ’s demographic characteristics the government sees at a given time period. Characteristics important to the government include _Ni_ , the person’s household size; _Si_ , the person’s state of residence; _Ii_ , the person’s gross income minus earned income deduction; _Ri_ , the person’s rent; and _Ui_ , the person’s utility expenditure. Let _p_ = _{b, o, u, h,_<sup>ˆ</sup> _h_<sup>ˇ</sup> _}_ represent the set of SNAP parameters, which are functions of _Xi_ . _b_ represents the maximum-benefits formula. The SNAP formula is given by 48
16 payments that HCFP provides, 32% of HCFP beneficiaries experienced catastrophic outof-pocket spending.

The lack of impact on out-of-pocket spending reflects the other main finding from the results—the large impacts that HCFP seems to have had on utilization. The effect is most pronounced for inpatient care: HCFP is estimated to have increased the probability of an inpatient spell by 30%, but the probability of an outpatient visit by only 16%; the number of inpatient spells by around 45%, but the number of outpatient visits by only around 20%. The marked increase in utilization of inpatient services helps explain the lack of reduction in out-of-pocket spending on inpatient care—a negligible change in cost per admission coupled with a rise in quantity of services used. The extra episodes of care brought about by HCFP have been delivered in just three of the nine types of facility examined: commune health stations, district hospitals, and provincial hospitals. None of the other public facility types have been significantly impacted by HCFP, with the possible exception of village clinics but the evidence is very weak. The private sector, by contrast, _has_ been significantly affected by HCFP, but the effect is _negative_ : HCFP has encouraged people to switch from private providers to public ones.

**Table 9** suggests that HCFP’s impact on average out-of-pocket spending has been similar for the poor and better off, but its impact on catastrophic spending has been felt largely among the bottom quintile. By contrast, as far as utilization is concerned, the impacts of HCFP seem to have been _smallest_ among the poor. Indeed, among the poorest decile, there is barely any evidence of significant impacts of HCFP on utilization at all. Among the second decile, there are five significant utilization impacts in **Table 9** , while among the richest 80% of the eligible subsample (most eligible individuals are in the bottom half of the income distribution, not the upper half), there are seven significant utilization impacts. Furthermore, HCFP has not significantly affected the use of the private sector by the poorest 10%—the significant (negative) impacts are to be found among the other deciles.
# **_4.2 Difference-in-difference-in-differences (DDD)_**

Our data allow us to know if a mother has any preschool-age child at any given year. We could also exploit the exogenous overlap in the time when mothers have an eligible child with the spatial and temporal variations in preschool access to estimate the preschool effect on maternal employment in a DDD setup. This affords us additional variation that we can difference withinyear and within-district. Specifically, we estimate: y��� �α�β TK�� ⋅Eligible�� �γ TK�� �δ Eligible�� �μ� �φ� �ψ Xijt �ε��� �2� where y���, TK��, μ�, φ�, and Xijt are as described above. Eligible�� is the dummy indicating if mother _i_ in year _t_ has any preschool-aged eligible children (age 3-6). The coefficient interacting preschool density and eligibility, β, is our DDD estimate.

Some studies studying the effect of childcare on maternal employment have used the age of the youngest child to define treatment category<sup>14</sup> ; we opt to define a more general treatment category pertaining to any child in the preschool age because, in Indonesia, the incidence of first birth has a more substantial negative effect on maternal employment than subsequent births (Halim, Johnson, and Perova 2017). Moreover, with detailed fertility history, we can infer the age of all children born to the mother at any given year and assign a treatment status to all mothers who might have benefitted from preschool access.<sup>15</sup> We can take a more naïve stance and we do not have to assume that mothers do not have the incentive to return to the workforce until the youngest child enters preschool.

Similar to Brodeur and Connolly (2013) and Herbst (2017), our non-eligible “comparison” group is the group of women without any preschool-aged children, which includes non-mothers and mothers with all children younger than or older than preschool ages. While employment trends of non-mothers may not be similar to those of preschooler-mothers, the identifying assumption in DDD is less stringent than that in DD. It requires that absent differences in preschool access (be it across regions or over time), the employment gap between mothers with preschool-aged children and mothers without preschool-aged children observe similar trends. Therefore, the common

> 14 See for example: Gelbach (2002), Baker et al (2008), Cascio (2009), Berlinski et al. (2011), Brodeur and Connolly (2013), Nollenberger and Rodriguez-Planas (2015), Herbst (2017), and Contreras and Sepulveda (2017). 15 In most cross-sectional data without detailed fertility history, e.g. Labor Force Survey or Census, we have to define treatment pertaining to the youngest child because the first child might have moved out of the household and, thus, is no longer observed. The youngest child, on the other hand, has a very small likelihood of living apart from her mother. With detailed fertility history, we know all children eligible for preschools and we define our treatment status accordingly.

11
across countries. Section III reports estimates of the impact of regulation on economic growth and the size of the informal sector. Section IV offers some concluding remarks.

# **II. Measuring Business Regulation**

In this section, we briefly describe our measures of regulation and their sources. We also discuss differences in regulation intensity across countries for different levels of economic development.

We construct indices to measure business regulation in the following seven areas: firm entry, labor markets, fiscal burden **,** trade barriers, financial markets, contract enforcement, and bankruptcy regulation. Each index is obtained as an average of related components, normalized to vary within a unit interval with higher values representing heavier regulatory burden.<sup>6</sup> The components used to construct the seven synthetic indices are obtained from the following data sources: Doing Business (The World Bank Group), Index of Economic Freedom (The Heritage Foundation), Economic Freedom of the World (The Fraser Institute), Labor Market Indicators Database (M. Rama and R. Artecona, 2000), The Corporate Tax Rates Survey (KPMG), and International Country Risk Guide (The PRS Group). These sources cover the largest number of countries and areas under regulation, and their measures use a clear methodology and are straightforward. Except for the Labor Market Indicators Database, all sources are public. Our sample covers 75 countries.

In most cases, data are based on surveys conducted in a single year (in the late 1990s) in a large group of countries; for components with observations for more than one year, we use average values over the period. Therefore, our indices should be interpreted as average regulation levels in the late 1990s. We should note, however, that regulation tends to stay constant over long periods of time.

Table 1 shows simple correlations between the seven regulation indices. The strongly positive correlations among all but the fiscal burden and labor indices suggest that regulation policy comes in “packages.” Judging from these correlations, we can distinguish three regulation categories: fiscal, labor, and product-market regulations, where

> 6 We refer the interested reader to Loayza, Oviedo and Servén (2005a) for details on the construction and components the business regulation indices.

3
|Non-agricultural self-employment|||0.09133**||||0.09147**||||0.09126**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||(0.04177)||||(0.04177)||||(0.04176)||
|Remittances & Others|||-0.00897||||-0.00891||||-0.00899||
||||(0.09640)||||(0.09645)||||(0.09638)||
|Property income and investments|||0.20760**||||0.20789**||||0.20747**||
||||(0.08436)||||(0.08439)||||(0.08435)||
|Transfers and other benefits|||0.11600***||||0.11615***||||0.11593***||
||||(0.04167)||||(0.04169)||||(0.04164)||
|Own > median size of land (2.5 acres)|||0.19984***||||0.19977***||||0.19987***||
||||(0.03191)||||(0.03192)||||(0.03191)||
|Observations|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|
|Kleibergen-Paap rk Wald F|17.39|17.39|13.58|12.70|20.40|20.40|15.57|14.46|14.46|14.47|11.71|11.04|
|Root MSE|0.594|0.594|0.534|0.362|0.594|0.594|0.536|0.364|0.594|0.594|0.534|0.362|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|education, occupation, other income sources,<br>|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|land ownership)|||||||||||||
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

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