rafmacalaba/datause-encoder-data huggingface dataset:
Next, we obtain the equation that determines the optimal public investment-to-output ratio. Using the government budget constraint, we obtain which can be substituted in (30) (rewrite those condition in terms of g) and obtain an expression for _x_ : Furthermore, we can use (51) and characterize, which combined with (31) leads to a condition for the optimal public investment size, _x_<sup>_+_</sup> : This expression is the same as in Marrero (2008), hence a similar discussion applies. As commented above, we characterize this condition with independence to the debt-sustainability condition. First, since (1+γ)<sup>σ</sup> −β(1−δγ) >0 (from the NPG) and ψ−π0<1 , x+ is positive. Moreover, + in principle, x might be above or below the threshold βθ(1−ψ−π0) . However, it cannot be above βθ(1−ψ−π0) by much, since it can be proved (see Marrero, 2008) that _x_ + cannot exceed θ(1−ψ−π0) which is the ratio that maximizes the steady-state growth rate (hence, it is also lower than one). Second, notice that an explicit expression for the optimal stationary policy cannot be obtained since _γ_ depends on economic fundamentals and policy parameters in a non-trivial manner.<sup>21</sup> Hence, changes in any parameter show a direct impact on _x_ + and an indirect effect through growth. Indeed, the private capital depreciation rate can only affect the optimal policy through the indirect channel. Moreover, although we do not elaborate on this in this paper, a second indirect channel (correlated with the first one) is through the debt sustainability condition.

The results of the model can be stated in accordance with their effects among key parameters, such as the EIS, 1/σ , or the public capital depreciation rate, δ g, on the optimal public investment ratio. The main findings of this sensitivity analysis are the following: (i) a lower IES (as well as a lower β ) lowers the optimal public investment-to-GDP ratio -- a low EIS means that households prefer flat consumption trajectories to volatile ones, hence they have little incentives to substitute

> 21 Futagami et al. (1993) pointed out that the optimal stationary public investment policy may differ substantially in an economy with transition dynamics, but they did not specify the condition for this. This condition contributes in this respect. The unique environment in which an explicit expression for the optimal public investment ratio can be determined is whenever u(c)=log(c) (i.e., σ=1 ) and δ g=1, where the optimal public investment-to-output ratio would be the standard βθ(1−ψ−π0) , which is the same than in Marrero and Novales (2007) but for π0 .

12
**Table 3.2. Macro indicators in 2022 and by scenario in 2030 (% of nominal GDP)**

|**Indicator**|**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta r**|**ec+ta+**|
|---|---|---|---|---|---|---|---|---|
|**Absorption**|136.7|138.5|138.9|138.7|138.7|137.7|137.8|137.7|
|**Consumption - private**|108.6|111.0|111.4|109.1|109.1|104.9|104.9|106.3|
|**Consumption - government**|19.3|18.8|18.8|15.9|15.9|15.4|15.3|14.0|
|**Investment - private**|8.0|8.0|8.0|11.2|11.2|13.7|13.7|13.8|
|**Investment - government**|0.7|0.7|0.7|2.5|2.5|3.8|3.8|3.6|
|**Exports**|7.5|7.2|8.1|8.9|8.9|10.1|10.1|11.9|
|**Imports**|44.2|45.7|47.0|47.6|47.6|47.9|47.9|49.6|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|3.1|2.6|2.5|2.3|2.3|3.0|2.9|1.9|
|**GDP at factor cost**|96.9|97.4|97.5|97.7|97.7|97.0|97.1|98.1|
|**GNI**|99.9|99.9|99.9|97.6|97.6|95.7|95.8|95.6|
|**GNDI**|136.6|138.5|138.9|135.4|135.4|131.6|131.7|131.6|
|**Foreign savings**|0.0|0.0|0.0|3.3|3.3|6.1|6.1|6.1|
|**Gross national savings**|8.7|8.7|8.7|10.4|10.4|11.4|11.4|11.3|
|**Foreign government debt**|43.1|43.7|44.1|41.7|41.7|38.7|38.6|38.7|
|**Domestic government debt**|6.5|10.9|10.4|9.7|9.7|8.7|8.7|8.2|

**Table 3.3. Government receipts and spending in 2022 and by scenario in 2030 (% of nominal GDP)**

|**Indicator**||**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta re**|**c+ta+**|
|---|---|---|---|---|---|---|---|---|---|
|**Receipts**|**Direct taxes**|3.0|2.3|2.0|1.7|1.7|2.5|2.4|1.1|
||**Import tariffs**|0.8|0.8|0.9|0.9|0.9|0.9|0.9|0.9|
||**Other indirect taxes**|2.3|1.8|1.6|1.4|1.4|2.1|2.1|0.9|
||**Private transfers**|1.1|1.1|1.1|1.1|1.1|1.1|1.1|1.1|
||**Foreign transfers**|14.0|14.8|14.9|14.1|14.1|13.1|13.0|13.1|
||**Domestic financing**|0.3|0.3|0.3|0.3|0.3|0.2|0.2|0.2|
||**Foreign financing**|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
||**Total**|24.5|23.9|23.9|23.0|23.0|23.7|23.7|21.9|
|**Spending **|**Consumption**|19.3|18.8|18.8|15.9|15.9|15.4|15.3|14.0|
||**Fixed investment**|0.7|0.7|0.7|2.5|2.5|3.8|3.8|3.6|
||**Private transfers**|4.4|4.4|4.4|4.5|4.5|4.5|4.5|4.3|
||**Total**|24.5|23.9|23.9|23.0|23.0|23.7|23.7|21.9|

18
**Table 11a:** Regression of log per capita food expenditure by treatment – household head characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.110***|0.114***|0.120***|0.059|0.091*|-0.054|0.083|0.030|-0.116|
||(0.038)|(0.038)|(0.043)|(0.086)|(0.048)|(0.057)|(0.056)|(0.064)|(0.096)|
|Recall long-list|0.214***|0.209***|0.176***|0.082|0.198***|0.134**|0.196***|0.145**|0.012|
||(0.041)|(0.040)|(0.043)|(0.091)|(0.044)|(0.056)|(0.057)|(0.063)|(0.096)|
|Recall short-list*educated|||0.009||||||-0.061|
||||(0.114)||||||(0.108)|
|Recall long-list*educated|||0.178*||||||0.126|
||||(0.104)||||||(0.096)|
|Recall short-||||||||||
|list*employed||||0.062|||||0.073|
|||||(0.095)|||||(0.093)|
|Recall long-list*employed||||0.161|||||0.096|
|||||(0.112)|||||(0.097)|
|Recall short-list*non-||||||||||
|wage|||||0.038||||-0.026|
||||||(0.101)||||(0.106)|
|Recall long-list*non-wage|||||0.031||||-0.016|
||||||(0.107)||||(0.097)|
|Recall short-list*floor||||||||||
|material||||||0.296***|||0.296***|
|||||||(0.074)|||(0.080)|
|Recall long-list*floor||||||||||
|material||||||0.144*|||0.082|
|||||||(0.074)|||(0.075)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.112||-0.070|
||||||||(0.082)||(0.113)|
|Recall long-list*water||||||||||
|(rainy)|||||||0.087||0.008|
||||||||(0.087)||(0.107)|
|Recall short-list*water||||||||||
|(dry)||||||||0.175**|0.126|
|||||||||(0.085)|(0.113)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.161**|0.113|
|||||||||(0.081)|(0.097)|
|Constant|10.791***|10.430***|10.727***|10.767***|10.749***|10.650***|10.671***|<br>10.672***|<br>10.571***|
||(0.034)|(0.046)|(0.034)|(0.057)|(0.035)|(0.039)|(0.044)|(0.053)|(0.066)|
|Observations|2,118|2,108|2,118|2,118|2,118|2,118|2,118|2,108|2,108|
|R-squared|0.017|0.179|0.079|0.023|0.041|0.135|0.072|0.074|0.192|

Note: Dependent variable is the log of per capita food consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes household head and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

41
# **Regional economic growth in Mexico:  Recent evolution and the role of governance**

Eli Weiss and David Rosenblatt

## **1. Introduction**

The evolution of how economists perceive corruption and its impact on economic development has changed radically in the last few decades. The “grease the wheel of growth” view dating back to the 1960s was gradually overtaken by a “sand the wheel of growth” approach. On the theoretical side, development economists began to incorporate concepts from the growing field of the economics of institutions. Since the mid 1990s, many economists have tried to empirically analyze the relationship between corruption and economic growth by using cross-country data, and these studies added an important new dimension to the empirical literature on economic convergence/divergence and the determinants of the cross country patterns of growth. This new line of research was made possible by the development of new measures of quality of governance, or “corruption”, at the international level.

Since 2001, the non-governmental organization “ _Transparencia Mexicana_ ” is constructing every second year a National Index of Corruption and Good Governance, based on behaviors of corruption, (in contrast to the perception based index of Transparency International). The creation of this new measure allows us to incorporate governance into traditional growth regressions of state growth in Mexico.
Policy Research Working Paper 9089

# **Abstract**

This paper uses data on directional trade flows and Regional Trade Agreements (RTA) to i) estimate the effects of RTA on trade flows and ii) assess the importance of Global Value Chains for these effects. Based on a Difference-inDifference identification strategy, we find that RTAs are associated with: (1) an increase in trade within the region, (2) a decrease in inflows to the region, and (3) an increase in outflows from the region. The first two findings can be understood as trade creation and trade diversion due to a shift in demand associated with the lower trade barriers within the region and Rules of Origin as an implicit trade barrier for imports from the rest of the world. Global Value Chains are most relevant to understand the third finding on the increase in outflows from the region. The key determinant of the increase in outflows is the importance of the _Regional Value Chains for imports of intermediates_ by members of the region.

This paper is a product of the World Bank’s _World Development Report 2020_ Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at francois.m.desoyres@frb.gov.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 961 M. A. Drupp, M. C. Freeman, B. Groom, and F. Nesje. Discounting Disentangled. _American Economic_ 962 _Journal: Economic Policy_ , 10(4):109–134, Nov. 2018. ISSN 1945-7731, 1945-774X. doi: 10.1257/ 963 pol.20160240. URL `https://pubs.aeaweb.org/doi/10.1257/pol.20160240` .

- 964 M. T. Dvorak, K. C. Armour, D. M. W. Frierson, C. Proistosescu, M. B. Baker, and C. J. Smith. Esti965 mating the timing of geophysical commitment to 1.5 and 2.0 °C of global warming. _Nature Climate_ 966 _Change_ , 12(6):547–552, June 2022. ISSN 1758-678X, 1758-6798. doi: 10.1038/s41558-022-01372-y. 967 URL `https://www.nature.com/articles/s41558-022-01372-y` .

- 968 European Commission. Directive 2003/87/EC of the European Parliament and of the Council of 13 969 October 2003 establishing a system for greenhouse gas emission allowance trading within the Union 970 and amending Council Directive 96/61/EC, Nov. 2005. URL `https://eur-lex.europa.eu/eli/` 971 `dir/2003/87/2023-06-05` .

- 972 European Commission. Directive (EU) 2023/959 of the European Parliament and of the Council 973 of 10 May 2023 amending Directive 2003/87/EC establishing a system for greenhouse gas emis974 sion allowance trading within the Union and Decision (EU) 2015/1814 concerning the establish975 ment and operation of a market stability reserve for the Union greenhouse gas emission trading 976 system, May 2023. URL `https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv:` 977 `OJ.L_.2023.130.01.0134.01.ENG` .

- 978 C. Fischer. Emissions pricing, spillovers, and public investment in environmentally friendly technologies. 979 _Energy Economics_ , 30(2):487–502, Mar. 2008. ISSN 01409883. doi: 10.1016/j.eneco.2007.06.001. 980 URL `https://linkinghub.elsevier.com/retrieve/pii/S0140988307000734` .

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- 985 P. Friedlingstein, M. O’Sullivan, M. W. Jones, R. M. Andrew, D. C. E. Bakker, J. Hauck, P. Land986 schtzer, C. Le Qu ́er ́e, I. T. Luijkx, G. P. Peters, W. Peters, J. Pongratz, C. Schwingshackl, S. Sitch,

- 987 J. G. Canadell, P. Ciais, R. B. Jackson, S. R. Alin, P. Anthoni, L. Barbero, N. R. Bates, M. Becker, 988 N. Bellouin, B. Decharme, L. Bopp, I. B. M. Brasika, P. Cadule, M. A. Chamberlain, N. Chandra, T.-

- 989 T.-T. Chau, F. Chevallier, L. P. Chini, M. Cronin, X. Dou, K. Enyo, W. Evans, S. Falk, R. A. Feely,

49
<!-- Start of picture text --> 3500 3500<br>3000 Phase 1 3000 Phase 3<br>Phase 2<br>2500 Phase 3 2500 Phase 1<br>2000 2000<br>1500 1500<br>1000 1000<br>500<br>500<br>0<br>0<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002<br>1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003<br><!-- End of picture text --> Fig. 4: Historical simulated payouts of drought insurance contracts based on (left) capped and (right) non-capped rainfall data from 1961 to 2005 With the capped data, the third phase is more frequently triggered with higher compensation than is the case for the non-capped data. Thus, capping has important implications both for the insured as well as the insurer. The contract in its present form provides insurance protection against frequent rather than catastrophic losses. However, this may change in the context of climate change.

# **3.3 Current and future rainfall scenarios**

The modeling of accumulated rainfall amount is based on input data of the Chitedze station in Malawi as analyzed and modeled by Tadross et al. (2005). Information of the input data used to construct current and future scenarios is based on the regional climate projection models MM5 and PRECIS (see Table 4).

<u>Table 4: Input data for the rainfall modeling.</u>

|<br>•<br>DailyRainfallamountfrom 1961till 2005fromChitedze station.|
|---|
|•<br>PRECIS rescaled projections (monthly rainfall) of the control and future period.<br>Control is January1960 to December 1979. Future period is January 2070 to<br>December 2089.|
|•<br>MM5 rescaled projections (monthly rainfall) of the control and future period.<br>Control is from January 1975 to December1984. Future period is January 2070 to<br>December 2079.|

The PSU/NCAR mesoscale model (known as MM5) developed by Pennsylvania State University and the National Center for Atmospheric Research is a limited-area, nonhydrostatic, terrain-following sigma-coordinate model designed to simulate or predict mesoscale atmospheric circulation.<sup>2</sup> Precis (Providing REgional Climates for Impacts Studies) is based on the Hadley Centre's regional climate modelling system.<sup>3</sup> Both regional climate models (RCM) are forced within the A2 emissions scenario global circulation model. The SRES A2 scenario is a standard scenario used in assessing future worlds with climate change and leads to rather high greenhouse gas emissions (Nakicenovic and Swart, 2001). The main difference of the two RCM is that they

> 2 See <u>http://box.mmm.ucar.edu/mm5/</u>

> 3 See <u>http://precis.metoffice.com/</u>

10
model of two such institutional settings . The rst is a cadaster/registry of property rights, thanks to which landowners have their registered rights recognized and enforced. The second setting relies on bilateral trusted relationships between buyers and sellers, based on group kinship. We present these institutional settings sequentially before comparing them.

# 3.1 A model of registration

In the rst extension, we add a property rights registration system to the benchmark model. Registration extinguishes competing claims on a land plot and makes it fully secure. Because it is observable by all, it allows buyers to identify a fraction of secure plots (i.e., those which are registered) with certainty. In this extension, sellers have the possibility to register their land before entering a transaction, anticipating that a registered plot will sell at a premium (that capitalizes both the increase in tenure security and the removal of information asymmetry). We assume that there is a registration cost, _k_ , which is the same for all plots, irrespective of the initial tenure security level.

## 3.1.1 Sellers and buyers' behavior

An owner now has two choices. He chooses whether to participate in the market (decision _P ∈{_ 0 _,_ 1 _}_ ) and, conditional on participating in the market, whether to register his plot (decision _F ∈{_ 0 _,_ 1 _}_ ). If choosing _F_ = 1 , the tenure security level of the plot is reset at the value 1 . There are thus two sale situations depending on sellers' decisions, each commanding a dierent price. Applying the same bid-rent approach as in the previous section, we see that the buyer of a registered plot will be willing to pay _ψ_ ( _x, u|F_ = 1) = _y − tx − u_ to reach utility level _u_ . Similarly, the buyer of a non-registered plot will be willing to pay _ψ_ ( _x, u|F_ = 0) = _{π_ ( _x_ ) + _q_ (1 _− π_ ( _x_ )) _}_ ( _y − tx − u_ ) .

## 3.1.2 Competitive equilibrium

Given the additional features, of the model, the notations are slightly modied in comparison with the benchmark case. As before, we continue to use _L_ 1( _x_ ) and _Lq_ ( _x_ ) to denote the quantities of secure and risky land that are transacted, while _R_ ( _x_ ) continues to denote the price of informally transacted land. However, we now introduce the notation _Lf_ ( _x_ ) to denote the quantity of registered (and transacted) land in each location _x_ , and _Rf_ ( _x_ ) to denote the price of formally registered land. We now allow the market participation and the registration decisions to depend on location, the intrinsic tenure security level, and both formal and informal market prices, with the notations _P_ ( _x, Q, Rf , R_ ) and 14
20 household level data is available for Malaysia. The 1995 Household Income Survey data covers a large sample, documenting the earnings, work activities and education profiles of 36,700 households in Malaysia. The extent of the survey's coverage of foreign workers is likely to be fairly representative, since non-citizens represent 4.5 percent of the sample (or about 1600 observations), which would be equivalent to nearly a million foreigners in a tota] population of 21 million. This is not inconsistent with official statistics that foreign workers number 1.2 million, clearly within the same order of magnitude. The survey does not include illegal foreign workers, however, estimated to account for an additional 800,000 workers.

Looking at the individual characteristics of foreign workers in Malaysia and Singapore compared to nationals gives a sense of the degree to which immigration and other policies have effectively targeted foreigners for unskilled and skilled positions. The evidence suggests that policies indeed affect the composition of foreign labor. The ethnic and sectoral segmentation observed in Singapore is consistent with policy that targets foreign workers by country of origin for certain categories of work. As shown in Figure 4, the majority of foreign workers are from Malaysia, the so-called traditional source country, and are primarily employed in manufacturing and services; Malaysians employed in the construction sector are typically skilled workers (Wong, 1997). Expatriates from Thailand and Bangladesh are predominantly employed in the construction sector, in semi-skilled and unskilled jobs, and an estimated 25 percent of all foreigners are employed as domestic servants, primarily women from the Philippines. Indonesia and Sri Lanka.

<!-- Start of picture text --> Figure 4<br>Singapore: Foreign Labor Composition<br>Sri Lanka<br>4%<br>Other<br>indonesia ' 6% Malaysia<br>4% . 43%<br>Philippines<br>17%<br>Bangladesh<br>11% Thailand<br>15%<br><!-- End of picture text -->
When we estimate the effect of the country risk on the level of the investment, we see that the energy sector remains the most sensitive to changes in country risk ratings not only in the likelihood of undertaking PPI investments but also when predicting the level of the investments.

A change of one point in country risk is linked on average to an increase of 3.8 percent in investments in energy. After energy comes transport with a 2.5 percent and then telecommunication with 1.6 percent, while “Water and Sewerage” does not have a statistically significant effect. Hence, improving an SD (13.8 points) in the country risk, implies a 53.4, 34.6, and 22.4 percent increase in the probability of undertaking PPI in energy, transport, and telecom sectors, respectively. In the “Water and Sewerage” sector the country risk explains the likelihood to commit but not the level of commitment as in other sectors, mainly explained by the fact that almost every water and sewerage investments in PPI database are greenfield projects (around 98%). The nature of the projects is analyzed in the following section.

**Table 6: The Effect of Country Risk on PPI Investment Levels by Sector**

|**VARIABLES**|**Log PPI Energy**|**Log PPI Telecom**|**Log PPI Transport**|**Log PPI Water**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Country Risk|0.0387***|0.0162***|0.0251*|0.0151|
||(0.0110)|(0.00552)|(0.0128)|(0.0184)|
|Log GDP ppp|0.396|1.816***|1.108***|1.071*|
||(0.255)|(0.118)|(0.324)|(0.622)|
|Log GDP Growth|0.00770|0.116**|0.432***|-0.340|
||(0.101)|(0.0515)|(0.142)|(0.216)|
|Log Inflation|-0.160*|-0.0350|-0.0751|-0.283*|
||(0.0920)|(0.0397)|(0.0988)|(0.149)|
|Log Openness|-0.230|0.0134|-0.160|-1.266|
||(0.474)|(0.202)|(0.556)|(1.023)|
|Constant|-5.224|-40.60***|-24.51***|-18.36|
||(5.475)|(2.556)|(7.161)|(14.10)|
|R2 within|0.0513|0.2987|0.1417|0.1323|
|R2 between|0.4441|0.7776|0.2745|0.0693|
|R2 overall|0.3687|0.6461|0.3108|0.0393|
|Observations|476|1,063|313|140|
|Number of countries|83|111|69|39|

*** p<0.01, ** p<0.05, * p<0.1

Source: Authors’ calculations.

13
**Objective Overview**

<!-- Start of picture text --> TOPIC<br>CLASSIFICATION<br>TAXONOMY  GEOLOCATION  Feature<br>Tweets<br>FILTER  FILTER  Loadings<br>SENTIMENT<br>ANALYSIS<br>ITERATIVE<br>REFINEMENT<br>MANUAL<br>TAGGING  AND<br>ALGORITHM<br>TRAINING<br><!-- End of picture text --> <!-- Start of picture text --> Feature<br>Loadings<br><!-- End of picture text --> <!-- Start of picture text --> Tweets<br><!-- End of picture text --> <!-- Start of picture text --> MANUAL<br>TAGGING  AND<br>ALGORITHM<br>TRAINING<br><!-- End of picture text --> Figure 1: An overview of the analysis pipeline used in the present study using Twitter data to understand the evolution of the public perception of the El Salvador gas subsidy reforms of 2011.

# **3 Data**

## **3.1 Source**

We consider the historical archive of the Twitter firehose of public tweets available through a paid service.

## **3.2 Taxonomy**

In order to filter relevant content from the period of interest, a taxonomy of Spanish keywords related to the topic was constructed. This step is of critical importance. The taxonomy must contain all permutations of words relevant to the topic of interest including slang, abbreviations and synonyms. For this reason, several domain experts and native Spanish speakers were consulted to advise on taxonomy content. Further, if the 4