rafmacalaba/datause-encoder-data huggingface dataset:
|Variable|Obs.|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Annual Growth Rate 52-98 (%)|57|2.91|1.47|-0.03|7.94|
|Annual Growth Rate 78-98 (%)|57|7.11|3.89|1.47|22.49|
|Famine Control|58|0.78|0.14|0.46|1.11|
|Death Rate (Death Per 1000)|58|13.4|5.8|5.8|33.8|
|FA3|59|0.19|0.39|0|1|
|YRD|59|0.81|0.39|0|1|
|Guerrilla|59|0.42|0.5|0|1|
|YRD×GuerrillaNo|59|0.44|0.5|0|1|
|YRD×GuerrillaYes|59|0.37|0.49|0|1|
|Ln_GVOPC_52|58|7.95|0.45|5.99|8.66|
|Ln_GVOPC_78|58|7.9|0.48|5.99|9.23|
|Ln_Pop_52|59|2.67|0.69|1.34|4.33|
|Ln_Pop_78|58|3.36|0.66|1.99|4.96|
|Share of Plain (%)|59|10.96|9.7|1.53|41|
|Distance to Xiamen (Km)|59|184.2|90.01|21|342|

Table 2: Summary Statistics of Main Variables.

cadres, particularly so in the post-reform period. The two graphs in the bottom panel show that the growth rates are higher, probabilistically, in counties that had local guerrilla presence than those that did not have guerrilla presence; and the difference is also much more striking during the 1978-1998 period.

In Table 3, we summarize the differences in the average annual real GVO growth rates between the counties led by cadres affiliated with FA3 and YRD, or between counties with or without guerrilla presence for the period of 1952-1998 and 1978-1998 separately. In the left side of Panel A, we show that, during 1952 to 1998, the average real GVO growth rates for FA3 counties was 2.10 percent, that for YRD counties was 3.08 percent, and the difference between YRD and FA3 counties was 0.99 percentage points, which is 0.67 standard deviations (SDs) of the mean growth rate of 2.91 percent. The difference in the growth rates between YRD and FA3 counties is statistically significant at the 10 percent level. However, if we focus on 1978-1998, the difference is much more striking: the average annual real GVO growth rate for FA3 counties was 4.37 percent, and that for YRD counties was 7.72 percent. The 3.17 percentage points per year YRD advantage in growth rate (about 0.82 SDs), is statistically significant at 1.4% level. Needless to say, the difference in economic growth rates between these two types of counties was huge, especially in light of the fact that they are from the same province.

In the right side of Panel A in Table 3, we examine the differences in growth rates by whether a county had guerrilla presence as of May 1948. The differences in the counties with and without guerrilla presence are also striking. The guerrilla-present counties had an advantage in growth of 0.89 percentage points (or 0.6 SDs) in the 19
city and its surrounding areas. The aggregate labor market clearing condition determines _<u>v.</u>_ Note that the symmetry in the initial equilibrium breaks down once there is a trade cost shock to one of the international trade routes.

## **Built-up Density**

The empirical analysis utilizes data on built-up area to explore the impacts of CIV crisis on sunk investments that can lead to persistent effects. We define built-up density in terms of density of residential housing and manufacturing structures.<sup>13</sup> The proportion of built-up area is defined as equal to the density of housing and manufacturing in the [ _K_<sup>0</sup> _, K_<sup>_z_</sup> ] zone of the integrated subregion. In rest of the areas, it equals the density of housing defined in turn by population density ( _ni_ ). This definition implies that built-up area should be more concentrated around the city center due to higher population density and concentration of manufacturing.

# **(4.3) Effects of the Transport Shock to One Port (Abidjan)**

We model the CIV-political crisis as an increase in _θk_ and no change in _θj_ along alternative routes. Though international prices at the port of entry were not influenced by the CIV crisis, transport cost from port (Abidjan) to city _K_ ( _θk_ ) increases as a result of the conflict. An increase in _θk_ increases price of import ( _m_ ) to the consumers ( _∂P∂θmKk_ 0 _>_ 0) but reduces prices of exportables ( _∂P∂θqKK_ 0 _<_ 0) to the producers. The CIV crisis did not affect transport cost from port of entry (non-Abidjan) to city _J_ , but it is indirectly affected due to labor mobility between cities. Propositions 1-3 summarize the impacts of a higher _θk_ .

## **Effects of Trade Cost Shock to the Abidjan Route: Testable Predictions**

_A trade shock due to conflict in CIV (Abidjan) leads to_ _(1) Shrinking of the spatial margins (i.e., SMI and SMS) around city K dependent on Abidjan port, and expansion of the margins around city J that uses ports other than Abidjan._

> 13Though agriculture is the most land-intensive activity, it does not involve buildings/structures. Similarly, small-scale manufacturing production for self-consumption usually does not need a building structure separate from the housing for shelter. More specialized large and medium scale manufacturing catering to international markets, in contrast, requires fixed structures and leads to a higher built-up density.

14
The cost of education is calculated separately for public and for non-governmental institutions. The average cost is weighted according to the share of the student population in each segment in 2003 (Table 5).

**-** **<u>Table 5 Participation Rate by Public and Non governmental Institutions</u>**

|**Country**|**Public (%)**|**Non-**<br>**governmental (%)**|**Total Enrollment**|
|---|---|---|---|
|Brazil|29|71|3,887,000|
|Colombia|42|58|1,045,000|
|Mexico|67|33|2,239,000|
|Peru|59|41|849,000|

Source: Instituto Internacional de la UNESCO para la Educación Superior en América Latina y el Caribe (IESALC) for Brazil, Colombia and Mexico. ANR (2005) Estadisticas Universitarias, Lima: Asamblea Nacional de Rectores for Peru.

Average costs of books for students in tertiary education are available from household surveys in Colombia, Mexico and Peru. In Brazil, the expenditures on books are available as a part of costs of study materials. Instead, we rely on information from previous cost studies on the International Comparative Higher Education Finance and Accessibility Project at the State University of New York at Buffalo. We obtained average cost information on books for public and non-governmental institutions for the academic year of 2000 to 2001.

## **Table 6 Data** **<u>Sources and Estimated Education Costs (US$2003 PPP)</u>**

|**Country**|**Source**|**Public**|**Non-**<br>**governmental **|**Average**|
|---|---|---|---|---|
||**Tuition**||||
|Brazil|UniversityWebsites|0|4,912|3,478|
|Colombia|Information System for Tertiary Education at the<br>Ministryof Education|573|2,386|1,661|
|Mexico|Household Survey|109|2,380|783|
|Peru|Telephone survey and||||
||Universitywebsites|0|5,276|2,581|
||**Other Study Costs (Books and S**|**tudy Materials**|**)**||
|Brazil|ICHEFAP-Region-Latin America- Brazil|220|220|220|
|Colombia|Household Survey|NA|NA|289|
|Mexico|Household Survey|273|778|418|
|Peru|Household Survey|455|509|531|

# <u>Estimating Student Assistance</u>

Reliable data on grants and loans were unavailable in all surveys. Thus, grant and loan expenditures are obtained from the relevant government sources in each country, see Table 7. The total grants dispersed to beneficiaries or students are divided by the total number of tertiary students in each country to estimate the average amount per student.

11
# _Data Used in this Study_

This study uses microdata from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC) which is based on household surveys conducted across 25 Latin American countries<sup>7</sup> . All household surveys are nationally representative with the exception of Argentina, where surveys cover the urban population only.<sup>8</sup> SEDLAC harmonizes survey data across countries and produces for each country comparable measures of individual and household income, demographic characteristics, education, employment, housing, infrastructure, and durable goods and services.<sup>9</sup> Individual income in SEDLAC is the sum of income from all sources received by an individual. Where possible, labor income (defined as income from the individual’s main occupation) is distinguished from non-labor income, which is divided into three categories: (i) pensions, (ii) capital and benefits and (iii) transfers. Household income is the sum of the total individual income across all household members.<sup>10</sup> Although consumption is generally the preferred measure of well-being, this study follows other literature focused on the region and uses income instead as this is the only measure of well-being available in most Latin American household surveys. Very

> 7 SEDLAC countries include Argentina, Bahamas, Belize, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominica, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Uruguay and Venezuela RB.

8 This is of limited concern for this study given that the urban population in Argentina represents more than 85% of the total population in the country.

9 For a complete description of survey instruments underlying the harmonized SEDLAC data and related methodological issues, see <u>http://www.depeco.econo.unlp.edu.ar/sedlac/.</u> 10 An additional issue arises due to spatial variation in prices and its implications for distributional studies (Deaton, 1997; Ravallion and Chen, 1997). This variation poses a challenge for SEDLAC data (and for the household surveys it is based on) since most LAC countries do not routinely collect information on local prices as part of household surveys, making it difficult to perform price adjustments. In order to correct for this, rural incomes in SEDLAC are increased by a factor of 15% to capture differences in rural-urban prices. This value is an average based on available studies of prices in the region and is certainly arbitrary, but arguably represents an improvement on unadjusted prices.

7
and mismanagement to implementing partners and local, sub-national, or national leaders. The trainings were managed by the Inspectorate of Government (IG), an independent arm of the government responsible for fighting corruption, and implemented in partnership with local civil society organizations (CSOs). The sample size for this intervention is between five and 20 times larger than sample sizes in similar research, covering more than 45 districts and 485 sub-counties throughout the northern half of the country of Uganda, with more than 10,000 direct beneficiaries. The design thus allows for a minimum detectible effect size of less than 10% for most outcomes.

As NUSAF2 comprised a wide range of project types, including building teachers’ houses, providing livestock to households, putting up fencing, and establishing enterprise development, we developed a normalized index of project quality obtained through physical assessments of the projects (similar to audits). These data were collected about six months after the mean completion of the local NUSAF2 projects and were used to measure the immediate impacts of the training. We then used the information collected from this assessment to create a scorecard that ranks the performance of the community projects relative to other community projects within a district. We randomly selected 283 communities to be given this information during a community meeting, which included a facilitated discussion about why communities did or did not perform well relative to others.

This produced a 2x2 design where communities received training, a scorecard, both training and a scorecard, or no intervention. This design allows us to test directly whether training communities on social accountability or simply providing information on relative project quality can lead to improved service delivery, or if a combination of the two is needed.

We conducted individual surveys with community members six months after the initial assessment and scorecards were delivered to measure household welfare impacts. The sample includes over 6,900 individuals. Almost two-thirds of the projects provided livestock to households, making these projects more easily comparable to one another and more likely to have welfare implications for individual households. For these reasons, we focus our welfare analysis on these projects, though we also present results from the full sample.

We find that the social accountability training led to a small increase in the overall quality of projects by 0.135 standard deviation. From a follow-up household survey conducted six months later, we find that neither the training nor the project quality scorecard alone had any 3
- De Mel, Suresh, David McKenzie, and Christopher Woodruff. 2009. “Are women more credit constrained? Experimental evidence on gender and microenterprise returns.” _American Economic Journal: Applied Economics_ 1(3), 1-32.

- De Mel, Suresh, David McKenzie, and Christopher Woodruff. 2014. “Business training and female enterprise start-up, growth, and dynamics: Experimental evidence from Sri Lanka,” _Journal of Development Economics_ 106, 199-210 _._

- Dollar, D., A. Kraay. 2004. “Trade, growth, and poverty.” _The Economic Journal_ 114 (493), F22-F49.

- Fairlie, R.W., Morelix, A., and Tareque, L. 2017. _2017 Kauffman Index of Startup Activity: National Trends._ Ewing Marion Kauffman Foundation _._

- Hanousek, J. A. Shamshru, J. Tresl. 2017. “Firm efficiency, foreign ownership and CEO gender in corrupt environments.” _Journal of Corporate Finance_ .

- Huang, J., D.J. Kisgen. 2013. “Gender and corporate finance: are male executives overconfident relative to female executives?” _Journal of Financial Economics_ 108, 822-839.

- Levi, M., K. Li, F. Zhang. 2014. “Director gender and mergers and acquisitions.” _Journal of Corporate Finance_ 28, 185-200.

- Faccio, M., M. Marchica, R. Mura. 2016. “CEO gender, corporate risk-taking, and the efficiency of capital allocation.” _Journal of Corporate Finance_ 39, 193-209.

- Galor, O., Weil, D. N. 2000. "Population, Technology, and Growth: From Malthusian Stagnation to the Demographic Transition and beyond." _American Economic Review_ 90(4), 806-828.

- Galor, O. 2005. “From Stagnation to Growth: Unified Growth Theory.” In Handbook of Economic Growth, Volume 1, pp. 171–293. Elsevier.

- <mark>Goldstein, Markus, and Christopher Udry. 2008. "The profits of power: Land rights and agricultural investment in Ghana."</mark> _<mark>Journal of Political Economy</mark>_ <mark>116(6), 981-1022.</mark>

- Guiso, L., A. Rustichini. 2018. “What drives women out of entrepreneurship? The joint role of testosterone and culture.” _European Economic Review._

- Klapper, L.F., S.C. Parker. 2010. “Gender and the business environment for new firm creation.” _World Bank Research Observer_ 26, 237-257.

- Koellinger P., M. Minniti, and C. Schade. “Gender differences in entrepreneurial propensity.” _Oxford Bulletin of Economics and Statistics_ 75(2), 213-224.

- Lan, Justine, Ben Shepherd. 2018. “Gender, structural transformation, and deindustrialization.” _Working Paper DTC-2018-5_ , Developing Trade Consultants.

- Lara, J.M.G., B.G. Osma, A. Mora, M. Scapin. 2017. “The monitoring role of female directors over accounting quality.” _Journal of Corporate Finance_ 45, 651-668.

- Legarraga, Iza, Alexandros Ragoussis. 2018. “Beyond capital: Monitoring development outcomes of multinational enterprises.” Policy Research Working Paper 8686, World Bank.

- Lucas, Robert. 1988. “On the mechanics of economic development.” _Journal of Monetary Economics_ 22(1), 3-42.

20
_A_ . Then FAI into country 1 itself falls by _β_ ∆ _FAI_ 1 _.A._ while in every other country _j_ , it rises by _β_ ∆ _FAIjδj_ 1 _A_ , so that fall in total investment elsewhere is _β_ ∆ � _j_ =1<sup>_FAIj_1</sup><sup>_δj_1</sup><sup>_A._Assuming</sup> further that in all _j_ the same weight is attached to tax rates in all countries, the condition for aggregate FAI to increase when a binding minimum rate is applied to the lowest tax country becomes that (1 _/_ ( _N −_ 1)<sup>�</sup> _j_ =1<sup>_FAIj>FAI_1:thatis,thelevelofFAIinthatlowest-tax</sup> country be below the average level elsewhere.

Generalizing this, it is shown in Appendix B that (in the continuous case as above) it is sufficient for an increase in the minimum rate to increase aggregate FAI that _E_ [ _FAI|T < m_ ] _< E_ [ _FAI_ ], so that the average level of FAI in countries constrained by the minimum is below that across all countries. To the extent that smaller countries tend to have lower statutory tax rates (as many models of tax competition predict, and as is the case in our dataset),<sup>68</sup> and given too that most of the host countries in our sample are, viewed in a global setting, relatively high tax ones, there are thus systematic reasons to suppose that a minimum tax rate, at least at moderate levels, will increase aggregate FAI into these countries.

This finding of an increase in total FAI as a consequence of adopting a global minimum tax is in stark contrast to the slight reduction found by others: of around 2 percent, for instance, in the baseline estimates of UNCTAD (2022).<sup>69</sup> This reflects two differences in approach. First, that here builds on fully articulated and empirically estimated cross-border relationships rather than applying presumed elasticities to constructed effective tax rates. Second, the result here is not an estimate of an overall global effect but of the effect on 32 host countries that, in global terms, are relatively high tax ones. Those countries, nonetheless, account for more than half of global FDI—and they are the ones that have been driving the international tax reform agenda.

reduction in _FAIh_ : more generally, the condition that follows is thus sufficient for an increase in aggregate FAI.

> 68On the theory point, see for instance Keen and Konrad (2013); on the data point, Supplementary Appendix Figure C.3 confirms that size, whether measured by GDP or population, is positively correlated with the statutory tax rate.

> 69Devereux et al. (2020), Hanappi and Cabral (2022) reach broadly similar conclusions.

40
shocks to output and prices is statistically different from estimates obtained when excluding the amplification mechanism through exchange rates.

Consistent with this literature, our results indicate that FX puzzles are prevalent in most EMEs, where currency depreciation impinges on domestic financial stability. More notably, the price puzzle is most evident in EMEs which display the foreign exchange rate puzzle. In contrast, in advanced economies and a few other EMEs with managed foreign exchange regimes, neither the FX puzzle nor the price puzzle is evident.<sup>35</sup>

# **VI. Conclusion**

The literature presents mixed results, at best, on the effectiveness of monetary policy in EMEs. This could stem from the challenges EME central banks face in stabilizing macroeconomic conditions or from limitations in accurately identifying the effects of monetary policy shocks due to the volatile nature of these economies. Our empirical findings, based on a standard SVAR model, suggest that in the major advanced economies monetary policy transmission operates effectively and in line with theoretical predictions. We document that in most EMEs, by contrast, monetary policy tightening often seems to result in rising prices (the price puzzle) and depreciating currencies (the FX puzzle).

We adopt a unified framework to appraise various solutions that have been proposed to address these empirical puzzles. We conclude that SVAR models augmented with forward-looking expectations show the most promise in resolving these puzzles. We also show that the two puzzles are closely related—eliminating the FX puzzle resolves the price puzzle. Our findings underscore the importance of modeling the forward-looking nature of monetary policy in EMEs when evaluating the effectiveness of their monetary transmission mechanisms.

# **References**

- Aktaş, Z., Kaya, N., and Özlale, Ü. (2005). The price puzzle in emerging markets: evidence from the Turkish economy using “model based” risk premium derived from domestic fundamentals. Central Bank of the Republic of Turkey Working Paper No: 05/02.

- Antolín-Díaz, J. and Rubio-Ramírez, J.F. (2018). Narrative sign restrictions for SVARs. _American Economic Review_ 108(10): 2802-2829.

- Arias, J.E., Caldara, D., Rubio-Ramírez, J.F. (2019). The systematic component of monetary policy in SVARs: an agnostic identification procedure. _Journal of Monetary Economics_ 101: 1-13.

- Baker, S.R., Bloom, N., and Davis, S.J. (2016). Measuring economic policy uncertainty. _Quarterly Journal of Economics_ 131(4): 1593-1636.

- Barth, M.J. and Ramey, V.A. (2002). The cost channel of monetary transmission. in NBER Macroeconomic Annual 2001 (16):199-240.

- Bauer, M. D. and Swanson, E.T. (2023). An alternative explanation for the “Fed Information Effect”. _American Economic Review_ 113(3): 664-700.

> 35 The results for countries with managed fixed exchange regimes are presented in Figure A10.

15
corresponds to one quarter. I run three separate simulations. First I have both shocks, then I shut off the trend shocks, and finally I shut off the temporary shocks.

Table 3 reports averaged simulated standard deviations of output growth, consumption growth, the trade balance, the current account, valuation effects and changes in NFA position. Numbers in brackets are the standard deviations of the statistics. In the first six rows, it is not surprising that the model can match the data, because I use output, consumption and investment data to estimate the model’s parameters. When it comes to trade, current account and valuation effects, the model can account for most of the volatility in the trade and current account of the U.S. economy, but only about one-third of the volatility of the U.S.’s valuation effects.

When only transitory shocks are present, the change in the NFA position is slightly less volatile than the current account because valuation effects offset some of the current account’s movements. However, when only trend shocks are present, the change in the NFA position is much more volatile than the current account because valuation effects here are strongly reinforcing.

# **5 Conclusion**

In light of the U.S.’s persistent current account deficits, both empirical and theoretical literature has pointed to “valuation effects” as a channel that could offset the deficits and mitigate the decline of the NFA position, that is, valuation effects are stabilizing. This paper shows that whether valuation effects are stabilizing or not depends on the the nature of the underlying productivity shocks. In response to transitory shocks, valuation effects are stabilizing; but in response to trend shocks, valuation effects amplify the impact of the current account on NFA position. This situation is clearly illustrated by the external imbalances between the U.S. and other G.7 countries during the late 1990s when the U.S. experienced both current account deficits and negative valuation effects. Quantitatively, the model can explain for most of the volatility of the U.S.’s trade and current account, but it only accounts for about one-third of the U.S.’s valuation effects. This quantitative result is obtained in a framework where agents are fully rational, financial frictions are limited to only “local expert” costs, and information about transitory and trend productivity shocks is perfect. Relaxing these restrictions is worth exploring in future research.

17