rafmacalaba/datause-encoder-data huggingface dataset:
|||2012; 743<br>men and<br>women, aged<br>15-65||||reading<br>proficiency|0.0648* - 0.0861**<br>for grit, 0.0522* -<br>0.0672** for hostile<br>bias, -0.0845** - -<br>0.0901** for<br>decisionmaking,<br>0.0408** - 0.0448**<br>for risk aversion||experience-<br>squared,<br>mother's<br>edcuation,<br>and indicator<br>variables for<br>wage<br>earners,<br>occupations<br>and<br>economic<br>sector|
|---|---|---|---|---|---|---|---|---|---|
|Valerio, Sanchez Puerta,<br>Tognatta & Monroy-<br>Taborda (2015)|Armenia,<br>Bolivia,<br>Colombia,<br>Georgia,<br>Kenya,<br>Ukraine,<br>Vietnam|STEP (Skills<br>Measurement<br>Survey)<br>2012; men<br>and women,<br>aged 15-65,<br>wage and<br>self-<br>employed<br>workers;<br>sample<br>ranging from<br>846 in<br>Bolivia to<br>1,948 in<br>Vietnam|Heckman-<br>correction<br>for selection<br>bias|Log hourly<br>earnings;<br>gender as<br>dummy<br>variable|0.025<br>(Ukraine) -<br>0.050***<br>(Bolivia) for<br>years of<br>schooling|-0.008<br>(Armenia) -<br>0.096**<br>(Kenya) for<br>standardized<br>literacy|-0.052* (Colombia)<br>- 0.0378* (Armenia)<br>for extraversion, -<br>0.025 (Colombia) -<br>0.075*** (Vietnam)<br>for openness, -<br>0.0684***<br>(Armenia) - 0.020<br>(Colombia) for<br>agreeableness, -<br>0.025 (Colombia) -<br>0.0483* (Armenia)<br>for grit|0.035<br>(Kenya), -<br>0.198***<br>(Vietnam), -<br>0.256***<br>(Colombia),<br>-0.402***<br>(Ukraine), -<br>0.4471***<br>(Bolivia), -<br>0.479***<br>(Armenia),<br>and -<br>0.564***<br>(Georgia)|Potential<br>experience<br>and<br>experience-<br>squared,<br>indicator<br>variable for<br>self<br>employment|

Footnotes: Estimation results are statistically significant as reported by the author(s): *** at 1% level; ** at 5% level; * at 10% level. This survey of the literature includes only those studies that estimate log wages or log earnings functions, published after or in 2000, that include measures of cognitive and noncognitive skills and information on gender. From these studies, we selected the results of specifications that include also years of schooling and other covariates.

12
depreciation. They became a permanent fixture of the economic setting, until their gradual dismantling that began in the 1960s.

More broadly, the term “capital controls” has come to encompass a wide diversity of mechanisms employed at one time or another by industrial and developing countries to restrict international capital movements. They range “from the explicit to the subtle, and from the market-friendly to the coercive”.<sup>35</sup> They include explicit taxes as well as implicit ones in the form of quantity restrictions and outright prohibition of specific financial or foreign exchange transactions. But ultimately all controls share a common rationale. In the textbook world of complete markets and rational investors, unrestricted capital mobility leads to an efficient allocation of resources through competition, risk diversification and equalization of risk-adjusted returns. In contrast, in the real world of incomplete markets, informational asymmetries, and less than fully-rational investors, the result may instead be moral hazard, excessive risk-taking, and disproportionate macroeconomic and financial instability.<sup>36</sup> In such context, capital controls offer a potential second-best tool.

From the point of view of crisis management, however, it is essential to distinguish between controls on capital outflows and controls on capital inflows. Controls on outflows represent primarily a crisis containment tool, while controls on inflows may serve a prudential purpose to prevent future crises. Hence this section focuses mostly on the former, but it also considers briefly controls on inflows. Throughout, the focus is on

> 35 Magud et al (2007).

> 36 These market imperfections have been stressed by many distinguished observers, going as far back as Tobin (1978), and including Harberger (1982), Diaz-Alejandro (1985), Bhagwati (1998), Cooper (1998), and Stiglitz (2002).

34
From the evidence available, in most countries, increasing returns with quantiles have been observed. In particular, increasing returns have been documented for 15 out of 16 European countries studied, the United States, and whites in South Africa. The countries are: Austria, Denmark, Finland, France, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom (Martins and Pereira 2004); South Africa (Mwabu and Schultz 1996); and the United States (Buchinsky (1998).

There are four different patterns of returns by quantile in economically developed countries (Pereira and Martins 2000). First, for Austria, Finland, France, Ireland, Netherlands, Norway, Spain, Sweden, Switzerland and the United Kingdom, a positive and stable relationship exists over time. Second, for Portugal, one sees a positive relationship which is becoming more acute over time. Third, for Denmark and Italy returns are very similar across the distribution over time. Fourth, for Germany (only marginally) and Greece the returns-quantile profile is negative. Rates of return increase by quantile for all countries (with Denmark, Germany and Italy being borderline cases) except Greece, for which returns decrease moderately with quantiles (Martins and Pereira 2004). However, the data for Greece do not allow for a straightforward comparison with the other countries, as they are based on net wages. The authors point out that progressive taxation is likely to have a strong impact in eroding the returns to education at the top of the distribution than at its bottom. This may explain the Greek results. A likely explanation for the observed pattern of returns is the interaction between ability and schooling, which results in an amplification of the impact of ability upon earnings. Another possible explanation has to do with school quality differences. In particular, it may be that individuals who do worse in the labor market (for a given school attainment), are those individuals who received lower quality schooling. On the other hand, a negative relationship between ability and returns to education (decreasing returns with quantile) may be interpreted as evidence of substitutability between education and ability (see, for example, Walker and Zhu 2001).

For the United States, using data for 1972, 1979, 1985 and 1992, Buchinsky (1998) finds that returns for college graduates are always higher at the higher quantiles. For high school graduates returns are lower at higher quantiles in 1972 and 1979, but this pattern is reversed in 5
- Birkhaeuser, Dean, Robert Evenson, and Gershon Feder. 1991. "The Economic Impact of Agricultural Extension: A Review." _Economic Development and Cultural Change_ 39 (3): 607-50.

- Boserup, Ester. 1965. _Conditions of Agricultural Growth: The Economics of Agrarian Change Under Population Pressure._ New York: Aldine Publishing Co.

# Bourguignon, Francoise, and Thierry Verdier. Forthcoming.

- Brainard, S., Lael and Thierry Verdier. 1994. "Lobbying and Adjustment in Declining Industries." _European Economic Review_ 38 (3-4): 586-95.

- Brandao, Antonio S., and Gervasio C. Rezende. 1989. "The Behavior of Land Prices and Land Rents in Brazil." In Allen Maunder, and Alberto Valdes (eds.), _Agriculture and Govemments in an Interdependent World._ Proceedings of the Twentieth International Conference of Agricultural Economists, Buenos Aires, Argentina, August 24-31. Dartmouth: Aldershot.

- Brenner, R. 1977. "The Origins of Capitalist Development: A Critique of Neo-Smithian Marxism." _NewLeft Review No._ 104.

- Bruce, John, and Shem Migot-Adholla (eds.). 1994. _Searching for Land Tenure Security in Africa._ Dubuque, Iowa: Kendall/Hunt Publishing Co.

- Cardenas, Mauricio and Adriana Pont6n. 1995. "Growth and Convergence in Colombia: 1950-1990." _Journal of Development Economics_ 47 (1): 5-37.

- Cain, Mead. 1981. "Risk and Insurance, Perspectives on Fertility and Agrarian Change in India and Bangladesh." _Population and Development Review_ 7 (3): 435-74.

- Calvo, Guillermo A., and Jacob A. Frenkel. 1991. "Credit Markets, Credibility, and Economic Transformation." _Journal of Economic Perspectives_ 5 (4): 139-48.

- Casella, Alessandra, and Bruno Frey. 1992. "Federalism and Clubs: Towards an Economic Theory of Overlapping Political Jurisdictions." _European Economic Review_ 36 (2-3): 639-46.

- Cavallo, Domingo, and Yair Mundlak. 1982. "Agriculture and Economic Growth in an Open Economy: The Case of Argentina." International Food Policy Research Institute Report 36, Washington, D.C.

- Chatterjee, Satyajit. 1991. "Effect of Transitional Dynamics on the Distribution of Wealth in a Neoclassical Capital Accumulation Model." Federal Reserve Bank of Philadelphia Working Papers, 91-22, Philadelphia, Penn.

- Cukierman, Alex, Steven Webb, and Bilin Neyapti. 1992 "Measuring the Independence of Central Banks and Its Effect on Policy Outcomes." _World Bank Economic Review 6_ (3): 353-98.

- Deininger, Klaus, and Lyn Squire. 1996. "New Ways of Looking at Old Issues: Inequality and Growth." World Bank, Washington DC, mimeo.

68
21 splits in Table 8 appear not to impact on our results on workers’ remittances and current account reversal. Such a robustness is confirmed also when we add interactions with continent dummies. In particular, as shown in Table 9, the beneficial effects of workers’ remittances on financial stability is neither specific to African economies nor to Asian developing countries.

# _6.2 Robustness: endogeneity of remittances_

How about endogeneity of remittances? Could it be the case that altruistic migrants increase their remittances when a financial crisis becomes more likely?

The issue is not so simple and requires to be developed further. Being remittances lagged one period in our specification, the risk is that forward looking migrants would react to an increase in the ex-ante probability of a current account reversal. Then two scenarios open up. In the first one, the associated increase in remittances is not capable of avoiding the reversal; in this case, endogeneity would work against our result in that biasing towards a positive indirect effect of remittances on the probability of current account reversals<sup>29</sup> . In the second one, the associated increase in remittances is capable of avoiding the current account reversal; in this case, endogeneity would bias toward finding a negative indirect effect of remittances on the probability of current account reversals. Only in this case, endogeneity would bias in favor of our result.

Besides recalling the high stability and low volatility of remittances, our best answer to the endogeneity issue is to show that the within-country dynamics of remittances is not crucial for the identification of their stabilizing effect. For this, we perform two exercises. The first one consists in computing the frequency of switches in DREM since in our specification an increase in remittances before the crisis raises an endogeneity issue only if it determines a switch in the dummy variable DREM. Should we observe a low share of switches, we would conclude that endogeneity is not a serious issue.

> 28 Miguel, Satyanath and Sergenti (2004) use this dataset to estimate the impact of economic conditions on the likelihood of civil conflicts. For details on the methodology and the set of countries with civil conflicts, we send to Strand, Wilhelmsen and Gleditsch (2004).
# **Table 11: Top 20 institutions using over the 31 RePEc criteria based on research output and citations in the field of development economics**

## Rank

- 1 World Bank Group, Washington

- 2 Department of Economics, University of Chicago, Chicago

- 3 Economics Research, World Bank Group, Washington

- 4 Department of Economics, Massachusetts Institute of Technology

- 5 Department of Economics, Harvard University

- 6 Department of Economics, Brown University

- 7 Kennedy School of Government, Harvard University

- 8 Department of Economics, Oxford University

- 9 London School of Economics (LSE), London

- 10 Department of Economics, New York University

- 11 Wirtschaftswissenschaftliche Fakutät, Universität Zürich

- 12 Department of Economics Department, London School of Economics

- 12 International Food Policy Research Institute (IFPRI)

- 14 International Monetary Fund (IMF), Washington

- 15<sup>Institut für Empirische Wirtschaftsforschung (IEW),</sup> Wirtschaftswissenschaftliche Fakutät, Universität Zürich, Zürich

- 16 Graduate School of Business, Stanford University

- 17<sup>Center for International Development, Kennedy School of Government,</sup> Harvard University, Cambridge

- 18 National Bureau of Economic Research (NBER)

- 19 Faculty of Economics, University of Cambridge

- 20 Woodrow Wilson School, Princeton University

Source: RePEc website: <u>http://ideas.repec.org/top/</u> 49
atively new technologies would be less abundant in low income countries. This is especially the case if the new technology is substitutable with labor (e.g., Autor, Levy and Murnane, 2003), a factor that is relatively abundant in low income countries.

Despite these plausible economic mechanisms, our findings suggest that the correlation between ICT abundance and income per capita is entirely accounted for by differences in industrial composition. Predicted values based on domestic industrial composition and US industry level ICT intensity exhibit the same relationship with income per capita. Moreover, we present evidence suggesting that, within industries, ICT spending is similar across countries of different levels of development.

The suggested implication is that technology adoption lags are not very important for the proliferation of ICT. On average, after controlling for industrial composition, the value of ICT capital per worker is the same as the general capital-labor ratio. A higher relative price of ICT capital in low income countries, which is perhaps reflective of higher costs of tradables, implies that the corresponding quantity of ICT capital goods is lower in low income countries.

Of course, our analysis here “explains” cross country differences in ICT abundance only in an accounting sense. This accounting exercise illustrates that in order to understand the source of the differences, one must look deeper into the fundamental questions of development economics, and understand why the capital-labor ratios are lower in developing countries; why non-tradables are relatively cheaper; and why production is concentrated disproportionately in agriculture and other industries that do not require much ICT capital.

# References

> Acemoglu D, Autor D. 2011. _Skills, Tasks and Technologies: Implications for Employment and Earnings_ , volume 4 of _Handbook of Labor Economics_ , chapter 12. Elsevier, 1043–1171.

> URL http://ideas.repec.org/h/eee/labchp/5-12.html

> Autor DH, Dorn D. 2013. The growth of low-skill service jobs and the polarization of the us labor market. _American Economic Review_ **103** : 1553–97.

> URL http://ideas.repec.org/a/aea/aecrev/v103y2013i5p1553-97.html

> Autor DH, Levy F, Murnane RJ. 2003. The skill content of recent technological change: An empirical exploration. _The Quarterly Journal_

31
# II.THE SHORT RUN PROCESS OF DETERMINATION OF THE REAL EXCHANGE RATE

Consider an economy producing two types of goods, Traded (T) and Non-Traded (N), with prices PT and PN. We define thz real exchange rate, e ,as the relative price of Traded vs.Non-Traded goods: e 8 PT/PN.\l/ \1/ The analysis in this Section draws and extends on the results presented in Rodriguez (1982).

Private sector nominal spending on goods is denoted by Gp and Government spending on goods is denoted by Gg. Total spending on goods (absorption) is the sum of private and government spending:

# (1) G = Gp + Gg

Nominal GDP is denoted by Y and the difference between GDP and Nominal absorption is the Trade Surplus (TS) : (2) TS = Y - G on the demand side, assume the private sector spends a fraction b(e) of total private spending on Non-Traded goods : (3) Gpn-b(e).Gp.

Similarly, the government spends a fraction bg on Non-Traded goods: (4) Ggn= bg.Gg.

Total nominal spending on Non-Traded gods is therefore: (5) Gn- Gpn + Ggn = b(e).Gp + bg.Gg Define the ratio of Government spending to GDP as the po,.icy parameter: (6) g = Gg/Y.

7
predictions and because they generate larger and more accurate estimates of uncertainty. Because the cost to accuracy for in-sample municipalities is minor, the larger estimates of uncertainty increase the coverage rate for in-sample municipalities. Failing to weight by state household size when aggregating the simulation results to municipalities has virtually no effect on the estimates because the population weights only vary at the state level and the benchmarking procedure aligns the estimates with the survey-based estimates at that level.

# _6.3 Model heterogeneity_

So far, the results shown have come from one estimated national model. However, it is possible that estimating separate models would improve the performance of the model by better modeling the heterogeneity across states or across urban/rural areas. Table 6 shows how the results change due to different types of variants of the estimation and simulation procedure. The baseline model considers estimates from one national model. Mexico has separate poverty lines for urban and rural areas, however, which must be considered when estimating headcount poverty rates during the simulation stage. Because the software package used allows for only one poverty line, the baseline model inflates the per capita income of rural areas in the sample by the ratio of the urban to rural poverty line. After making this adjustment, the urban poverty line threshold is applied to all households in the synthetic census, in both urban and rural areas. The second model considers a similar approach, but instead estimates six models, each corresponding to different groups of states.<sup>21</sup> This may better capture the heterogeneity across these different state groupings. The third model takes a different approach to estimating separate urban and rural poverty lines. This approach simulates urban and rural poverty rates separately, based on a single national model, and then takes the population-weighted average of the urban and rural estimates for each municipality.<sup>22</sup> When taking this population-weighted average, we assume zero covariance between the urban and rural poverty estimates of common municipalities, which further

> 21 These are the same six groups used in official government statistics.

> 22 It is less straightforward to estimate the mean squared error of the population weighted urban and rural average. Partly this is because the random component of the municipal mixed effect will be drawn independently for rural and urban areas, underestimating the mean squared error. We therefore estimate the covariance of urban and rural poverty rates across municipalities, assuming the poverty estimates are unbiased and that the MSE estimates can be used to estimate variance. We then estimate the covariance between urban and rural municipalities and calculate the variance of the population-weighted average.

27
the case with remittances, individuals can consume more of both goods and leisure (i.e. the income effect dominates) and thus their labor supply is reduced. In a general equilibrium setting, wages are set to clear the labor market and reduced labor supply implies raising wages. This triggers second order effects: higher wages raise the opportunity cost of leisure and the substitution effect can push individuals to increase their labor supply, up to the point where the income effect dominates again (the backward bending labor supply curve). Given the initial equilibrium values and our parameter choices (derived from Kim, 2006), shocks that directly increase the wages received by workers, such as a reduction in payroll taxes (when these have an incidence cost for employees), generate a positive supply response. Therefore, in the current model setup the introduction of an endogenous labor supply results in additional pressure on wages and exacerbates the effects of real exchange rate appreciation described earlier.

# **3.2 Jamaican economy in 2002: data sources and sectoral structure**

The initial benchmark equilibrium for the CGE model in our study is a 2002 Social Accounting Matrix (SAM), which was constructed specifically for this exercise.<sup>9</sup> This SAM has been assembled from various sources and includes 22 sectors, 22 commodities, 3 factors (skilled and unskilled labor and one composite capital), an aggregate household account,<sup>10</sup> and other accounts (government, savings and investment, and the Rest of the World). Macroeconomic and sectoral data come from various publications of the Statistical office of Jamaica (STATIN), while microeconomic data on sectoral employment, wages and household consumption are estimated from the 2002 Labor Force Survey and the 2002 Survey of Living Conditions. International trade data and tariff protection rates are obtained from the UN COMTRADE and TRAINS databases. The quality of the initial dataset represented by this SAM directly influences the quality of the model results. For this reason, particular attention has been devoted in estimating the value added (especially employment and average wages by skill and by sector), trade, and tariff components of the SAM.

The Jamaican economy is dominated by service sectors, which account for more than 80 percent of the GDP at factor cost.<sup>11</sup> Household consumption consists mainly of food products, where the processed foods and sugar sectors account for 30.5 percent of total. Services account for an additional 35 percent of household consumption, and the rest is spread fairly evenly across

> 9 For more details on the SAM, see the Annex 7.2.

> 10 One version of the SAM has two separate household groups defined according to main income source of the household head—see Section 4.4.

> 11 This figure includes public services in the definition. Private services represent 68 percent of GDP at factor cost (labor and capital value-added).

10
**Table 13: Means of Variables in HEAZ Models DHS, 1996 & 2000**

|_Variables_|_Mean_|_Std.Dev_|
|---|---|---|
|HAZ|-1.11|1.34|
|PerCapitaVLDistrictExpenditures|31.90|7.63|
|AssetIndex|-0.10|0.90|
|Maledummy|0.50|0.50|
|Multiplebirthdummy|0.01|0.11|
|Birthorder--2ndChild|0.22-|0.42|
|Birthorder-- 3rdChild|0.16|0.37|
|Birthorder--4thChild|0.10|0.30|
|Birthorder--5th Child|0.07|0.25|
|Birthorder--6th+Child|0.14|0.35|
|Age0-6months|0.10|0.30|
|Age7-12months|0.10|0.31|
|Age13-18months|0.10|0.30|
|Age25-35months|0.19|0.39|
|Age36-59months|0.41|0.49|
|ShareHHmembersage0-5|0.30|0.13|
|Share HH girlsage6-15|0.10|0.12|
|ShareHHboysage6-15|0.10|0.12|
|ShareHH women16-25|0.10|0.12|
|ShareHHwomen26-65|0.16|0.11|
|ShareHHmen16-25|0.07|0.11|
|ShareHHmen26-65|0.17|0.10|
|NumberofHHmembers|6.38|2.56|
|Headismale|0.88|0.32|
|Headis indigenous|0.12|0.33|
|Mother'sage|29.24|6.77|
|Mother'sheight(cm)|150.33|5.67|
|Mother'seduc- primary|0.36|0.48|
|Mother'seduc- secondary|0.39|0.49|
|Mother'seduc-postsec.|0.16|0.37|
|Father'seduc- primary|0.30|0.46|
|Father'seduc- secondary|0.44|0.50|
|Father'seduc- postsec.|0.20|0.40|
|Housefloordirtdummy|0.51|0.50|
|Pipeddrinkingwaterdummy|0.55|0.50|
|Flushtoiletdummy|0.40|0.49|
|Urban dummy|0.68|0.47|
|Numberofobservations|19,053||

The effect of VL expenditures is insignificant in the basic OLS model (Table 14, model 1). Moreover, it is negative in the IV models without district fixed effects (3 and 5).

_27_