rafmacalaba/datause-encoder-data huggingface dataset:
# _Quality of Indices_

For detailed information on the component indices used, we refer those interested to the original works for discussions of the index strengths and weaknesses. In cases where the indices have been produced for several years (HDI and gini coefficients), the authors have had time to respond to criticisms of the initial versions. The indices used from Mauro were constructed by a commercial organization, Business International Corporation, which sells updated versions of its indices. Presumably, the version made public by Mauro is old enough that its commercial (and, perhaps, academic) value is low. Unfortunately, an updated version was out of the range of our research budget.

The political freedom index created by Haq is new both in the sense that there are no previous versions and in that it addresses a topic that had previously not been subjected to formal indexing. The corruption index is updated annually and reflects the results of several surveys on bureaucratic honesty. The outward orientation index is an updated version of an index first created in the mid-1980s. The central bank independence index is relatively new and represents the legal characteristics of a country's central bank. As the authors note, there is often a discrepancy between the statutory independence of a central bank and its independence in practice. The authors attempted to capture this effect, by developing a second index which measures the frequency of turnover of heads of central banks. This second index has not been incorporated into this paper because of the small number of countries covered.

Note that although all of these indices have been published in the past 3 years, the data from Mauro and Cukierman, Webb and Neyapti are somewhat older. This may bias results for, or against, specific countries which have experienced rapid change since the early 1990s. The Mauro indices, for example, have assessments of Liberia and Nigeria which, given more recent events, seem high. The indices have not been adjusted to reflect these changes in order to preserve the internal consistency of these indices.

# _Adjustments_

Political Freedom: mean shifted to 50. Political Stability, Red Tape, Judicial Efficiency: mean shifted to 50. Corruption: unchanged. Human Development Index: mean shifted to 50. Gini Coefficients: 10 - sqrt(gini), mean shifted to 50.

Outward Orientation: ln(speed of integration index) used as percent change, then applied to initial index, mean shifted to 50.

Central Bank Independence: mean shifted to 50.

Debt to GDP Index: 2 - debt/gdp, mean shifted to 50.

Means were shifted by: Initial Index Number * 50 / Average of Initial Index 22
across various percentiles of the distribution of trade openness, using results from Table 8. The estimates, as shown in Table 9, show that the nonlinear effects are statistically significant, and suggest that the negative effect of exchange rate flexibility diminishes with an increase in trade openness.

# **4 Exchange rate regimes and financial development**

The focus of this study has been on the interaction between financial development and exchange rate regimes and their impact on economic growth. However, the choice of exchange rate regime tends to be endogenous to the level of financial development especially in economies with less developed financial sector. Flexible exchange rates can be instrumental in minimizing incentives for foreign currency borrowing which helps to minimize currency mismatches, and deepen domestic financial markets particularly for local currency debt instruments and those for hedging against exchange rate risks (Gadanecz and Mehrotra, 2013). Thus, fixed exchange rate regimes may discourage the development of the financial system.

Bordo and Flandreau (2003) show that the level of financial development is higher in countries with a flexible exchange rate regime in the post-Bretton-Woods era, and further suggest that financial development is critical to the evolution of the international monetary system. On a different note, Lin and Ye (2011) address the question of whether financial development matters for the choice of exchange rate regime, and whether financial development plays a role in the decision to transition from a fixed exchange rate regime to a flexible one. Using data for 102 countries, and domestic credit to the private sector as share of GDP as the measure of financial development, they find that financial development has a statistically significant and negative impact on the probability of adopting a fixed exchange rate regime; the impact being quantitatively sizable. The study also finds that countries with a higher level of financial development are less likely to opt for a fixed exchange rate regime, and that conditional on having been under a fixed exchange rate regime, a country is more likely to exit under a higher level of financial development. The analysis, however, acknowledges that financial development may be endogenous, such that exchange rate regime choices may impact financial development outcomes.

18
||MAC|parameter|MIC parameter|
|---|---|---|---|
||<sup>_m_</sup><br><br>|$/tCO2<br><br>�|_c_<sup>_m_</sup><br><br>�<br>$/tCO2<br><br>�|
||_γ_<br>_i_<br>|GtCO2_/yr_<br>|_i_<br><br>GtCO2/yr<sup>3</sup><br>|
|Waste||34|3732|
|Industry||17.6|1932|
|Forestry||15.9|1745|
|Agriculture||11.9|1306|
|Transport||11.6|1273|
|Energy||10.3|1131|
|Buildings||3.6|395.2|

Table 3: Calibration of the Marginal Investment Cost (MIC) functions and of the Marginal Abatement Costs (MAC) functions. This gures are consistent with our modeling framework (18, 20) and the data from IPCC reported in the Tab. 2.

We also ensure that relative costs (when comparing two sectors) are equal in the two models. This way, dierences in optimal strategies arise only from the introduction of economic inertia.

We rst solve the problem exposed in section 2, assuming quadratic abatement costs. Quadratic costs are a simple specication that grants that the _γi_ are convex, and simplies the resolution as marginal abatement costs are linear: Where _γi_<sup>_m_</sup> are parameters specic to each sector. We calibrate these costs functions using the abatements corresponding to a 20 $/tCO 2 marginal cost in gure SPM.6 of IPCC (2007) presented in Tab. 2; the result of this calibration is presented in the rst column of Tab. 3.

The numerical resolution allows one to compute the optimal strategy in nancial ( _γi_<sup>_′_(</sup><sup>_ai_(</sup><sup>_t_)))and</sup> physical ( _ai_ ( _t_ ) ) terms (see (34) and (36) in annex D and Fig. 3).

We call Γ the discounted cost of the optimal mitigation strategy: The numerical resolution gives Γ = 3351 G$.

Then, we solve the model with economic inertia, still assuming quadratic costs.

This allows one to simplify the model (see (38) and (41) in annex E). We calibrate the _c_<sup>_m_</sup> _i_<sup>parameters</sup> by adapting the methodology from Grubb et al. (1995): rst, we assume that the relative MICs are equal to the relative MACs from IPCC: And second, we constrain the total costs of the mitigation strategy to be equal in the two models: A numerical resolution provides _c_<sup>_m_</sup> _i_<sup>(Tab. 3), as well as the optimal abating strategy</sup><sup>_xi_and</sup><sup>_ai_(Fig. 3)</sup> Finally, we introduce annualized marginal investment costs. In our modeling framework, evanescent options are implemented paying a MAC, while permanent options are implemented paying a MIC. Investing _xi_ ( _t_ ) at _t_ reduces GHG emissions from _t_ onwards, but the cost of doing so, _ci_ ( _xi_ ( _t_ )) , is paid only at time _t_ . To make this cost easier to compare with evanescent abatement costs, it is useful to introduce the annualized cost of investment _r · ci_ ( _xi_ ( _t_ )) and its marginal value. Indeed, paying the 9
This is also a constraint for a panel analysis with longer temporal structure. The key question pertinent to this analysis is: “What is the monthly wage or salary for which you would accept to work?” to which the respondent could provide an answer in seven intervals. For our analysis, the middle point of the interval is taken and hence the variable is treated as continuous. The answer has been provided only by unemployed persons, i.e. those who are without a job and actively searching for such. Annex 1 provides the definitions of all variables used throughout.

The following **Table 1** provides information for our sample. We have on disposal 10,787 observations of unemployed people distributed among 5,955 unique individuals. Of these, 4,172 observations or 1,364 unique individuals appear in at least two time periods and have both employment and unemployment experience, so that for them we observe both the reservation and the market wage over a fairly short period of time.

**Table 1 – Samples size**

||**Total sample**|**Individuals**<br>**appearing at**<br>**least two**<br>**periods**|**Unemployed**<br>**for at least**<br>**one quarter**<br>**over the**<br>**observed**<br>**period**|**Unemployed**<br>**with**<br>**employment**<br>**experience**<br>**over the**<br>**observed**<br>**period**|
|---|---|---|---|---|
|**Observations**|69,712|61,112|10,787|4,172|
|**Individuals**|31,588|22,988|5,955|1,364|
|_Source: Authors’_|_calculations base_|_d on LFS._|||

# **4. Stylized facts**

Reservation wages in North Macedonia follow a fairly typical normal-like distribution ( **Figure 1** ). However, compared to market wages, the reservation wage distribution is considerably narrower and bulged. Still, when both are compared in average and median terms, then no significant differences are evident ( **Table 2** ). This initial observation is against the widespread perception that reservation wages in North Macedonia are frequently set high so as being an obstacle to accepting a job.

5
polynomial regression of the dummy for the extensive margin with respect to the distance between OD pairs for the alternative cut-offs. Consistent with a priori expectation, the probability of being isolated increases with an increase in distance between the OD pair. We consider an OD pair isolated if the predicted probability is more than 0.5 (blue dashed horizontal line in Figure 4). The higher cut-off implies an extensive spatial margin at around 3,900 km which coincides with the maximum distance we observe in our trip data. We use the higher cut-off for the rest of the analysis. When the probability of being isolated is averaged by states, the estimates indicate that three Indian states (Mizoram, Nagaland, and Sikkim) and two territories (Puducherry, Daman and Diu) can be considered isolated in the sense that 50 percent of the potential trade links in these states and territories see very few trips between them (Figure 7).

# 5 **Short-term Impact of the Pandemic**

## 5.1 **Impact of transport cost shock on the intensive margin**

In this subsection, we use the estimates from Tables 3 and 6 to simulate the impacts of the freight rate increase on trade flows focusing on the post-lockdown period. This exercise limits its scope to look at the immediate impacts of a freight rate increase in the very short-run and thus does not attempt to utilize the general equilibrium framework. The rationale behind the narrow focus is that the COVID-19 shock has affected nearly all facets of the economy, and a broader evaluation of its impacts on spatial development would have to wait in light of the more devastating wave that started after March 2021.

The simulation exercise takes the heterogeneous pattern of freight rate changes during the postlockdown period from Table 3 (column 2) and predicts freight rates along existing pre-pandemic routes. These predicted rates are then used in conjunction with trade elasticity to predict the change in trade flow along each route. The estimated decrease in trade flows under two alternative trade elasticity estimates (2.8 and 3.9) are plotted in Figure 5. The average decrease in trade flow due to freight rate shock is about 70 percent when we take the elasticity of trade with respect to the freight rate as equal to (3.9) and 58 percent when elasticity is equal to (2.8). Figure 5 confirms a sharper reduction in trade flows between OD 21
through higher lending costs and could be the outcome of a temporary shortage of liquidity following a crisis accompanied by financial sector stress and excessive pressure on banks’balance sheets. In this context, failure to restore adequate liquidity levels may result in a tightening of credit supply to the economy.

The paper uses a novel panel dataset on output and domestic bank credit for a sample of advanced and emerging countries between 1980 and 2014 to analyze the role of demand and supply-side drivers of bank lending growth during credit-less recoveries. We argue that during these episodes the evidence points to the prevalence of demand-side frictions to credit growth, reflected in lower demand for liquidity by the private sector in the aftermath of recessions. During these episodes output recovery takes place while the economy is deleveraging, i.e. the stock of credit is decreasing. However, as in Biggs et al. (2009) we show that as long as the pace of deleveraging is decreasing a rebound in output can occur in the presence of negative credit growth.

Our work is carried out in two steps. In the first one, we model credit-less recoveries in a simple endowment economy framework featuring representative infinitely lived households. In this model, credit dynamics are a function of the policy rate and of liquidity shocks. These shocks reflect frictions in financial intermediation and affect the value of liquidity needed to finance consumption and investment.

In the second step, we test empirically the hypotheses. We first exploit simple information on the correlation between prices and output during the recession phase of the cycle to identify demand and supply driven recessions originating in the real and financial markets. Subsequently, we examine the role of demand and supply-driven recessions in influencing the likelihood of a credit-less recovery in our sample of advanced and emerging countries.

Related literature: Our work builds on the theoretical literature on credit constraints and is closely related to empirical studies on financial crises and credit-less recoveries. From an economic theory perspective, the relationship between output and credit has been analyzed in the context of macroeconomic equilibrium models featuring real and financial markets. A large body of literature concerned with the notion of financial accelerator (Bernanke and Gertler, 1989; Bernanke, Gertler and Gilchrist, 1999 and Kiyotaki and Moore, 1997) has documented the channels through which financial markets affect the real economy. In these models, the interaction between real and financial variables stems from the need of firms to access funds in credit markets. Due to financial market imperfections, firms’ability to access liquidity is dependent on the value of their financial wealth, which is assumed to be pro-cyclical. Shocks affecting the economy impact on borrowers’ creditworthiness and on their ability to access funds to finance investment, contributing to amplify the magnitude of business cycle fluctuations.

3
- Dix-Carneiro, R., Goldberg, P. K., Meghir, C., & Ulyssea, G. (2021). _Trade and domestic distortions: the case of informality_ (Working Paper No. 28391). National Bureau of Economic Research.

- Dix-Carneiro, R., & Kovak, B. K. (2019). Margins of labor market adjustment to trade. _Journal of International Economics_ , _117_ , 125–142.

- Flaaen, A., Shapiro, M. D., & Sorkin, I. (2019). Reconsidering the consequences of worker displacements: Firm versus worker perspective. _American Economic Journal: Macroeconomics_ , _11_ (2), 193–227.

- Foster, L., Grim, C., & Haltiwanger, J. (2016). Reallocation in the great recession: cleansing or not? _Journal of Labor Economics_ , _34_ (S1), S293–S331.

- Fr ́ıas, J. A., Kaplan, D. S., Verhoogen, E., & Alfaro-Serrano, D. (2022). Exports and wage premia: Evidence from Mexican employer-employee data. _Review of Economics and Statistics - forthcoming_ .

- Garin, A., & Silv ́erio, F. (2022). How responsive are wages to demand within the firm? evidence from idiosyncratic export demand shocks. _Review of Economic Studies - forthcoming_ .

- GC Britto, D., Pinotti, P., & Sampaio, B. (2022). The effect of job loss and unemployment insurance on crime in Brazil. _Econometrica_ , _90_ , 1393–1423.

- Gerard, F., & Gonzaga, G. (2021). Informal labor and the efficiency cost of social programs: Evidence from unemployment insurance in Brazil. _American Economic Journal: Economic Policy_ , _13_ (3), 167–206.

- Gerard, F., Lagos, L., Severnini, E., & Card, D. (2021). Assortative matching or exclusionary hiring? The impact of employment and pay policies on racial wage differences in Brazil. _American Economic Review_ , _111_ (10), 3418–57.

- Gerard, F., & Naritomi, J. (2021). Job displacement insurance and (the lack of) consumptionsmoothing. _American Economic Review_ , _111_ (3), 899–942.

- Gerard, F., Naritomi, J., & Silva, J. (2021). Cash transfers and formal labor markets: Evidence from Brazil. _CEPR Discussion Paper_ (DP16286).

- Goldberg, P. K., & Pavcnik, N. (2003). The response of the informal sector to trade liberalization. _Journal of Development Economics_ , _72_ (2), 463–496.

29
loarn repayiments, leci to low recovery ra{-l..i In mtlany ( Icial c >. u ;A ii -,u, [, i ates have usually varied between 50 and 6 percent, but sometimes have even fallen below 20 percent. In Bolivla, for example, delinquencles were around 47 percent In the 1970s. Over 50 percent of the loans made through the cooperative system in Thailand were In arrears In the 1980s. Delinquency rates of 50 percent were observed In Indla, 40 percent In Malaysia and Nepal, and about 80 percent In Bangladesh.1 6 19. Because of ineffIcient operations and low collection rates of many government supported lending Institutlons, officlal agricultural credit programs have often been extremely costly to governments and remained without adequate developmental Impact. For example, the official rural financial system of Mexico has cost the government over an extended Inflationary perlod more than one dollar for each dollar lent. It Is not a coincidence that the rural credit systems of the World Bank's three most importani agricultural credit borrowers, Brazil, Mexico, and India, have all experlenced severe problems and placed enormous burdens both directly on governmernt budgets and less directly on the external debt position. The government of Brazil has at times allocated about one-fourth of Its total agricultural expenditure to maintain the agricultural credit system. A major lesson cited by an OED report of a credit project In Brazil Is "the Inappropriateness of subsidized credit as a tool for agrlcultural development."17 A Bank-sponsored study of credit

> 16 Aslan Productivity Organization, Farm Credit Situathon in Asia, 1984, p. 47, and Asian Productivity Orgarizab'on, Farm Finance and Agricultural Development, 1988, p. 250.

> 17 OED Audit Report on Agricultwral Credit and Export Development Project, Report 7992, June 1989.
**Table** **<u>7: The effect of trading with neighbors on conflict, revisited</u>**

||(1)<br>any onset|(2)<br>any onset|(3)<br>any onset|(4)<br>any onset|(5)<br>major<br>onset|(6)<br>major<br>onset|(7)<br>major<br>onset|(8)<br>major<br>onset|
|---|---|---|---|---|---|---|---|---|
|Trade with|-0.077*|-0.106|-0.084**|-0.113*|0.000|-0.006|-0.005|-0.008|
|neighbors(RTA)(t-1)|(0.040)|(0.065)|(0.040)|(0.064)|(0.021)|(0.025)|(0.022)|(0.026)|
|Instrumented|NO|NO|NO|NO|YES|YES|YES|YES|
|Time trends|NO|YES|NO|YES|NO|YES|NO|YES|
|Weights|[t-12; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|[t-12; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|
|Observations|3,464|3,464|3,461|3,461|3,461|3,461|3,461|3,461|
|R-squared|0.071|0.158|0.071|0.158|0.071|0.158|0.071|0.158|
|Nr. of countries<br>First stage<br>Kleibergen-Paap|114|114|114|114|114|114|114|114|
||(9)|(10)|(11)|(12)|(13)<br>|(14)<br>|(15)<br>|(16)<br>|
||any onset|any onset|any onset|any onset|major<br>onset|major<br>onset|major<br>onset|major<br>onset|
|Trade with|-0.077**|-0.108*|-0.077**|-0.108*|-0.007|-0.015|-0.007|-0.015|
|neighbors(RTA)(t-1)|(0.038)|(0.055)|(0.038)|(0.055)|(0.018)|(0.021)|(0.018)|(0.021)|
|Instrumented|NO|NO|NO|NO|YES|YES|YES|YES|
|Time trends|NO|YES|NO|YES|NO|YES|NO|YES|
|Weights|[t-12; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|[t-12; t-2]|[t-12; t-2]|[t-4; t-2]|[t-4; t-2]|
|Observations|3,860|3,860|3,857|3,857|3,857|3,857|3,857|3,857|
|R-squared|0.026|0.067|0.026|0.065|0.026|0.065|0.026|0.065|
|Nr. of countries<br>|115|115|115|115|115|115|115|115|
|First stage<br>Kleibergen-Paap|22743|6948|22743|6948|27094|8503|27094|8503|

_Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; all regressions include country fixed effects, year effects and the trade shocks variables as in the previous tables and other controls (any conflict since 1946 and war border (t-1));trade shock variables are weighted using the slow-moving averages (no country-specific time trends)._ 32
Table 10. Human capital returns measured by height and schooling among the rural population of <u>PROSPERA</u>

||Males||Females||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|VARIABLES|ln(w)|Works<sup>a)</sup>|ln(w)|Works|
|**ln of Height in cm (predicted)**|**10.736*****<br>**(2.152)**|**9.708*****<br>**(2.923)**|**8.764*****<br>**(2.660)**|**2.527**<br>**(2.380)**|
|**Schooling (years)**|**0.034*****<br>**(0.007)**|**-0.026**<br>**(0.016)**|**0.048*****<br>**(0.010)**|**0.024****<br>**(0.010)**|
|BMI|-0.021**<br>(0.008)|-0.020*<br>(0.012)|-0.031***<br>(0.012)|0.004<br>(0.010)|
|Age|0.018***<br>(0.004)|0.029***<br>(0.009)|0.019***<br>(0.006)|0.022***<br>(0.006)|
|Speaks indigenous language|-0.053<br>(0.063)||-0.042<br>(0.093)||
|Migrated = 1|0.473***<br>(0.047)|0.325**<br>(0.144)|0.203***<br>(0.066)|0.446***<br>(0.072)|
|Household with children||0.223||-0.190***|
|from 0 to 5 years of age||(0.141)||(0.065)|
|Has a partner||-0.346<br>(0.338)||-0.772***<br>(0.179)|
|Lives with the partner||0.622*<br>(0.344)||-0.132<br>(0.176)|
|ln Income in the household<sup>b)</sup>||-0.049***<br>(0.013)||-0.061***<br>(0.010)|
|Constant|-52.349***<br>(10.900)|-48.051***<br>(14.790)|-41.515***<br>(13.270)|-13.010<br>(11.848)|
|Observations|1,992||2,358||

Note: a) To estimate the selection equation, the following variable were added to the Mincer model: Household with children from 0 to 5 years of age; has a partner/spouse; lives with the romantic partner; and ln of the monthly income of the household. b) Corresponds to the monthly income of the household deducting the monthly income of the index young adult. /athrho of 0.046 and standard error of (0.054) for males; -0.068 and (0.089) for females. /lnsigma of -0.176***and standard error of (0.031) for males; -0.091**and (0.040) for females. Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on the data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000 In summary, the results of the Mincer model of human capital returns indicate that for the population raised in rural households with maximum exposure to PROSPERA, there is a premium in terms of labor income, the result of the gains in height and schooling. We also observe that the population with the highest levels of education earns the highest labor income, on average.

# **VIII. Conclusions**

This study focused on the analysis of the data collected as part of the Evaluation of Rural Households Survey 2017 (ENCEL 2017) and on the previous rounds of ENCEL and ENCASEH 1997. In particular, we use 1997-2000 as the baseline period in order to investigate the potential for intergenerational mobility, and the magnitude of the absolute intergenerational mobility of human capital (height as measurement of health and schooling as measurement of education), 33