rafmacalaba/datause-encoder-data huggingface dataset:
# **Empirical approaches to money demand estimation**

There have been repeated efforts at providing empirical content in a countryspecific context, to the theoretical approach to money demand briefly described above. In recent years in particular, there has been a proliferation of studies using a vector error correction approach to mcdeling money demand (VECM).<sup>3</sup> This approach has, among many others, the advantage of jointly estimating the long- and short-run components of the demand for money, thus facilitating the task of ensuring that short-run specifications are associated with long-run components consistent with established economic theory. In practice, the VECM approach does appear to perform relatively well in the estimation of models that exhibit long-rum behavior consistent with economic theory. This appears to be the case despite the f,act that changes to the short-run model specification often dramatically alter associated estimates of the long-run component.

In many cases, however, the short-run autoregressive modeling framework used in the VECM approach becomes parameter intensive, and more often than not, there is little guidance from economic theory on the appropriate lag structure to be selected. The autoregressive framework may sometimes obscure, or at least does not help in making transparent, the presence of potentially interesting short-run components (e.g. of one or more cycles) common to tie endogenous variables in the system. Conventional unit root and cointegration tests, as well as parameter estimates using a VECM approach, may also be overly sensitive to the presence of seasonal unit roots.<sup>4</sup> As in the VECM approach, the structural time series modeling approach allows joint estimation of long- and short-run components of money demand. By imposing the restriction that the endogenous variables in the system share a common stochastic trend (i.e., that variables are cointegrated), it is possible to find specifications that are consistent with economic theory at the zero frequency.

3For a recent survey of empirical studies using error-correction methods in the estimation of money demand please refer to Sriram (2001). Cartaya et al (1997) used a cointegration framework in the study of money demand at the monthly f[requency in Venezuela. More recently, Ramajo (2001) used time-varying parameter error correction models, applied at a quarterly frequency to Venezuela.

**4** Seasonal error-correction models have been proposed and applied to the estimation of money demand. See for example a recent application to Chile, in Soto and Tapia (2001).

4 Money Demand in Venezuelai, Mario A. Cuevas
- Costinot, A. (2008). A comparative institutional analysis of agreements on product standards. _Journal of International Economics_ , 75(1):197–213.

- Crozet, M. and Koenig, P. (2010). Structural gravity equations with intensive and extensive margins. _Canadian Journal of Economics/Revue canadienne d'économique_ , 43(1):41–62.

- Disdier, A.-C., Fontagné, L., and Cadot, O. (2014). North-south standards harmonization and international trade. _The World Bank Economic Review_ , 29(2):327–352.

- Disdier, A.-C., Stone, S., and van Tongeren, F. (2020). Trade and Economic Effects of IRC: Further Empirical Evidence from SPS and TBT provisions. PSE Working Papers hal-02953673, HAL.

- Espitia, A., Pardo, S., Piermartini, R., and Rocha, N. (2020). Technical barriers to trade. In _Handbook of Deep Trade Agreements_ , pages 343–366. The World Bank.

- Fernandes, A. M., Ferro, E., and Wilson, J. S. (2019). Product standards and firms’ export decisions. _The World Bank Economic Review_ , 33(2):353–374.

- Fernandes, A. M., Freund, C., and Pierola, M. D. (2016). Exporter behavior, country size and stage of development: Evidence from the exporter dynamics database. _Journal of Development Economics_ , 119:121–137.

- Fontagné, L. and Orefice, G. (2018). Let’s try next door: Technical barriers to trade and multi-destination firms. _European Economic Review_ , 101:643–663.

- Fontagné, L., Orefice, G., Piermartini, R., and Rocha, N. (2015). Product standards and margins of trade: Firm-level evidence. _Journal of International Economics_ , 97(1):29–44.

- Freund, C. and Pierola, M. D. (2015). Export superstars. _Review of Economics and Statistics_ , 97(5):1023–1032.

26
**Table 10. Family and reproductive health outcomes (ITT), 2010, 2011 and 2012 cohorts**

||P<br>|ooled 2010‐2<br>|012 Cohorts||
|---|---|---|---|---|
||Baseline mean<br>[Std Dev]|<br>OLS<br>(1)|IPSW<br>(2)|NN<br>(3)|
|Desired number of children|2.19|‐0.001|‐0.010|0.001|
||[0.767]|(0.021)|<br>(0.024)|(0.020)|
|Pregnant (1= Yes), if married|0.038|0.005|0.007|0.006|
||[0.192]|(0.010)|<br>(0.011)|(0.011)|
|Number of children, if|1.126|‐0.014|‐0.013|‐0.015|
|respondent has children|[1.322]|(0.012)|<br>(0.013)|(0.012)|
|Ever argue with spouse/|0.799|‐0.006|‐0.002|‐0.009|
|partner, if applicable (1=Yes)|[0.401]|(0.023)|<br>(0.023)|(0.024)|
|Use any type of contraception|0.768|0.022|0.019|0.018|
|(1=Yes), if sexually active|[0.422]|(0.022)|<br>(0.023)|(0.023)|
|HIV Knowledge score (0‐3)|1.147|0.030|0.021|0.031|
||[0.764]|(0.040)|<br>(0.041)|(0.039)|
|Household food insecurity (0‐4)|0.552|‐0.030|‐0.061**|‐0.033|
||[0.772]|(0.029)|<br>(0.030)|(0.032)|
||0.348|‐0.003|0.011|0.001|
|Weekly protein consumption|||||
|<br>(1= more than 5x per week)|[0.476]|(0.029)|<br>(0.028)|(0.025)|
|Household receives remittances|0.241|0.016|0.008|0.016|
|(1=Yes)|[0.428]|(0.022)|<br>(0.025)|(0.020)|
|Household received more|0.104|0.019|0.014|0.019|
|remittances than last year|[0.306]|(0.018)|<br>(0.018)|(0.017)|
|(1=Yes)|||||
|Clustered Standard Errors||Yes|Yes|No|

Notes: All columns report difference‐in‐difference estimates. "ITT" indicates that everyone whose score qualified them for a given training event is included in the "treatment" group. Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

33
know” responses to other questions in the section of the LiTS questionnaire as an instrument for the selection equation (4).

# _5.2 Addressing the non-response bias_

<u>Table 3</u> shows the results of the maximum likelihood estimation of ordered probit with the Heckman correction model <u>(De Luca and Perotti 2011).</u><sup>10</sup> The model’s correction factor – indicative of the correlation between a respondent’s likelihood to provide a response and the nature of that response itself – shows significant coefficients for Haiti, Chernobyl, and Fukushima. This result suggests that the decision to respond is not random but is correlated with the respondents’ knowledge or perceptions about each catastrophe. Ignoring this bias could distort the representation of public sentiment in our findings. In addition, the _p-_ values from the likelihood ratio tests in Table 3 suggest the capacity of the model correction to account for the bias present in the Haiti, Chernobyl, and Fukushima catastrophe questions. However, the selection-corrected coefficients in <u>Table 3</u> are qualitatively and even quantitatively similar to those shown in <u>Table 2,</u> suggesting that, despite the presence of a sample selection bias, it does not appear to significantly affect the main pattern of results.

# _5.3 Beliefs about climate change_

<u>Table 4</u> presents the results of estimating equation (5) with a binary probit model for four dependent variables capturing beliefs that: climate change is real, climate change is manmade, climate change will affect the respondent personally, and climate change will have an impact on the children of today during their lifetime.<sup>11</sup> The estimations show strong qualitative similarities. Except for the question about whether climate change is real, respondents who overestimate catastrophe deaths are more likely to think that climate change is manmade and will affect them and current children. A one standard deviation increase in the overestimation index is associated with a 1.5-percentage point increase in the probability of responding that climate change is manmade, a 2.5-percentage point increase in the probability of believing that climate change will affect the respondent during his/her lifetime, and

> 10 Inclusion of the Heckman adjustment helps tackle the nuanced issue of selection bias, particularly bias stemming from non-responses. By accounting for the link between respondents’ inclination to respond and the content of their responses we obtain insights that better reflect perceptions and knowledge across the population.

> 11 Distances to a catastrophe site are insignificant in the regular and IV ordered probit estimations. For that reason, we dropped these variables from the estimations shown in <u>Tables 4, 5, and 6</u> for conciseness. We show the linear regression estimates of the models shown in Tables 4 and 5 in Tables B1 and B2 in Appendix B.

10
**Figure 2. Tobacco Price Elasticities, Lower, Medium, and Upper Bounds (%)**

The upper-bound elasticity tends to reflect a longer-term scenario, echoing the effect the tobacco tax would have on younger smokers (table 3). After a few decades, these people will represent the majority of the population as older smokers die or quit smoking. The total average effect of the price increase over the long term would therefore be approximated more closely by the upper-bound price elasticity.

**Table 3. Tobacco Price Elasticities, by Income Decile: Fuchs and Meneses (2017) (%)**

|_Price elasticity_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|_Average _|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|−39|−31|−32|−26|−24|−23|−22|−21|−16|−13|−25|
|Medium bound|−59|−51|−52|−46|−44|−43|−42|−41|−36|−33|−45|
|Upper bound|−79|−71|−72|−66|−64|−63|−62|−61|−56|−53|−65|

_Source_ : Estimates based on data of the household budget survey, 2010–13 Once changes in tobacco expenditure due to price increases are calculated, we proceed to calculate the incidence of tobacco consumption on medical treatments, lost years of work, and related costs. Denisova and Kuznetsova (2014) estimate the incidence of tobacco on medical spending. As a secondary source of information, the Ukrainian government and WHO have estimated the disease prevalence in each age-group and the monetary cost of treatment. This information allows a calculation of the average cost of medical treatment for each disease. The cost of lost years of employment can be estimated using the household consumption survey (see below).

# **c. Mortality age patterns**

Early mortality related to tobacco consumption is analyzed to obtain the elasticities. Medical events associated with tobacco show a strong relationship with age and the number of years of smoking. Table 4 illustrates age patterns in tobacco-related deaths. Lost years of life are distributed among households using the proportion of households that have smokers, and the income (or wages) of this population segment is used to estimate the working years lost.

11
other. The results from regressions of this type are reported in Table 7. Column (1) suggests that there is a negative association on average between the belief that treatment of homeowners is unfair and agreeing with the statement that the country is going in the “right direction” on reform related issues. Column (2) suggests a strong negative correlation between the view that homeowners are treated unfairly, and that the government is engaging in open dialogue and including citizens in the reform process. Colum (3) suggests a strong positive correlation between holding the view that homeowners are treated unfairly and that the respondent is “worse off financially than two years ago”, and even larger that the government is not doing enough on corruption (column 4). Columns (5) and (6) suggest a strong negative correlation between holding the view that homeowners are treated unfairly and feeling that it is either a good time to find a job or a good time to start a business. Thus, holding a view that homeowners are treated unfairly is strongly correlated with negative views on the reform program and pessimism about economic conditions.

**Table 7: Descriptive OLS Regression on Fairness of Policy for Current Homeowners**

||Country<br>going in<br>right<br>direction|Gov.<br>engages<br>citizens<br>in<br>reform|Finances<br>worse<br>than 2<br>years<br>ago|Gov. not<br>doing<br>enough<br>on<br>corruption|Good<br>time to<br>findjob|Good<br>time to<br>start<br>business|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Unfair Treatment|-0.01**|-0.10***|0.08***|0.09***|-0.19***|-0.11***|
||(0.003)|(0.015)|(0.017)|(0.017)|(0.022)|(0.019)|
|Constant|1.00***|0.95***|0.14***|0.05***|0.68***|0.81***|
||(0.001)|(0.004)|(0.007)|(0.004)|(0.010)|(0.008)|
|Observations|7,583|7,583|7,583|7,583|7,583|7,583|
|R-squared|0.00|0.02|0.01|0.02|0.02|0.01|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 This analysis can be strengthened by moving to an investigation of _changes_ in views, rather than focusing on descriptive regressions in terms of levels. Table (8) provides the results of such panel regressions, and more clearly establishes the association between views on reform and the fairness of urban development policy. The approach accounts for both “observed” characteristics that are enumerated in the survey, as well as “unobserved” sources of heterogeneity. However, it is important to note that these analyses do not rule out alternative explanations for the relationships found. For instance, omitted variable bias may be present if there exist time-varying factors that can affect both views on the fairness of urban development policies and the other L2CU questions described. However, given the events that precipitated the change in views on fairness, in the opinion of the 42
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# **6 Annexes**

## **1 – The Social Accounting Matrix**

The Social Accounting Matrix for Nicaragua has been estimated using the 2000 input output table provided by the Central Bank of Nicaragua as a starting point. The Central Bank base year for the national accounts is 1994 and the latest update for the input output table is the year 2000.

Given our interest in income distribution and trade, the input output table has been modified to include more factors of production and multiple trading partners. We have disaggregated the value added accounts and estimated payments from the various production sectors to three separate factors: skilled labor, unskilled labor and composite capital (which includes payments to land). The sectoral employment and average wages for these factors have been derived from the 2001 LSMS survey and the classifications of sectors used in the survey and the input output table have been merged in a consistent common classification shown below in the list of the SAM accounts. Using the household survey to modify the value added reported in the input output table is a crucial step given that the main link between the macro and micro modules is given by the sectoral factor returns. Inconsistencies in the estimation of value added between the input output and the household survey – each uses different definitions, sampling, and reference moments in time (recall for instance that the National Accounts are based on some technical coefficient estimated for the year 1994) – cannot be fully eliminated. However, our simple ‘reconciliation’ approach assures a minimum of compatibility across the two data source so that our macro-micro model can produce sensible results.

The imports and exports accounts as well as the tariff rates have been estimated using the UN Comtrade database for different trading partners, including the U.S., the rest of Central America, the European Union and others (although in the model version used here, we just distinguish between the U.S. and the Rest of the World). Tariff rates for the commodities included in the model have been calculated as weighted averages using imports flows as weights. Tariff revenues calculated with this procedure have been 49
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# **<u><mark>Table 2. Effect of earmarked relations on interest rate spreads and volume of working capital loans</mark></u>**

||Int<br>|erest rate spre<br>|ad<br>|V<br>|olume (log<br>|s)<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Earmarkfbt|0.340***|0.236***|0.334***|0.030**|0.024**|-0.010|
||(0.115)|(0.059)|(0.101)|(0.011)|(0.010)|(0.017)|
|Observations|397,427|297,406|264,639|397,427|297,406|264,639|
|R-squared|0.723|0.797|0.839|0.878|0.913|0.921|
|Mean dependent variable|27.13|26.98|27.81|11.11|11.08|11.16|
|Firm*Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Location*Sector*Month FE|No|Yes|No|No|Yes|No|
|Firm*Year FE<br>Notes: The table shows estimates of OLS r|No<br>egressions on our loan|No<br>-level data for th|Yes<br>e sample of firms|No<br>that at any tim|No<br>e in our sampl|Yes<br>e period begin|

Notes: The table shows estimates of OLS regressions on our loan-level data for the sample of firms that at any time in our sample period begin an earmarked credit relationship with a bank. The dependent variables correspond to the interest rate spread (columns 1 to 3) and log volume (columns 4 to 6) of working capital loans. _Earmarkfbt_ is an indicator variable that equals one for all consecutive periods after a firm receives its first earmarked loan from a bank, and zero otherwise. Other controls include the length of the relationship duration of each firm-bank pair and an indicator variable that equals one for firms headquartered in locations above the median banking concentration (measured by the Herfindahl Index) and zero otherwise. Columns 1 to 3 control for maturity, volume and a dummy variable indicating if the loan was collateralized at the time of issuance. Columns 4 to 6 control for maturity, interest rate spread and a dummy variable indicating if the loan was collateralized at the time of issuance. All regressions include fixed effects for the ten loan rating categories established by regulators, as well as fixed effects for the type of loan (whether the loan has a fixed rate or a rate indexed by Brazil's CDI). Standard errors are doubled clustered at the bank and time level.

20
# Revisiting the Gains from Trade in EMDEs: The Case of Selected East Asian and East African Economies<sup>∗</sup>

Enkhmaa Battogtvor,<sup>†</sup> Socrates Kraido Majune,<sup>‡</sup> and Angella Faith Montfaucon<sup>§</sup>

## _JEL Classification_ : F18, D22, Q56

_Keywords_ : Gains from trade, Gains from variety, Elasticity of substitution, Welfare effect of trade liberalization, Economic integration

> ∗The authors thank Csilla Lakatos, participants and reviewers of the 2023 East Asian Economic Association (EAEA) International conference (Republic of Korea) and the 2024 Mongolia National Research and Consulting Center (NRCC) Seminar for helpful comments. The elsticities for each country are available at https: //datacatalog.worldbank.org/data/dataset/0066672/elasticities_of_substitution_for_28_countries_ All errors are our own.

> †National University of Mongolia, enkhmaab@num.edu.mn

> ‡University of Nairobi, skmajune@uonbi.ac.ke

> §World Bank, amontfaucon@worldbank.org Corresponding author
**Table 2. Regression results on log population**

||||**Taipin**|**g Regime is me**|**asured as:**||
|---|---|---|---|---|---|---|
|||**T**|**aiping**||**Taiping*ln(No. of**<br>**battles)**|**Taiping*ln(Taiping**<br>**durations)**|
||||**Full-High**||||
||**Full**|**Full**|<br>**War**<br>**Casualty**|**Full**|**Full**|**Full**|
|Taiping *Year 1851|-0.015|-0.003|-0.003|-0.004|-0.004|-0.001|
||[0.018]|[0.020]|[0.020]|[0.020]|[0.009]|[0.003]|
|Taiping *Year 1880|-0.469***|-0.456***|-0.444***|-0.460***|-0.211***|-0.089***|
||[0.101]|[0.095]|[0.096]|[0.095]|[0.034]|[0.012]|
|Taiping *Year 1910|-0.470***|-0.438***|-0.426***|-0.442***|-0.204***|-0.082***|
||[0.095]|[0.092]|[0.094]|[0.092]|[0.034]|[0.013]|
|Taiping *Year 1953|-0.562***|-0.514***|-0.500***|-0.513***|-0.232***|-0.090***|
||[0.095]|[0.093]|[0.095]|[0.093]|[0.035]|[0.013]|
|Taiping *Year 1982|-0.499***|-0.444***|-0.429***|-0.443***|-0.206***|-0.079***|
||[0.088]|[0.085]|[0.085]|[0.084]|[0.033]|[0.012]|
|Taiping *Year 2000|-0.484***|-0.431***|-0.419***|-0.430***|-0.210***|-0.079***|
||[0.092]|[0.089]|[0.090]|[0.089]|[0.033]|[0.012]|
|Occupied *Year 1851|-0.024*||||||
||[0.013]||||||
|Occupied*Year 1880|-0.023||||||
||[0.076]||||||
|Occupied*Year 1910|-0.061||||||
||[0.073]||||||
|Occupied*Year 1953|-0.091||||||
||[0.066]||||||
|Occupied*Year 1982|-0.106*||||||
||[0.063]||||||
|Occupied*Year 2000|-0.103||||||
||[0.064]||||||
|Ln (frequency of war since 1776)|0.008|0.006|0.007|0.006|||
||[0.007]|[0.008]|[0.008]|[0.007]|||
|Number of Palace Scholars (jinshi)||||-0.002|||
|In each period||||[0.001]|||
|Observations|1,862|1,862|1820|1862|1862|1862|
|R-squared|0.797|0.796|0.798|0.796|0.801|0.804|
|Number of prefectures|266|266|260|266|266|266|
|Control, prefecture FE, year FE|Y|Y|Y|Y|Y|Y|
|Cluster at Prefecture Level|Y|Y|Y|Y|Y|Y|

Note: The full sample (“Full”) contains the prefecture-year panel for 266 prefectures and seven time periods (from 1820 to 2000). The Full-high war casualty sample (i.e., excluding high Taiping war casualty cities) contains the prefecture-year panel for 260 prefectures (after dropping six prefectures including Wuchang, Anqing, Songjiang, Jiangning, Suzhou, and Hangzhou), which were heavily affected by the Taiping) and seven time periods (from 1820 to 2000).

Only the estimation results of the key variables are presented. The time-varying control variables are the duration of treat ports, the duration of concessions and leased territories, the frequency of wars since year 1776. Also included are time dummies interacted with the following time-invariant controls: the distance to Yangzi River, to the coastline, and to the Grand Canal; the number of neighboring provinces; the pre-war level of land taxes per mu; the total number of palace graduates per million people from 1793-1820; silk and tea prefectures dummy; the four post designations classified by the Qing government in 1820.

In Column 4, as robustness check, we also controlled the number of newly elected palace graduates for each period. Standard errors are robust, clustered at the prefecture level.

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

47