rafmacalaba/datause-encoder-data huggingface dataset:
- 16 -

We had to assume, in effect, that the quality adjustments applied equally to domestically used and exported products. Since only some products were covered, and since most of the quality corrections did not go beyond 1983, we felt that the adjustments we made were minimal even for SITC Division 7 for the Unitad States, and must surely understate what a comprehensive quality correction would show.

For the export prices of the United States, based on the weights of developed-country exports of manufactures to developing countries, the effect of the quality adjustments is as follows, for all manufactures and for SITC 7 (Table 6): Table 6 Effect of Quality Corrections on Price Indexes for Manufactured Exports from the United States, 1991/1953 1975, and 1986 Weights of Developed-Country Exports to Developing Countries

||WeiEh|ts of|
|---|---|---|
||1975|1986|
|SITC 7|||
|Unadjusted Index|4.353|3.516|
|Adjusted Index|3.134|2.545|
|Ratio: Adjusted/Unadjusted|.720|.724|
|All Manufactures|||
|Unadjusted Index|4.165|3.700|
|Adjusted Index|3.511|3.079|
|Ratio: Adjusted/Unadjusted|.843|.832|

Source: Appendix Tables 4 and 5 For SITC 7, the group in which the quality adjustments were carried out, the rise in U.S. export prices was considerably less with the quality adjustment, by about one quarter, or .86 per cent per year- And that quality adjustment in SITC 7 alone results in a reduction in the estimated price increase for all U.S.
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Table 11: Gender and Soft Skills

|(1)<br>(2)<br>Rejected<br>Profle<br>viewed|(3)<br>N of profle<br>views|(4)<br>Contacted|(5)<br>Interview<br>requested|
|---|---|---|---|
|Female<br>0.005<br>-0.017|-0.067|0.015|0.009|
|(0.030)<br>(0.030)|(0.090)|(0.018)|(0.014)|
|Team<br>-0.038<br>-0.026|-0.085|0.005|0.015|
|(0.029)<br>(0.030)|(0.090)|(0.017)|(0.014)|
|Lead<br>0.005<br>-0.021|-0.059|0.001|0.006|
|(0.030)<br>(0.030)|(0.092)|(0.017)|(0.014)|
|Female _×_ Lead<br>-0.020<br>0.005|0.095|-0.001|-0.011|
|(0.043)<br>(0.042)|(0.131)|(0.025)|(0.020)|
|Female _×_ Team<br>-0.014<br>0.053|0.144|-0.010|-0.010|
|(0.042)<br>(0.042)|(0.126)|(0.025)|(0.020)|
|Constant<br>0.340***<br>0.342***|0.748***|0.076***|0.046***|
|(0.021)<br>(0.021)<br>|(0.065)|(0.012)|(0.009)|
|_R_<sup>2</sup><br>0.002<br>0.001|0.000|0.001|0.001|
|N<br>2,995<br>2,995|2,995|2,995|2,995|
|_Note:_ This table presents the results of speci<br>Rejected takes the value of 1 if the company <br>Column 2 Profle viewed takes the value of 1 i<br>least once. Column 3 is the number of visits to<br>takes the value of 1 if the Company contacted t<br>5 Interview requested takes the value of 1 if the<br>an interview. Robust Standard errors. * p_<_0.1,|fcation 8.<br>The <br> disregarded the <br>f the profle was<br> the website pro<br>he candidate thr<br>Company contac<br> ** p_<_0.05, ***|outcomes are: <br> application as <br> visited by the<br>fle. In column <br>ough email. Fi<br>ted the candida<br> p_<_0.1|In column 1<br> not-suitable.<br> company at<br> 4 Contacted<br>nally, column<br>te requesting|

30
The analysis disaggregates the youth population by gender and poverty status. The gender disaggregation is necessary due very different time use patterns by men and women, including women’s greater propensity to spend significant periods out of the labor force and women’s higher unemployment rates. The sex variable is used to proxy gender.

The poverty disaggregation is intended to identify if the poor have more difficulty than in the non-poor in securing or keeping employment. Since the three surveys do not include consumption data, we use a crude measure to proxy poverty level of the individual. We calculate total labor income for each household, and we divide the sample into household income quintiles. Once the quintile is assigned to a household, we assign the corresponding quintile level to each individual in our sample.

Seven different sectors are defined in order to better understand where the unemployed were before entering this state and to what sector they go upon leaving the state. Three non-working sectors are identified. The “unemployed” are those who are not working, not studying, and report having searched for a job for at least an hour in the week prior to the interview. Those who are “out of the labor force” are not in school, not working, and not looking for a job. “Students” are in school, not working, and not looking for a job. Four labor market sectors are considered. Those who own a firm and do not have any paid employees are identified as “self-employed.” The “unpaid” are those who state that they work but do not receive remuneration. Those who work for pay are divided into two categories: “formal salaried employees” are employees who receive a salary and whose employer also pays into the social security system on their behalf and “informal salaried employees” earn a salary but the employers do not contribute to the social security system on the workers’ behalf.

# _Summary Statistics_

The unemployment rates in our sample are given in Table 3. The youth rates range from 6 percent in Mexico to 6.5 percent in Brazil to 35 percent in Argentina. Among young adults, the rates are around 4 percent, 8 percent, and 22 percent, respectively. Adults have the lowest rates at 2 percent, 4 percent, and 10 percent, respectively. The rates differ slightly between the first and second observations in our sample, but there is no trend in any country of a uniform increase or decrease in rates across group. They also differ slightly from those in Table 1 since the table presents unemployment rates in the indicated year for each country 9
Policy Research Working Paper 8597

# **Abstract**

Through substitution and income effects, remittances can alter an individual’s allocation of time between market activities and household production, decreasing labor supply. This paper uses propensity score matching and household surveys for 2006 and 2014 to estimate the impact of remittances on labor supply in the three countries of the Northern Triangle (El Salvador, Guatemala, and Honduras). The results show that remittances are associated with a reduction in labor force participation, particularly among women. This effect is largest for Salvadoran women (13 percentage points). A sensitivity analysis finds that the negative effect on labor force participation rates of men in El Salvador and Guatemala and women in El Salvador is robust to potential selection bias. Receiving remittances is also associated with a lower likelihood of young adults being in school or at work, with this effect being robust to selection bias for young men in Guatemala. At the same time, the evidence suggests that remittances may be supporting small enterprises and self-employment in El Salvador and Guatemala. The analysis does not find robust evidence of remittances affecting the labor supply in Honduras in 2014..

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at lsousa@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
7 unlike the case of many QR allocation schemes in developing countries, the US system of QRs allows for market-clearing prices. 8/ Since in 1984 quotas were already in place in autos and textiles, we assume that observed purchases were at the premia inclusive pricGs in that year. (The estimation of premia is discussed in section 3). Finally, it is worth noting that, following the calibration procedures in applied general equilibrium models, we are assuming that the prices and quantities observed in 1984 correspond to an equilibrium of the US economy with normal capacity utilization and in which the only distortions are the QRs and tariff on imports. This may be viewed as a strong assumption but it is implicit as well in all other estimates of the costs of protection.

Host previous GE studies of the costs of protection (e.g. Deardorff and Stern (1986) and Whalley (1985)) that have dealt with QRs have done so in the context of global models where QRs have been treated by their tariff equivalents without including the associated rent transfer that occurs with the US QRs. Moreover, the choice of functional forms has resulted in excessively large terms-of-trade effects for unilateral changes in trade policy (see footnote 6). 9/ Both problems are remedied in our approach.

# 3. Elasticity and Premia Estimates

The accuracy of our estimates of the welfare costs of protection depend on the assumptions embodied in the model, on the estimates of preexisting premia due to QRs in existence and on the supply and demand elasticity estimates. With respect to the modelling assumptions, with the possible exception of noncompetitive behavior in autos (see Winston and Associates (1987) and Dixit (1987)), our assumptions are certainly
Since the objective is to assess the partial correlation between the digital economy and long-term or frictional unemployment, the econometric estimations also control for a correlate of the business cycle, namely the log difference between observed GDP and trend GDP. The GDP series are in local currency at constant prices and were downloaded from the World Bank’s World Development Indicators database. In turn, we applied the HodrikPrescott filter to the log of GDP series from 1970-2017 on a country-by-country basis using the standard filtering window for annual data. Finally, we use the average log difference between the observed and trend GDP series from 2000-2017 as the proxy for the phase of each country’s business cycle during the estimation sample period. Although the averaging of unemployment across 2000-2017 by itself should remove some of the cyclical component of unemployment, the sample period covers the years of the Global Financial Crisis and there is no guarantee ex-ante that average unemployment rates during this time period were unaffected by economic fluctuations associated with the global downturn as it spread across the world. Consequently, to err on the side of caution, the results section discusses results with and without controlling for the business cycle.

Another potentially important control concerns policy-induced labor market frictions. Specifically, we use an indicator of the costs of severance pay for dismissal, reported as a percentage of the average yearly salary. The underlying data are from 20152017 and were downloaded from the World Bank’s Doing Business Database.<sup>6</sup> We work with country averages. The costs of employment separations, under certain wage-setting conditions, can be interpreted as de facto hiring costs. Indeed, there is a long-standing

> 6 https://www.doingbusiness.org/

10
There are two key points that come out of these tables. First, on extremely basic competencies such as recognizing words on the language test and recognizing numbers or solving addition or subtraction on the math test, the sample of children in MP and UP perform dismally and children in Karnataka do way better. In UP, only 46 percent of children after four years of schooling can read 3 or more words from a list of five. And barely 22 percent in UP and 33 percent in MP at the end of grade 4 can read a simple sentence compared to 73 percent in Karnataka.

The percent of children answering correct is much higher in Karnataka compared item wise to MP and UP. However, as one goes to the slightly higher level competencies such as writing a sentence or solving a division problem, the performance of children in Karnataka falls compared to their own performance on the more basic competencies.

|**Table 2**<br>**What do grade 4 c**<br>Percentage childrencorrect|**hildren**<br>UP|**know in**<br>MP|**languag**<br>KA|**e***<br>Grade level at<br>the<br>competen<br>specified<br>in<br>framework|which<br>cy<br>is<br>MLL|
|---|---|---|---|---|---|
|Can read at least 3 of 5 words(without matra)|46|54|92|I & II||
|Can read at least 3 of 5 words(with matra)|33|38|89|I & II||
|Can read simple sentence|22|33|73|II||
|Can write at least 2 of 3 words(without matra)|31|35|78|I||
|Can write at least 2 of 3 words(with matra)|16|22|68|I||
|Can write short sentence|13|19|47|II||

|**Table 3**<br>**W**|**hat do grade**|**4 children**|**know in**|**mathem**|**atics***|
|---|---|---|---|---|---|
||||||Grade level at which<br>the<br>competency<br>is<br> <br> <br>|
|Percentage childrencorrect|Test item|UP|MP|KA|specified<br>in<br>MLL<br>framework|
|Number recognition<br>2digit||31|46|93|I|
|Number recognition<br>3 digit||16|19|84|III|
|2 digit addition without<br>carryover|62+35|53|64|91|II|
|2 digit addition with carry<br>over|85+46|26|42|80|III|
|2 digit subtraction without<br>borrowing|54-32|41|48|84|II|
|2 digit subtraction with<br>borrowing|84-39|18|20|63|II|
|Multiply2 digit by1 digit|43 х 8|15|25|59|III|
|Multiply2 digit by2 digit|35 х 24|10|14|40|IV|
|Divide 2 digit by1 digit|54÷6|14|19|60|III|
|Divide 3 digit by1 digit|585÷9|8|11|41|IV|

*End of school year for grade 4 in MP/UP and start of school year for class 5 in KA

8
- Gruber, J.W., and S.B. Kamin. 2012. “Fiscal Positions and Government Bond Yields in OECD Countries.” _Journal of Money, Credit and Banking_ 44:1563–1587.

- Hansen, B.E. 1996. “Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis.” _Econometrica_ 64:413–430.

- —. 1999. “Threshold effects in non-dynamic panels: Estimation, testing and inference.” _Journal of Econometrics_ 93:345–368.

- Hoshi, T., and T. Ito. 2014. “Defying gravity: can Japanese sovereign debt continue to increase without a crisis?” _Economic Policy_ 29:5–44.

- Ichiue, H., and Y. Shimizu. 2015. “Determinants of long-term yields: A panel data analysis of major countries.” _Japan and the World Economy_ 34-35:44–55.

- Jaramillo, L., and A. Weber. 2013. “Bond yields in emerging economies: It matters what state you are in.” _Emerging Markets Review_ 17:169–185.

- Joyce, M.A., A. Lasaosa, I. Stevens, and M. Tong. 2011. “The Financial Market Impact of Quantitative Easing in the United Kingdom.” _International Journal of Central Banking_ 7:113–161.

- Joyce, M.A., Z. Liu, and I. Tonks. 2017. “Institutional Investors and the QE Portfolio Balance Channel.” _Journal of Money, Credit and Banking_ 49:1225–1246.

- Lagarias, J.C., J.A. Reeds, M.H. Wright, and P.E. Wright. 1998. “Convergence Properties of the Nelder–Mead Simplex Method in Low Dimensions.” _SIAM Journal on Optimization_ 9:112–147.

- Laubach, T. 2009. “New Evidence on the Interest Rate Effects of Budget Deficits and Debt.” _Journal of the European Economic Association_ 7:858–885.

- Longstaff, F., J. Pan, L. Pedersen, and K. Singleton. 2011. “How Sovereign Is Sovereign Credit Risk?” _American Economic Journal: Macroeconomics_ 3:75–103.

- Luukkonen, R., P. Saikkonen, and T. Terasvirta. 1988. “Testing Linearity Against Smooth Transition Autoregressive Models.” _Biometrika_ 75:491–499.

24
# **References**

Boyce, J. K., and M. Ravallion, 1991, A Dynamic Econometric Model of Agricultural Wage Determination in Bangladesh, Oxford Bulletin of Economics and Statistics, 53(4): 361-76 Budd, J. W., 1993, Changing Food Prices and Rural Welfare: A Non-Parametric Examination of the Cote d’Ivoire, Economic Development and Cultural Change, 41(3): 587-603.

Coudouel, A., J. Hentschel, and Q. Wodon, 2002, Poverty Measurement and Analysis, in J. Klugman, editor, A Sourcebook for Poverty Reduction Strategies, Volume 1: Core Techniques and Cross-Cutting Issues, World Bank, Washington, DC.

Barrett, C. D. and P. A. Dorosh, 1996, Farmers' Welfare and Changing Food Prices: Nonparametric Evidence from Rice in Madagascar, <u>American Journal of Agricultural Economics, 78(3): 656-69.</u> Budd, J. W., 1993, Changing Food Prices and Rural Welfare: A Non-Parametric Examination of the Cote d’Ivoire, Economic Development and Cultural Change, 41(3): 587-603.

Christiaensen, L. and L. Demery, 2007, <u>Down to Earth: Agriculture and Poverty Reduction in Africa, Directions in Development, World Bank, Washington, D.C.</u> Deaton, A., 1989, Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis, <u>The Economic Journal, 99(395):1-37.</u> International Monetary Fund (2008) Food and Fuel Prices: Recent Developments, Macroeconomic Impact, and Policy Responses, mimeo, Washington, DC: IMF.

Ivanic and Martin, 2007, Implications of Higher Global food Prices for Poverty in Low-Income Countries, Policy Research Working paper 4594, World Bank, Washington, DC.

Minot, N, and F. Goletti, 1998, Export Liberalization and Household Welfare: The Case of Rice in Vietnam, American Journal of Agricultural Economics, 80(4): 738-49.

Niimi, Y., P. Vasudeva-Dutta, and A. L. Winters, 2004, Storm in a Rice Bowl: Rice Reform and Poverty in Vietnam in the 1990s, Journal of the Asia Pacific Economy, 9(2):170-190.

Nouve and Wodon, 2008, Impact of Rising Rice Prices and Policy Responses in Mali: Simulations with a Dynamic CGE Model, mimeo, World Bank, Washington, DC.

Ravallion, M. 1990, Welfare changes of food price changes under induced wage responses: Theory and evidence for Bangladesh, Oxford Economic Papers, 42: 574–85.

Ravallion, M. and D. van de Walle, 1991. The impact on poverty of food pricing reforms: a welfare analysis for Indonesia, Journal of Policy Modeling, 13(2):281-99.
# **<u>Table 8: The Financing of  Trade Credit Supply: Is Trade Credit Demand More Important for Credit Constrained Firms?</u>**

The reported estimates are from logit (Colums 1-4) and OLS (Columns 5-8) regressions. AR_d is a dummy indicating the use of accounts receivable and AR_per is the percent of monthly sales sold on credit. We transform AR_per in a continuous variable by using the inverse function of the logistic model. See Table 1 for variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p-values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||_AR_d _|_AR_d _|_AR_d _|_AR_d _|_AR_per_|_AR_per_|_AR_per_|_AR_per_|
|L_age|-0.13|-0.25|-0.18|-0.22|-0.69|-0.66|-0.54|-0.59|
||[0.11]|[0.01]**|[0.09]*|[0.04]**|[0.00]***|[0.02]**|[0.06]*|[0.05]*|
|L_emp|0.06|0.08|0.05|0.08|0.27|0.28|0.19|0.25|
||[0.15]|[0.11]|[0.30]|[0.16]|[0.02]**|[0.05]**|[0.18]|[0.09]*|
|D_Foreign|0.39|0.49|0.61|0.51|0.21|0.45|0.78|0.36|
||[0.07]*|[0.05]**|[0.02]**|[0.06]*|[0.76]|[0.59]|[0.40]|[0.70]|
|D_State|0.03|-0.02|0.01|-0.03|0.10|-0.03|0.37|0.32|
||[0.85]|[0.93]|[0.94]|[0.86]|[0.79]|[0.95]|[0.43]|[0.52]|
|D_Export|-0.33|-0.53|-0.39|-0.47|0.29|-0.51|-0.31|-0.30|
||[0.10]*|[0.02]**|[0.11]|[0.05]*|[0.65]|[0.47]|[0.69]|[0.70]|
|AP_d<br>AP_per|1.40<br>[0.00]***|1.53<br>[0.00]***|1.72<br>[0.00]***|1.44<br>[0.00]***|0.09<br>[0.00]***|0.12<br>[0.00]***|0.10<br>[0.00]***|0.08<br>[0.00]***|
|LC_unused<br>LC*AP_d<br>LC*AP_per|0.11<br>[0.78]<br>0.10<br>[0.84]||||0.84<br>[0.33]<br>-0.01<br>[0.79]||||
|D_Bank||0.07||||0.26|||
|D_Bank *AP_d<br>D_Bank *AP_per||[0.68]<br>-0.33<br>[0.20]||||[0.53]<br>-0.06<br>[0.00]***|||
|D_RE|||0.38<br>[0.04]**||||0.59<br>[0.18]||
|D_RE*AP_d|||<br>-0.54<br>[0.05]**||||||
|D_RE*AP_per|||||||-0.03<br>[0.16]||
|D_Fam_Informal||||-0.11<br>[0.66]||||-0.53<br>[0.30]|
|D_Fam_Inf*AP_d||||-0.03<br>[0.93]|||||
|D_Fam_Inf*AP_per||||||||0.06<br>[005]**|
|Constant|-2.97|-3.27|-3.44|-2.90|-13.2556|-13.5008|-13.606|.<br>-13.387|
||[0.00]***|[0.00]***|[0.00]***|[0.00]***|[0.00]***|[0.00]***|[0.00]***|[0.00]***|
|Observations|1,881|1,299|1,207|1,187|1,869|1,291|1,203|1,182|
|Pseudo R-squared|0.13|0.15|0.17|0.15|0.18|0.19|0.2|0.2|

45
reporting marginal budget shares on each expenditure type for households without migrants, households with international migrants, and households with internal migrants. These were obtained from the estimated unrestricted demand system given in equation 8. The general formula for the marginal budget shares is: In this formula, “^” refers to an estimated parameter. The marginal budget share for nonmigrants is evaluated by setting the migration variables _M h_ in Equation 10 equal to zero, thereby eliminating all migration effects from the system.<sup>9</sup> The marginal budget shares for a given class of migrants (international or domestic) were calculated by setting the corresponding migration variables equal to 1.0 and the migration terms for the other migration class to 0. All other variables in the system were set equal to their means. For each household type, the marginal budget shares add up to 1.0.

It is important to keep in mind that the econometric analysis makes it possible to compare marginal budget shares between households with migrants and _otherwise similar_ households without migrants. The findings reported in Table 5 control for all of the explanatory variables included in the expenditure system and described in Table 3.

> 8 The χ2 statistic (degrees of freedom) corresponding to the null hypothesis that all migration effects are nil is equal to 156.12(28), significant at well below the .01 level.

25