rafmacalaba/datause-encoder-data huggingface dataset:
# **Works Cited**

Adato, Michelle, and Terry Roopnaraine. 2004. “A Social Analysis of Red de Protección Social in Nicaragua”. Washington, DC.

Aheeyar, M.M.M. 2006. “Cash Delivery Mechanisms in Tsunami-Affected Districts of Sri Lanka”. London.

- Aker, Jenny. 2013. “Cash or Coupons? Testing the Impacts of Cash versus Vouchers in the Democratic Republic of Congo.”

- Akresh, Richard, Damien de Walque, and Harounan Kazianga. 2012. “Alternative Cash Transfer Delivery Mechanisms: Impacts on Routine Preventative Health Clinic Visits in Burkina Faso”. Washington DC.

- Angelucci, Manuela. 2008. “Love on the Rocks: Domestic Violence and Alcohol Abuse in Rural Mexico.” _The B.E. Journal of Economic Analysis & Policy_ 8 (1).

- Armand, Alex. 2013. “Who Wears the Trousers in the Family? Intra-Household Resource Control, Subjective Expectations and Human Capital Investment”. University College London.

- Attanasio, Orazio, Erich Battistin, Emla Fitzsimons, Alice Mesnard, and Marcos Vera-Hernández. 2005. “How Effective Are Conditional Cash Transfers? Evidence from Colombia.”

Attanasio, Orazio, and Luis Carlos Gómez. 2004. “Evaluación Del Impacto Del Programa Familias En Acción - Subsidios Condicionados de La Red de Apoyo Social. Informe Del Primer Seguimiento (Ajustado)”. Bogotá.

- Attanasio, Orazio, and Alice Mesnard. 2005. “The Impact of a Conditional Cash Transfer Programme on Consumption in Colombia”. The Institute of Fiscal Studies.

Bailey, Sarah. 2009. “An Independent Evaluation of Concern Worldwide’s Emergency Response in North Kivu, Democratic Republic of the Congo”. London.

Baird, Sarah, Francisco H.G. Ferreira, Berk Özler, and Michael Woolcock. 2014. “Relative Effectiveness of Conditional and Unconditional Cash Transfers for Schooling Outcomes in Developing Countries: A Systematic Review.” _Journal of Development Effectiveness_ (September). doi:10.4073/csr.2013.8.

Banerjee, Abhijit, and Esther Duflo. 2006. “The Economic Lives of the Poor.”

- ———. 2007. “The Economic Lives of the Poor.” _Journal of Economic Perspectives_ 21 (1) (January): 141– 167. doi:10.1257/jep.21.1.141.

- Banerjee, Abhijit, and Sendhil Mullainathan. 2010. “The Shape of Temptation: Implications for the Economic Lives of the Poor”. Cambridge, MA.

- Banks, James, Richard Blundell, and Arthur Lewbel. 1997. “Quadratic Engel Curves and Consumer Demand.” _Review of Economics and Statistics_ LXXIX (4): 527–539.

15
11 Using a sample of 21 countries, a study by the International Monetary Fund. 1983, analyses the cross-section relationship between average real interest rates, GDP growth and growth of broad money over the decade 41971-80. As expected, tthere is a close relationship between output and money growth, but, as output growth would be expected to promote growth of financial assets just as the latter might raise output growth, causation is not clear. However, the paper also finds significant association between the real interest rate regime and the growth of output and money, where the interest rate regime is a dummy variable, taking the values 1, 0 and -1, depending on whether the average real interest rate were positive, slightly negative or strongly negative over the period. Under the assumption that the interest rate regime so defined represents an exogenouse policy variable, this suggests that output growth rates are indeed influenced by interest rate policies, in the direction suggested by McKinnon and Shaw. The paper does not address the question of the transmission mechanism between interest rates and growth in more detail; neither does it directly consider the role of interest rate policy as opposed to inflation.

Using a sample of 31 developing countries, AgArvala. 1983, examines the relationship between two performance variables, growth and equity, and seven distortions in product, factor and foreign exchange markats for the decade of the 1970s. The level of real interest rates was included as one of the seven distortions. Only a weak relationship was found between interest rates and growth. but this may have been due to the ranges chosen to classify the countries: only two were classified as having low interest rate distortions. A composite distortion index, formed by arithmetic averaging of the seven distortions, explains 34% of the cross-country variation in growth rates.

Hanson and Neal, 1987, estimate the demand for broadly defined money, estimating a partial adjustment model of asset demand for 36 countries over periods of approximately 20 years. Inflation was a powerful determinant of financial asset holdings, while nominal term deposit rates, though generally positive in impact, had a less clearly identifiable role. Part of the reason for this may have been that nominal rates were typically administered, and adjusted too little and too late to compensate for inflation and other corrosive influences. In fact cross-section regression of the average nominal interest rate on average inflation showed that rates were adjusted by only 65% of inflation, so that the more inflationary countries typically had lower real interest rates. Cross-section regressions of financial asset holding that included as explanatory variables both average inflation and the average nominal interest rate produced a negative, though insignificant, sign for the latter, probably because of this interrelationship. Although the study did not go on to investigate the consequences of financial repression, it raises the question of whether the emphasis should be on interest rate policies or on the fiscal, monetary and
Table 3. Evolution of employment across different population groups

||**Net change **<br>**to prep**|**in employment relative**<br>**andemic level (%)**|**Sample**|
|---|---|---|---|
||**Apr-Jun20**|**Jul-Dec20**<br>**2021**|**Countries**<br>**N**|
|**All countries**|_−_30_._6|_−_16_._8<br>_−_8_._2|75<br>246,618|
|_Country panel_|_−_31_._0|_−_19_._2<br>_−_12_._0|31<br>140,798|
|**Female**|_−_33_._6|_−_20_._1<br>_−_10_._0|73<br>103,900|
||_−_35_._8|_−_22_._6<br>_−_13_._0|31<br>60,950|
|**Male**|_−_27_._2|_−_14_._6<br>_−_7_._4|73<br>139,142|
||_−_27_._7|_−_16_._1<br>_−_10_._4|31<br>79,353|
|**Non-urban/Rural**|_−_28_._7|_−_15_._1<br>_−_7_._5|67<br>92,426|
||_−_28_._4|_−_16_._6<br>_−_11_._6|27<br>53,174|
|**Urban**|_−_31_._5|_−_17_._3<br>_−_9_._3|69<br>135,127|
||_−_31_._3|_−_20_._0<br>_−_14_._2|27<br>73,999|
|**Low education**|_−_33_._6|_−_19_._4<br>_−_8_._1|59<br>92,320|
||_−_35_._2|_−_22_._5<br>_−_12_._6|23<br>55,169|
|**High education**|_−_29_._1|_−_17_._8<br>_−_7_._9|59<br>89,509|
||_−_28_._2|_−_20_._7<br>_−_12_._7|23<br>47,497|

_Source_ : World Bank estimates based on COVID-19 High-Frequency Phone Surveys.

in the initial phase of the pandemic, and subsequent gains in employment either proceed at roughly the same pace among women and men, or gender disparities actually widened over time (Figure A6.1).

Employment also fell proportionately more among those with lower levels of education in April-June 2020, following the onset of the pandemic. Relative to prepandemic employment levels, this gap appears to close in subsequent HFPS waves, in the sample of all countries and in the country panel. However this is driven by previously unemployed workers in the low education group starting jobs at a faster rate, rather than those that stopped working since the pandemic returning to jobs more quickly (Table A6.2 and Table A6.3).

To get a cleaner picture of job losses and subsequent recovery, we employ the panel structure of the data to analyze the characteristics associated with employment loss and, separately, with employment recovery. In particular, we draw on a subsample of almost 40 countries with panel surveys to predict conditional probabilities that those who stopped working subsequently returned to work, as reported in a later HFPS wave. Figure 4a shows that those working before the pandemic were significantly more likely to stop working within each country if they were women, lower educated, and during a period of higher policy stringency. Workers in urban places were also marginally more likely to stop work. Figure 4b shows women were also less likely to gain or recover employment during the pandemic period, especially if they had children in their household. Those with lower education were 16
# **Figure 8 Fiscal policy volatility and procyclicality**

# **A. Correlation between fiscal volatility and procyclicality**

<!-- Start of picture text --> Correlation<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>World AEs EMDEs<br>exporters importers<br>Commodity Commodity<br><!-- End of picture text -->

# **C. Correlation between fiscal volatility and procyclicality, by income groups**

<!-- Start of picture text --> Correlation<br>0.4<br>***<br>***<br>0.3 ***<br>0.2<br>0.1 ***<br>0.0<br>UMCs HICs LICs LMCs<br><!-- End of picture text -->

# **B. Correlation between fiscal volatility and procyclicality over time**

<!-- Start of picture text --> Percent 1980-2006 2007-2020<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>World AEs EMDEs<br>exporters importers<br>Commodity Commodity<br><!-- End of picture text -->

# **D. Correlation between fiscal volatility and procyclicality, by regions**

<!-- Start of picture text --> Correlation<br>0.6<br>***<br>***<br>***<br>***<br>0.3<br>***<br>***<br>***<br>0.0<br>-0.3<br>ECA MNA EAP AEs LAC SSA SAR<br><!-- End of picture text --> _Sources_ : International Monetary Fund; World Bank.

_Note_ : AEs = advanced economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; EMDEs = emerging market and developing economies; HIC = high-income country; LAC = Latin America and the Caribbean; LIC = lowincome country; LMC = lower-middle-income country; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa; UMC = upper middle-income country.

A.B.C.D. Bars represent the correlation between fiscal procyclicality and the measure of volatility that does not exclude the effect of the business cycle on fiscal policy changes.

A.C.D. Blue diamonds and asterisks represent statistical significance.

37
With that in mind, there are a few characteristics that differentiate Vietnam from other low income countries. First, it had a much better coverage of basic services infrastructure across most regions even during 1993.<sup>7</sup> According to the data both on services availability and the perceptions of commune leaders, the infrastructure has improved significantly during this five year period. The key question asked in this paper is what type of households, in which locations, having access to what type of services, benefited more from the reforms of the 1990s? Thus, the question is of a relative response of the households and income growth rather than the absolute response of the rural economy. An equally important question would be to test whether the overall fast response observed in Vietnam was due to the better coverage of services. Given that almost all regions have access to the basic services infrastructure, it is very hard to show that the fast overall growth is related to the better services availability in Vietnam. For that, one would need to compare Vietnam to other countries, however, to the best of our knowledge, comparable studies of this type do not yet exist.

In order to answer the question of which services and household characteristics have an impact we estimate the determinants of income growth using household data for a panel of households. To avoid simultaneity we only include initial household characteristics and services, which are predetermined, and examine their impact on income growth. Our results show that overall service availability and quality has a positive impact on income growth. More specifically, access to electricity, daily markets, and credit, quality of roads, and a higher initial level of integration into the market had a positive impact on growth. On the other hand, the availability of extension services, public transportation, and communication services were not significant.

The rest of the paper is organized as follows. The next section describes the data and presents detailed discussions of the availability, quality, and access to services across regions and income levels, and the production related services in Vietnam. Following this discussion, Section 3 specifies the variables used in the empirical analysis, illustrates the

> 7  In addition to the existence of a major reform, Vietnam as a case study was picked since it was thought that there would be generally low quality of services and there would be regions or communities with very limited and uneven services access. However analysis of data showed that the services coverage was more extensive and better than other low income countries. Even by 1993, Vietnam had managed to create the basic services infrastructure in rural areas.

5
# Appendix 2. Definitions and sources of variables

|Variable|Definition|Source|
|---|---|---|
|Economic activity|||
|Output|GDP in constant 2010 U.S. dollars (market weighted), taken from the World Bank. Prior to 1960, the<br>series is extended using data from the Penn World Tables 9.1 (PWT 9.1). Sample includes 180<br>countries, including 36 advanced economies and 144 EMDEs. For PPP-weighted series, GDP is in<br>constant 2010 international dollars.|Feenstra, Inklaar, and<br>Timmer (2015); World<br>Bank.|
|Output per<br>capita|GDP per capita, in constant 2010 U.S. dollars (market weighted), taken from the World Bank. Prior<br>to 1960, both GDP and population series are extended using data from PWT 9.1. Sample includes 180<br>countries. For PPP-weighted series,GDPper capita is in constant 2010 international dollars.|Feenstra, Inklaar, and<br>Timmer (2015); World<br>Bank.|
|Industrial<br>production|Industrial production index (if not available, manufacturing production index is used). Data are<br>obtained at a quarterly frequency and used as annual averages of year-on-year growth rates. For<br>aggregated groups, market-weighted output is used as a weight. The main source of the series is the<br>World Bank and, for countries and quarters without data, the series is extended using growth rates of<br>data from the Organisation for Economic Co-operation and Development (OECD) and Haver<br>Analytics.|Haver Analytics, OECD,<br>World Bank.|
|Trade|Exports plus imports of goods and services, in constant 2010 U.S. dollars, taken from the World Bank.<br>Prior to 1960, the series of exports and imports are extended using data from PWT 9.1. Trade for<br>aggregatedgroups is the sum of exports and imports of individual countries.|Feenstra, Inklaar, and<br>Timmer (2015); World<br>Bank.|
|Unemployment<br>rate|Unemployment, in percent of labor force, taken from the International Monetary Fund (IMF). Data<br>for aggregated groups are computed as the sum of those unemployed in individual countries divided by<br>the sum of labor force.|IMF.|
|Oil consumption|Oil consumption, in thousand barrels per day. Oil consumption in advanced economies and EMDEs is<br>computed as the sum of consumption in individual countries. If there are differences between the<br>world total and the sum of advanced economies and EMDEs, the residuals are added to the EMDE<br>aggregate. The number of world oil consumption for 2019 is based on data over the first nine months<br>of theyear.|British Petroleum, U.S.<br>Energy Information<br>Administration.|

62
28. Lucas, Robert E.B. and Oded Stark, “Motivations to Remit: Evidence from Botswana,” J. Polit. Econ., 93 (5), 901-918, October 1985 29. Mansuri, Ghazala, “Migration, School Attainment and Child Labor: Evidence from Rural Pakistan,” DECRG, The World Bank, Washington DC, April 2006 (mimeograph) 30. McCall, B.P and J.J. McCall, A Sequential Study of Migration and Job Search, Journal of Labor Economics, Oct. 1987, 5(4), 452-76 31. McCormick, Barry and Jacqueline Wahba, “An Econometric Model of Temporary International Migration and Entrepreneurship,” Department of Economics, University of Southampton, Southampton, 2001 (mimeograph) 32. McFadden, D., “Conditional Logit Analysis of Qualitative Choice Behavior”, in P. Zarembka (ed.), Frontiers in Econometrics, Academic Press, New York, pp.105-42, 1974 33. McKenzie, David, John Gibson and Steven Stillman, “How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration,” IZA Discussion Papers 2087, Institute for the Study of Labor, 2006 34. Mesnard, Alice and Martin Ravallion, “Is Inequality Bad for Business? A Nonlinear Empirical Model of Entrepreneurship,” ARQADQ, University of Toulouse and DECRG, The World Bank, Toulouse and Washington D.C., 2001 (mimeograph) 35. Munshi, Kaivan and Mark Rosenzweig, “Why Is Mobility in India so Low? Social Insurance, Inequality and Growth,” Economic Growth Center, Yale University, New Haven, July 2005 (mimeograph) 36. Munshi, Kaivan, “Networks in the Modern Economy: Mexican Migrants in the US Labor Market,” Quarterly Journal of Economics, 118(2), 549-99, May 2003 34
# **Annex 1. Data sources**

For Latin American Countries we use the SEDLAC dataset, which covers all countries in mainland Latin America and four of the largest countries in the Caribbean. Most household surveys included in the sample are nationally representative. For comparability purposes this dataset computes income using a common method across countries and years. In particular, it constructs a common household income variable that includes all the ordinary sources of income and estimates of the imputed rent from home ownership (see the Guide in the Web site of the SEDLAC for methodological details). Note that this is the only set of countries for which hours of work are available, allowing for further decomposition in the report.

For Bangladesh, Moldova, Romania, Peru, and Thailand, we use the Household Income and Expenditure Survey for each year. We make temporal and spatial adjustments for comparability reasons. For Ghana and Nepal we use the Rural Income Generating Activities (RIGA) data set, http://www.fao.org/economic/riga/en/.

<mark>Table A1. Data Sources</mark>

||Initial<br>Final|Initial<br>Survey<br>Name|Final<br>Survey<br>Name|
|---|---|---|---|
|In|come-Based Poverty|Measures||
|Argentina|2000<br>2010|EPH|EPH-C|
|Brazil|2001<br>2009|ECH|ECH|
|Chile|2000<br>2009|PNAD|PNAD|
|Colombia|2002<br>2010|CASEN|CASEN|
|Costa Rica|2000<br>2008|ECH|GEIH|
|Ecuador|2003<br>2010|ENFT|ENFT|
|El Salvador|2000<br>2009|EHPM|EHPM|
|Honduras|1999<br>2009|EPHPM|EPHPM|
|Mexico|2000<br>2010|ENIGH|ENIGH|
|Panama|2001<br>2009|EH|EH|
|Paraguay|1999<br>2010|EIH|EPH|
|Cons<br>|umption-Based Pove<br> <br>|rtyMeasures<br>|<br>|
|Bangladesh|2000<br>2010|HIES|HIES|
|Ghana|1998<br>2005|RIGA|RIGA|
|Moldova|2001<br>2010|HBS|HBS|
|Nepal|1996<br>2003|RIGA|RIGA|
|Peru|2005<br>2009|ENAHO|ENAHO|
|Romania|2001<br>2009|HBS|HBS|
|Thailand|2000<br>2009|SES|SES|

35
## **Table 3 Main Bank Variables for Developing and Developed Countries**

This table presents the mean and number of observations of main variables by country type and t-statistics for mean comparison. _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding_ is the share of non-deposit short-term funding in total deposits & short-term funding. _Return on assets_ is the profit before tax as a percentage of total assets of a bank. _Z-score_ is a measure of bank solvency defined _ROA_ + _CAR_ as , where ROA is return on assets, CAR represents capital assets ratio and SROA stands for

# _SROA_

standard deviation of return on assets. The data for these bank level variables are from Bankscope.

|Country||Fee income|Non-deposit<br>fundingshare|Return on assets|Z-score|
|---|---|---|---|---|---|
|Developing<br>countries|Mean|0.385|0.064|0.024|17.692|
||N|1887|1862|1911|1687|
|Developed countries|Mean|0.342|0.088|0.016|35.263|
||N|5379|5350|5505|4867|
|Total|Mean|0.353|0.081|0.018|30.740|
||N|7266|7212|7416|6554|
|Mean comparison||||||
|test|t-statistics|7.336|-5.954|8.030|-24.159|

37
# Non-Technical Summary

This paper explores an empirical methodology to assess the impacts of trade reforms, such as those being proposed in the WTO Doha Round negotiations, on household behavior in developing countries. I focus on consumption and income responses. When price reforms take place, households are affected on both the expenditure and the income sides. By facing changed prices for consumer goods, households modify consumption decisions. By facing different wages, household income responds. The paper proposes a joint estimator of demand and wage price elasticities from survey data.

The method uses an empirical model of demand to extract price information from unit values, and uses this information to estimate the response of households to price reforms. By correcting unit values for quality effects and measurement error, the method overcomes the problem of the endogeneity of unit values. By endogeneizing household income, the model corrects potential biases in the estimation of own- and cross-price elasticities in consumption. I apply the method to an expenditure and income survey for rural Mexico. It is shown that using unit values instead of prices may lead to inconsistent results, and that the corrections suggested in this paper are empirically important. It is also shown that allowing for consumption and income responses is a key element of an accurate empirical assessment of trade policy. Concretely, I show that the standard proposition of the literature, that after an increase in the price of a good net consumers will be worse off but net producers will be better off, may be misleading. In a static context, the welfare effects of a price change can be assessed by comparing budget shares and income shares. This argument, however, fails to consider dynamic household responses. Consumers may respond by substituting away from the more expensive goods. In rural areas, farmers may increase agricultural production, farm employment and wages, and purchases of inputs and services in local markets. Importantly, thus, the net position of the household becomes endogenous: sufficiently large consumption and income responses may cause an ex-ante net consumer become an ex-post net producer, thus benefiting from the price increase. My empirical application to rural Mexico reveals that the evaluation of price/trade reforms is indeed sensitive to the inclusion of these consumption and income responses.
|Table R.SA2: Linear Probabili<br>|ty<br>Estim<br>|ates,<br>De<br><br>|pendent var<br><br>|iable: E-filing<br><br>|
|---|---|---|---|---|
|P||t|hose|Partialout.||
|Turnover|0,.023*<br>{O.013)||<br>0.018<br>{0.019}|<br>0.034*<br>(0.019)|
|Employment|0.024<br>{O.017)||O.037<br>(0.025)|O.006<br>(0.023)|
|Productivity||0.011<br>(0.013)|||
|Gauteng|O.141***|O.1357**|o.027|O.L78**=|
||f(O.054)|(0.053)|(0.098)|(0.065)|
|Western Cape|0,109*|0.103*|“0.026|0.176**|
||(0.058)|(0.057)|(O.097}|(O.076)|
|Narth West|O.168*=|oO.172**|0.115|0.154|
||(O.083)|(0.082)|{0.133}|(104)|
|Mpumalanga|0.055|0.057|-0.265**|O.327***|
||(0.076)|(0.077)|{0.110}|(0.108)|
|Narth Cape|0.139*|0.145*|0.050|0.160|
||(O.079)|(0.081)|{O. 128)|(097)|
|Service (Finance & Business)||0.238***<br>f(O.059)|0,.213***/<br>(0.060)|o.224***<br>(O.085}|o.174*<br>(0.096)|
|Personal Services|-0.007|-0.032|| -0.236**|0.152|
||(0.100)|(0.099)|{0.101}|(0.155)|
|Hotels and Restaurants|o.074|0.050|0.423**|-0.194**|
||{0.103}|(0.101)|{0.165}|(O.078)|
|Catering|O.466**|0.492"||0.435*|
||(O.236)|(0.242)||(0.256)|
|VAT|O.169***|0.190***]|o.229%+*|O.087|
||(0.048)|(0.048)|(0.076)|(0.068)|
|Constant|-0.491**<br>(O.204)|-0.302<br>(0.192)|“0.331<br>(O.297)|-0.698**<br>(279)|
|Observations|a72|872|419|453|
|R-squared|o.107|0.096|O71|O.110|

Sampling weights are uted in all regressions. “AM” includes all firms either partially auteurce or de all the TC in-house. “In-house” includes firms which do all TC in house and “partial out” cavers partially qubsaurcing firms, Turnover, total employment and productivity (tumoverfamployment) are in lags, 4lso, aut of all industry, legal farm, pravineé and major tax durmnies, only the statistically significant ones are reported. *** p<O.01, ** p<d.05, * pxd.d