rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4**<br>**. Bound Anal**|**ysis for Male Business O**|Unadjusted<br>**wners**||
|---|---|---|---|
||Lower Bound|Treatment Effect|Upper Bound|
||(1)|(2)|(3)|
|**Business Practices**||||
|Allows purchases on credit (1=Yes)|-0.106**|-0.012|0.107**|
||(0.045)|(0.044)|(0.044)|
|Recorded sales last month (1=Yes)|-0.084*|0.058|0.161***|
||(0.046)|(0.046)|(0.046)|
|Records money taken for household needs (1=Yes)|-0.109***|0.055|0.126***|
||(0.038)|(0.041)|(0.044)|
|Record anything (1=Yes)<sup>1</sup>|-0.038**|0.034*|0.054***|
||(0.018)|(0.019)|(0.020)|
|_Aggregate Business Practices_|-0.166**|0.098|0.317***|
||(0.071)|(0.075)|(0.073)|
|**Business Operation**||||
|Business has fixed location (1=Yes)|0.045|0.062**|0.134***|
||(0.029)|(0.027)|(0.021)|
|Operates all months of the year (1=Yes)|-0.052|0.001|0.179***|
||(0.042)|(0.040)|(0.038)|
|Index of Business Appearance<sup>1</sup>|-0.208|0.319|1.076***|
||(0.222)|(0.210)|(0.166)|
|Investment in Marketing (1=Yes)<sup>1</sup>|-0.088***|-0.018|0.003|
||(0.018)|(0.023)|(0.024)|
|Business is open to the public (1=yes)<sup>1</sup>|-0.187***|-0.107**|0.020|
||(0.052)|(0.049)|(0.048)|
|Has secured buyer (1=Yes)<sup>1</sup>|-0.119***|0.106***|0.163***|
||(0.035)|(0.040)|(0.044)|
|Index of business assets<sup>1</sup>|-0.389***|0.005|0.368***|
||(0.110)|(0.130)|(0.133)|
|_Aggregate Business Operations_|-0.051|0.056|0.169***|
||(0.034)|(0.035)|(0.033)|
|**Employment, Sales and Profits**||||
|Number of Workers|-0.400***|0.111|0.372**|
||(0.113)|(0.136)|(0.144)|
|Log Sales in a Good Month|-0.238**|0.129|0.465***|
||(0.092)|(0.102)|(0.093)|
|Log Sales Average Month|-0.276***|0.055|0.397***|
||(0.097)|(0.103)|(0.091)|
|Log Sales in a Bad Month|-0.258**|0.070|0.408***|
||(0.108)|(0.117)|(0.104)|
|Log Sales November 2008<sup>1</sup>|-0.519***|0.061|0.592***|
||(0.162)|(0.151)|(0.132)|
|Log Profit<sup>1</sup>|-0.350***|0.026|0.459***|
||(0.108)|(0.122)|(0.118)|
|_Aggregate Sales and profits_|-0.124*|0.025|0.259***|
||(0.067)|(0.074)|(0.069)|

Note:<sup>1</sup> Variable collected only during follow-up. Aggreggate variables for each family of outcomes are averages of the standardized z-score of each variable in the family. See Appendix B for a definition of the aggregates. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline and gender). Standard errors reported in parentheses are clustered at the CO level. The following symbols *,** and *** denote significance at the 10, 5 and 1 percent level, respectively. Bounds are computed based on Lee (2002).
used a uniform recall period of 30 days for all consumption items, seven of the survey rounds over this period have used instead a mixed-recall period (MRP), with shorter (one week) recall for some items (for food in the 55<sup>th</sup> round) and longer (one year) for others (mainly non-food items).<sup>22</sup> On a preliminary investigation of the data we found that the use of a mixed recall period reduced the log of the headcount index at a given level of mean consumption by about 0.2 and the effect is (highly) significant.<sup>23</sup> All our regressions include a control for MRP survey rounds.

We use the NSSO’s urban-rural classification.<sup>24</sup> Over such a long period, some rural areas would have become urban areas. To the extent that rural (non-farm) economic growth may help create such re-classifications, as successful villages evolve into towns, this process may produce a downward bias in our estimates of the (absolute) elasticities of rural poverty to rural economic growth. The impact on the urban elasticities could go either way, depending on the circumstances of new urban areas relative to the old ones. We have little choice but to use the NSSO's classification, given that the unit record data are unavailable for the full period covered by this exercise. But nor is it clear what the best corrective would be with access to that data.

Figure 2 gives the urban share of total consumption in the NSS data, which has risen steadily since about 1960. The increase clearly predates the reform period, but the share has increased appreciably since the 1980s.

The poverty line is based on the line defined by the Planning Commission (1979), and endorsed by Planning Commission (1993). This is based on a nutritional norm of 2,400 calories per person per day in rural areas and 2,100 calories for urban areas. The poverty lines for rural and urban sectors were defined as the level of average per capita total expenditure at which these caloric norms were typically attained. The rural poverty line was thus determined at a per capita monthly expenditure of Rs. 49, and the urban at Rs. 57 at 1973-74 prices.

> 22 Mixed reference periods have been used for rounds 55, 56, 57, 58, 59, 60 and 62.

> 23 Regressing the change in the log of the headcount index across 42 rounds on the change in the log of the survey mean and the change in a dummy variable for MRP rounds, the latter had a regression coefficient of -0.20 with a t-ratio of 16.7. Similarly, MRP rounds tended to yield significantly lower inequality (as measured by the Gini index) in both rural and urban areas.

> 24 The NSS has followed the Census definition of urban areas which is based on a number of criteria including a population greater than 5000, a density not less than 400 persons per sq. km. and three-fourths of the male workers engaged in non-agricultural pursuits.

11
Policy Research Working Paper 6538

# **Abstract**

Using longitudinal data on more than 2,000 Russian families spanning the period between 2007 and 2010, this paper estimates the impact of the 2009 global financial crisis on food expenditures, health care expenditures, and doctor visits in Russia. The primary estimation strategy adopted is the semi-parametric difference-in-difference with propensity score matching technique. The analysis finds that household health and nutritional behavior indicators do not vary statistically between households that were crisis-affected and households that were not affected by the crisis. However, the analysis finds that crisis-affected poor families curtailed their out-of-pocket health expenditures during and after the crisis more than poor families that were not affected by the crisis did. In addition, crisis-affected vulnerable groups changed their health behavior. In particular, households with low educational attainment of household heads and households with more elderly people changed their health and nutrition behavior response when affected by the crisis. The results are invariant to the propensity score matching techniques and parametric fixed effects estimation models.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. 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://econ.worldbank.org. The author may be contacted at majwad@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
<!-- Start of picture text --> Grid capacity<br>65<br>6055 7’<br>50 7<br>45 se<br>:o :<br>35 ra<br>30 4 = Benchmark<br>25 7<br>20 _ eao ——=— Counterfact<br>15 =<br>10<br>2<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> SectoralGVA shares<br>3B<br>7065 po,<br>60 ha wo<br>55 aere =e Bence farming<br>50 cen" .<br>25 - ———Benc industry<br>40 . rms ———Benc services<br>35 ~s. ive<br>30.25 ~‘ ” oa, -----Coun-—----Coun farmingindusry<br>.20<br>35100 inte =----Coun services<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Average no. of workers per establishment<br>35<br>30<br>23 eee Bence farming<br>20 ——Benc industry<br>s<br>15 .< tone! mo Bec services<br>‘eer === Counfaming<br>10<br>°eeini xy =<a= CounCounservicesindusry<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Predicted sectoral real GVA<br>14<br>12 Rs. Nv<br>10 ..,Ys i 44 ———— Benc farming.<br>98 Sere { wa ——— Benc industry<br>gg = —s,enc SVE<br>{> =----Coun farming<br>o8 Poa eee =----Coun industry<br>0.2 eee— _——_—— —~ -----Coun services<br>0.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Normalized GDP per capita<br>4<br>aswae<br>ae<br>¢<br>2 4 ¢ ———— Benchmark<br>y Counterfact<br>1<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Predicted expenditure shares<br>09<br>os ws Bernc cons<br>07 etic we aoa eee Bene priv inv<br>0.6 Sean == Benc pub! inv<br>05 — Benc fix cost<br>9-4 === Councons<br>0.3 -————\ =< Counprivinv<br>ee0.1 semen oie ps, > ===aCounfx cot<br>0.0 = eon ee<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->
an equal volume of sales in February-August. In July and August, the state stores steadily lost sales. August volume in state stores was about one-fourth of their February volume.

For 17 of the 19 core commodities, sales volurie in the state sector increased following January 1992. The exceptions are eggs, for which the precipitous fall in volume, followed by roughly level sales thereafter, suggests a reporting mistake. The decline in cigarette sales in stores is sustsined after the original drop, suggesting that cigarette sales may have indeed moved out of the sampled stores, perhaps to sidewalk kiosks. For the remainder of items, the increase in volume in stace stores is not mirrored by a fall on private markets. These data support anecdotal reports that, after liberalization, fGio **d** id in fact return to shelves of state stores. Although the liberalization did little directly to promote reorganization of the trading system, this evidence suggests that the precipitous fall in volume that caused alarm in fall 1991 was halted and reversed in January and February 1992.

For several commodities-smoked sausage, butter, milk, cheese, 9ggs, sugar, bread, vermicelli-volume in the private markets remained low or negligible throughout January-August 1992. Other ptoducts were sold hAvily in the private markets. By August 1992 private market volume exceeded state-store volume for potatoes, apples, and beef in these sampled oudets. In most cases the private-market price substantially exceeded the price in state stores. Only for potatoes, cabbage, onions, and apples did the market and state-stor. prices tend to converge over time. (See Appendix for price and quantity charts for each of the 19 commodities.) Figure 3 shows a volume weighted price index for all 19 commodities (using February 4, 1992 quantity weights with December 1991 = 100 as a base period). Separate indexes were constructed for state-owned retail stores ("state price") and the collective-farm markets ("market price"). Market price data do not begin until the last week in January. The state-store price index rose sharply between December 1991 and January 1992, but not to the level of private market prices. Both indexes appear to have cvershot the deregulated equilibrium, so that the indexes fell in February. Subsequently, both 8
b Estimation by using the census data as benchmarks and link them with the adjusted unemployment data. See Xue & Zhong (2003) for details.

Between 1996 and 2001, the number of urban and township workers fell from 149 million to 108 million, a net reduction of 41 million or 28%.8 This includes a main drop of 52.5 million in state-owned and urban collectives and an increase of 12 million in the emerging formal private sector (joint stock and shareholding companies and foreignfunded enterprises). Meanwhile, at the same time period, the employment in the urban informal private and self-employed sectors enjoyed a 13.3 million, or 57% increase. The number of rural emigrants is also rapidly increasing. Current estimates of the number of migrant workers fluctuate between 40 and 80 million, which is about 6-10 percent of the total labor force.9 According to the survey data from the National Statistics Bureau, from 1997-2000, the share of rural migrant labor as a percentage of the total rural labor force increased from 18.1% to 23.6%, i.e., from 83.2 million to 113.4 million, increasing 10.07 million or 10.8% annually on average.

With the “revitalizing China with science and education” policy, China has achieved great success in several new industries in the past five to seven years, particularly high technology and ICTs. China has also been successful in modernizing and restructuring the economic base of coastal cities through the “special economic zone” approach. However, these new industries are limited in terms of employment-generating capability. In the late 1990s employment in the 53 designated high-technology parks (mostly in the coastal areas) had reached only 2.2 million people, even though several parks have enjoyed rapid and sustained growth (annual growth in sales of 50%). 10 Electronics and telecommunications industries, despite considerable efforts toward their development, do not generate many new jobs. 11 Altogether these industries’ share reached 8.7% of the manufacturing total labor force in 2000, up from 7.3% in 1994 (table 2). On the other hand, the labor-intensive light industries employed more than 30% of the labor force in 2000. Growing steadily, the traditional basic industries—such as metal products, chemicals, and machinery, which have not received priority attention from the authorities—had about 57% of the labor force in 2000.

|**Table 2. Number and Share of Workers in Manufacturing**|**Industries (1994-2000)**<br>**a**|
|---|---|
|1994<br>1996|1998<br>2000|

> 8 Calculated based on data from _China Statistical Yearbook 1999_ , and _China Labor Statistical Yearbook 2002_ , China Statistics Press, Beijing.

> 9 The lower estimate reflects published data of Ministry of Public Security, the higher estimate is by the World Bank based on the 2000 census.

> 10 Dahlman, C. and J. Aubert, _China & Knowledge Economy: Seizing the 21st Century_ , 2001, the World Bank, Washington, DC.

> 11 Ibid.

5
# **References:**

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Baccus, D. and Driffill, J. (1985), Rational expectations and policy credibility following a change in regime. _Review of Economic Studies_ , 52, 211-221.

Banerjee, A., Banerji, E., Duflo, E., Glennerster, R., Keniston, D., Khemani, S. and Shotland, M. (2007), Can information campaigns raise awareness and local participation in primary education? _Economic and Political Weekly_ , 42, 1365-1372.

Bardhan, P. (2006), Political economy of India. In K. Basu (ed.), _The Oxford Companion to Economics in India_ , Oxford University Press, New Delhi.

Bardhan, P. and Mookherjee, D. (2005), _Decentralization of local governments in developing countries: A comparative perspective_ . Cambridge, MA: MIT Press.

Bardhan, P. and Mookherjee, D. (2006), Decentralization and accountability in infrastructure delivery in developing countries. _The Economic Journal_ , 116, 101-127.

Besley, T., Pande, R. and Rao, V. (2004), The politics of public good provision: Evidence from Indian local governments. _Journal of the European Economic Association_ , 2, 416-426.

Chattopadhyay, R. and Duflo, E. (2004), Women as policy makers: Evidence from a randomized policy experiment in India, _Econometrica_ , 72, 1409-1443.

Dinar, A. (ed.) (2000), _The political economy of water pricing reforms_ . Oxford: Oxford University Press.

Estache, A., Foster, V. and Wodon, Q. (2000), Infrastructure reform and the poor. Learning from Latin America’s experience. LAC regional studies program, WBI studies in development. Part I: Main report.

Estache, A., Laffont, J. J. and Zheng, X. (2006), Universal service obligations in LDCs, _Journal of Public Economics_ , 1155-1179.

29
Table 1: Summary Statistics

|Variable|Obs|Mean|Std.<br>Dev.|Me|an|Difference|
|---|---|---|---|---|---|---|
|||Total||No Priming|Priming||
|Urban|4579|0.41|0.49|0.404|0.415|0.011|
|Household head|4579|0.74|0.44|0.743|0.731|-0.012|
|Female|4579|0.37|0.48|0.356|0.375|0.019|
|Age|4579|50.37|15.67|50.436|50.312|-0.131|
|Years of education|4579|6.79|5.66|6.752|6.751|0.046|
|Married|4579|0.74|0.44|0.751|0.736|-0.015|
|Married (monogamous)|4579|0.57|0.5|0.567|0.572|0.005|
|Married (polygamous)|4579|0.17|0.38|0.184|0.164|-0.020|
|Muslim<br>|4579|0.43|0.5|0.422|0.44|0.018|
|Other or unknown<br>religion|4579|0.02|0.13|0.016|0.018|0.0007|
|Log per capita<br>consumption in<br>|||||||
|2012/2013 (annualized<br>average between post-|4316|11.39|0.705|11.424|11.456|0.032|
|planting and post-<br>harvest data)|||||||
|Hours past week hh non-<br>farm or ag, wage|4579|32.53|24.74|32.704|32.365|-0.339|
|Household size|4579|5.52|3.19|5.522|5.522|-0.000|
|Head of HH is female|4579|0.21|0.41|0.214|0.207|-0.007|
|Head of HH age|4579|53.09|14.62|53.095|53.076|-0.019|
|Covariate shock 2014-<br>2016|4579|0.17|0.38|0.178|0.167|-0.011|
|Idiosyncratic shock<br>2014-2016|4579|0.19|0.39|0.200|0.178|-0.022*|
|Experienced HH death<br>2014-16|4579|0.11|0.31|0.115|0.109|-0.006|
|Conflicts between 2014-<br>2016|4579|0.04|0.19|0.035|0.043|0.008|
|Experienced shock,<br>conflict, or death 2014-<br>2016|4579|0.36|0.48|0.365|0.346|-0.019|
|Depressive<br>symptomatology (CES-<br>D Score_>_10)|4579|0.22|0.42|0.232|0.216|-0.016|
|CES-D score(max 30)|4579|7.05|5.19|7.173|6.93|-0.243|

20
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Weig|ht-for-age z-sc|ores|||
|Asset index 5 (Wealthiest)|0.184*|0.184*|0.166*|0.143|0.169*|0.065|0.131|
||(0.095)|(0.096)|(0.096)|(0.098)|(0.098)|(0.100)|(0.100)|
|Rural|-0.165**|-0.162**|-0.155*|-0.140*|-0.154*|-0.097|-0.126|
||(0.079)|(0.080)|(0.079)|(0.080)|(0.080)|(0.082)|(0.081)|
|Eastern|0.061|0.067|0.071|0.094|0.089|0.091|0.099|
||(0.072)|(0.072)|(0.072)|(0.073)|(0.073)|(0.073)|(0.073)|
|Central|0.017|0.019|0.016|0.038|0.031|-0.000|0.012|
||(0.067)|(0.067)|(0.067)|(0.067)|(0.067)|(0.067)|(0.068)|
|Mid Western|-0.107|-0.100|-0.094|-0.098|-0.111|-0.126*|-0.129*|
||(0.076)|(0.077)|(0.076)|(0.076)|(0.076)|(0.076)|(0.077)|
|Far Western|-0.082|-0.075|-0.063|-0.089|-0.089|-0.066|-0.100|
||(0.087)|(0.088)|(0.088)|(0.087)|(0.087)|(0.087)|(0.087)|
|Constant|-0.480**|-0.483**|-0.476**|-0.447**|-0.457**|-0.497**|-0.440**|
||(0.198)|(0.198)|(0.198)|(0.198)|(0.199)|(0.197)|(0.199)|
|Observations|2,481|2,481|2,481|2,493|2,493|2,493|2,493|
|R-squared|0.168|0.168|0.169|0.171|0.169|0.176|0.171|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in the total food expenditure, FCS is the food consumption score and DDS is the household level dietary diversity score, PC consumption is the per capita consumption, and Food share is the share of food expenditure on total expenditure.

68
Figure 9: Property tax compliance: men respond earlier to the letter, but women catch up (a) Distribution of payment date for women and men (October 2020 bill) <!-- Start of picture text --> Fraction<br>.15<br>Due date Women<br>Oct'20 bill Men<br>.1<br>.05<br>0<br>30sep2020 14oct2020 28oct2020 11nov2020 25nov2020 09dec2020 23dec2020<br><!-- End of picture text --> (b) Treated vs Control (October 2020 bill) <!-- Start of picture text --> Treatment<br>effect (p.p.)<br>Due date<br>7 Intervention Oct'20 bill Men<br>begins<br>6<br>5<br>4<br>3<br>2 Women<br>1<br>0<br>-1<br>-2<br>25sep2020 02oct2020 09oct2020 16oct2020 23oct2020 30oct2020<br>Calendar date<br><!-- End of picture text --> _Notes_ : Panel (a) shows the fraction of men and women paying the October 2020 bill before and after the due date (October 9th, 2020). The area of each histogram integrates into one. A larger bar on a particular date means that the payment frequency of the corresponding group is higher than that of the other group. Panel (b) shows the coefficients and 95% confidence intervals from a saturated regression that computes, on each calendar day, the payment rate difference between treatment and control. Standard errors are clustered at the individual level. The first vertical bar denotes the start of the intervention. The second vertical bar indicates the due date for the October 2020 bill. The letters were delivered between September 28th and October 7th. While men respond earlier to the tax communication campaign, women eventually catch up.

25
- 18 -

Figure 3: Evolutlon of **Local** Real Monet **- Balanc** (In ml.o **d** local cura.) 8189 <!-- Start of picture text --> 10<br>85<br>9~<br>1q81 *82 98$ 1984 ;8 1 9 "I S<br><!-- End of picture text --> Figure 4 Evolution **of** Real Bank Time Deposig (in mis. o Il cal cur. ) **_81_ -a** <!-- Start of picture text --> 35<br>4,<br>9.<br>I 0<br>1941 1q8t2 983 1q64O 19t85 194 1987 1q88 'qsq<br><!-- End of picture text --> Figure S: (.voIution of Bank DeposIts InForeIgn Currency (in mil.*. & real terms) 81889 <!-- Start of picture text --> 1'2<br>* g<br>tea' t,oa 198) 184 19 08Ut t998 158 1080 58<br><!-- End of picture text -->