rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2b: Estimation results based on Equation (2) with double interactions including subsidies**

||(1)|(2)|(3)|
|---|---|---|---|
||Sales per worker<br>(log)|Sales (log)|Employment (log)|
|Energy intense|-0.334|-0.468|-0.590**|
||(0.787)|(0.794)|(0.257)|
|Price oil, log|12.21***|11.74***|-2.176***|
||(0.474)|(0.475)|(0.143)|
|Price electricity, log|-1.979***|-2.083***|-0.520***|
||(0.260)|(0.266)|(0.0923)|
|Explicit petroleum subsidy (% GDP)|63.85***|61.58***|-13.66***|
||(12.83)|(13.58)|(5.187)|
|Explicit electricity subsidy (% GDP)|-6.694***|-6.935***|1.567***|
||(1.561)|(1.599)|(0.545)|
|log Price oil*Energy intense|0.476***|0.482***|-0.0394|
||(0.179)|(0.180)|(0.0542)|
|log Price elect*Energy intense|0.712***|0.655***|-0.478***|
||(0.171)|(0.173)|(0.0573)|
|log Price elect*Subsidy elect (% GDP)|-6.125***|-6.195***|0.783***|
||(0.647)|(0.662)|(0.215)|
|log Price petroleum*Subsidy fuel (% GDP)|-13.59***|-13.09***|2.868***|
||(2.712)|(2.868)|(1.090)|
|Constant|-58.61***|-54.55***|8.842***|
||(2.391)|(2.404)|(0.752)|
|Observatons|21,629|21,629|21,629|
|<br>R-squared|0.230|0.335|0.474|
|<br>CountryFE|YES|YES|YES|
|Robust standard errors in parentheses||||
|<br>*** p<0.01, ** p<0.05, * p<0.1||||
|Weighted by country sample size||||
|Controls for size clusters<sup>[1]</sup>, age, wave, and subsector||||

[1] Constructed using employment in columns (1) and (2), and sales in column (3) 20
“normal” growth (the pattern of observed weight gain) are added. This adjustment uses the 2 kilocalories per gram of tissue synthesis suggested in FAO (2001) combined with the average annual weight gain for each age and gender in the National Nutrition Monitoring Bureau data. Adjustments for the additional energy required by pregnant or nursing mothers are difficult due to lack of data, but energy requirements of infants themselves can be included. REE for infants (children under age 1) is set such that male babies have TEE of 650 calories and female babies have TEE of 600 calories, consistent with the (Indian Council of Medical Research 2009) report. Second, values outside of the 1% of the tails of the distribution are replaced with the percentile cutoff values prior to estimation.

# **B Estimation of Activity Levels (AL)**

Appendix Table S.2 lists the time-use survey activity codes and headings, the average share of time spent on the activity by rural and urban individuals, and the matched activity factors from FAO (2001). Estimates are not very sensitive to using alternative sources of activity factors, such as those developed for richer countries that tend to contain more detailed classifications of sports and exercise activities. Appendix Table S.2 includes the matched activities and activity factors from (Ainsworth et al. 2000) (used by (Cutler et al. 2003)) for comparison, and Appendix Table S.2 Panel C shows that the difference in activity levels using the measure used in the text and this alternative set of activity factors is quite small.

The estimates reported in the text make two additional assumptions about activity factors to derive activity levels. First, since the time-use data reports time spent on “related” or “other” activities within certain headings, one must assign an activity factor in these cases. The activity factor used in this case is equal to the average for the activities matched under the same heading. Second, since mode of travel is unobserved, the estimates use a single activity factor equal to 3 for all travel. This seems reasonable given that car ownership is very low in India and many other forms of transport (including bicycles, motorcycles, animal-carts and public transit) are equally and sometimes more energy intensive than walking. For example, FAO (2001) reports activity factors for “sitting on a bus/train” (1.2), “driving a car/truck (2.0), “walking around/strolling” (2.1), and more intense activities like “carrying a 20-30kg load on head” (3.5), “walking quickly” (3.8), and “cycling” 21
The impact of the three policies on inequality depends on flood location and urban configuration. Figure A2 provides comparable figures to figure 4 in the core text for all six cases: central, uniform and peripheral floods in cities where poor households live in center and those where they live in the outskirts. In summary, we find that:

- In the low-income–high-income configuration, subsidized insurance maintains inequality levels constant. Zoning increases inequality if floods are located in the city center, as it prevents lowincome households from living in their preferred location, and decreases inequality if floods are located in the periphery, as it prevents high-income households from living in the periphery and enjoying large dwellings. A risk-based insurance stands between these two cases.

- In the high-income–low-income configuration, subsidized insurance increases inequality as lowincome households subsidize the consumption of amenities by high-income households. When floods are in the periphery, their impact is extremely limited in terms of inequality and social welfare. When floods are distributed proportionally to the city center and zoning or risk-based insurance policies are implemented, an increase in the frequency or extent of floods has little or no impact on inequality, as both income classes are impacted by the floods, similarly to the low income-high income configuration. But when floods are concentrated in the city center and riskbased insurance is implemented, an increase in the extent and frequency of flooding decreases inequality because, even with land market mitigation, high-income households are more impacted by floods.

# APPENDIX B. MATHEMATICAL APPENDIX

## **This appendix is largely derived from Brueckner, Thisse and Zenou (1999). We want to show that, assuming exogeneous amenities with more amenities in the city center, if the amenity gradient is steep enough, with our utility specification, high-income households live in the city center.**

Two forces are at play in opposite directions:

1. Conventional forces push high-income households to the periphery.

2. Demand for amenities pushes high-income households to the city center.

Formally, we denote r the distance to the CBD, and consider an exogenous level of amenities at each distance from the CBD denoted A(r) . We consider two income classes: high- and low-income households. We denote Y0 and Y1 their incomes, assuming that Y0 < Y1 , and T0 and T1 their transportation costs, assuming that T1 > T0 . Households consume quantities of housing q1 and q0 with rents per unit of housing R1 and R0 and a composite good z1 and z0 with prices normalized at 1.

Utility is given by: We consider that high-income households live where their bid-rent is higher than the bid-rent of lowincome households. Writing r ̂ the tipping point between high- and low-income households, highincome households live in the city center if the difference in the bid-rent slopes at r ̂ for the two groups is negative—that is, if: (A1) 19
ertheless, our finding that employment creation predominantly happens at low-productivity firms is a so far undocumented feature of the policy. Furthermore, while there are many similarities in the responses of younger and older workers in Hungary, there are also notable differences. For instance, we find no indication for wage changes for younger workers, while we estimate clear wage differences for older workers. This difference could be driven by wage rigidities that constrain firms’ pass-through differently for younger and older workers. For instance, passing through the tax cut to younger workers could mean a wage increase for a 22-24 years old and then a wage cut once they reach age 25. At the same time, passing through the tax cut would simply mean that once age 55 is reached a pay raise is implemented. The latter might be more feasible than the former because workers dislike pay cuts (Bewley, 1998; Kaur, 2019).

Another difference is that the heterogeneity in employment effects seems to be less pronounced for younger workers than for older workers, at least for certain quality measures. Through the lens of our illustrative model, this could be explained by the fact that there are many more new entrants in the labor market for younger workers than in the labor market for older workers. Workers who are entering the labor market, or workers in probationary period, have no credible outside option and so firms can hire them and extract all the rents. If the share of these types of workers is large in a labor market, there will be smaller differences in the hiring incentives of low- and high-productivity firms. In line with that interpretation, we show that the employment differences between low- and high-TFP firms are less pronounced among workers who enter the labor market at later ages and so have shorter work history (if any) by age 22-24. At the same time, more experienced workers entering the labor market at younger ages (age 18-19) seem to be affected by the tax cut just like older workers: employment increases only at low-TFP firms (Table D2).

# **8 Conclusion**

This paper studies the implications of payroll tax cuts in a setup where firms play an active role in wage determination. A key prediction of our framework is that the tax policy can have heterogeneous impacts across firm types. High-productivity firms offering good jobs respond to the tax cut by raising wages, while low-productivity firms will respond mainly on the employment margin. As a result, the tax policy will change the composition of jobs in the economy.

We exploit the introduction of age-dependent payroll tax reductions in Hungary and use rich administrative data to provide empirical support for these predictions. In response to a large tax cut, we estimate that both employment and wages increased among treated 36
government.7/ Individual enterprises should not be subject<sup>to their own</sup> financial constraints, rather 'prices should be raised on those goods<sup>with a high ratio of</sup> marginal gain to marginal distortionary costs, and lowered for those where that ratio is low . . this leads to the well-known rule that the retio of consumer price to<sup>producer price should be</sup> inversely proportional to the own-price<sup>elasticity of demand" 8/.</sup>

# Optimal Prices for Individual Firms

7. Pricing rules for individual enterprises<sup>facing financial</sup> const-aints were esrived by Boiteux 9/ and Baumol and Bradford.10/<sup>Many</sup> public sector firms are characterized by increasing<sup>returns to scale,</sup> which means that marginal cost pricing would<sup>result in negative profits</sup> and financial deficits.11/ They therefore determined<sup>the optimal prices</sup> above marginal costs which allowed a nationalized<sup>industry to earna</sup> specified level of profits.12/ Maximizing consumer welfare (the compensating variation) subject to the enterprise's<sup>budget constraint</sup> yielded the now well-known equations for optimal<sup>prices: the percentage</sup> markup of price over marginal cost is inversely<sup>proportional</sup> to the own7/ C. Heady, Public Sector Pricing in a Fiscal<sup>Context, The World Bank,</sup> PPR Working Paper No. WPS-179, 1989, p. 13-30. 8/ Ibid, p. 26. Heady also considers the distributional<sup>impact of</sup> public sector pricing and recommends weighting consumer losses<sup>according</sup> to the distribution of income.

9/ H. Boiteux, 'On the Management of Public Monopolies<sup>Subject to</sup> Budgetary Constraints," Journal of Economic Theory, 3, 19'1, p. 219-240. English translation of the original French article "Sur<sup>la gestion des</sup> Monopoles Publiques astreints a l'equilibre budgetaire,"<sup>Econometrica,</sup> Vol. 24, No. 1, Jan. 1956, p.22-40.

10/ W. Baumol and D. Bradford, (1970) "Opti:rAl Departures from Marginal Cost Pricing," American Economic Review, Vol.<sup>60, p.265-283.</sup> 11/ In transport, fixed costs include the initial<sup>capital investment</sup> and maintenance expenses to protect infrastructure from weathering.

12/ These profits could he equal to zero.

5
countries with surges and the countries without surges. It shows that at the beginning of the period, three-quarters of the countries in the surge sample and the no-surge sample were closed. However, at the end of the period, only 18 percent of the surge countries were closed, as compared with 34 percent of the non-surge countries. The t-test of the difference between the means at the beginning of the sample is not significant. At the end of the sample, it is significant at the 5 percent level, implying that countries in the surge sample are significantly more likely to have liberalized than countries in the nonsurge sample.

One criticism that has been leveled at this measure is that it tends to pick up exchange rate management and/or state ownership more than trade liberalization (see discussion in Wacziarg and Welch 2008). An alternative way to examine trade policy more directly is to look at average tariffs during the surge. Figure 4 shows the average tariff around the time of the surge.<sup>12</sup> There is a general trend towards liberalization, especially in developing countries, over the period. However, liberalization immediately before the surge tends to be modest (2-4 percentage points) and there is little evidence of a discrete jump.

_Time series view of macro variables_ In this section, we examine how various macro variables change before and during the surge. As above, all of the graphs are shown for a balanced sample to ensure that

> 12 The tariff data are from the World Bank, Trade Research Division ,“Trends in average applied tariff rates in developing and industrial countries, 1981-2007” (http://econ.worldbank.org/WBSITE/EXTERNAL <u>/EXTDEC/EXTRESEARCH/0,,contentMDK:21051044~pagePK:64214825~piPK:64214943~theSitePK:4</u> 69382,00.html). The data are compiled from UNCTAD, IMF, WTO, and country sources.  The graph shows the unweighted overall annual average applied tariff rates.

12
|Of which: Dining out|0.580***<br>(0.164)|0.501***<br>(0.165)|1.260***<br>(0.267)|0.423**<br>(0.211)|0.777***<br>(0.231)|1.528***<br>(0.495)|
|---|---|---|---|---|---|---|
|Clothes|0.306***|0.269***|0.720***|0.257*|0.376***|0.590**|
||(0.102)|(0.103)|(0.163)|(0.131)|(0.125)|(0.287)|
|Utilities|0.181***|0.180***|0.275**|0.183***|0.154***|0.469|
||(0.033)|(0.032)|(0.138)|(0.040)|(0.047)|(0.314)|
|Communications|0.242***|0.210***|0.565***|0.184***|0.309***|0.545***|
||(0.051)|(0.050)|(0.078)|(0.058)|(0.069)|(0.198)|
|Local transport|0.435***|0.384***|0.732***|0.328***|0.502***|0.518**|
||(0.092)|(0.093)|(0.138)|(0.115)|(0.096)|(0.253)|
|Travel|0.872***|0.770***|1.595***|0.751**|0.899***|2.131***|
||(0.211)|(0.214)|(0.250)|(0.283)|(0.157)|(0.583)|
|Entertainment|0.655***|0.547***|1.171***|0.546**|0.852***|0.441|
||(0.169)|(0.174)|(0.325)|(0.235)|(0.163)|(0.404)|
|Automobiles|0.474***|0.394***|1.406***|0.285*|0.615***|1.329***|
||(0.127)|(0.115)|(0.291)|(0.148)|(0.202)|(0.323)|
|Other vehicles|0.182|0.116|0.841**|0.071|0.380|0.162|
||(0.136)|(0.126)|(0.377)|(0.137)|(0.371)|(0.566)|
|Durable goods|0.444***|0.357***|1.029**|0.371**|0.420**|0.621|
||(0.118)|(0.104)|(0.397)|(0.158)|(0.181)|(0.958)|
|Medical|0.166**|0.154*|0.309|0.071|0.287**|0.672|
||(0.075)|(0.085)|(0.201)|(0.086)|(0.127)|(0.432)|
|Health and fitness|0.395***|0.327***|0.534***|0.351*|0.381***|0.636**|
||(0.126)|(0.123)|(0.195)|(0.188)|(0.138)|(0.299)|
|Education|-0.102|-0.091|0.008|-0.157*|0.100|-0.165|
||(0.118)|(0.125)|(0.274)|(0.091)|(0.204)|(0.402)|
|Home repair|-0.043|-0.072|0.116|-0.001|-0.109|-0.280|
||(0.083)|(0.086)|(0.314)|(0.131)|(0.149)|(0.707)|
|Gifts|0.061|0.073|0.454|0.013|0.210*|0.281|
||(0.107)|(0.099)|(0.276)|(0.140)|(0.120)|(0.545)|

Note: The model of estimation is the same as Table 1, the dependent variable is the logarithmic form of household per capita consumption growth, and the variables of control include lagged log value of household expenditure per capita, average age/10, age squared/100, dependency ratio, urban-rural dummies, and regional dummies. For readability, we present only the results of interest - the coefficients of the z-score of online purchase / population in 2013. Fully results available upon request. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

First, for goods and services regularly consumed by most households,<sup>22</sup> including expenditures on cosmetics and beauty, food, clothes, utilities, communication, local transportation, and entertainment, we see a positive association between the z-score of online purchases over

> 22 From the CFPS data, over 50 percent of the households reported such consumption expenditures.

·· 15
Policy Research Working Paper 5726

# **Abstract**

The authors employ a novel dataset on almost 30,000 trade credit contracts to describe the broad characteristics of the parties that contract together; the key contractual terms, such as the discount for early payment; and the days by when payment is due. Whereas prior work has typically used information on only one side of the buyerseller transaction, this paper utilizes information on both.

The authors find that the largest and most creditworthy buyers receive contracts with the longest maturities from smaller suppliers, with the latter extending credit to the former perhaps as a way of certifying product quality. Discounts for early payment seem to be offered to riskier buyers to limit the potential nonpayment risk when credit is extended for non-financial reasons.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 lklapper@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
Table 10: Labor Cost by firm size, ownership and components (OOOHUF per worker per month): 1992

||-_C|ompone|nts---||--------||Ownership-|
|---|---|---|---|---|---|---|---|
|||||labor|**fully**|part||
|Firm size|wage|taxes|bonus|cnst|priv|priv|state|
|<10|32.4|13.4|18.1|63.9|53.7|64.5|112.3|
|11-20|29.3|12.7|11.6|53.6|48.0|60.0|59.2|
|21-50|29.3|12.6|11.3|53.2|47.7|64.9|51.0|
|51-200|19.4|8.7|5.6|33.7|33.8|38.3|31.7|
|201-300|16.6|**7.5**|4.1|28.2|28.2|26.6|28.7|
|>300|18.9|8.6|3.9|31.4|30.2|31.3|3i.7|
|Source: Min|istry of|Financ|e|||||

Table 11: Wage and employment changes for value subtracting and other firms, 1992

||--- Fir|ms<br>with:-|----|Firms with--|
|---|---|---|---|---|
||Other|Sales<Material Costs|Other|Sales<Wages|
|Employment<br>|89.3|42.1|87.6|52.9|
|change (dL92)|||||
|Wage change|119.4|133.0|122.7|172.4|
|(dw92)<br>Bonuses (000||9.9<br>13.5||9.9<br>14.2|
|HUF per month)|||||

[Note: **finns** with sales<material costs comprise 9.4% of the sample; firms with sales<wages **6.5%]** Source: Ministry of Finance and World Bank 24
18 _Table 4: Basic results for 1991-2000 CHNS panel_

||High o|ut-of-pocket p|ayments|Catastrophi|c out-of-pocke|t payments|
|---|---|---|---|---|---|---|
|Variable|Sample|Urban|Rural|Sample|Urban|Rural|
|Health insurance|0.328|0.561|0.190|0.307|0.59|0.12|
||(2.20)|(2.40)|(0.97)|(1.85)|(2.20)|(0.55)|
||_0.069_|_0.113_|_0.041_|_0.074_|_0.145_|_0.029_|
|SAH good|0.353|0.197|0.419|0.241|0.036|0.357|
||(1.68)|(0.56)|(1.59)|(1.04)|(0.09)|(1.24)|
|SAH fair|1.218|1.151|1.243|0.997|0.638|1.184|
||(5.55)|(3.16)|(4.49)|(4.15)|(1.54)|(3.97)|
|SAH poor|2.432|1.947|2.627|2.233|1.656|2.474|
||(9.71)|(4.51)|(8.44)|(8.29)|(3.43)|(7.48)|
|Per capita income|70.888|39.156|101.722|-210.39|-205.95|-208.96|
||(1.54)|(0.54)|(1.69)|(3.55)|(2.29)|(2.62)|
|Years of schooling|-0.008|0.000|-0.013|-0.009|-0.004|-0.012|
||(0.79)|(0.02)|(1.01)|(0.83)|(0.18)|(0.92)|
|1993 wave|-0.634|-0.486|-0.711|-0.62|-0.569|-0.641|
||(5.87)|(2.51)|(5.41)|(5.25)|(2.55)|(4.57)|
|1997 wave|-0.239|-0.122|-0.315|-0.154|-0.008|-0.224|
||(2.27)|(0.63)|(2.46)|(1.35)|(0.04)|(1.65)|
|2000 wave|-0.036|-0.148|0.036|0.064|0.002|0.113|
||(0.34)|(0.73)|(0.28)|(0.55)|(0.01)|(0.83)|
|N|14905|5128|9796|14905|5128|9796|
|Av. no. waves|2.4|2.2|2.5|2.4|2.2|2.5|
|Log likelihood|-906.84|-294.61|-607.02|-771.57|-225.51|-542.34|
|Chi squared|300.265|71.902|238.779|265.023|58.704|213.762|
|Prob value for hypothesis of<br>constant effects across|0.037|0.952|0.027|0.310|0.901|0.270|
|provinces|||||||

Note: Models are fixed effects logits, estimated on the 1991, 1993, 1997 and 2000 waves of the CHNS. Income expressed in millions of RMB for the estimation. The first number for each covariate is the coefficient, the second (in parentheses) is the t-statistic, and the third (in italics, and listed only in the case of the insurance dummy) is the marginal effect (conditional on a zero fixed effect).