rafmacalaba/datause-encoder-data huggingface dataset:
employment rates of 13 year olds in the post-intervention period. For most other ages there is an increase in employment rates in the months of May-December, but the increase is statistically insignificant. The exceptions to this are among 10 year old boys and 14 year old girls where there is a slight decrease in employment rate for these children in the months following the implementation of the Act.

**Table 1: Employment rate for children age 10-14**<sup>**8**</sup>

|||**_1991_**|||**_Jan-April_**|||**_May-Dec._**||
|---|---|---|---|---|---|---|---|---|---|
|**_Age_**|**_Boys_**|**_Girls_**|**_Total_**|**_Boys_**|**_Girls_**|**_Total_**|**_Boys_**|**_Girls_**|**_Total_**|
|10|12.6|20.6|16.4|14.1|14.1|14.1|12.0|23.2|17.3|
||(1.4)|(1.8)|(1.1)|(2.9)|(2.9)|(2.0)|(1.6)|(2.2)|(1.3)|
|11|14.7|19.9|17.0|12.2|20.0|15.7|15.6|19.9|17.5|
||(1.7)|(2.2)|(1.4)|(3.1)|(4.2)|(2.6)|(2.1)|(2.6)|(1.6)|
|12|22.4|24.7|23.4|20.2|23.0|21.5|23.2|25.3|24.1|
||(1.6)|(1.9)|(1.2)|(3.1)|(3.6)|(2.3)|(1.9)|(2.3)|(1.5)|
|13|26.4|25.5|25.9|34.4|32.2|33.3|23.5|23.3|23.4|
||(2.4)|(2.3)|(1.6)|(5.0)|(5.0)|(3.6)|(2.7)|(2.6)|(1.8)|
|14|33.7|33.1|33.4|25.2|36.6|31.0|36.4|31.9|34.1|
||(2.4)|(2.3)|(1.7)|(4.4)|(4.8)|(4.8)|(2.8)|(2.6)|(1.9)|

Note: figures in parentheses are standard errors Figure 1 shows the monthly employment rates by age. Except for 13 year old children, there does not appear to be much decrease in employment rates of the children in the months following the implementation of the EoCA 1991. The monthly employment rates of children in the four main occupation groups (figure 2) shows some seasonal decrease in employment in agriculture in the months of June, July, and August which increases again in last four months of the year, this is not surprising since the agriculture sector is not a prohibited sector of work under the EoCA 1991. As opposed to agriculture sector, the manufacturing sector shows a slight reduction in employment rates in the post-implementation months. Similarly participation in other the other two sectors is also either steady or decreasing for all ages.

> 8 The reference period for work in PIHS 1991 is 7 days.

10
distribution bear a greater burden of direct and indirect taxes, relative to _market income_ , the proportional tax burden does not vary considerably across the market income distribution.

Strong progressive, in-kind education and health transfers boost the final position of low-income Cambodian households. The bottom 40 percent are net beneficiaries of the fiscal system, while richer households are net contributors. The reduction in the Gini from 32.4 to 31.4 is due to in-kind transfers (Figure 8).

Figure 16: Distribution of direct taxes and direct transfers <!-- Start of picture text --> A. Direct tax burden/benefit by decile B. Direct tax burden/benefit by decile (% of<br>(KHR/month)<br>market income)<br>5000 1.00<br>0 0.50<br>-5000<br>0.00<br>-10000<br>-0.50<br>-15000<br>-1.00<br>-20000<br>-25000 -1.50<br>-30000 -2.00<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Income decile Income decile<br>SSC: EIC - ER HI: ER Salary tax SSC: EIC - ER HI: ER Salary tax<br>Witholding tax Rental tax Registration tax Witholding tax Rental tax Registration tax<br>MoT tax Property tax Gov. stipends MoT tax Property tax Gov. stipends<br>Welfare (inc. pensions) Total Welfare (inc. pensions) Total<br>Figure 17: Distribution of direct and indirect taxes, direct transfers, subsidies and in-kind transfers<br>A. 0verall tax burden/benefit by decile B. 0verall tax burden/benefit (% of market<br>(KHR/month) income)<br>40000 15.00<br>20000<br>10.00<br>0<br>-20000 5.00<br>-40000 0.00<br>-60000<br>-5.00<br>-80000<br>-100000 -10.00<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Income decile Income decile<br>Education transfers Health transfers Total transfers - cash Education transfers Health transfers Total transfers - cash<br>Electricity subsidy Total direct taxes Total indirect taxes Electricity subsidy Total direct taxes Total indirect taxes<br>Total Total<br>KHR / month<br>Proportion of income (%)<br>KHR / month<br>Proportion of income (%)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Welfare transfers have limited poverty-reducing and redistributive effects, however, because they provide low coverage, are small, and are poorly targeted. In 2019, about 10 percent of Cambodian households possessed an IDPoor “equity” card and only 2 percent received conditional cash transfers 25
# **4.2 Traffic, Weather and Air Pollution**

Our second exercise tests the impact of motor vehicle emissions on air pollution, using hourly data from the CAMS-3 monitor and a .01-degree square area whose centroid has the coordinates of the monitor (Figure 1). Within that area, we use the previously-described methodology to compute hourly average traffic speed, which is an inverse index of congestion: emitting vehicles occupying road space in that area. Among the weather variables, prior experimentation showed that only temperature, humidity and wind speed have consistent and highly-significant effects. We therefore exclude rainfall and pressure from the final estimation model. We specify it in log form to minimize outlier effects. (1) 0 + 1l 2l l 0<sup>l</sup> 0<sup>l</sup> tt tt tt<sup>+ ββ</sup> tt<sup>+ ββ</sup> tt<sup>+ εε</sup> tt where (measured at date/time t): PP = ββ ββ llSS + ββ<sup>ll</sup>

- P  =  Pollutant concentration measured by CAMS-3 [PM 2.5 (ug/m<sup>3</sup> ); NO2 (ppb)]

- S  =  Average traffic speed (km/hr) in the .01-degree cell surrounding CAMS-3

- H  =  Humidity (%)

- T  =  Temperature (C)

- W  =  Wind speed (km/hr)

- ε = Random error term

Table 4 reports our results for PM2.5 and NO2 readings from the CAMS-3 monitor. Our primary estimates use variable levels, but we also include estimates for time-differenced<sup>7</sup> variables to test the possible effect of serial correlation on the parameter estimates. The time-differenced version provides a weaker test of the weather variables, whose dominant variation is seasonal rather than hourly. Table 4 shows that all variables in the two levels equations are highly significant, while traffic speed has similar parameter estimates and high significance in the difference equations. As expected, weather-related variations in measured pollution are statistically weaker in the timedifference variant.

> 7 We use one-hour differences for this exercise.

12
perspective is warranted. A person who adapts to iow food energy intake through diminished activity leveis (r-ecalling the aforementioned example) is someone who one would want to help through anti-hunger policies. Yet some approaches found in both research and practice would (. t least implicitly) dictate otherwise. For example, the use of child anthropometric measures (such as weight-for-age) to indicate nutritional need is suspect from this point of view. (It has been found that seemingly satisfactory physical growth rates on children are maintained at low food energy intake levels by not playing; see Beaton's 1983 survey. That is surely a serious food related deprivation for any child.) The broader concept of hunger used by Dreze and Sen is motivated by the above ideas of "functioning" and "capability". This approach will be novel to many present day economists, and others (though it does have roots in classical political economy, and, indeed, Aristotle's views on "political distribution"; see Sen, 1989a). Thus it might be considered a significant departure from present-day "mainstream" approaches to the welfare economics of public policy. Is it?

Dreze and Sen emphasize the inadequacies of viewing c -wsption of commodities as an end in itself (also a prominent theme of the Human Development ReDort). However, I do not think that the real difference lies here, at least in theory. The current mainstream approach in economi- theory, which is clearly seen by the authors as a good deal less than adequate for the task at hand, does not insist that income, or command over consumption, is the final objective of personal well-being. Nor does it insist that income is all that matters in determining individual well-being, or that personal characteristics do not matter, at given consumption levels. By either 5
23

# TABLE 3 : Basic Reolantinf Eauation

Dependent variable Is RPBAHR2 18 observations used for estimation from 1968 to 1985

|Regressor|Coefficient|T-Ratio|
|---|---|---|
|CONS|.0295<br>3|4.6063|
|AREVPROD(-1)|1995xlO 3|6.8853|
|AREVPROD(-2)|.0022xlO3|.3235|
|REALINT(-1)|-.2452xlO 3|-5.3344|
|PRICOCO(-1)|-<br>.1152xlO|-6.2994|
|OLDPROD(-1)|-.0492|-4.6606|
|R-Squared|.9664||
|F-statistic F( 5, 12)|69.0101||
|R-Bar-Squared|.9524<br>-2||
|S.1.<br>of Regression|.2038xlO3||
|Residual Sum of Squares|.0498xlO||
|Mean of Dependent Variable|<br>.00920||
|S.D. of Dependent Variable|<br>.00934||
|DW-statistic|2.1109||

# Diagnostic Score (LK) Test Statistics

|A:Serial Correlation|CHI-SQ( 1).|0.2632|
|---|---|---|
|B:Functional Form|CHI-SQ( 1)-|2.3738|
|C:Normality|CHI-SQ( 2)-|1.3139|
|D:Heteroscedasticity|CHI-SQ( 1)-|.0562|

# Notes

A:Lagrange multiplier test of residual serial correlation B:Ramsey's RESET test using the square of the fitted values C:Based on a test of skewness and kurtosis of residuals D:Based on the regression of squared residuals on squared fitted values
Unlike most other studies, we use a more up-to-date econometric methodology and superior data. In particular, unlike other studies we a) control for the endogeneity of explanatory variables, b) account for the presence of a spurious scale effect introduced by the varying size across units of observation (districts), and c) use long panels of comparable monthly data from all districts in the countries that we analyze. Unlike most studies, we also employ both static and dynamic specifications and estimate on contiguous panels to allow for dynamic adjustment and regime changes. Like other studies, we do not address the issue of the matching of vacancies with employed individuals (job-to-job mobility), an issue that should be taken up in future research.

# **3. The Estimating Framework**

Theories of search and matching generally do not imply a particular functional form of the matching function. Like most studies, we use the Cobb-Douglas function given by equations (2) and (3) above. Taking equation (2) for simplicity, using lowercase letters for logarithms of variables and introducing unobserved (time invariant) district specific effects _α i_ =ln _Ai_ as well as an idiosyncratic error term _εi,t_ , we can write (2) as In estimating (6), one has to take into account the specific features of the matching model. Estimating by the ordinary least squares (OLS) method is not appropriate if the unobserved district specific effects ® _i_ are correlated with explanatory variables _ui,t-1_ and _vi,t-1_ . This correlation exists on account of structural differences between districts caused by unobserved factors that affect both ® _i_ and _ui,t-1_ or _vi,t-1_ . One important factor is district size, although this factor and its impact on cross-sectional estimates can be

> (1995), who use monthly data for districts/regions in Poland (1992 93), Hungary (1991-94), the Czech Republic (1991-94), and Slovakia (1990 93), Burda and Lubyova (1995) who use monthly and quarterly Czech and Slovak data from 1992 to 1994, Boeri and Burda (1995), who  use Czech district-level data over the period 1992 1994, Burda and Profit (1996), who use

− 14 −
Table 2: Production functions using modified data _(continued)._

|**1982**Cluster mean for<br>distance to AW roads|-0.064**<br>(0.022)|-0.035<br>(0.023)|-0.020<br>(0.026)|
|---|---|---|---|
|**1982**Cluster mean for<br>distance to dirt roads|0.035<br>(0.047)|0.045<br>(0.051)|0.021<br>(0.060)|
|**1982**Cluster mean for|0.104**|0.094**|0.108**|
|distance to transport|(0.024)|(0.025)|(0.029)|
|1982 Cluster mean for|-0.076*|-0.123**|-0.075|
|cropped area|(0.036)|(0.038)|(0.043)|
|**1982**Cluster mean for|0.037**|0.032*|0.028|
|farm size|(0.014)|(0.014)|(0.015)|
|**1982**Cluster mean for|0.013|0.015|0.001|
|livestock value (000 Ksh)|(0.018)|(0.018)|(0.024)|
|**1982**Cluster mean for<br>cash crop value (000 Ksh)|-0.438**<br>(0.060)|-0.227**<br>(0.068)|-0.228**<br>(0.082)|
|**1982**Cluster mean for|0.040*|0.013|0.010|
|other crop value (000 Ksh)|(0.018)|(0.020)|(0.021)|
|**1982**Cluster mean for|-0.018|-0.020|-0.009|
|non-farm income (000 Ksh)|(0.043)|(0.043)|(0.046)|
|Medium Potential Zone|-0.849**|-0.398|-0.484*|
|Dummy variable|(0.177)|(0.208)|(0.224)|
|Low Potential Zone|-0.802**|-0.396|-0.602|
|Dummy variable|(0.250)|(0.276)|(0.314)|
|Hilly area|0.054|0.211|0.234|
|Dummy variable|(0.140)|(0.140)|(0.147)|
|Undulating area|0.165|0.191|0.211|
|Dummy variable|(0.097)|(0.117)|(0.138)|
|AEZ (range)|0.62 to 1.77|0.216 to 1.572|0.263 to 1.617|
|Jt. Signif. (F value)|(3.319)**|(3.533)**|(3.263)**|
|N|455|455|455|
|Adj.**R**2|0.705|0.738|0.744|
|Test for Joint significance<br>of District effects||9.695**|5.023**|
|(F value)||||
|Test for the sum of Sfratio x<br>district effects<br>(F value)|||1.737|
|Test for the weighted sum<br>of sfratio x district effects<br>(F value)|||1.150|

# Notes:

Dependent variable is log of the reconstructed 1990 value of farm production. The extension variable is the revised variable used by B&E, 1997.

Standard errors reported in parentheses.

* and ** indicate significance at 5 and 1% levels, respectively.

District effects, with Machakos as the base case, range from -0.07 to 1.36 for model II and from

-1.09 to 0.15 for model III.

AW roads refer to all-weather roads.

16
models: the Canadian Climate Center (CCC) scenario (Boer et al. 2000), Centre for Climate System Research (CCSR) (Emori et al. 1999), and the Parallel Climate Model (PCM) scenario (Washington et al. 2000). We use country level climate change scenarios in 2020, 2060, and 2100 from each climate scenario. The change in temperature predicted by each climate model is added to the baseline temperature in each district. The percentage change in precipitation is multiplied by the baseline precipitation in each district. This gave us a new climate for every district in Latin America for each scenario.

Table 2 summarizes the climate scenarios of the three models for the years 2020, 2060, and 2100. The models predict a broad set of scenarios consistent with the range of outcomes in the most recent IPCC (Intergovernmental Panel on Climate Change) report (Houghton et al. 2001). In 2100, PCM predicts a 2 ° C temperature increase in Latin America whereas CCC predicts a 5 ° C increase. Rainfall predictions are noisier: PCM predicts rainfall to increase by 8% by 2100 whereas CCC predicts rainfall to decrease by 8%. Examining the path of climate change over time reveals that temperatures are predicted to increase steadily until 2100 for all three models but precipitation will vary across time.

The parameters from the estimated multinomial logit models are used to simulate the impacts of climate change on the probabilities of choosing a particular animal for each climate scenario in Table 3. The dry and hot CCC and CCSR scenarios predict that farmers would choose beef cattle and sheep more often and dairy cattle, pigs, and chickens less often. With the wetter and milder PCM scenario, farmers will pick sheep more often and beef and dairy cattle less often.

# **6. Conclusion**

This paper uses a multinomial choice model to capture the choice of species made by farmers.

8
# Policy Implications of Non-linear E¤ects of Tax Changes on Output<sup>�</sup>

Samara Gunter Daniel Riera-Crichton Colby College World Bank Carlos Vegh Guillermo Vuletin World Bank World Bank JEL Classi...cation: E32, E62, H20.

Keywords: tax multiplier, tax policy, tax rate, value-added tax, non-linear, narrative.

�We would like to thank sem inar participants at the International M onetary Fund, Inter-Am erican D evelopm ent Bank, World Bank, Federal Reserve Board, Europ ean Stability M echanism , Central Bank of Argentina, Central Bank of Chile, Central Bank of Spain, UN Econom ic Com m ission for Latin Am erica and the Caribb ean, G eorge Washington University, Johns Hopkins University, W illiam s College, D avidson College, Universidad Nacional de La Plata, Sao Paulo School of Econom ics-G etulio Vargas Foundation, Escola Sup erior d’Adm inistració i D irecció d’Em preses (ESAD E), Institute of Education and Research (INSPER), International M acro Workshop-RID G E, Latin Am erican and Caribb ean Econom ic Association (LACEA), Annual Sym p osium of the Spanish Econom ic Association, and XLIV M eeting of the Network of Central Banks and Finance M inistries-ID B for m any helpful com m ents and suggestions. We would also like to thank Alb erto Alesina, Silvia Albrizio, Leop oldo Avellan, Frank Bohn, Fernando Broner, Eduardo Cavallo, Javier G arcia-Cicco, Aitor Erce, D avide Furceri, Vitor G aspar, Alejandro Izquierdo, Herm an Kam il, G raciela Kam insky, Aart Kraay, G erardo Licandro, Alessandro Notarpietro, Peter M ontiel, Eduardo M oron, Ilan Noy, Pablo O ttonello, Peter Pedroni, Javier Perez, Rob erto Ram os, D avid Robinson, D iego Saravia, O lena Staveley-O ’Carroll, Jay Shambaugh, Ham ilton Taveras, Teresa Ter-M inassian, and M artin Urib e for helpful discussions, and José Andrée Cam arena Fonseca, D iego Friedheim , Pablo Hernando-K am insky, and Luis M orano for excellent research assistance.
firms in Bangladesh, India and Sri Lanka, median employment in small formal firms is 10 (compared to 35 employees for all formal firms in these three countries), and in rural firms is 1.5. In the comparison of small formal vs. informal manufacturing firms in India, median employment in small formal firms is again 10 (compared to 20 employees for all formal manufacturing firms in the India surveys), and in informal firms is 4. The remaining size disparity between small formal firms, on the one hand, and rural or informal firms, on the other, is why we condition on size in specification 2 as described above.

# _Levels comparisons between rural and formal sector firms_

The results for the comparison of rural with formal sector firms are clear and echo the results reported above in Section 7 for the large city / small city comparison using the Enterprise Survey data (Table 16). Rural firms almost always report that the elements of the business environment represent less serious obstacles to their firm’s production and growth than is the case for managers of formal sector firms. This is highlighted by the predominance of red shaded cells in both specifications. (Note that the number of observations for “Competition” and “Crime, theft and disorder” is very small and these results should be treated with caution.) The results in the lower part of the table are interesting because they accord with what one would expect of rural firms: they have much lower engagement with officials (bribes, inspections, management time) and report lower costs of corruption and the range of institutional constraints. Rural firms report similar levels of power outages and they are more likely to have a generator than formal sector firms. They rate the problems associated with access to electricity as less problematic than do formal sector firms, which suggests that they make less use of electricity in their production process.

# _Comparison of rankings of constraints by rural and formal sector firms_

Table 17 presents the results in a slightly different way in order to highlight how the different firm types rank the constraints. Using the unconditional means in the first column, we see the results familiar from above, namely that electricity, political instability, and corruption are the highest ranked constraints for formal sector firms.

36
**<u>Table A.12. The placebo test of the program effect on individuals’ outcomes</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|Log of<br>monthly<br>wage (wage<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Log of<br>education<br>subsidy for<br>students<br>(children<br>aged 6-17)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Year 2008|0.0276|-0.0009|-0.0262*|0.1003|-0.1977*|0.0259|0.0187|-0.4615|
||(0.0346)|(0.0149)|(0.0156)|(0.2917)|(0.1086)|(0.1326)|(0.1283)|(0.3139)|
|(Poverty rate – 50) * Year 2008|-0.0044|0.0001|0.0013|0.0110|0.0123|-0.0081|-0.0061|0.0023|
||(0.0038)|(0.0019)|(0.0022)|(0.0251)|(0.0110)|(0.0150)|(0.0141)|(0.0285)|
|Program * (Poverty rate – 50) * Year|0.0051|-0.0000|-0.0007|-0.0181|-0.0093|0.0146|0.0098|0.0363|
|<br>2008|(0.0039)|(0.0019)|(0.0023)|(0.0287)|(0.0122)|(0.0159)|(0.0152)|(0.0332)|
|Age|0.0277***|0.0039***|0.0077***|0.1213***|0.0420***|-0.0050|-0.0054|0.3567***|
||(0.0017)|(0.0007)|(0.0006)|(0.0087)|(0.0086)|(0.0043)|(0.0042)|(0.0581)|
|Age squared|-0.0003***|-0.0000***|-0.0001***|-0.0015***|-0.0005***|0.0003***|0.0002***|-0.0171***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0025)|
|Male|-0.0314***|-0.0062|0.0476***|0.9325***|0.0348|-0.1440***|-0.1333***|0.1443***|
||(0.0082)|(0.0038)|(0.0055)|(0.0706)|(0.0294)|(0.0276)|(0.0265)|(0.0386)|
|Ethnic minorities (yes=1, Kinh=0)|0.2366***|-0.1104***|-0.0764***|-0.3756*|-0.2764***|-0.2440**|-0.2373**|0.8376***|
||(0.0419)|(0.0175)|(0.0242)|(0.2245)|(0.0455)|(0.1173)|(0.1150)|(0.1686)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1410***|0.0497***|-0.0162|-0.7904***|6.7972***|0.7029***|0.6362***|-1.2519***|
||(0.0428)|(0.0140)|(0.0205)|(0.2558)|(0.1504)|(0.0908)|(0.0885)|(0.3508)|
|Observations|41,780|41,780|41,780|41,780|7,773|62,232|62,232|19,133|
|R-squared|0.220|0.083|0.102|0.161|0.201|0.099|0.094|0.417|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. The sample used in these regressions includes VHLSS 2004, 2006 and 2008. The VHLSS 2008 is used as the survey after the placebo program. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

62