rafmacalaba/datause-encoder-data huggingface dataset:
_Fareed M. A. Hassan, ECSPE_ 14

# **3.2. A More Progressive, Three-Bracket System Can Raise Even More Revenue**

The main arguments made in favor of a simple system with fewer tax brackets and flat rate(s) is simplicity, revenue potential and other potential supply side incentives related to saving and investment<sup>6</sup> . Its biggest disadvantage is the lack of progressiveness. Besides the acknowledged need to generate revenue, personal income tax is also often expected to perform a redistributive function. The significant excess of the income of the rich above mean income suggests that -- on the basis of the principle of ability to pay -- it is desirable to collect substantial tax revenues from the rich 7. To explore the revenue implications of alternative, but more progressive income tax than the previously discussed flat-rate tax., I have constructed an alternative tax structures with broad, yet realistic, tax brackets derived from the 1992 income distribution. Panel c of Table 3 shows an altemative system with four brackets (including the lowest tax-free bracket, as in the flat-rate system) and four marginal rates of zero, 20 percent, 30 percent, and 40 percent. This system has the potential to raise an average 6/The analysis does not attempt to assess longer-term effects of tax policy through changes in the level of capital formation and growth. Lower flat rates have the obvious advantage of reducing disincentives to a wide range of economic activities, includiing work effort and saving and investment. Nevertheless, it is difficult to quantify these effects although there is often no question about their direction. For instance, Marsden's (1986) study uses evidence from 20 countries, spanning almost the entire spectrum of world economies, to show that those with lower taxes experienced faster economic growth and higher employment and productivity. Hall and Rabushka (1985) estimate the potential gains from their proposed simple flat wage and business tax as a 6 percent increase in output from increased total work in the US economy and an additional increment to total output of 3 percent from added capital formation, p. 75. Eissa (1995, 1996) analyzed the labor supply response to the US Tax Reform Act of 1986. She found that while married women at the top of the income distribution increased their labor supply by 18 percent, male responsiveness was very little. For a comprehensive survey of the economic effects of the tax reform Act of 1986 on the US economy, see Auerbach and Slemrod (1997).

7/ Stern (1987) shows that greater inequality in income-earning ability did indeed result in higher optimal tax rates. On the other hand, Brennan and Buchanan (1980) and Harberger (1993) argue that the design of tax policy should be guided primarily by practical considerations such as simplicity, uniformity and transparency as opposed to the rates implied by the optimal tax theory. Tanzi (1992), Bird (1989), and Slemrod (1990) have also stressed the need for tax policy to pay more attention to the practical and administrative constraints.
# **2.1. Evolution of Per Capita Income and Inequality**

Table 2.1 presents a summary of the distribution of per adult equivalent<sup>7</sup> expenditure based on the 1996, 2001 and 2007 household surveys conducted by the National Statistical Office. All these surveys follow the sampling frame of the 1987 population census. The samples are stratified and the 1996 survey has the smallest sample size with 1,728 observations 36 percent of which represent the rural sector. The National Statistical Office (2002) has noted this under-representation of the rural areas in the 1996 household survey. For the other two surveys, the sample size is 10,992 observations for 2001 and 11,391 observations for 2007.

On the basis of the means reported in the second column of table 2.1, we find that (see table 2.2) the average per adult equivalent expenditure grew 5.4 percent per year over the period of 1996-2007 in nominal terms. Looking within sub-periods, the mean per adult equivalent expenditure grew by about 9 percent per year between 1996 and 2001, and by about 2.5 per year between 2001 and 2007. In real terms, these average rates of growth fall respectively to 1.9 percent, 4.1 percent and 0.5 percent. National account statistics tell a different story. The real per capita GDP is believed to have grown only by 1.57 percent per year between 1996 and 2001, and by 0.57 percent between 2001 and 2007 (National Statistical Office 2002, 2008).

Table 2.2. Growth in Average per Adult Equivalent Expenditure in Cameroon

||(1996-2007)|
|---|---|
|Period|Average Growth Rate (percentage)|
||Nominal<br>Real|
|1996-2001|9.0<br>4.1|
|2001-2007|2.5<br>0.5|
|1996-2007|5.4<br>1.9|

Source: Authors’ Calculations

> 7 The underlying scale assigns weights to individual members of the household according to their age and gender.  However there is no gender differential for children up to the age of 10.  Thus children who are at most 1 year old get a weight of 0.255.  Those with age between 1 and 3 years get assigned a weight of 0.45. Between the age of 4 and 6, the weight is 0.62 while it is 0.69 for the 7-10 age group.  Starting from age 11, males get assigned the following weights: 0.86 between 11 and 14, 1.03 between 15 and 18, 1 between 19 and 50 and 0.79 above 50.  All females between 11 and 50 get a weight of 0.76 and those above 50 get a weight of 0.66.

6
**Table B5. Deprivation proportions by Venn diagram region in Figure 6: urban and rural populations**

||Urban<br>2011|Rural<br>2011|Difference<br>(Rural-Urban)|
|---|---|---|---|
|money poor|0.32|0.36|0.04*|
|female circumcision deprived|0.24|0.36|0.12***|
|girls' education deprived|0.14|0.46|0.32***|
|_Distribution of the population_||||
|not deprived|0.47|0.22|-0.25***|
|only money poor|0.19|0.11|-0.09***|
|only female circumcision deprived|0.12|0.14|0.02*|
|only girls' education deprived|0.07|0.20|0.12***|
|money poor, female circumcision deprived|0.08|0.07|0.00|
|female circumcision, girls' education deprived|0.02|0.09|0.06***|
|girls' education deprived, money poor|0.03|0.12|0.09***|
|allthree deprivations|0.02|0.06|0.04***|

34
increases demand of goods, but only when they signal ability. Our first finding is very similar to theirs: both observability and ability-signals are necessary conditions for conspicuous consumption.<sup>8</sup> However, our paper differs to Clingingsmith and Sheremeta (2018) in several important ways. Most importantly, our main purpose is to establish a nexus between inequality, access to loans and conspicuous consumption. By implementing access to costly loans, we demonstrate that loan-taking increases with conspicuous consumption, and that this is mostly driven by the low-income subjects seeking pecuniary emulation. We also document a vicious cycle between inequality and costly loan-taking.

The rest of the paper proceeds as follows: the next section discusses the experimental design, followed by the results and then the conclusion.

# **II. Experimental design**

The experiment consists of a total of eight treatments, varying (i) observability of purchasing, (ii) ability signaling, and (iii) availability of loans, yielding a 2 X 2 X 2 between-subjects design. Simulating consumption was one of the main challenges for this paper. We take a context-heavy approach, first by utilizing pictures and descriptions of real-world luxury items that are well outside the range of affordable items for our subjects. The 25 items consist of expensive mobile phones, famous paintings, estates, private islands, and yachts. We took care to select items that were as gender neutral as possible, which is why we did not use expensive cars or clothing/jewelery. In all treatments, subjects were shown a description of the item, but had to spend real money to view the item. In all sessions, we enforced removal of all personal items so that subjects would not be able to look up the items on the internet during the session itself. The full list of items, descriptions and prices can be found in Table A.1 in Appendix A.

It is important to note that subjects’ purchasing decision was for a virtual picture of the item, with prices scaled to real-world prices. This was made clear to all subjects in the instructions, that purchasing was limited to viewing a picture of the item. This is in contrast to some of the literature involving real goods (see for example studies eliciting willingness to pay for real-world objects, such as Plott and Zeiler, 2005; Isoni, Loomes, and Sugden, 2011; among others). However, in keeping with the context of goods that signal status, it would be cost-prohibitive for

> expenditure, and those that have greater threatened self-worth are more likely to defer the disutility of expenditure. Our results are in contrast to this, however, with little possibility of any long-term deferment, we still find higher loan-taking, but cannot attribute this to those with threatened self-worth, but do attribute it to status-seeking behavior.

> 8 For more on signaling behavior, see Crawford and Sobel, 1982; Glazer and Konrad, 1996; Feltovich, Harbaugh, and To, 2002; among others.

5
Table 3: More than a quarter of world trade is subject to an average preference margin of 7.4 percent

||**Type of regime**|**Share of global**<br>**imports(%)**|**Avg. Bound**<br>**MFN rate**|**Avg. Applied**<br>**MFN rate**|**Avg. Applied**<br>**Preferential Rate**|
|---|---|---|---|---|---|
|**Trade not**|MFN rate > 0|21|27.9|9.9||
|**covered by an**<br>**Agreement**|Zero‐MFN rate|21|10.5|0.0||
|**Trade covered**|Zero‐MFN rate|21|13.4|0.0|0.0|
|**by an**<br>**At**|Total Liberalization|28|24.8|7.4|0.0|
|**greemen**|Partial Liberalization|3|27.7|14.1|7.7|
||No Liberalization|2|34.5|15.1|15.1|

Source: Authors’ calculation using ITC/World Bank database How are preference margins distributed? Of the 31 percent of global trade subject to positive preference margins, 16 percent is subject to preferences below 5 percent, 10.2 percent is subject to preferences between 5 and 10 percent and 5 percent is subject to preference margins over 10 percent (see Figure 13).

Figure 13: Distribution of preference margins <!-- Start of picture text --> 15.7%<br>10.2%<br>5.0%<br>PFM less 5% PFM between 5% and 10% PFM over 10%<br>Total Liberalization Partial Liberalization<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database Preferential margins vary significantly across economic sectors. Preferential liberalization efforts have been significant for sectors such as agroindustry and apparel, where initial trade‐weighted MFN rates were above 10 percent. Over 45 percent of animal and animal products, foodstuffs, and textiles preferential trade was subject to preferential margins over 10 percent (62, 47 and 46 percent, respectively). On the other hand, sectors such as machinery/electrical, transportation and raw hides, skins, leather where initial MFN rates were moderate (between 5 and 10 percent) were mainly subject to preferential margins under 5 percent (see Appendix Table A. 6).

Given the proliferation of PTAs, the advantage conferred by a preferential tariff to a given exporter does not depend only on the difference between the MFN tariff and preferential rate, but also on tariffs faced by competing suppliers from other countries in the same market. Low et al. (2009) introduced the concept of competition‐adjusted preference margins to account for 14
districts to work.<sup>16</sup>

# **5 Results**

## **5.1 Overall Enrollment**

In order to evaluate the effect of MGNREGA on enrollment, we estimate equation 1 and present the results in Table 2. Column (i) presents the basic difference-in-difference specification with school and year fixed effects. This result is robust to controlling for state specific time trends as reported in Column (ii). Both specifications control for district level controls that influenced the roll-out timing. The study controls for the Scheduled Castes and Scheduled Tribes population from the Census of India 1991, agricultural wage in 199697, and output per agricultural worker in 199093, interacted with time indicators. This accounts for differential trends in districts with the backward district status that influenced selection into the program. In addition, we control for trends in districts level total population, percentage of urban population, population growth rate, overall literacy rate, and women’s literacy rate in a similar manner.<sup>17</sup> The school-level controls include any attendance scholarships being offered at the time, uniform, books, stationery and other such subsidies offered to girls, the number of classrooms, the number of classrooms in good condition, availability of common toilets, girls toilets, drinking water facilities, electrification status, number of male teachers, and number of female teachers.

The coefficient in Columns (i) and (ii) is -2.23 and is statistically significant at the 5 percent level. Overall, enrollment in this period is increasing and thus this coefficient indicates that introduction of MGNREGA results in a smaller annual increase in school

> 16Also, anecdotal evidence suggests that beneficiaries use the employment guarantee in summer months. One concern might be that schools are already closed for vacation. However, schools in India generally close for only around 40 days and the timing varies spatially ranging from mid-May to end of June in North to mid-June to end-July in the South. Also, Imbert and Papp (2015) show that the program impacts rural wages in a general equilibrium framework. Given that, there is an incentive to substitute for adult labor year round.

> 17We observe that the effects are no different if we do not include these co-variates.

13
Figure A18: Specification Charts for Maximum Daily Temperature <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .2<br>.05 .1<br>0 0<br>* Weather Product MERRA-2* -.05-.1 * Weather Product MERRA-2* -.1-.2<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.6 .3<br>.4 .2<br>.2<br>0 .1<br>* Weather Product * -.2 * Weather Product * 0<br>MERRA-2 -.4 MERRA-2 -.1<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>* Weather Product * -.1 * Weather Product * -.1<br>MERRA-2 -.15 MERRA-2 -.15<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

66
<!-- Start of picture text --> 100.0%<br>90.0%<br>80.0%<br>70.0%<br>60.0%<br>50.0% 5 5x Ssx x& x6 ¥=<br>a a a a a a<br>40.0% S =} S s a a<br>30.0%<br>20.0%<br>10.0%<br>xx Rx xx x8 x2 zx xx xx xx<br>0.0% 2=o Lea6 Ssa6 ZSa6 eSa)—_ 2Sa6—= 2Saé eoa6 es3<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>=Micro mSmall = Medium s Large<br><!-- End of picture text -->
_30_ _Price Support at Any Price?_ Such analysis leads us to construct two gradual integration scenarios:

- (a) The FULL FINANCING scenario assumes that the nation decides to allow for transfers to the agricultural sector (collected by increasing indirect taxes) that reach the maximal total transfer level agreed within the GATT (equal to total policy costs in the feasible price supporting policy presented in the FAST PATH/OUTPUT REDUCTION experiment). Let us remind, that as previous experiments show, such a policy may be, at least initially, quite costly for urban households (less costly, however, than price supporting).

- _(b)_ The BUDGET CONSTRAINED scenario assumes that the disposable funds are equal to costs for taxpayers in the feasible price supporting policy, and therefore considerably reduced.

Both the scenarios assume the same policy mix: encouraging old farmers to retire (as in the MORE PENSIONERS experiment), promoting rural job creation (as in the JOB PROMOTION FOR FARMERS experiment), avoiding price supporting schemes until the moment of integration into CAP (as in the LAST MOMENT INTEGRATION scenario). The only difference is the scale of resources that may be used (the BUDGET CONSTRAINED scenario represents the same policy that the FULL FINANCING, realized with a considerably smaller financing).

# _5.2_ Integration scenarios: simulation experiments

The policy costs of both scenarios are presented in table 5.1. The costs reach an assumed level in the period 1995-2005. It is only in the last years of the scenario that additional costs for consumers appear because of targeting EU price level.

**Tab.5.1 EU membership - adjustment path Simulation experiments: POLICY COSTS**

||Fast path<br>reduc<br>(EU pr. by 20<br>|/output<br>tion<br>00, quotas)<br>|Gradual int<br>full fina|egration<br>|<br>ncing|Gradual in<br>budget con<br>|tegration<br>strained<br>|
|---|---|---|---|---|---|---|
|l____________________|2000j|<br>2010|2000|2010|2000|2010|
|_In $ billions(91 prices):_<br>(1) Cost for taxpayers|1.00|0.68|2.91|1.35|1.16|1.58|
|(2) Cost for consumers|1.56|1.81|0.00|0.86|0.00|0.88|
|**Totalcost**(1)+(2)<br>(excl.social security)|2.56|**2.49**|**2.91**|**2.21**|**1.16**|**2.46**|
|Increase of social<br>security expenditures|-0.06|-0.09 ||0.05|0.14|0.05|0.14|
|Producer Subsidy<br>Equivalent (PSE) in %|28.8%|23.8%|0.0%|13.7%|0.0%|14.4%|

_Source: Model POLAGR, simulation Jan.1995_
employment of women in the organized sector. ASI surveys have a similar design<sup>10</sup> and stratification as the NSSO surveys, and we construct population-level estimates of employment in the organized sector at the state-industry level similar to above. We capture separate men’s and women’s employment in the organized sector based on fields in the ASI reporting plant employment by gender. We also use the 1989 ASI to check for pre-trends in women’s employment by state in the organized sector.

Unfortunately, ASI surveys do not collect the gender of the establishment owners, and so we can only examine ownership and entrepreneurship outcomes in the unorganized sector. Even within the unorganized sector, our results below emphasize household-based establishment entry rather than independent facilities. This pattern suggests that the lack of ownership data in the ASI is not a material concern as it is unlikely that we would observe heightened women’s entrepreneurship in the organized sector.

Table 1 provides descriptive statistics on our sample. The first three columns provide aggregate establishment counts, employment, and output for each year across all of India. Since we have data on the timing of political reservation implementation for all states and territories in India, we do not restrict our sample geographically. These aggregates are broken down into organized- versus unorganized-sector contributions. For the unorganized sector, we further report women-owned establishments and then household- versus non-household-based establishments for women. Columns 4 and 5 describe the growth rate of each economic activity from 1994 to 2005, and Columns 6-8 provide shares relative to the total.

As noted earlier, the unorganized sector accounts for a large share of establishment counts and employment. Moreover, these shares are very persistent, as discussed further in Ghani et al. (2013c). Women-owned establishments in the unorganized sector account for a small share of total manufacturing activity: in 2005, they represent 36% of establishments, 19% of employment, and 1% of output. Most of this activity is household based. For example, 90% of employment in women-owned establishments was in household-based operations in 2005.

While being a small share of total activity, women-owned establishments have experienced much more rapid growth during the 1994-2005 period than the manufacturing sector

> 10 The ASI sampling frame is based on business registers rather than the Economic Census.

12
set. Estimates on the time it takes to load and unload the goods at the ports is taken from Slack e al. (2018).

Before transportation infrastructure projects are implemented, bilateral trading times between BRI economies are on average 383.1 hours, equivalent to 16 days, and they range between a minimum of 0.9 hours to a maximum of 40.6 days (see Table 1 ). To perform the econometric analysis on the impact of trading times on bilateral trade flows, the minimum time it takes to connect cities is aggregated at the country pair level. To control for the fact that the economic activity varies across cities, we compute a population weighted average of the shipping times between each pair of cities that are in a pair of countries.

_Table 1: Summary statistics of bilateral shipping times pre‐BRI_

||**Obs.**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|_Travel Time (hours)_|4,891|383.112|192.625|0.89|974.781|

At the regional level, before BRI transport projects, the East Asia & Pacific region has the highest trading times vis‐a‐vis all the other regions, particularly with countries in Central & Eastern Europe and Central & Western Asia (see Table 2). For example, it takes on average more than 30 days to ship goods between China and countries in Central & Eastern Europe such as Estonia, Poland and Croatia. Shipping times between China and Central Asian countries such as Georgia and Armenia are also high, amounting to 32.3 and 32.1 days on average.<sup>7</sup> _Table 2: Average pre‐BRI trading times between regions_

|**Avg. Travel time(days)**|**CEE**|**CWA**|**EAS**|**MEA**|**SAS**|**SSF**|
|---|---|---|---|---|---|---|
|_Central and Eastern Europe_|3.6||||||
|_Central and Western Asia_|13.1|10.8|||||
|_East Asia & Pacific_|25.9|23.4|8.1||||
|_Middle East & North Africa_|13.1|14.2|20.7|8.9|||
|_South Asia_|22.3|19.6|16.2|14.9|11.8||
|_Sub‐Saharan Africa_|19.6|22.3|21.4|14.3|17.6|4.0|
|**Regional**|13.9|16.1|19.7|14.1|17.8|18.5|

Note: Averaged over all country‐pairs in each region‐pair 7 Table A. 8 presents simple average trading times for all countries.

7