rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 8: Test for heterogeneity of imp**|**act among n**|**ew, con**|**verted and**|**non-par**|**ticipants**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Variables**|||**ATET f**|**or**||||**Differe**|**nces**|
||**New part**|**’s (1)**|**Con**|**v. (2)**|**Non-pa**<br>**(3)**|**rt’s**<br>|**(1) - (2)**|**(1) –**|**(3)**<br>|
|Female social capital (change)|1.28|***|1.50|***|1.15|***|-0.22|0.12||
|Female econ. empowerment (change)|0.71|***|0.92|***|0.54|***|-0.21|0.17||
|Female political participation||||||||||
|(change)|0.09|***|0.09|***|0.04|***|0.00|0.06|**<br>|
|Energy intake p.c. (kcal/day)|185|**|7||21||178<br>*|164|*|
|Protein intake p.c. (g/day)|7.38|***|3.62|**|2.19||3.76|5.19|**|
|Consumption p.c. (Rs/year)|1073|***|459||26||614|1048|**|
|Income p.c. (Rs/year)|-514||-217||393||-296|-907||
|Asset (Rs)|313||-289||-194||602|507||
|Numberofobservations|846||14|16|196|4||||

Significance of difference is as follows: * significant at 10%; ** significant at 5%; *** significant at 1% 24
we document formal estimations of factor misallocation and financial outcomes. We finally conclude with our main findings and directions for future work in section 6.

# **2. Literature Review**

Access to finance is critical for innovation and investment, entrepreneurship, participation in international trade, and growth in GDP and productivity.<sup>1</sup> In the recent World Bank Enterprise Survey, 17% of firms in developing countries report access to finance as the biggest obstacle to their operations. Evidence also confirms that this inequality in access to credit is even more severe for smaller firms. For instance, the World Development Report finds that only 30% of capital needs for small firms is financed from external sources, while this figure is close to 50% for large firms. This is broadly true across all regions of the world (World Bank, 2004).

Several barriers constrain access to external finance: lack of collateral to pledge against loans, banks’ unwillingness to lend to risky businesses or smaller entities, wedges created due to costly evaluation processes for loans, problematic tax payment reports, unsound business plans, and the impact of asymmetric information on lending rates. Studies reveal that small and medium enterprises (SMEs) are more financially constrained than larger firms and are less likely to have access to formal finance (e.g. Calomiris and Hubbard, 1990; Beck et al., 2006). This inequality in access is more acute in the case of developing countries (e.g. Beck et al., 2005, 2006a). Moreover, even when accessing financing through informal markets or trade credits, the cost of these loans can be substantially higher (e.g. Tang, 1995; Malesky and Taussig, 2009). For instance, the top five banks serving SMEs in non-OECD countries reach only 20% of formal micro enterprises and SMEs, and this figure is as low as 5% in Sub-Saharan Africa. In emerging

> 1 Representative papers for innovation and investment include Hubbard, 1998; Carpenter et al., 1998; Guariglia, 2000; Benito, 2005; Kerr and Nanda, 2015; for entrepreneurship, Klapper et al., 2006; Alfaro and Charlton, 2006; Aghion et al., 2007; Midrigan and Xu, 2014; for participation in international trade, Greenaway et al., 2007; Berman and Hericourt, 2010; Chaney, 2013; Manova, 2006; and for growth in GDP and productivity, Beck et al., 2000; Arcand et al., 2012; Levine, 2005; Beck, 2008; Demirguc-Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Beck et al., 2007. Important theoretical literature evaluates the interplay between financial constraints and entrepreneurship (e.g. Stiglitz and Weiss, 1981; Evans and Jovanovic, 1989; Lloyd-Ellis and Bernhardt, 2000) and firm growth (e.g. Albuquerque and Hopenhayn, 2002).

6
|3-member household|0.37***|0.45***|
|---|---|---|
||(45.73)|(46.13)|
|4-member household|0.24***|0.29***|
||(38.64)|(38.89)|
|5-member household|0.12***|0.14***|
||(22.63)|(20.35)|
|Multigeneration family|-0.00|0.01|
||(-0.94)|(1.29)|
|Extended family|0.03***|0.06***|
||(3.90)|(7.59)|
|Share of 0 to 18 years old members in family|-0.18***|-0.22***|
||(-21.21)|(-20.27)|
|Share of 61+ years old members in family|-0.04*|-0.03|
||(-2.34)|(-1.50)|
|Female headed households|-0.04***|-0.04***|
||(-6.12)|(-5.24)|
|Log (age of household head)|0.03***|-0.02|
||(3.34)|(-1.71)|
|Any member with higher than middle to high school level of education|0.02***<br>(3.41)|0.03**<br>(2.98)|
|Share of members with middle to high school level of education|0.12***|0.13***|
||(12.49)|(10.95)|
|Any member with diploma to post graduate level of education|0.05***|0.05***|
||(7.87)|(8.10)|
|Muslim household|0.03***|-0.02*|
||(5.18)|(-2.29)|
|Christian household|0.09***|0.03*|
||(5.54)|(2.05)|
|Sikh household|0.14***|0.03|
||(9.41)|(1.50)|
|Jain household|0.07|-0.01|
||(1.03)|(-0.26)|
|Buddhist household|-0.03|0.04|
||(-1.07)|(1.75)|
|Zoroastrian and other religions|-0.07|0.07|
||(-1.40)|(1.00)|

29
35 Table 2 (continued): Contributions to total agricultural NRA and CTE from different policy instruments,<sup>a</sup> by region, 1981-84 and 2000–04 (percent)

|(b) Consumer Tax Equivalents<sup>c</sup>||1981-84|||2000-04||
|---|---|---|---|---|---|---|
||All<br>developing<br>countries|High-<br>income<br>countries|All focus<br>countries|All<br>developing<br>countries|High-<br>income<br>countries|All focus<br>countries|
|Border measures|||||||
|Import tax equivalent|10|46|24|10|32|19|
|Export subsidies|1|2|1|1|1|2|
|Export tax equivalent|-22|0|-13|-2|0|-2|
|Import subsidy equivalent|-3|0|-2|-1|0|-1|
|**_ALL BORDER MEASURES_**|**-14**|**48**|**10**|**8**|**33**|**18**|
|Domestic measures|||||||
|Consumption subsidies|-1|0|-1|-1|-6|-3|
|Consumption taxes|0|0|0|1|0|1|
|**_ALL DOMESTIC CONSUMPTION MEASURES_**|**-1**|**0**|**-1**|**0**|**-6**|**-2**|
|**TOTAL CTE (covered farm products only)**|**-15**|**48**|**9**|**8**|**27**|**16**|
|**_Consumer tax equivalent, in real 2000 US$ billion_**|**_-67_**|**_146_**|**_73_**|**_34_**|**_79_**|**_125_**|

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008). a In the absence of data, it is assumed the share of input tax/subsidy, domestic production tax/subsidy and border tax/subsidy payments for noncovered farm products are the same as those for covered farm products. The first period begins in 1981 because that was the first year for which estimates for China are available. b All entries have been generated by dividing the producer subsidy equivalent of all (including NPS and ‘decoupled’) measures by the total agricultural sector’s gross production valued at undistorted prices. c All entries have been generated by dividing the consumer tax equivalent of all measures by the total consumption value (at the farmgate level, valued at undistorted prices).
decade, a country with an agency awarded a quality score of 60 percent (on a 0-100 percent scale, as assessed by the Global Investment Promotion Benchmarking initiative of the World Bank Group) received, on average, 25 percent higher FDI inflows than did a country whose agency obtained a score of 45 percent (controlling for country-specific characteristics).

Once FDI enters a country, governments may wish to maximize the productivity benefits associated with FDI by assisting local firms in becoming suppliers to foreign affiliates. Econometric evidence from the Czech Republic suggests that less credit constrained firms are more likely to become suppliers to foreign affiliates (Javorcik and Spatareanu 2009b). This finding is consistent with the survey evidence from the same country indicating that foreign firms often require their prospective suppliers to make improvements prior to signing a contract (Javorcik 2008). It is also in line with the cross-country evidence suggesting that FDI stimulates economic growth in host countries with well-developed financial markets (Alfaro et al. 2007). Thus, authorities may wish to consider extending subsidized credit to prospective suppliers of foreign affiliates. Another possible policy intervention involves establishing supplier development programs that bring together local firms and foreign affiliates to help local firms meet the expectations of foreign customers.

30
that there is an on account payment to farmers every 2 in 10 years with average on account payment of 5% of total sum insured. Finally, suppose that in one quarter of those years there is no yield-triggered claim payable.

Figure 2: Motivation for on account load Claims mass from yield trigger Claims mass from on account trigger In this hypothetical example, the on account payment would increase the pure premium rate from 7.50% to 7.75% of total sum insured.

The actuary may judge that additional benefits from the on account payment do not merit an adjustment to be made to rates. Any such judgment should be noted and clearly documented.

# _Pure Premium Rate_

# <u>Description of step</u>

The pure premium rate at the crop/district level is then taken to be: For cotton in Gujarat, assuming that the _Rate for other benefits_ is zero, the pure premium rate calculation for each district are given in Table 15.

Table 15. Cotton in Gujarat: pure premium rates

|**District**|**Base Pure Rate**|**Capped Excess**<br>**Load**|**Assumed rate for**<br>**other benefits**|**Pure Premium Rate**<br>|
|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|
|GJ1|4.2%|0.6%|0.0%|4.8%|
|GJ2|8.0%|0.6%|0.0%|8.6%|
|GJ3|5.6%|0.6%|0.0%|6.1%|
|GJ4|4.3%|0.6%|0.0%|4.9%|
|GJ5|3.3%|0.6%|0.0%|3.9%|
|GJ6|9.4%|0.6%|0.0%|10.0%|
|GJ7|3.3%|0.6%|0.0%|3.9%|
|GJ8|12.4%|0.6%|0.0%|12.9%|
|GJ9|7.2%|0.6%|0.0%|7.8%|
|GJ10|5.7%|0.6%|0.0%|6.3%|
|GJ11|3.9%|0.6%|0.0%|4.5%|
|GJ12|16.9%|0.6%|0.0%|17.5%|
|GJ13|6.1%|0.6%|0.0%|6.7%|
|GJ14|4.6%|0.6%|0.0%|5.1%|

24
<!-- Start of picture text --> 100%<br>90%<br>80%<br>cB]<br>= 70%<br>cB]<br>3 60%<br>oc<br>5 50%<br>= 40%<br>1)<br>o 30%<br>oO<br>20%<br>10%<br>0% <<br>0% 20% 40% 60% 80% 100%<br>Percent of Consumption<br><!-- End of picture text -->
# Table S9. Relationship Between Inequality and Human Capital (Additional Controls)

|Dependent Variable is:|Share of Pop|ulation Tertiary Education||
|---|---|---|---|
||(1)|(2)|(3)|
|Inequality Variable is:|Gini<br>Brueckner et al. (2015)|Net Gini<br>Solt (2015)|Market Gini<br>Solt (2015)|
|Inequalityit|0.36***<br>(0.12)|0.31***<br>(0.15)|0.35***<br>(0.18)|
|Inequalityit|-0.05**|-0.04**|-0.04*|
|*ln(yit-1)|(0.02)|(0.02)|(0.02)|
|ln(yit-1)|0.01**<br>(0.01)|0.02***<br>(0.01)|0.02*<br>(0.01)|
|OPSit|-0.01<br>(0.11)|-0.04<br>(0.06)|-0.03<br>(0.05)|
|TWWIit|0.01<br>(0.01)|0.00<br>(0.01)|-0.00<br>(0.01)|
|Investment/GDPit|0.04<br>(0.03)|0.05<br>(0.03)|0.04**<br>(0.02)|
|PopulationGrowthit|-0.06<br>(0.06)|-0.05<br>(0.06)|-0.05<br>(0.05)|
|Relative Price of Investmentit|0.01**<br>(0.00)|0.01<br>(0.01)|0.01*<br>(0.01)|
|Kleibergen Paap F-Statistic|571|299|113|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|440|543|543|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

53
Compensation of losers from reform also played a role in the reform’s apparent success. While fiscal constraints often make it challenging for a government to provide direct compensation, the November 2012 reforms were accompanied by a generous cash transfer that was designed to fully compensate the bottom 70 percent of Jordanians from the losses due to the removal of subsidies. The cash transfer in fact appeared to over-compensate a large swath of the population (Araar et al. 2013). The speed with which the transfer took place is also a factor in the reform’s apparent success. While the petroleum price hikes were announced on November 12, within the next few weeks a large number of people started receiving the cash compensation.

In enacting future reforms the government can learn from its own experience. There have been concerns regarding the targeting efficiency of the cash program accompanying the 2012 subsidy reforms (see, e.g., Araar et al. 2013). The government itself has taken measures to improve the targeting, specifically by setting up a National Unified Registry (NUR) database to better target beneficiaries for future cash compensation programs. Nevertheless, tackling electricity subsidy reform appears to be daunting for the government because of its sheer scale.

# **6. Conclusions**

This paper examined the distributional and fiscal implications of petroleum and electricity subsidy reforms. Both subsidies are pro-rich in nature, and in absolute monetary terms, richer households benefit more than poorer households as their consumption levels are higher. Thus, these universal subsidies are costly and inefficient as a majority portion of the total subsidies ‘leak’ to the non-poor households, and significant amounts actually leak to the top quintile households. However, the analysis also suggests that the poorer segments of the population benefit quite substantially from the subsidies and removal of subsidies would impose economic hardship on these groups.

Nevertheless, as the government wishes to strike a balance between protecting its population from price increases and ensuring fiscal prudence, a move away from the universal subsidies system appears imperative. This move, however, would require a considered analysis of both technical and political economy considerations. A generous cash transfer can be put in place to help build broad based public support for reforming universal subsidies, but the government needs to target these transfers well through developing a sound social protection system.

Finally, it is important to note that while this paper has presented several findings, the scope of analysis was necessarily constrained by time and data availability. The focus was limited to microanalysis of household level impacts. A more comprehensive analysis would involve broader sectors of the economy (such as non-household users of petroleum products and electricity), as well as involve a political economy and stakeholder analysis to identify who would gain and who would lose from reform and how.

20
# Table 4c. Multinomial logistic regression of ECED duration

||**Duration i**<br>**(base: 1 t**|**n playgroup**<br>**o 2 years)**|**Duration**<br>**(base:**|**in kindergarten**<br>**1 to 2 years)**|
|---|---|---|---|---|
||<1 year|≥ 2 years|<1 year|≥ 2 years|
|_Child characteristics_|||||
|Age|1.197***|0.801***|0.940**|0.890***|
||(0.0383)|(0.0333)|(0.0285)|(0.0275)|
|Girl|0.898**|1.117*|0.897**|1.049|
||(0.0415)|(0.0738)|(0.0393)|(0.0545)|
|Stunted|0.795***|1.182*|0.795***|<br>1.015|
||(0.0585)|(0.104)|(0.0567)|(0.0741)|
|_Family characteristics_|||||
|Wealth Z-score|0.839***|0.823***|0.576***|<br>0.985|
||(0.0379)|(0.0441)|(0.0318)|(0.0568)|
|Mother's education (years)|0.959***|1.031**|0.877***|<br>1.054***|
||(0.0117)|(0.0158)|(0.0124)|(0.0152)|
|Parenting practices|1.007|1.108***|0.990|1.139***|
||(0.0299)|(0.0437)|(0.0288)|(0.0340)|
|_ECED quality_(base: Low quality)|||||
|Medium quality|1.042|1.234|1.157|1.222|
||(0.157)|(0.181)|(0.214)|(0.213)|
|High quality|0.753**|1.194|0.748*|1.145|
||(0.108)|(0.164)|(0.129)|(0.192)|
|_ECED fees_(base: No fees)|||||
|≤ 10,000 IDR|0.596***|0.853|0.713|0.827|
||(0.0963)|(0.126)|(0.166)|(0.144)|
|> 10,000 IDR|0.649***|0.657***|0.537***|<br>0.715*|
||(0.109)|(0.105)|(0.126)|(0.124)|
|Constant|2.628***|2.865***|14.69***|<br>1.388|
||(0.831)|(1.003)|(5.110)|(0.451)|
|Observations|12,|710||12,710|

Note: Exponentiated coefficients. Standard errors clustered at the village level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 29
2

# **_Introduction_**

Many countries in Africa suffer high rates of under-employment or low-productivity employment. In addition, because of demographic factors, many countries anticipate that large numbers of people will enter the workforce in the near future. Therefore, there is an urgent need to understand the drivers of job creation in the private sector. In particular, what drives job creation? And what are the constraints that prevent firms from hiring more workers? One explanation is that market imperfections and weak business environments lower the productivity of firms and prevent resources from being allocated toward better- performing firms, thereby reducing the overall potential for job creation.

We use the existing literature on firm growth to construct a simple model.<sup>2</sup> We draw from learning models of firm growth, which indicate that there is a positive correlation between firm growth and productivity. These models emphasize the role of the entrepreneur (or manager) in the learning process. Early learning models describe managerial capacity as fixed or innate (Jovanovic, 1982), while subsequent theoretical models allow for human capital formation to impact managerial efficiency and firm growth (Pakes and Ericson, 1989). A firm “learns” about its productivity over time-efficient firms invest and expand while less productive ones stay small, shrink or exit. This class of models also predicts that firm age and size are both negatively correlated with firm growth: as firms grow older or become larger, their rate of growth slows. After controlling for age, larger firms grow more slowly because they are already at a higher level of productivity. Consequently, they have limited scope to increase productivity further (Evans and Leighton, (1989); Bates (1990); McPherson (1996); Sutton (1997)).

A recent study by Hsieh and Klenow (2012) is particularly relevant. They show that in the U.S., the average 40 year-old plant employs almost eight times as many workers as a typical plant five years or younger. In contrast, Indian plants grow very little in terms of employment or output. Mexico is in the middle--the average 40 year-old Mexican plant

> 2 Models of firm growth can be separated into two groups--those that rely on “stochastic” models of growth based on Gibrat’s Law, and those that rely on “learning” models.<sup>2</sup> Stochastic models describe firm growth as drawn from a distribution.  “Lucky” firms are those firms that repeatedly draw high rates of growth over time.  In these models of firm evolution, firm size and firm growth are independent of each other. However in practice various models may include both “stochastic” features as well as “learning” features such as in Jovanovic (1982).
The relative polarization indices, shown in Table 3, capture these changes well. The MRP index is always positive and statistically significant ( _p_ -value = 0.00). Decomposing the MRP into the contributions to distributional change made by the segments of the distribution above and below the median, it appears that “downgrading” dominated “upgrading” in the polarization upswing over the course of the first two sub-periods: the value of the lower relative polarization index (LRP) is indeed greater than that of the upper relative polarization index (URP) – 0.26 vs. 0.17 and 0.27 vs. 0.11, respectively – which is consistent with the visual impression from the shape shifts above. The values of the indices in the 2005-12 period denote instead a nearly perfectly symmetric polarization in each tail.

In sum, while often less noticeable in any single period when compared to the large swings in median household consumption expenditure, the growth in polarization was a major contributor to the overall changes in the Ghanaian consumption distribution since the early 1990s. Behind these shape shifts, however, was probably a set of key drivers. The following section relies therefore on GLSS data to examine how the changes above have been associated with consumption growth and, thereby, identifies the main drivers behind the polarization upsurge.

# **3.3 The drivers of growing polarization in Ghanaian household consumption**

The presentation of polarization results over the three sub-periods requires a considerable amount of space. For the sake of brevity, we chose to present only part of the results and made an effort to present the main findings in an abridged format. For example, we decided not to comment on the econometric results of the unconditional quantile regression, and to place the decomposition tables in the appendix, and regarding the polarization decomposition results, to focus our attention only on the top percentiles results (top two and bottom two).

Overall this is not a big limitation since, as shown in panels (a) and (b) of Figure 5, the inter-quantile analysis has detected a significant variation in the percentiles’ cut-offs (between deciles inequality, measured by interquartile ratios) primarily among these deciles and a very limited one among the rest of the distribution. Furthermore, the other component of polarization, the so-called “identification” (measured by deciles’ coefficient of variation, CV) tended to be more accentuated in these deciles rather in the central ones. Looking at sub-periods, it clearly emerges that, in 1991-98 and 2005-12, the between component was compensated by a high identification component, thus neutralizing the modification of inequality; differently, in the sub-period 2005-12 it appears both a sustained growth of between component and an important reduction of identification component (growth of CV) especially for what concern the 10-th and 90-th deciles.

17
Treatment households and 19 percent of Control households use wood. These groups are also similar in terms of the distances from the nearest facilities – primary school, secondary school, primary health-care center, paved road, bus stop, hat/bazaar, NGO office and bank. There is no significant difference in these variables among the two groups. It appears unlikely for the time trends to be different between these two groups. Table 1B also shows there was no significant difference in total, food and non-food consumption expenditures<sup>20</sup> of these households at baseline. However, the consumption patterns of these households in November 2012 have significant differences. These differences also hold when we disaggregate the sample by poverty status. These figures are presented in Table 4.

**Table 4: Monthly Consumption expenditure at endline (in ‘000BDT)**

||Full S<br>T1|ample<br>C1|p-value|Po<br>T1|or<br>C1|p-value|Non-<br>T1|poor<br>C1|p-value|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)||(3)|(4)||(5)|(6)||
|Food<sup>a</sup>(A)|5.83|4.84|0.30|5.32|4.59|0.32|6.64|5.33|0.43|
|Non-food<sup>b</sup>(B)|3.15|2.18|0.02|2.67|2.09|0.03|3.92|2.33|0.09|
|Non-food (monthly)<sup>a</sup>|0.90|0.68|0.02|0.95|0.65|0.02|0.81|0.74|0.56|
|Total (A+B)|8.98|7.02|0.08|7.99|6.69|0.12|10.56|7.66|0.15|

Notes: T1=Treatment group, C1= Control group. aFor the month of November 2012. bnon-food annual expenditure While there is no significant difference in food consumption between these two groups of households, the differences in total non-food consumption expenditure and non-food expenditure incurred monthly are statistically significant at 5 percent level. These are presented in columns 1 and 2 of Table 4. Columns 3 and 4 present the same results for households who are poor and columns 5 and 6 present those for the non-poor households.<sup>21</sup> Since EGPP essentially offers wage income to households during lean seasons, we expect to see some increase in their consumption expenditures relative to similar non-EGPP households. This is consistent with what we see in Table 4. Since consumption data was calculated in the

> 20 Food expenditure was collected for last seven days, non-food monthly expenditure was collected for last one month and non-food annual expenditure were collected for last one year. All of these items were then converted to a 30-day expenditure to generate monthly consumption expenditure.

> 21 Households with consumption expenditure below or above the 2010 national poverty line are defined as poor and non-poor households respectively.

20