rafmacalaba/datause-encoder-data huggingface dataset:
C";.: and nature of key relationships, data analysis may indicate if one or two observations exert an undue influence on the results. Any observation that has an X value far removed from the mean for that series is a point of leverase, with the potential to exert a large influence on the result. An influential point is defined as one whose exclusion would result in a substantially different regression line. Under such circumstances it may be more appropriate to use robust regression, which will reduce the weight given to the influential point.

The term robust regression is used both to apply to this specific technique and more generally. In tne general sense, a robust result is one which is not dependent upon the sample (and therefore not the result of influential points) but also one that holds up in the face of different (but appropriate) specifications.<sup>6</sup> These points may be illustrated by the well known, but not robust result that aid displaces domestic savings.

Griffin (1970, 1971) claimed that aid displaces savings<sup>7</sup> and backed up his argument by reporting the results from regressions of the savings rate on the aid ratio. He found statistically significant slope coefficients from cross-section data of -0.67, -0.73 and -0.82.!

Using more recent cross section data for 66 developing countries I obtained a coefficient of -0.87 (White, 1992b and 1992c). But the regression of the savings rate on the aid ratio is clearly misspecified: other factors play a role in determining savings. Indeed, from Griffin's own algebraic presentation of his model (contained in the 1971 paper) it is clear that the estimated equation should include the inverse of income (1/Y), which he omits. Re-estimation of the equation with my data set including this variable changes the coefficient from -0.87 to -0.40!9 A graphical illustration of this change in impact is shown in Figure 2. 1. The upper graph shows the simple scatter of the savings and aid rates and the fitted simple regression line. The lower box shows the partial scatter plot - that is one the effect of 1JY has been removed from both S/Y and ANY. The fitted line in this plot (which has the same slope as the coefficient on A/Y from the multiple regression of SlY on A/Y and 1IY) is much less steep than that from the simple plot.

16
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Next, we examine the heterogeneity in the impact of the crisis. This builds on the literature on the distributional impacts of financial crises.<sup>12</sup> This literature, which largely relies on household survey data, has mixed findings on whether such crises have affected the poor more severely than the middle class, or rural households more than urban households. Our study can add to this literature in two ways. The first contribution is methodological: most existing studies are based on a before-after comparison which, unlike our differences-in-differences approach, does not account for other aggregate shocks. The second contribution is to highlight specific channels of distributional impacts, namely heterogeneity in job losses by worker attributes.<sup>13</sup> Table 5 investigates which type of workers were more severely affected by firms’ credit tightening conditions. The dependent variables are the share of workers in different job categories; hence the � ’s in these regressions capture the impact of the credit crunch on the composition of employment. Overall, the results suggest that unskilled workers with precarious working conditions suffered greater employment losses.

Column (1) shows that the share of employees in temporary contracts decreased an additional 0.6 percentage point in financially dependent sectors when access to credit declined by about 20 percentage points, a sizable impact given that the baseline share of temporary workers in high FD sectors is around 11%. This heterogeneity is to be expected, given that the costs of laying off temporary workers when their contracts end is lower than that of laying off permanent workers.

Column (3) shows that the share of workers with a second job increased slightly more among workers whose main occupation is in sectors highly dependent on finance. This is consistent with second12 See Fallon and Lucas (2002), Brown (2013), Lustig (2000), McKenzie (2006), McKenzie (2004), Cunnigham and Maloney (2000), Goh et al. (2005), Robilliard et al. (2001), Friedman and Levinsohn (2002), Lokshin and Ravallion (2000) and Lopez-Acevedo and Salinas (2000).

> 13 It is important to note that the overall distributional impact of such crisis depends on other factors as well, such as changes in non-labor income and other ways in which households are able to respond to a job loss.

12
|Table 6. Poverty Pr<br>Februa|ofile by Educ<br>ry 1996 and<br>Febru|ation Level of H<br>February 1999 (<br>ary 1996|ousehold H<br>%)<br>Febru|ead,<br>ary 1999|
|---|---|---|---|---|
|Education level of<br>household head|Poverty<br>incidence|Contribution<br>to total poor|Poverty<br>incidence|Contribution<br>to total poor|
|Not completed primary and<br>illiterate|31.23|27.67|47.51|19.84|
|Not completed primary but<br>literate|21.63|35.09|36.68|31.82|
|Completed primary|15.03|30.15|29.66|35.34|
|Completed junior secondary|7.04|4.80|16.85|7.61|
|Completed senior secondary|2.44|2.19|8.59|5.05|
|Completed tertiary|0.41|0.11|1.98|0.34|

The table shows that even just removing illiteracy has a large impact on reducing poverty incidence, i.e. by almost 10 percentage points. The table also shows that by the level of junior secondary education, the poverty incidence is already lower than the national average. This implies that poverty incidence at the national level is very much affected by high poverty incidence among those who have only primary education or less. At the tertiary level, the poverty incidence is indeed very small, less than 2 percent in February 1999. Before the crisis, it was almost non-existent at 0.4 percent.

All of this points to the fact that improving the education level of the people in general is one of the best long-run strategies in reducing poverty. However, this is not as straightforward as it seems. There is an endogeneity between welfare and education. So it is not only education level affects welfare, but also the initial level of welfare affects educational achievement. Hence, if education is left entirely as a 22
outcomes. Nine variables are indicated for inclusion on the basis of their PIP scores. Of these, three are robustly significant (PIP > 16/19 _and_ |unconditional mixed t-statistics| > 2).

||**TABLE9: **|**AGGREGATE**|**EXPENDITU**|**REDEVIAT**|**ION, ****_e_**|||
|---|---|---|---|---|---|---|---|
|||**Encompa**|**ssing**|**Bayesian O**|**utcomes**|**Prelimi**|**nary**|
||**Hypothesized**<br>**Si**|**Equatio**|**n 10a**|**k/K=1**|**6/19**|**Equation**|**10b**|
||**gns**|Coeff.|Prob. 2/|PIP|Mixed-t 1/|Coeff.|Prob.|
||1|2|3|4|5|6|7|
|_Constant_|0|-13.193|0.509|..|..|-1.237|0.877|
|_PCT_INT_<br>|+|0.211|**0.040**|**0.918**|1.907|0.119|0.139|
|_Z_PCT_INT*Rpos_<br>|+|0.595|**0.032**|**0.908**|1.825|0.389|**0.040**|
|**Common Pool**<br>||||||||
|_H01 FCTN_ETHN_|+|-0.074|0.127|0.614|-0.841|..|**..**|
|_H01 FCTN_LANG_|+|0.008|0.858|..|..|..|**..**|
|_H01 FCTN_RELIG_|+|0.060|0.134|0.579|0.776|..|**..**|
|_H02 DRO_|+|0.495|**0.043**|**0.866**|1.590|0.429|**0.055**|
|_H02 DRY_|+|-0.184|**0.001**|**0.951**|**-2.249**|-0.154|**0.003**|
|_H03 PCT_CSE_|+|-0.045|0.604|0.340|-0.203|..|**..**|
|**Political Institutions**||||||||
|_H04 POLITY_|−|0.634|**0.060**|**0.924**|1.868|0.276|**0.032**|
|_H05 MPP_|−|-0.026|0.739|..|..|..|..|
|_H06 SYSTEM_|−|4.639|0.150|0.646|0.902|..|..|
|_H07 PR_|−|-3.549|0.145|0.776|-1.247|..|..|
|**Capacity for PFM**||||||||
|<br>_H08 PEFA_|−|-1.545|0.616|0.510|-0.646|..|..|
|_H09 ln(POP)_|−|1.347|**0.053**|**0.940**|**2.032**|0.292|0.461|
|_H10 ln(GNIPC)_|−|-0.023|0.990|..|..|..|..|
|_H11 ODAGDP_|−|-9.490|0.509|0.483|-0.591|..|..|
|**Interactive Terms**||||||||
|_H03 R∙Z(PCT_CSE)_|+|-0.808|**0.042**|**0.985**|**-2.816**|-0.525|**0.023**|
|_H04 R∙Z(POLITY)_|+|0.348|0.415|0.489|0.587|..|**..**|
|_H05 R∙Z(MPP)_|+|-0.065|0.901|..|..|..|**..**|
|_H06 R∙Z(SYSTEM)_|+|0.003|0.985|..|..|..|**..**|
|_H07 R∙Z(PR)_|+|-0.127|0.534|0.350|-0.139|..|**..**|
|_H08 R∙Z(PEFA)_|+|0.829|0.184|0.763|1.192|..|**..**|
|_H09 R∙Z(lnPOP)_|+|-0.802|0.107|**0.858**|-1.513|-0.560|**0.011**|
|_H10 R∙Z(lnGNIPC)_|+|0.571|0.401|**0.860**|1.576|1.117|**0.000**|
|_H11 R∙Z(ODAGDP)_|+|0.055|0.875|..|..|..|..|
|_BATTLE_|+|0.143|0.777|..|..|..|..|
|_Ghana 2008_<br>||||||22.667|**0.005**|
|_India 2007/08_<br>||||||31.405|**0.000**|
|_Pakistan 2007/08_||||||21.691|**0.007**|
|_Sierra Leone 2007_||||||25.172|**0.001 **|
|Observations||144||144||144||
|R-squared||0.544||||0.572||
|Adjusted R-squared||0.443||||0.529||
|S.E. of regression||8.182||||7.523||
|Sum squared resid.||7,833||||7,358||
|Log likelihood||-492||||-488||
|F-statistic||5.374|**0.000**|||13.357|**0.000**|
|Normal error term 3/||0.750||||0.487||
|Heteroskedasticity 4/||0.013||||0.325||
|Omitted variables 5/||0.008||||0.246||

1. Unconditional inclusion.

2. White heteroskedasticity-consistent standard errors & covariance.

3. Jarque-Bera probability.

4. White n∙R<sup>2</sup> probability (without cross terms).

5. Ramsey RESET F-statistic probability (1 term).

32
lockdown measures were imposed due to Covid-19 and therefore cannot speak to changes in poverty headcounts in the aftermath of the pandemic.

The rest of the paper proceeds as follows. Section 2 provides a detailed overview of the known differences between the survey instrument and sample design of CPHS and NSS and sets up both datasets to achieve closest possible comparability based on this knowledge. Section 3 examines the results of the reweighting exercise while Section 4 introduces our two approaches to estimating NSS-consistent measures of consumption. Section 5 reports headline poverty and inequality estimates and reports the results from robustness checks. Section 6 corroborates our findings using a range of independent data sources. We conclude in Section 7.

# **2 Data**

## **2.1 Consumer Pyramid Household Survey (CPHS)**

The CPHS is a stratified multi-stage survey with towns and villages from the 2011 population census as its primary sampling units (PSU) and households as its ultimate sample unit (USU). CPHS’ first stage stratum is a spatial unit called Homogeneous Region (HR), which is a set of contiguous districts with similar agroclimatic conditions, urbanization levels, female literacy rates and number of households. The latest round of CPHS consists of 102 HRs spread over 28 states and 514 districts in India (out of total of 36 states and 718 districts in India), with each HR further divided into rural and urban sub-strata. The latest round of CPHS’ rural sample comprises 63,430 households selected randomly from 3,965 villages and 110,975 households from 7,920 urban census enumeration blocks (CEBs).

The CPHS’ consumption module contains monthly household expenses for about 115 unique items. A quarter of these relate to food, while others include expenditures on clothing, footwear, cosmetics, toiletries, appliances, restaurants, utilities, transport, communication, education, health, monthly loan repayments and other miscellaneous items. CPHS interviews households three-times a year, at four-month intervals referred to as waves. Households report item-wise consumption for each of these four months. Household interviews are scheduled such that survey estimates are nationally representative for each month of the CPHS wave. In addition to consumption expenditures, CPHS collects data on demographic information, incomes, employment status of members, asset ownership and consumer sentiments of the household. The CPHS does not conduct a listing exercise. Instead, it uses household and population growth projections from Registrar General and Census Commissioner of India to calculate household and 5
_Fareed M. A. Hassan, ECSPE_ 8 percent for the third one. This means that the first positive tax bracket was rather narrow in relation to the income group it covered.

The second positive income tax bracket of 24 percent covered 78 percent of the fourth income decile, all of the fifth through ninth deciles, and 65 percent of the highest income group in 1992. This tax bracket had the broadest base, covering nearly two-thirds of the total number of households. Potential revenue as a share of total personal incoime was in the range of 4.7 to 16.5 percent, depending on the niumber of households subject to tax in each of the income decile in question.

The next three tax brackets of the 1992 schedule given in Table 2 covered a small part of the households. The 28 percent rate was applicable to only 73 households in the survey or one third of the top income decile. Also, tax rates of 32 and 36 percent covered only three households in the 1992 survey. These brackets were extremely narrow relative to the tax base which they were designed to tap. Perhaps even more striking is the fact that the maximum 40 percent tax rate applied to incomes of over 240, 000 Lev exceeded the maximum income reported in the 1992 household budget survey, indicating that this tax bracket was applied to a nonexistent base! Figure 1 shows the misrnatch between the 1992 income distribution and bracket/rate tax structure.

Total potential personal income tax revenue (TPPITR) can be expressed as follows: TPPITR **=j Ei** (Xij t1 + aj), (2) where: j = 1, 2, 3 ... , 7 (i.e., seven tax rates as given in Table 2); i = 1, 2, 3 ........ 2202 households included in the survey; and Xij tj, and a;j are as defined in equation 2.

The application of equation 2 to household income as recorded in the 1992 survey yielded potential revenue amounting to Lev 4.1 million with an average income tax payment of Lev 1850 per capita, or a potential average income tax rate of 8.3 percent. This amount was substantially higher than the actual 1992 income tax payment of Lev 1.5 million, giving an
food markets. Public employment programs for those needy who are able to work have been successful in cases where it has been possible to keep the reward for work low enough to be unattractive to those with other employment alternatives.<sup>19</sup> The proposed modest reserve could be crucial for tackling local humanitarian crises. But its impact would be negligible on the global market volatility that is the focus of this paper.

# **National strategic reserves**

One reason that grain prices have not declined further from recent peaks is that many countries are rebuilding or expanding their grain reserves in reaction to the export bans and export taxes observed recently.<sup>20</sup> Such actions appear almost inevitable at the national level given the inability of exporters to commit to being reliable suppliers in emergencies. According to a recent report, the United Arab Emirates, presumably capable of offering a logical food-for-oil deal, were unable to obtain blanket assurances from Pakistan that grain produced from the Emirates’ planned agricultural projects in that country would not be subject to export controls.<sup>21</sup> Futures contracts eliminate counterparty risk but can expose countries to location-basis risk and sudden large margin calls. Further, a futures market might be shut down or exports banned in an emergency; both actions were taken in India in 2007 at a time when the situation in world grain markets fell far short of emergency conditions.

A key question is how large the reserve should be. The answer must depend on the facts of each case, including the diversity of food supplies, dependability of traditional suppliers, and cost of the program. Such stocks tie up capital for the substantial intervals between releases and can be expensive to maintain, especially in humid tropical countries.<sup>22</sup> Their efficient management also uses scarce human capital and temptations for corruption can easily arise.

If the public stock’s management can commit to hold the stocks for release only in circumstances in which private stocks would be exhausted, the disincentives to storage by the private market can be reduced. For a landlocked country, this type of emergency situation might be the second year of a severe drought. For an importer, it might be the second year of a global shortage. In

> 19 See, for example, Subbarao (2003).

> 20 Recent reports indicate that Saudi Arabia, Egypt, Iran, China, Russia, Jordan, Mozambique, Morocco, and Malawi are among the countries placing grain in national reserves. (Marc Sadler, personal communication, April 30, 2009.)

> 21 Oxford Analytica, _Global Strategic Analysis_ , April 20, 2009.

> 22 Stocks would be “rolled over” with no net release as frequently as needed to maintain quality.

– 26 –
indicator examines laws that constrain a woman’s freedom of movement. The _Workplace_ indicator evaluates laws that may constrain a woman’s ability to work. The _Pay_ indicator assesses legislation that may affect a woman’s pay. The _Marriage_ indicator looks at how married men and woman are treated under the law. The _Parenthood_ indicator assesses the legislation that may impact a woman’s ability to partake in the workforce after having a child. The _Entrepreneurship_ indicator examines how legislation may impact a woman’s ability to start and run a business. The _Assets_ indicator considers how the law may constrain a woman’s ability to own and manage assets. Finally, the _Pension_ indicator examines how the law may affect the size of a woman’s pension upon her retirement. Each indicator is scaled from 0 to 100, where 100 is a perfect score indicating no legal gender discrimination. The aggregate WBL index is an unweighted average of the underlying eight indicators. The WBL data cover the period 1970–2020; according to the most recent data, the global average score is 76.1, indicating that women have, on average, just over three-quarters the rights of men in the areas covered by the index.<sup>8</sup>

# <u>Other macro-level variables</u>

In our regressions examining the relationship between legal gender equality and the probability that a firm began informally, we control for income level, rule of law and religion. Income level is measured as real per capita gross domestic product (GDP), from the World Bank’s World Development Indicators database.<sup>9</sup> Our measure of the rule of law is from the _Worldwide Governance Indicators_ (WGI).<sup>10</sup> This variable captures the perceptions of citizens’ confidence in and adherence to rules within their society; it captures the quality of contract enforcement, property rights, policing, the courts, and the likelihood of crime and violence (Kaufmann, Kraay and Mastruzzi, 2010).

Data on religion are from the Pew Research Center (2015). For each country, the data show the percentage of the population that belongs to each of the following religious groups: Islam, Hinduism, Christianity, Judaism, Buddhism, folk religions, other religions, and unaffiliated. The Pew data set projects global religious trends from 2010 to 2050; we use the 2010 values.

# _2.2 Descriptive statistics_

The data displayed in Table 1 shows that, for the average firm in our sample, the WBL index score in the year the firm began operations was 65.9, implying that women had just under two-thirds of the rights of

> 8 A more detailed description of the index is available at https://wbl.worldbank.org/en/methodology and in Hyland, Djankov and Goldberg (2020).

> 9 https://datatopics.worldbank.org/world-development-indicators/

> 10 http://info.worldbank.org/governance/wgi/

7
a moment of relative weakness. The international community, by promoting the completion of the CPA-mandated census and other technical issues related to the CPAmandated general elections of 2009 as well as by urging more transparency on both sides’ military expenditures can reduce information asymmetries and the risk of an arms race. In addition, the international community could decrease the risk of renewed violence by supporting a process of democratization of Sudanese politics (including the 2009 elections). Democratization is shown in the model to provide a useful commitment device and resolve some of the credibility issues that are currently contributing to excessive militarization by both sides and might result in unwanted and unintended conflict. Democratization is also likely to have much more significant impact beyond its positive influence associated with reduced military expenditure. A genuine democratic transformation might actually make unity attractive to Southern Sudanese; and even if the latter opted for partition, the two emerging democracies are likely to assign a higher value to economic interdependence and cooperation than any payoffs that might be reaped by conflicts and war. The opportunity cost of miliatarization and conflict in terms of human development suggest that all actors have an incentive to create commitments for a lasting and credible peace. Finally, the international community’s promotion of investment in crucial sectors (e.g., oil) could play an important role in increasing economic complementarities between North and South, thus potentially reducing the probability of both partition and war.

47