rafmacalaba/datause-encoder-data huggingface dataset:
and liability portfolios (both in terms of volume and composition) may help to identify vulnerabilities as well as potential opportunities to benefit from a consolidated balance sheet approach.

# **IV.I. Countries with Net FX Asset Positions**

# **A. South Africa**

South Africa has a liquid and relatively developed local currency bond market. The central government uses this market to meet most of its financing needs, so external debt represents a small share (around 10 percent) of the government’s total debt portfolio (Table 2). This falls below the current government benchmark of 15 percent (external debt as percent of total debt).

**<mark>Table 2 – South Africa: Composition of Debt</mark>**

_<mark>as of March 31, 2017, USD bil eq.</mark>_

||Amount|Percent|
|---|---|---|
|Domestic Debt|149.0|89.5|
|External Debt|17.5|10.5|
|Total|166.5|100.0|

Most of the external debt share is comprised of USD-denominated bond issuances in international capital markets (93 percent). The National Treasury has also issued small amounts of EUR- and JPY-denominated bonds and used cross-currency swaps to hedge its exposure to this non-USD denominated debt. The South African Reserve Bank (SARB) maintains a significant amount of reserves—its external debt-to-reserves ratio is 0.43—and the USD share of its reserves sufficiently covers its amount of USD-denominated external debt. The USD debt-to-reserves ratio is 0.7 (Table 3).

**<mark>Table 3 – South Africa: Composition of Foreign Currency Debt and Reserves</mark>** _<mark>as of March 31, 2017, USD billion eq.</mark>_

|||USD|EUR|JPY|GBP|Other|Total|
|---|---|---|---|---|---|---|---|
||USD bil. eq.|23.2|7.6|-|2.5|7.9|41.2|
|Reserves|Percent of total|56.4|18.3|-|6.0|19.3|100.0|
||Percent of GDP|7.8|2.6|-|0.8|2.7|13.9|
|External Debt (by|USD bil. eq.|16.3|0.7|0.5|-|-|17.5|
|currency of|Percent of total|93.0|4.0|3.0|-|-|100.0|
|issuance)|Percent of GDP|5.5|0.2|0.2|-|-|5.9|
|Concentration|Ratio FX debt/<br>reserves|0.70|0.09|n/a|n/a|n/a|0.43|

Source: National Treasury, Bloomberg and South African Reserve Bank _Applied FX Rate: 13.4136 ZAR/USD as of March 31, 2017_ 14
Figure A.6. Geographical Distribution of Taliban, Allies, and Contested Districts- All Sample <!-- Start of picture text --> District Presence in 2017<br>Taliban Control<br>Government Control<br>Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Presence in 2019<br>Taliban Control<br>Government Control<br>Contested<br>53<br><!-- End of picture text --> <!-- Start of picture text --> District Presence in 2021<br>Taliban Control<br>Government Control<br>Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Presence in 2018<br>Taliban Control<br>Government Control<br>Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Presence in 2020<br>Taliban Control<br>Government Control<br>Contested<br><!-- End of picture text --> <!-- Start of picture text --> District Presence in Aug. 2021<br>Taliban Control<br><!-- End of picture text --> _Notes:_ Source: FDD’s Long War Journal
in that year.

Let us also inspect time-trends in inequality. Figure 16 shows our estimates of the Gini coefficient for the years under consideration. Both approaches are found to produce qualitatively similar results.<sup>26</sup> We observe a slight moderation in consumption inequality in India since 2011. This could in part be attributed to the fact that top-income households are under-represented in household surveys (whether NSS or CPHS). Consequently, consumption inequality estimated from household survey data capture distributional changes for households that are in the bottom 95 percent, say, of the distribution. To the extent that the income or consumption growth since 2011 is largely concentrated in the top end of the distribution (Chancel and Piketty, 2019), our household survey-based estimates of consumption inequality will be downward biased.

Figure 14 in Appendix 4.3 reveals that the moderation of inequality has been larger in rural than urban areas. Since 2015, changes in rural inequality have been less pronounced than urban areas. Urban inequality dropped in 2018 which coincides with the year in which the rate of poverty reduction was its highest. Figure 15 in Appendix 4.4 shows that other measures, namely, poverty gap and mean-log deviation yield trends in poverty and inequality dynamics that are consistent with the main results.

Finally, in Figure 17, we connect our estimates of poverty and inequality for India over the last decade with estimates dating back to 1993. It can be seen that our estimates of headcount poverty preserve the long-term trend of poverty reduction that is observed in India over this period. By the same token, our estimates suggest that the current poverty rate is higher than the forecasts based on pass-through adjusted consumption growth from national accounts (under the assumption of distribution neutrality). For consumption inequality we observe a trend reversal around 2011 (see Figure 18). Inequality is estimated to have steadily increased between 1993 and 2011. By our estimates inequality has started to moderate after 2011.

# **5.2 Robustness analysis**

Our preferred specification in approach 2 assumes a linear relationship between observed CPHS consumption and NSS consumption. We allow for heterogeneity (i.e. different relationships) between urban and rural India. It is possible however, that there are additional heterogeneities that should be accounted for. For instance, Gibson and Kim (2007) observe that the measurement errors in household consumption are systematically correlated with household size. Similarly, Beegle, et al. (2012) find that

> 26The inequality based on reported CPHS consumption range between 0.2965 and 0.3213 across years (not included in the figure) -- considerably lower than the estimates of inequality obtained using NSS-type consumption measures.

40
18 _Poor prime-age workers display lower employment-to-population ratios than the non poor, whereas youth and seniors living in poor households are more likely to be employed than those from non poor families_ (Table 8) _._ While 51.6 percent of the poor aged 25 to 54 are employed, this figure stands at 54.4 percent for the non poor. The poor work through older age than the non poor. As shown in Table 8, 60.4 percent of poor individuals aged 55 to 64 are employed compared to 50.9 percent for the non poor, which suggests that the poor cannot afford to stop working as early as the non poor. Results not included here also show that 30.9 percent of the poor aged 65 or more are employed against 21.2 percent for the non poor of the same age group.

_The non poor exhibit similar employment-to-population ratios in urban and rural areas, whereas the poor are less likely to be employed when living in urban centers_ (Table 8) _._ Indeed, 42.8 percent of the rural non poor and 42.2 percent of the urban non poor are employed, whereas these figures stand at 42.7 percent and 38.5 percent for the rural and urban poor respectively. This gap appears to be mostly due to the fact that the poor are less likely to participate in the labor market when they live in urban areas as compared to rural ones as unemployment rates are roughly equal for the poor wherever they live.

**Table 9. Types of Employment and Poverty (in percentages)**

||_Non Poor_|_Poor_|_Extremely Poor_|
|---|---|---|---|
|_Wage earners_|66.7|33.3|19.3|
|_Day Laborers_|60.4|39.6|22.5|
|_Unpaid family labor / apprentices_|39.9|60.1|36.9|
|_Self-employed_|47.5|52.5|28.4|

Source: EPCV 2004. Figures are displayed in percentage.

_Similarly to what was observed for the overall population, self-employment is the main form of employment for both the poor and the non poor_ . Indeed, 56.7 percent of workers living in poor households and 48.5 percent of those from non poor households are self-employed (Figure 8). Hence, over half the self-employed are poor and 28.5 percent are extremely poor (Table 9). The vast majority of self-employed people who are poor received no education (85 percent, see Figure 8). The same stands for the non poor self-employed (66 percent never went to school) although they include more individuals with primary and secondary education (respectively 17 percent, and 18 percent), thus suggesting that the latter are somewhat more likely to engage in higher value-added independent activities.
# **Table 8.-Dynamic firm growth equations: GMM-sys estimates, by state group, only businesses reportedly constrained by corruption**

Dependent variable: annual sales growth= Δ _y it_

||Constra|ined bycorrup|tion||Unconstrained bycorruption****|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||High-income<br>high- growth<br>states|Low-income<br>high-growth<br>states|Low-income<br>low-growth<br>states|High-income<br>low-growth<br>states|High-income<br>high- growth<br>states|
|_y_|0.017|0.056|-0.089|-0.159|-0.076|
|1<br>−<br>_it_<br>|(0.46)|(1.44)|(1.78)|(4.67)**|(2.30)*|
|1<br>−<br>Δ<br>_it_<br>_y_|-0.041|0.061|-0.033|0.061|0.009|
||(2.99)**|(2.01)*|(1.24)|(4.88)**|(0.82)|
|(<br>)<br>1<br>/<br>−<br>Π<br>_it_<br>_K_|-0.346|-0.424|-0.448|-0.142|0.010|
||(3.70)**|(3.10)**|(3.81)**|(4.53)**|(0.09)|
|(<br>)<br>1<br>/<br>−<br>_it_<br>_K_<br>_D_|0.048|0.034|-0.066|-0.579|-0.078|
||(1.25)|(0.31)|(1.30)|(19.75)**|(2.41)*|
|Outage loss|-0.010|-0.025|0.001|-0.012|0.005|
||(1.26)|(2.08)*|(0.15)|(2.72)**|(0.49)|
|Constrained by labor regulation|-0.230|0.053|-0.072|-0.152|-0.168|
||(1.20)|(0.33)|(0.57)|(3.62)**|(2.38)*|
|Constant|0.382|0.061|1.544|1.980|0.801|
||(1.15)|(0.10)|(4.01)**|(6.51)**|(2.34)*|
|Observations|714|280|207|205|320|
|Numberofbusinesses|449|233|159|93|222|
|Overidentification test:||||||
|Chi-square|44.5|24.7|33.5|37.2|48.5|
|P-value|0.73|0.94|0.79|0.42|0.53|
|AR in first dif. error (z-stat):||||||
|m1|-3.47|-1.26|-2.86|-3.12|-2.17|
|m2|-0.35|1.05|-0.73|-0.71|1.45|

Absolute value of z-statistics in parentheses. All columns include state, industry and year dummies

* significant at 5% level; ** significant at 1% level

- *** There are too few observations to estimate equations for unconstraine firms separately for each of the other three groups of states

Instruments: lags of growth, sales, profitabilability and indebtedness variables t-2 onwards, one step lagged first differences of the same, state of location, year of observation lagged business envirnoment variables, and city averages of business environment variables 25
# **Figure 5**

Estimates of Migrant Share Against Years Since IDs Issued, using Dummy Variable, Quadratic, and Quartic Specifications <!-- Start of picture text --> 0 5 10 15 20<br>Years Since IDs Issued<br>Dummy Coefficients Quadratic<br>Quartic<br>.15<br>.1<br>.05<br>Predicted Migrant Share<br>0<br><!-- End of picture text --> Source: RCRE panel (1986-2003) and Supplemental Survey (2004).

34
The study found no demand at all (exactly zero take-up) for the EduRemesa among migrants in the “no match” treatment group, a small amount of take-up (7%) in the 1:1 match treatment, and 19% take-up in the 3:1 match treatment. The 3:1 match treatment led to statistically significant increases in total educational expenditures on the target student, a reduction in that student’s labor supply, and an increase in the likelihood the target student attended private school. The increase in target student educational expenditures did not appear to come at the expense of reduced expenditures on other students in the household.

All told, the EduRemesa study finds no evidence that migrants have an unsubsidized or “pure” demand for control over the use of remittances for educational purposes. But migrants do appear interested in channeling remittances toward educational expenditures of specific students when given matching funds to do so, and when this occurs there are positive impacts on beneficiary students.

In a complementary study, De Arcangelis et al (2014) partnered with a Philippine bank (Bank of the Philippine Islands) with branches in Rome to design and pilot-test a new remittance product, called EduPay. This product allowed migrants overseas to channel tuition payments for particular students directly to those students’ educational institutions in the Philippines, avoiding the need to send tuition payments via family members or others in the Philippines who might not be completely trusted to make such payments reliably. This pilot study consisted of two components: 1) the pilot implementation of new remittance product, to provide proof of concept and estimate the level of demand, and 2) a survey to better understand how enhanced control over education expenditures in the home country might affect the volume of remittances sent home by migrants.

Proof of concept was demonstrated by successfully implementing a total of 178 EduPay payments for 55 students in the Philippines. In a relatively population-representative sample (not screened for interest or suitability in any way), 6.6% of those approached went as far as to fill out an authorization form allowing our study to contact the Philippine student and school and to make payment arrangements, although bureaucratic delays from some of the schools acted as a constraint on some transactions being made. In another sample screened for suitability and who were willing to respond to an extensive research survey, 43.9% filled out the authorization form.

23
In Figure 20, we plot the gap between back casted poverty projections and the actual poverty rate for 2004 across studies. Estimates closer to the horizontal axis show that the predicted poverty rates were close to the actual rate observed in 2004. The graph shows that approach 1 of our study predicts 2004 poverty rate to be 3.4 percentage points lower than the actual headcount across India and 3.2 percentage points lower rate for urban samples.<sup>27</sup> In comparison, estimates from Newhouse and Vyas (2019) are 2.2 percentage point apart from the actual national rate but the differences for urban samples are 9.2 percentage points higher. Deviations from the actual poverty rate in Edochie, et al. (2022) are in the same direction as our estimates but the magnitude is considerably higher in their study across all samples. Overall, these out-of-sample predictions for NSS-2004 suggest that our approach yield estimates that are closer to the actual headcount rate across rural, urban and all-India samples.

We believe that the inability to model changes in household asset ownership overtime could have led the earlier papers to overestimate poverty reduction in 2015 and 2017 and produce incompatible back casted estimates of poverty for 2004 (asset indicators were unavailable in the surveys used in the two papers). Our analysis in Section 6 using PLFS shows that asset indicators are important predictors of household consumption; failing to capture these indicators leads to divergent poverty estimates even within the same survey.

# **6 Corroborative evidence**

Our estimates of poverty are at odds with findings from the leaked NSS-2017 survey which shows a rise in poverty between 2011 and 2017. Both sources point to a moderation of inequality since 2011, but the magnitude of changes to inequality are significantly higher in the NSS-2017 relative to our estimates. In this section, we corroborate our main findings using a range of independent data sources.

## **6.1 Headcount poverty has declined after 2011 with larger reductions in rural areas**

**Estimated consumption levels sit well with private final consumption expenditure (PFCE) reported in national accounts** . A number of earlier studies have shown that there are systematic differences in consumption growth reported in national accounts statistics (NAS) and household surveys (see e.g. Ravallion, 2003; Datt

> 27Mean consumption per capita in the 2004 survey is 83.88 PPP dollars. The mean imputed 2004 consumption is 82.684 (1.4 percent lower than the survey mean).

47
# 4.4 Beds

Approximately half of the sample’s health centers have beds available to patients, including about 1/6 reserved for maternity wards. In rural areas, 53% of the beds available are in religious centers, while in the capital it is mainly the private clinics that offer beds (i.e. 64% of the 197 total beds available).

# 4.5 Competition

Competition among health providers could have important effects on service quality and prices. Despite some improvements of access to health services in the country over the last decade, the rural populations still suffer severe shortages in health services supply, and competition is very limited. Indeed, most of health centers visited in rural areas are the sole provider of care within a 2 km radius (Table 4) and thus enjoy a monopoly status (84%).

Table 4: Competition

|||TY|PE||L|OCATION<br>||Total|
|---|---|---|---|---|---|---|---|---|
|||||||r|||
||Public|Private|Religious|NGO|Capital|<br>Urban|Rural||
|Percentage (%)|||||||||
|Monopoly|63.4|25.6|77.3|14.3|1.9|13.2|83.8|59.1|
|Private Competition|11.0|41.0|0.5|28.6|51.9|21.1|3.1|14.6|
|Number (of<br>competitors)|||||||||
|Total Competition|0.7|2.4|0.4|1.7|3.0|1.7|0.2|0.9|
|Private sector|0.16|1.0|0.05|0.29|1.08|0.29|0.04|0.26|

Source: Chad 2004 QSDS and authors’ calculations.

The capital offers a strikingly different picture since only 2% of the health centers can be considered as monopolies (in the sense defined above), 52% of them face fierce competition from the private for-profit sector. Overall, however, competition is weak, with 0.3 clinics located in a 2-km radius of health centers visited. Religious clinics tend to locate in areas less occupied by other health centers; only 0.6 centers are found within 2 km of such non-profit clinics, including less than 0.1 private clinics in their neighborhood.

8
**difference in prices means that informal trade continues even in cases where sanctions are severe.**<sup>**11**</sup>

**This situation clearly leads to strained relations between the authorities and the local population. As local populations depend on cross-border trade, they worry that the local authorities may take action against it, as is the case in western Tunisia. Meanwhile, customs officials are concerned about the risk of local protests if they strictly enforce the tariffs in place, as is the case at the Libyan border.**

**The current situation of increasing informal trade derives from a sense of being left out from Tunis (especially at the Algerian border) in the context of weakened a state without a political consensus. Therefore, informal trade surge would have more chance to be contained whenever a political agreement is reached.**

Our estimate of the scale of informal trade between Tunisia and Algeria and Tunisia and Libya is set out in the following sections. It is based on official trade data, using the mirror statistics methodology (Section 2) as well as on interviews conducted on site with customs officials and various individuals involved in informal trade (Section 3). Section 4 presents an assessment of the positive and negative impacts of informal trade and Section 5 describes the loss of state revenues.

> 11 In 2012, customs seized informal imports for 48 million USD (Leaders 2013.2).

5
**<u>Table 4. Logit regression of treatment</u>**

|||**Overall**<br>||**Pa**<br>|**rticipants**<br>||**Non-**<br>|**participan**<br>|**ts**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**Coeff.**|**(s.e.)**|**Sig.**|**Coeff.**|**(s.e.)**|**Sig.**|**Coeff.**|**(s.e.)**|**Sig.**|
|**_Household demographics_**||||||||||
|Household lived in hamlet|-0.020|0.126||0.054|0.214||-0.114|0.161||
|Scheduled tribe/caste|0.777|0.179|***|0.974|0.336|***|0.465|0.217|**|
|Backward caste|0.235|0.152||0.166|0.301||0.219|0.180||
|Household size|0.157|0.034|***|0.111|0.054|**|0.174|0.044|***|
|Household female headed|-0.214|0.157||-0.184|0.269||-0.245|0.198||
|Some member can write|0.030|0.137||-0.155|0.242||0.064|0.171||
|Located in Telangana|-0.646|0.147|***|-0.639|0.225|***|-0.706|0.199|***|
|Located in Rayalaseema|0.338|0.131|***|1.353|0.295|***|0.070|0.164||
|**_Household initial conditions_**||||||||||
|Poorest of poor household|0.352|0.153|**|0.412|0.263||0.286|0.195||
|Poor household|0.105|0.139||0.072|0.240||0.110|0.177||
|Land ownership|0.217|0.117|*|0.361|0.199|*|0.022|0.149||
|Consumption p.c. (10,000Rs/year)|-0.164|0.123||0.000|0.000||0.000|0.000||
|Energy intake p.c. (1,000Kcal/day)|-0.594|0.236|**|0.000|0.000|***|0.000|0.000||
|Protein intake p.c. (g/day)|0.044|0.010|***|0.074|0.017|***|0.025|0.013|*|
|Total non-financial asset p.c. (10,000Rs)|0.159|0.123||0.324|0.263||0.082|0.136||
|Constant|-0.864|0.331|***|-0.525|0.572||-0.773|0.422|*|
|Pseudo R-squared|0.050|||0.103|||0.035|||
|Number of observations|2406|||1111|||1295|||

Significance of coefficient is as follows: * significant at 10%; ** significant at 5%; *** significant at 1% 20
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