rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>11</sup>

2002). These are appropriate time periods not only for growth, but for any fundamental aspect of economic development, such as financial sector growth.

Corresponding to the two periods of financial sector development are two base empirical specifications,

- (1) _Growth of the financial sector i_ ( _1975-2000_ ) = _β1_ + _β2ln(initial financial sector) i_ +

_β3(_ **initial political/institutional variable** _) i_ + **Xi ′β4** + _εi_ and

- (2) _Size of the financial sector i_ ( _2000_ ) = _β1_ + _β2 (_ **initial political/institutional variable** _) i_ + **Xi ′β3**

+ _εi_ .

Equation (1) allows medium term growth of the financial sector to vary with the initial size of the financial sector, analogous to medium-term growth regressions in which growth varies with the level of initial income per capita. In the second equation, the initial size of the financial sector is omitted in order to preserve the long run character of the investigation.<sup>2</sup> These specifications raise numerous issues, the most important of which are: what should be in **X** and how should one should address the potential endogeneity of the political and institutional variables?

With respect to the first question, the base specification is parsimonious, following Beck, et al. (2001) in the financial literature and many others in the growth literature. The argument behind parsimony is simple: the effects of political institutions on financial sector development are likely to be both direct and indirect. As a first approximation we would like to know the total effects of both. Some specifications control in addition for income per capita in 1975. This is a challenging test for the political hypotheses under consideration

> 2 Including 1975 financial sector size in equation 2 would render it nothing more than an investigation of growth over the period 1975 – 2000.
- 14 -

Table 3: C0-INTEGRATION TEST: iAEL CCO1DITY iPRICE lDICES ND iEAL AINTEiEST RATES

||Indepen|dent Variable|||Co-i_ntegrat_ion|Tests|
|---|---|---|---|---|---|---|
|Dependent<br>variable|Intercept|TFtB|LnPOIL|CRDW|OF|AOF|
|LnlCP33|4.889<br>(0.046)|-3.729<br>(0.803)||0.892|-3.311|-3.822|
|LnlCPAs|4.902<br>(0.051)|-4.044<br>(0.893)||0.784|-2.887|-3.337|
|LnlcCPl|4.946<br>(0.059)|-3.795<br>(1.031)||0.703|-3.043|-3.854|
|LnlCPAGF|4.857<br>(0.050)|-3.458<br>(0.879)||0.759|-2.823|-2.910|
|LnICPAGF|4.687<br>(0.096)|-4.782<br>(1.195)|0.072<br>(0.045)|0.855|-3.079|-3.080|
|LnICPAGW|5.081<br>(0.070)|-6.548<br>(1.223)||0.929|-3.315|-3.968|

__ Standard errors are in parenthesis. Critical values for CibW: 0.369<CiRw0.S70 at 95% conf idence Interval and 0.515<Ci 0.720 at **99%** confidence Interval (see Sargan and Bhargava. 1983). Critical values for OF and AOf as In Table I.

Table 4: C0-iNTEGRATION TEST: REAL CUWODITY PRICE INDICES ADNO iY SUPPLY

||CRDW|DF|AOF|h<br>I<br>t-l|ytse<br>t-i|
|---|---|---|---|---|---|
|LnICP33|0.519|<br>-2.381|-1.406||5|
|LnlCPAG|0.588|<br>-2.477|-1.212||5|
|LnWCPI|0.360|-1.971|-1.247||5|
|LnlCPAGF|0.676|-2.700|-1.634||5|
|LnICPAGNF|0.520|-2.321|-0.911||5|

Notes; See Table 3.
concerns to an extent, but is often insufficient. To this end, smoothing methods have been developed to deal with sampling error and produce estimates of the empirical distributions and equipercentile relationship best characterizing the underlying population. This enables interpolation at each point on the curve, enhancing precision of the equating exercise.

Two general types of smoothing can be conducted. In _presmoothing_ , the score distributions are smoothed using polynomial loglinear presmoothing (Holland and Thayer, 2000); in _postsmoothing_ , the equipercentile equivalents are smoothed using cubic-spline postsmoothing (Kolen, 1984). We use the _presmoothing_ loglinear method, which is the same method used by the ETS, and is based on Von Davier et al. (2004) and Holland and Thayer (1987, 2000)<sup>11</sup> .

Three assumptions must hold for the linking methods above to be valid. First, they must test the same underlying population. Given we are using sample-based ISATs and RSATs and equate using overlapping countries, this assumption is satisfied if the population tested is similar and participation rates reach a certain threshold or non-participation is random. Second, tests should measure similar proficiencies. We link across precise dimensions such as subject and schooling level (primary vs. secondary) to increase the likelihood of proficiency overlap. Finally, the distribution of proficiency should be similar across tests. We address this assumption by equating using an average across countries that participate in both tests. The reliability of the equating exercise is enhanced with an increase in the number of countries that take both tests being equated _._ We include robustness checks to demonstrate the sensitivity of our results to this effect. We also include confidence intervals for our estimates to quantify the degree of uncertainty.

We compute two types of education quality metrics: (a) the proportion of students achieving international benchmarks of performance; and (b) mean scores. For the first set of metrics, threshold levels of achievement, we use the presmoothed equipercentile method to capture the distribution of scores. For the second metric, mean scores, we use pseudo-linear linking. This methodology enables credible over-time comparisons, a central feature of our panel dataset, and is consistent with a growing literature in economics on globally comparable education quality data.

# _3.2 Features of the Methodology_

# _3.2.1. Comparability across Countries and over Time_

> 11 We used R Statistics software for the equipercentile linking. In particular, we use the “equate” package. See Albano (2016) for more information.

17
<!-- Start of picture text --> Male LFP Female LFP<br>°o<br>©<br>~co ~Cc vt<br>FS} 3<br>ot Og<br>N<br>Oo oO<br>-.3 -.2 -.1 0 A 2 -.4 -.2 0 2 4<br>Deviation from actual value Deviation from actual value<br>Male unemployment Female unemployment<br>Oo fo}<br>vt ive)<br>oOo oO©<br>€ €<br>3° 30°<br>(6)ON (6)ov<br>oO- oON<br>fo}A °| J<br>-1 -.05 0 .05 A 15 -1 -.05 0 .05 J 15<br>Deviation from actual value Deviation from actual value<br>In sample (SAE) Out of sample (SAE) Direct estimate<br>All deviations are winsorized by one percent in each tail for the sake of presentation.<br><!-- End of picture text -->
Panel B shows that, these patterns are driven by advance countries. EM in Panel C have the same responses to the VIX, however, the cyclical behavior of capital flows in EM is very different. Only reserve outflows responds procyclically to GDP growth and all else is acyclical.

Table 7: Quarterly Drivers of Other Investment Debt Outflows by Sector, 2004–2014

||Panel A:|All Countri|es||
|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|
|log(VIX_t−_1)|-5.321<sup>_∗∗∗_</sup>|0.104|-4.370<sup>_∗∗∗_</sup>|-1.056|
||(1.909)|(0.805)|(1.591)|(0.543)|
|GDP Growth_it_|_−_1<br>0.126<sup>_∗∗∗_</sup>|0.00452|0.102<sup>_∗∗∗_</sup>|0.0203|
||(0.0411)|(0.0152)|(0.0345)|(0.0100)|
|Observations<br>|1408|1408|1408|1408|
|_R_<sup>2</sup>|0.041|0.000|0.043|0.016|
|CountryFE|Yes|Yes|Yes|Yes|
||Panel B: Adv|anced Econ|omies||
||(1)|(2)|(3)|(4)|
||Total|Public|Banks|Corp.|
|log(VIX_t−_1)|-9.375<sup>_∗∗_</sup>|0.544|-8.129<sup>_∗∗_</sup>|-1.791|
||(3.614)|(1.748)|(2.948)|(1.084)|
|GDP Growth_it_|_−_1<br>0.306<sup>_∗∗_</sup>|0.0219|0.256<sup>_∗∗_</sup>|0.0275|
||(0.111)|(0.0423)|(0.0876)|(0.0277)|
|Observations|660|660|660|660|
|_R_<sup>2</sup>|0.071|0.001|0.084|0.020|
|CountryFE|Yes|Yes|Yes|Yes|
||Pan|el C: EM|||
||(1)|(2)|(3)|(4)|
||Total|Public|Banks|Corp.|
|log(VIX_t−_1)|-1.447<sup>_∗∗∗_</sup>|-0.268|-0.737<sup>_∗∗_</sup>|-0.442<sup>_∗∗_</sup>|
||(0.461)|(0.278)|(0.330)|(0.185)|
|GDP Growth_it_|_−_1<br>0.0477<sup>_∗∗_</sup>|-0.00515|0.0353|0.0175<sup>_∗∗_</sup>|
||(0.0187)|(0.00711)|(0.0188)|(0.00805)|
|Observations<br>|704|704|704|704|
|_R_<sup>2</sup>|0.040|0.001|0.019|0.022|
|CountryFE|Yes|Yes|Yes|Yes|

Sample is from 2004q1-2014q4, countries as listed in Appendix A.4. Other Investment Debt outflow data is primarily from IMF BOP, as described in the text. Public outflows are defined as the sum of General Government and Central Bank outflows. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 37
**Table 5: Key summary statistics on differential progress by wealth toward the health MDGs**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||An<br>%|nual grow|th<br>%<br>countries|Median||Relative i|nequality|as % of||Absolute<br>%|inequality|as % of<br>median|
|||countries<br>|Median<br>|where<br>|excess<br>||||median<br>||countries<br>|Median<br>|initial<br>|
||Median|where|growth|mean for|growth||%|Median|initial|Median|with fall|change|absolute|
||growth|pop|rate|poorest|of|Median|countries|change|CI<sup>§</sup>|initial|in|in|CI<sup>§</sup>|
|Indicator|rate<br>pop|mean<br>improved|poorest<br>40%|40%<br>improved|poorest<br>40%|initial<br>CI<sup>§</sup>|with fall<br>in CI<sup>§</sup>|in CI<sup>§</sup><br>p.a.|(absolute<br>value)|absolute<br>CI<sup>§</sup>|absolute<br>CI<sup>§</sup>|absolute<br>CI<sup>§</sup>p.a.|(absolute<br>value)|
|Stunting|-0.0084|64|-0.0087|64|0.0023|-0.1035|58|-0.0009|0.8|-0.0406|45|0.0001|0.3|
|Underweight|-0.0269|80|-0.0239|74|0.0064|-0.1345|56|-0.0012|0.9|-0.0246|43|0.0004|1.8|
|IMR|-0.0281|93|-0.0283|93|-0.0004|-0.1039|49|0.0010|0.9|-0.0080|34|0.0003|3.4|
|U5MR|-0.0307|88|-0.0328|90|0.0019|-0.1209|61|-0.0009|0.7|-0.0143|34|0.0003|2.1|
|Full immunization|0.0242|73|0.0295|72|0.0105|0.0934|68|-0.0040|4.2|0.0419|58|-0.0008|1.9|
|Measles|0.0169|78|0.0217|78|0.0091|0.0655|75|-0.0025|3.8|0.0386|73|-0.0012|3.1|
|ANC4+|0.0310|78|0.0445|78|0.0164|0.2001|78|-0.0049|2.4|0.0571|44|0.0003|0.6|
|SBA|0.0183|83|0.0277|80|0.0130|0.2807|73|-0.0043|1.5|0.1080|49|0.0004|0.3|
|Contraception|-0.0143|41|0.0065|50|-0.0009|0.0691|53|-0.0015|2.2|0.0097|53|-0.0005|5.6|
|HIV-prevalence|-0.0101|67|-0.0026|56|0.0198|0.1526|67|-0.0035|2.3|0.0066|89|-0.0004|5.4|
|HIV-condom|0.0740|93|0.1095|100|0.0412|0.2446|93|-0.0113|4.6|0.0625|57|-0.0002|0.4|
|Malaria-net children|0.1691|78|0.3353|74|0.0480|0.2950|65|-0.0304|10.3|0.0171|52|-0.0001|0.8|
|Av. health status|-0.0208|78|-0.0193|75|0.0060|-0.0620|58|-0.0011|1.1|-0.0162|49|0.0002|2.6|
|Av. interventions|0.0456|75|0.0821|76|0.0196|0.1783|72|-0.0084|4.2|0.0478|55|-0.0003|1.8|

Source: Authors’ calculations from original DHS and MICS datasets.<sup>§</sup> CI is concentration index – see text for definition.

24
Table 6 (cont.)

|Independent|Wage Wo|rkers|Nonwage|Workers|
|---|---|---|---|---|
|variables|OLS<br> <br>(1)|Sel.Adj.<br>(2)|<br>OLS<br>(3)|Sel.Adj.<br>(4)|
|Union in the firm|0.237*<br>(2.79)|0.233*<br>(2.74)|||
|Social Security participation|0.330*<br>(4.43)|0.340*<br>(4.42)|||
|Total capital of<br>enterprise|||0.003<br>(0.23)|0.004<br>(0.21)|
|Number of hired workers in<br>enterprise|||0.093<br>(2.30)|* 0.090*<br>(2.28)|
|Number of family workers in<br>enterprise|||0.011<br>(0.34)|0.011<br>(0.28)|
|A||00.009<br>(0.43)||0.025<br>(0.35)|
|R<sup>2</sup>adjusted|0.339|0.339|0.085|0.085|
|Mean dependent<br>variable|1.141|1.141|0.898|0.898|
|No. observations|543|543|898|898|

Notes: * Statistically significant at the 10 percent level or better. t-values in parentheses.

a/ Omitted because of small number of cases.

A comparison of the figures for wage workers in Table 6 with prior results for males H/ shows that male and female salaried workers in the private sector have similar returns to secondary schooling. Hcwever, salaried women have lower returns to an additional year of schooling at primary and higher education levels than their male counterparts, possibly because women are concentrated in few occupations and industries. A comparison of selfL See, Arriagada, AM., op. cit., Table 7.

- 35 -
# **<u>Table A.23. The district fixed-effect RDD-DD regressions of individual-level outcomes using a large-specification model</u>**

|||||Dependen|t variables||||
|---|---|---|---|---|---|---|---|---|
|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)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1112**|0.0521***|0.0296|0.5192*|0.3505**|0.3591***|-0.0217|1.2441***|
||(0.0553)|(0.0153)|(0.0522)|(0.2789)|(0.1483)|(0.1368)|(0.0355)|(0.4751)|
|(Poverty rate – 50) * Post-program|0.0070|-0.0056***|-0.0038|-0.0337|-0.0572**|-0.0540**|0.0060|-0.0475|
|period|(0.0056)|(0.0018)|(0.0043)|(0.0262)|(0.0223)|(0.0215)|(0.0039)|(0.0506)|
|Program * (Poverty rate – 50) * Post-|-0.0027|0.0046**|0.0030|0.0027|0.0567**|0.0528**|-0.0082**|0.0523|
|program period|(0.0061)|(0.0019)|(0.0049)|(0.0294)|(0.0227)|(0.0219)|(0.0041)|(0.0536)|
|Age|0.0268***|0.0039***|0.0089***|0.0962***|-0.0027|-0.0034|0.1373***|0.3337***|
||(0.0013)|(0.0005)|(0.0005)|(0.0058)|(0.0038)|(0.0037)|(0.0109)|(0.0466)|
|Age squared|-0.0003***<br>(0.0000)|-0.0000***<br>(0.0000)|-0.0001***<br>(0.0000)|-0.0012***<br>(0.0001)|0.0002***<br>(0.0001)|0.0002***<br>(0.0001)|-0.0088***<br>(0.0005)|-0.0198***<br>(0.0022)|
|Male|-0.0589***|-0.0096***|0.0803***|0.9564***|-0.1448***|-0.1342***|0.0290***|0.1097***|
||(0.0072)|(0.0031)|(0.0054)|(0.0595)|(0.0254)|(0.0245)|(0.0087)|(0.0299)|
|Ethnic minorities (yes=1, Kinh=0)|0.1224***|-0.0879***|0.0062|0.0210|-0.2176**|-0.2159**|-0.0344*|0.8635***|
||(0.0227)|(0.0101)|(0.0162)|(0.1591)|(0.0961)|(0.0943)|(0.0180)|(0.1237)|
|Additional control variables|-0.1112**|0.0521***|0.0296|0.5192*|0.3505**|0.3591***|-0.0217|1.2441***|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2852***|0.0129|-0.1131***|-0.7239***|0.8918***|0.8090***|0.4349***|-0.9731***|
||(0.0391)|(0.0123)|(0.0228)|(0.2336)|(0.0900)|(0.0866)|(0.0628)|(0.2742)|
|Observations|128,446|128,446|128,446|128,446|86,980|86,980|50,645|50,645|
|R-squared|0.232|0.072|0.215|0.193|0.100|0.095|0.284|0.336|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. The additional control variables include education levels of individuals (dummy variables), education levels (dummy variables), household size, the proportion of children in households, the proportion of older members in households.

Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

73
and (9) private investment is equal to I<sup>P</sup> = [ <sup>P</sup> K<sup>P</sup> /(K<sup>I</sup> /K<sup>P</sup> )<sup>1-K</sup> ]<sup>1/K</sup> , with the standard case (no direct complementarity, or  K = 1) corresponding to I<sup>P</sup> = <sup>P</sup> K<sup>P</sup> .

# **4.  Calibration**

The model is now calibrated using various data sources, including DRC’s National Institute of Statistics and the Central Bank of Congo, the World Bank’s African Development Indicators (ADI), the IMF’s World Economic Outlook (WEO), as well as parameter estimates from Agénor (2016) and various other papers.

For households, the intertemporal discount factor is set at 0.898, based on the estimates of the real interest rate and the depreciation rate of private capital provided below. The intertemporal elasticity of substitution, ς, is set at 0.2, in line with the evidence for lowincome countries reported in Agénor and Montiel (2015). The Frisch elasticity of labor supply is set at 0.125 (implying that ψ = 8) to capture a fairly inelastic supply of labor. This is a fairly reasonable assumption for a low-income country like DRC. The preference parameter ηL is set at 0.14, to account for a weak effect of leisure on household utility. The share of nontradables in total consumption, θ, is set at 0.56, as in Rabanal and Tuesta (2013) for instance. The fraction <sup>R</sup> of the share of resource revenues not going to the government but instead to domestic households is assumed to be 0.1; thus, 90 percent of the resource income that is not going to the government budget accrues to non-residents.

The share of capital in the nonresource tradable sector,  K, is calibrated at 0.6, to capture that the nonresource tradable sector is more capital intensive than the nontradable 20
**Table 6.3 Cost of Illness Per Case in Mexico for the General Population** (unless stated otherwise)

(costs in US$, 2010 values in 1999 prices)

||||_CostofIlln_|_essa_||
|---|---|---|---|---|---|
||_Public_|||||
|_Endpoints_|_services_|_IMSS_|_Private_|_Others_|_Averageb_|
|_Hospitaladmission_||||||
|Respiratory|939|1,252|3,131|1,565|1,870|
|CardiocerebrovascularC|2,818|3,757|9,392|4,696|5,611|
|Congestiveheartfailure(elderly)d|939|1,252|3,131|1,565|1,870|
|_Emergencyroomvisits_||||||
|Respiratory|211|50|83|50|91|
|Restrictedactivitydayse|10|10|10|10|10|
|_Minorrestricted activitydays_||||||
|Total (adults)|ng|ng|ng|ng|ng|
|Effectsinasthmatics||||||
|Asthma attacks|271|199|572|199|337|
|Coughwithoutphlegm(children)|ng|ng|ng|ng|ng|
|Cough withphlegm(children)|ng|ng|ng|ng|ng|
|Somerespiratory symptoms(children)|ng|ng|ng|ng|ng|
|Respiratorysymptoms|10|10|10|10|10|
|_Chronicmorbidity_||||||
|Chronicbronchitis|153|168|326|168|218|
|Chroniccough(children)|169|136|279|136|190|

ng = Assumed negligible.

a. From Hernandez-Avila and others (1995).

b. Based on National Health Survey, ENSA 111994, 18.6% public insurance, 31.9% IMSS, 33.3% private insurance, and 16.2% other.

c. Assumed three times respiratory hospital admissions d. Assumed same as respiratory hospital admissions.

e. Assumed same as respiratory symptoms. Only 46% of the work-loss-day portion of restricted activity days are valued with a COI component (Krupnick 2000).

# **_Human Capital Loss_**

The human capital approach is used for valuing the lost productivity associated with mortality. This approach assumes that the value of a person is equal to what he or she would have produced, that is, the discounted present value of a person's expected future earnings. The value of lost productivity may also include nonmarket productivity, for example, the value of household production. Other dimensions of illness and death, such as pain, suffering, and loss of leisure are excluded. The difference between the productivity loss and human capital is that the former accounts for the short-term production losses caused by morbidity, while the latter focuses on the production losses in the long term caused by increased mortal_33_
13-a

# Table 7 : BCEAO BALANCE SHEET (September 1986)

# Assets

|Gold||21|
|---|---|---|
|**IMF**Reserve Position and SDRs||16|
|Foreign Exchange||135|
|(of which Operations Account)|(95)||
|Claims on Banks and OFIs||596|
|Claims on National Treasuries||222|
|Operations on Behalf of National Trea|suries|431|
|(of which IMF and Trust Fund)|(399)||
|Fixed Assets, etc.||47|
|Suspense Accounts and Miscellaneous||341|
|Total Assets-<br>Total Liabilities||1809|
|Liabilities|||
|Currency||528|
|Claims of Foreign Banks, etc.||57|
|Claims of Local Banks and OFIs||234|
|Claims of National Treasuries. etc.||80|
|Transfers Being Executed||10|
|Obligations in Foreign Exchange||0|
|IMF, SDRs and Trust Fund||426|
|Capital and Reserves||107|
|Suspense Accounts and Miscellaneous||340|

In billions of CFAF; US$1-CFAF 331.
**Figure 4: Poverty projections Official poverty line**

<!-- Start of picture text --> COVID-<br>19<br><!-- End of picture text --> <!-- Start of picture text --> 25.3 25.3<br>24.9<br>23.7<br>21.9 22.0 21.6<br>24.1<br>21.9 22.0<br>18.4<br>20.5 20.6<br>18.7<br>FLOODS +<br>16.7<br>COVID- POST-PANDEMIC RECORD PROJECTED<br>19 RECOVERY INFLATION RECOVERY<br><!-- End of picture text --> <!-- Start of picture text --> Nowcasting<br><!-- End of picture text --> <!-- Start of picture text --> Forecasting<br><!-- End of picture text --> Source: Authors’ calculation from HIES (2018/19) data.

Note: Projections use preferred specifications following equation (1), including information from real growth from eleven economic sectors to model labor income. _Projections using Headline CPI_ (solid line) use headline CPI for the whole series. _Projections using HH inflation_ (dashed line) are based on household-specific inflation, as described in the methodological section, until FY23. However, due to the lack of disaggregated inflation projections post-FY23, later poverty projections are deflated using expected headline inflation.

The household-level poverty rate from FY23 to FY25 is forecasted using headline CPI instead of COICOP-disaggregated inflation. These indicate the possibility of an economic recovery in later years, which is projected to bring poverty back to its initial level, marking a period of five years without additional poverty gains.

Figure 4 also shows that using an aggregate measure of inflation (headline CPI) underestimates the projections of poverty rates compared to using disaggregated COICOP-level data on inflation (subject to availability). As indicated by the dashed line for FY19 to FY23, accounting for differential price trends and consumption baskets when projecting inflation, produces poverty rates that are consistently higher than those based on average national inflation. This is because the approach does not capture how lower-income households and those close to the poverty line whose food consumption is a large share of the total consumption basket, likely experienced higher inflation rates than the national average. This is important given that higher food prices mostly drove inflationary pressures.

Ignoring inflation heterogeneity across income groups results in an underestimation of poverty rates. Similar results are found for income inequality. The comparison between inequality (Gini 12