rafmacalaba/datause-encoder-data huggingface dataset:
Households with incomes _below_ this threshold but above the poverty line are classified as vulnerable, they are neither poor nor middle class. The upper threshold of the middle class, , is defined as the value associated with the quantile _q_<sup>_U,_</sup> that has a predicted probability of falling into poverty of 1 percent. YY<sup>UU</sup> Households with an income between qqiitt and are classified as middle class. YY YY<sup>VV</sup> YY<sup>UU</sup>

# **IV. Results**

The main objective of the approach outlined above is to identify the thresholds that define the middle class, the most relevant of which is the vulnerability threshold, i.e. the level of permanent income above which the probability of falling into poverty is lower than 4 percent.

Applying this approach separately to the panels for 2005–08 and 2015–18 highlights the fact that the vulnerability threshold increased in Europe between these two periods. This is the specific manifestation of the crisis of the middle class emphasized by this paper. Between these two periods, the vulnerability threshold rose, for the average country, by 10 percent in real terms. The average, across all countries, of the permanent incomes associated with the 4 percent probability of falling into poverty was $50.5 per day in 2008 and became $55.5 in 2018 (Table 2).<sup>12</sup> This 10 percent increase could be interpreted as an insurance premium to maintain the same level of protection against the risk of poverty. Households that did not “pay” this premium, and thus remained at the same level of income of the earlier period, faced an increased risk of falling into poverty which meant that they were no longer part of the middle class. The values reported in table 2 show that in 10 countries, the middle-class lower threshold increased by more than 10 percent between 2008 and 2018. The largest increase was in Bulgaria, one of the poorest countries in Europe, where the predicted income associated with the lower middle-class threshold soared 70 percent, increasing from $30 to $51 per day. Within Central Europe and the Baltic countries, the lower threshold increased from $42 to $52 per day in Latvia and from $44 to $50 per day in in Estonia. In the rest of Europe, substantial increases were seen in Austria, Denmark, Italy, the Netherlands, and Norway. Only in Cyprus did the threshold drop by more than 10 percent. That drop likely reflects the sharp decline in incomes after the global financial crisis, which disproportionately affected the bottom half of the

> 12 In Table A.5 we report the change of the vulnerability threshold between 2015-18 and 2017-20, that is, including the effect of the COVID-19 pandemic. In most countries for which data is available there was a decrease in the vulnerability threshold in that time period, most probably explained by the social protection mitigation measures implemented during 2020. There is not yet a 4-year panel in EU-SILC that completely excludes the years of the pandemic, so it is still early to know whether this change is permanent or not.

10
levels are most notable in states where illiteracy (or below primary level education) among adults is high. Reweighting is seen to be more successful in correcting biases in 2017 and 2018 than in 2019. But even in 2019, reweighting comes a long way in reducing the bias in states with high shares of illiterate or non-formal education. The estimates for higher education levels are largely scattered along the diagonal, confirming the successful bias correction. Figure 1 in Appendix 1.1 shows that the large bias in female illiteracy using reported CPHS data as documented in Somanchi (2021) is largely resolved after reweighting.

The NSS survey on education consumption conducted in 2017-18 provides another opportunity to compare education statistics derived from the (reweighted) CPHS against. As this survey is not used in the reweighting procedure, this comparison helps provide external validity of the adjustments made to the sampling weights. Panels (a), (b) and (c) from Figure 5 show the results for all adults, males and females above the age of 15, respectively. Reassuringly, all education level shares obtained using the adjusted CPHS sampling weights are within 1 percentage points from the benchmark survey. This denotes a notable improvement compared to the estimated obtained using the reported CPHS weights.

_Labor force indicators:_ Abraham and Srivastava (2019) observe a 3.2 percentage point gap in labor force participation rates among males between the CPHS-2017 and the PLFS from the same year. Labor force participation rate for females in the CPHS are about half that of what is estimated by the PLFS. Basole, et al.(2021) finds that the average real incomes in the CPHS of 2018 are about 30 percent higher when compared to the PLFS from the same year<sup>19</sup> . Despite the higher average incomes, wage inequality is lower in the CPHS relative to the PLFS: estimates of the Gini coefficient of income inequality for the two surveys are 0.42 and 0.44, respectively (excluding zero wage earners). Our analysis furthermore finds that the share of casual wages workers is higher in the CPHS than in the PLFS.

Figure 6 shows log monthly salaries and log daily wages for both the CPHS and benchmark surveys (these indicators are not included in the set of target variables used in reweighting). Reweighting closes the gap in monthly salaries and daily wages that is observed when using reported CPHS weights. The bias correction is larger for rural than for urban wage incomes. Unlike Basole, et al.(2021), we exclude income from self-employment in our analysis as determining profits from work requires detailed enumeration of cost and revenue parameters of an enterprise -- which are not recorded in either survey.

Reweighting is also seen to account for the gap in wage inequality between the CPHS 19Basole, et al. (2021) include earnings from self-employed work in their analysis.

17
||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|OLS (Full sample)|IV (Full sample)|OLS (rural only)|IV (rural only)|
|In(Cost to Market)|-0.0115**|-0.105***|-0.0118**|-0.0822***|
||(-2.429)|(-5.425)|(-2.465)|(-4.523)|
|Ln(No. of<sup>fatalities)</sup>|0.00789***|-0.121***|0.00713***|-0.0902***|
||(4.761)|(-4.464)|(4.311)|(-3.557)|
|In{Population)|0.530***|0.530***|0.527***|0.528***|
||(170.1)|(153.3)|<br>(169.7)|(159.5)|
|In(Population)*2|0.0359***|0.0357***|0.0365***|0.0363***|
||(128.8)|(114.5)|(127.9)|(117.7)|
|In(Cassava potential yield)|0.488***|0.548***|0.547 ***|0.595***|
||(5.666)|(5.648)|(6.375)|(6.418)|
|In(Cassava potential yield)*2|-0.0366***|-0.0395***|-0.0420***|-0.0444***|
||(-5.774)|(-5.568)|(-6.649)|(-6.543)|
|In(Maize potential yield)|0.451***|0.348**|0.367**|0.286*|
||(3.003)|(2.065)|(2.459)|(1.775)|
|In({(Maize potential yield)*2|-0.0420***|-0.0388***|-0.0340***|-0.0311**|
||(-3.686)|(-3.058)|(-2.995)|(-2.566)|
|In(Groundnut potential yield)|-0.322***|-0.384***|-0.308***|-0.354***|
||(-6.244)|(-6.562)|(-6.017)|(-6.360)|
|In(Groundnut potential yield)*2|0.0422***|0.0509***|0.0406***|0.0468***|
||(8.274)|(8.590)|(8.010)|(8.345)|
|In(Distance to mine)|-0.0764***|-0.0298***|-0.0709***|-0.0346***|
||(-13.55)|(-2.601)|(-12.52)|(-3.115)|
|Province fixed effects|Yes|Yes|Yes|Yes|
|Constant|-8.953***|-9.561***|-8.962***|-9.426***|
||(-42.29)|(-35.67)|(-42.65)|(-37.00)|
|Observations|26,330|26,330|26,179|26,179|
|R-squared|0.920|0.901|0.916|0.905|
|t-statistics in parentheses|||||
|*** 9<0.01, ** p<0.05, * p<0.1|||||
|Data:NighttimeLightslocalGDP,Ghosh(2010)|||||
economic growth and price changes.<sup>14</sup> In all of the scatter plots, there are some potentially influential outliers, including Azerbaijan, Angola, and Venezuela, which are three oil producers, several Eastern European countries (Armenia, Latvia, Estonia, and Ukraine) and several African countries (Tanzania, Mali, and Malawi). Note that these outliers are sometimes driven by large changes in the dependent variable as well as by unusual economic growth or inflation rates. Measurement error is therefore a problem in both the left- and right-hand side variables.

To gauge the influence of outlying observations, we calculated _dfbetas_ (an indicator of the influence of outliers) and earmarked observations with _dfbetas_ greater than 0.2.<sup>15</sup> One option is to run regressions that exclude outliers, which we do in the case of fixed effects regressions. Another option is to use a robust regressor that downweights outlying observations without completely discounting them. Hence, we use both robust regressors and fixed effects estimates that exclude these outlying observations. Furthermore, we report ordinary least squares regressions in appendix S1, in which all outliers are included.

14 Note that in all our regressions, we exclude observations for Zimbabwe because of its hyperinflationary episode, which leaves the country as an enormous outlier on the food inflation-food insecurity relationship. 15 This cut-off is fairly conservative. The usual cut-off for this sample size, 2/sqrt(N), is equal to 0.12. We calculate these _dfbetas_ for various models and exclude a common set of outlying observations: Algeria, 2009; Angola, 2008; Armenia, 2007 and 2009; Azerbaijan, 2007; Botswana, 2008; China, 2008; Denmark 2007 and 2008, and 2009; Djibouti, 2009; Iraq, 2008 and 2009; Kenya, 2007; Kuwait, 2009 and 2010; Romania, 2007; Rwanda, 2009; Tanzania, 2008; Trinidad and Tobago, 2008; Vietnam,

> 2009; and Zimbabwe, all observations. A good explanation of _dfbetas_ can be found in _Stata Web Books: Regressions with Stata,_

> _Chapter 2 – Regression Diagnostics_ : http://128.97.141.26/stat/stata/webbooks/reg/chapter2/statareg2.htm

18
17 needs, foreign labor (both legal and illegal) expanded. This was facilitated in part by easing migration restrictions: dependency ceilings were liberalized (raised to 45 percent in manufacturing, and up to a ratio of 5:1 in the construction sector) and a two-tier levy system was implemented in the manufacturing sector in which a premium was required for each foreign worker in excess of the 35 percent cut-off. For example, up to the 35 percent dependency ceiling, the per worker levy was S$300 per month; for additional workers between 35 percent and 45 percent, the unit levy was S$450 per month.<sup>9</sup> This resulted in sustained and increasing growth in the non-resident population, which grew at an annual rate of 2.7 percent in 1991, rising to 6.5 percent annual growth by 1996, outpacing the growth of residents (see Figure 3). It was at this time that more radical policy reform was being considered in terms of an auction system for permits (discussed below). By end- 1995 the number of foreigners reached more than half a million (about 16 percent of the population), 70 percent of whom held unskilled work permits (Wong, 1997). Enforcement measures shifted toward targeting employers who are the main contractors, holding them responsible for permit abuses among their sub-contractors, with infractions punishable by large fines.

<!-- Start of picture text --> hgure 3<br>Population Growth in Singapore<br>7% _<br>5%<br>4%<br>3%'<br>2<br>0% '<br>1991 1992 1993 1994 1995 1996<br>+ Residents A, Non-residents|<br><!-- End of picture text -->

In response to the increasing pressures of international competition, there has been a concerted effort to attract "talent", namely highly skilled experts, through additional incentives such as housing benefits. Not only is immigration policy integrally linked to macroeconomic policy to achieve growth targets and increase market share, but it also complements population policy. In effect, immigration policies have been used to mitigate the negative demographic trends of a declining indigenous population by encouraging young foreign professionals to settle permanently. Persistent labor shortages have led to more prevalent easing of requirements through exceptions, such as allowing municipal cleaning contractors to hire from non-traditional sources in order to meet demand _(Straits Times Interactive,_ December 3, 1996).

- 9 In June of 1997, the levy was reduced to S$400, and the two-tiered dependency ceilings were raised to 40 and 50 percent. The levy was subsequently raised to S$440.
that Ghana’s GDP was rebased in 2010. This resulted in statistically increasing Ghana’s GDP by 60 per cent from 2007. With the rebasing of GDP, the debt-to-GDP ratios looked very satisfactory on the surface. What policymakers may have overlooked was the fact that the GDP was rebased without an attendant increase in foreign exchange liquidity. In this situation, taking comfort from an apparently low debt-to-GDP ratio was potentially very misleading.

# **6 Declining capital expenditure**

According to Hartwick’s rule (Hamilton and Hartwick 2005), resource-rich countries, in order to maintain wealth and build strong foundations for economic growth, should offset the depletion of their natural resources by commensurate levels of investment in produced capital—primarily infrastructure and human capital. But in the case of Ghana, notwithstanding the new oil revenues and the massive increase in the debt stock, capital expenditure as a percentage of GDP has actually been in decline since 2007. From an average of 12 per cent of GDP between 2004 and 2008, with the lowest level over this period being 9.1 per cent in the 2008 election year, capital expenditure declined to 4.8 per cent by 2014 (Figure 5). This means that before the oil discovery Ghana was spending a much higher proportion of its income on capital investment than it has been since the oil discovery. This decline in infrastructure investment runs counter to what one would expect, and to what one would recommend if the oil revenues were prudently invested.

Figure 5: Capital expenditure as percentage of GDP, 2000–14 <!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Data from Ministry of Finance and Economic Planning, Ghana.

17
Figure 16 displays the Gini coefficients from the various checks for 2010 and 2016. These results indicate no significant differences across the scenarios, reinforcing one of the analysis's central messages that inequality has remained relatively stable over the past 12 years.

Figure 16. Gini coefficients in 2010 and 2016 under different scenarios <!-- Start of picture text --> 36<br>35<br>34<br>33.49 33.47<br>33.08<br>33 33.02<br>32.96 33.04 32.67<br>32.67<br>32<br>31<br>30<br>Current approach Sc.2: Remove decile Sc.3: Use only 2022 in Sc. 2 + Sc. 3<br>constraint training stage<br>2010 2016<br><!-- End of picture text --> Source: Own estimations based on 2010, 2016, and 2022 HIES.

These additional scenarios and their outcomes enhances the robustness of the analysis. None of the alternative scenarios significantly change the overall trends or conclusions drawn from the study of poverty and inequality evolution in Bangladesh over the past 12 years.

# b. The SWIFT method

A second round of robustness checks was conducted using the SWIFT methodology (Yoshida et al., 2022) as an alternative method. SWIFT typically is implemented for estimating total household consumption. However, it was adapted to predict only the additional consumption from previous rounds. This adjustment allowed for a comparison of the SWIFT results with those obtained through the methodology outlined.

The application of the SWIFT method necessitated a cross-validation process before prediction, resulting in a p-value of 0.04. This value indicated the most suitable fit for minimizing the mean square error and the mean absolute difference in poverty rate estimations within the test sample. Multiple imputations were conducted, each with 20 repetitions. Findings showed that the estimated poverty rates for both 2016 and 2010 were consistent across both methodologies. However, in terms of extreme poverty, the SWIFT method produced lower estimates, as presented in Figure 17. These lower estimates of extreme poverty do not fully align with the expected reduction trend, especially when considering the significant improvements observed in various other indicators between 2016 and 2022. The decline in extreme 23
in Ghana were inadequate, which led to an arrears accumulation. Such service disruptions are a major risk to connectivity.

**_Finding 15: Nonavailability of technical expertise has become a key vulnerability for system sustainability in some countries. In many low-income countries, the preferred option is to create an organizational unit within government that attracts professionals on long-term contracts. In more advanced countries, consideration should be given to outsourcing technical operations and maintenance using cloud services._**

3.61 **Technical expertise is critical for systems operation, but it is often difficult to attract at regular government pay scales.**<sup>**50**</sup> The FMIS becomes indispensable for budget management once it is operational and widely applied. It underpins the day-to-day functionality of all government accounting and budget entities. Therefore, any disruption in the system operation can cause significant disruption in the government’s financial operations. Establishing and staffing the supporting organization is crucial for the system’s long-term operational sustainability and the continuity of the government’s budget and financial management operations and reporting. Typically, a central organization is responsible for maintaining database integrity and ensuring that the systems network operation is smooth and uninterrupted. Mostly technical experts should staff this organization, which also provides technical support for end users. However, in many countries the technical skills required for these jobs are not generally available at the normal government salary scales. Government pay for technical staff for such an organization, combined with the lack of career prospects for technical information technology staff within government, is a major area of concern for FMIS project sustainability (in Maldives, Nepal, Pakistan, and Zambia, for example).

3.62 **Countries used several strategies to incentivize staff retention.** Government must plan the central institution’s organizational structure to perform the key technical tasks associated with the FMIS’ operation and maintenance and develop strategies to attract and retain the technical skills this unit requires. Retaining the highly skilled technical personnel required for system operation and maintenance is a common problem many governments face. Several options countries have used to address this issue include the following:

- **Option A - Create an organizational unit:** Establish an organizational unit within government and staff it with employees paid at special market-based salary scales (for external personnel and qualified civil servants) to attract professionals on long-term contracts.

> 50 See project documentation for FMIS operations in Afghanistan, Bangladesh, Guatemala, Hungary, Kazakhstan, Maldives, Moldova, Nepal, Nicaragua, Pakistan, Turkey, Uganda, and Zambia.

**45**

**45**
IHME and UNPD data are quite similar for most countries. The ratio of the upper (lower) bound to the point estimate of ASR in the IHME data is applied to the point estimate of ASR in the UNPD data to obtain upper (lower) bounds on ASR. The median uncertainty interval is 4.4 percentage points wide, while the median adult survival rate is 86 percent. Uncertainty intervals are substantially smaller (larger) for countries with higher (lower) ASR. The 25<sup>th</sup> and 75<sup>th</sup> percentiles of the width of the uncertainty interval are 2.5 and 7.2 percentage points respectively.

- <u>Stunting:  The UNICEF‐WHO‐World Bank Joint Malnutrition Estimates reports 95 percent</u> confidence intervals around estimates of stunting for about 40 percent of observations – primarily those where the JME team has access to the record‐level survey data and can do reanalysis.  These also correspond to the set of surveys for which gender‐disaggregated stunting rates are available, and confidence intervals are reported for all gender‐disaggregated rates.  For the median observation, the 95 percent confidence interval is just under four percentage points wide.  Absent better alternatives, for the remaining observations in the JME database, confidence intervals are imputed using the fitted values a regression of the width of the confidence interval on the stunting rate. Looking at the cross‐section of most recently‐available data for all countries in 2017, and after this imputation, the 95 percent confidence interval is 3.5 percentage points wide, while the median stunting rate is 22 percent.

Transforming the uncertainty intervals for the individual components of the HCI into uncertainty intervals for the overall HCI is complicated by the fact that there is no information on the joint distribution of uncertainty across components of the HCI. To see why this matters, note that if measurement error were uncorrelated across the different components, then the uncertainty intervals for the overall HCI would be smaller than for the components since over‐estimates of some components would be offset by under‐estimates of other components. If by contrast measurement error were highly correlated across components, then uncertainty intervals for the overall HCI would be larger than for the individual components, as over‐estimates on one component would be compounded by over‐ estimates on other components, and vice versa.

Absent any information on the extent of correlation of measurement error across components, the HCI uses the simple approach of constructing a lower (upper) bound of the uncertainty interval for the overall HCI by assuming that each of the components is at its lower (upper) bound. This approach is conservative in the sense that it amounts to assuming that the measurement error is highly correlated 51