rafmacalaba/datause-encoder-data huggingface dataset:
characterized by the observed aggregate moments of the income distribution, together with a single key unobserved mobility parameter. In Section 3, we show how to obtain bounds on this mobility parameter using only information embedded in the evolution over time of the aggregate moments of the income distribution. Section 4 validates our methodology in the PSID data, and Section 5 provides estimates of mobility for a large cross-section of countries based only on the aggregate summary statistics on the distribution of income available in the WID and PovcalNet databases. In Section 6 we apply the lognormality assumption and the estimates of mobility from the previous sections to document the differences between anonymous and non-anonymous growth rates of group average incomes. Section 7 concludes.

# **2.  Lognormal Income Distribution Dynamics**

Throughout the paper, we rely on the following assumption regarding the process generating individual incomes: _<u>Assumption A1: The logarithm of income of individual</u>_ � _at time_ � _,_ ��� _, is generated by the following autoregressive process:_ _where_ � _is an autoregressive parameter satisfying_ 0 ≤�< 1 _;_ �� _is a common factor; the innovations_ �� � � _and_ ��� _are independent and normally distributed with zero mean and variances_ �� _and_ ��� _; and initial_ �� _income is_ ��� = + �� + ��� _._ ��� Although very simple, this data generating process gives rise to non-trivial income distribution dynamics through the interplay of two forces. On the one hand, realizations of the idiosyncratic shock ��� generate changes in individuals’ relative incomes over time. On the other hand, there are two sources of persistence over time in relative incomes: the autoregressive term ������ , and the individual effect �� . Overall income distribution dynamics will reflect the balance of these sources of changes and persistence in relative incomes. Note also that we allow the common factor, �� , and the variance of the idiosyncratic � errors, ��� , to vary over time, but we require the autoregressive parameter � and the variance of the � individual effect �� to be stable over time.

6
**<mark>Table 2: Specification 1 – Correlates of stunting with exposure to E. Coli contamination at household point (N=816 children)</mark>**

|**Dependent Variable: Stunting**|**6 to 23**|**months**|**6 to 60**|**months**|**12 to 6**|**0 months**|**24 to 6**|**0 months**|**0‐60**|**months**|
|---|---|---|---|---|---|---|---|---|---|---|
||(1.1a)|(1.2a)|(1.1b)|(1.2b)|(1.1c)|(1.2c)|(1.1d)|(1.2d)|(1.1e)|(1.2e)|
|High E. Coli Contamination at HH (=1)||‐0.050||0.064*||0.069*||0.125***||0.062*|
|||(0.059)||(0.037)||(0.039)||(0.045)||(0.035)|
|Some E. Coli Contamination at HH (=1)|‐0.032||‐0.002||‐0.007||0.010||‐0.001||
||(0.060)||(0.036)||(0.038)||(0.044)||(0.035)||
|Appropriate water treatment (=1)|‐0.236**|‐0.233**|‐0.212**|‐0.208**|‐0.207**|‐0.202**|‐0.188|‐0.173|‐0.219***|‐0.217***|
||(0.110)|(0.112)|(0.086)|(0.085)|(0.095)|(0.094)|(0.120)|(0.119)|(0.079)|(0.079)|
|**Water Sources**|||||||||||
|_Tube Wells_|‐0.181|‐0.195|‐0.093|‐0.060|‐0.092|‐0.056|‐0.055|0.011|‐0.112|‐0.083|
||(0.188)|(0.188)|(0.096)|(0.099)|(0.099)|(0.102)|(0.112)|(0.115)|(0.085)|(0.087)|
|_Piped Systems_|‐0.114|‐0.110|0.064|0.087|0.083|0.109|0.106|0.159|0.011|0.033|
||(0.267)|(0.268)|(0.135)|(0.136)|(0.137)|(0.139)|(0.152)|(0.155)|(0.127)|(0.127)|
|_(baseline: Unimproved Water)_|||||||||||
|Log of Child Age|‐1.559|‐1.529|1.368***|1.401***|2.306***|2.357***|‐0.550|‐0.079|0.174**|0.182**|
||(1.099)|(1.099)|(0.332)|(0.330)|(0.628)|(0.629)|(2.390)|(2.378)|(0.082)|(0.082)|
|Log of Child Age Squared|0.370*|0.363*|‐0.210***|‐0.215***|‐0.348***|‐0.356***|0.047|‐0.016|‐0.021|‐0.022|
||(0.217)|(0.217)|(0.053)|(0.053)|(0.094)|(0.094)|(0.326)|(0.325)|(0.016)|(0.016)|
|Female Child (=1)|0.024|0.027|0.021|0.021|0.016|0.017|0.019|0.020|0.013|0.013|
||(0.058)|(0.058)|(0.035)|(0.035)|(0.037)|(0.037)|(0.044)|(0.043)|(0.033)|(0.033)|
|Child was ever breastfed (=1)|‐0.023|‐0.023|‐0.297***|‐0.301***|‐0.301***|‐0.308***|‐0.273**|‐0.282**|‐0.281**|‐0.284**|
||(0.217)|(0.211)|(0.105)|(0.104)|(0.108)|(0.107)|(0.115)|(0.114)|(0.112)|(0.111)|
|**Mother's Education**|||||||||||
|_Primary Education_|‐0.037|‐0.033|‐0.038|‐0.037|‐0.046|‐0.043|‐0.049|‐0.042|‐0.039|‐0.037|
||(0.066)|(0.066)|(0.040)|(0.040)|(0.043)|(0.043)|(0.049)|(0.048)|(0.038)|(0.038)|
|_Secondary Education or Higher_|‐0.272***|‐0.274***|‐0.146**|‐0.145**|‐0.134*|‐0.133*|‐0.078|‐0.077|‐0.135*|‐0.132*|
||(0.102)|(0.101)|(0.072)|(0.071)|(0.078)|(0.078)|(0.093)|(0.091)|(0.070)|(0.070)|
|_(baseline: No Education)_|||||||||||
|Community Improved Sanitation Ratio|‐0.065|‐0.048|0.084|0.079|0.090|0.085|0.112|0.111|0.104|0.103|
||(0.158)|(0.159)|(0.093)|(0.092)|(0.098)|(0.097)|(0.112)|(0.111)|(0.087)|(0.086)|

13
Operational Risk. Here SSA uses what is referred to as the Basic Indicator approach, discussed below, that boils down to a capital requirement of 15% of a bank’s gross annual operating income (based on a 3 year average).

The SSA is likely to be relevant for developing countries because it represents the simplest way to comply with Basel II. Indeed, the SSA was developed in part with developing countries in mind. However the real question is, does it imply an improvement to Basel I? The use of Export Credit Agency ratings for sovereign exposures provides little in terms of linking capital to risk for developing country banks. It is unlikely that the reductions in minimum requirements for mortgages are appropriate for developing countries that tend to be weak on creditor protection or have slow and inefficient legal systems. And the reductions in capital requirements for retail exposures may not be appropriate where there is high systemic risk and little real diversification. The preferential treatment in lending to a bank’s own sovereign may be more relevant to higher rated countries than those with weaker ratings where many would advise supervisors to take a much tougher stance. Moreover, it will be interesting to see how many developing countries supervisors apply the export credit agency ratings that may be politically associated to the OECD, to lending to their own sovereign. It seems likely that the situation here may remain as it is now with developing countries following their own rules.

The main change will then be with regards to operational risk. As discussed <u>below, this will tend to increase capital requirements for developing countries that apply SSA (or even SA)28. As discussed below a quantitative impact</u> study that focuses squarely on developing countries is clearly required urgently to consider the effects<sup>29</sup> .

# **2.4.2 The Standardized Approach**

The Standardized Approach (SA) adds the possibility of using private credit Rating Agencies as well as Export Credit Agencies to establish credit risk assessments that would then feed into capital requirements. As credit rating agencies rate corporates and banks, as well as sovereigns, this adds the possibility of using these assessments to link capital to risk more finely.

> 28 However, there may be little difference between applying SSA at 8%, but including operational risk, with Basel I but a higher requirement

> 29 The BCBS has conducted 3 quantitative impact studies - see BCBS (2003c) - but the results are presented on banks from the 13, “committee countries” and, we are told, 30 “other countries”.  The other countries then include industrialized, developing and offshore centers.  It is then difficult to disentagle the effects of the different components of changes in capital requirements for developing country banks.

18
# TABLE OF CONTENTS

|1.<br>INT|RODUCTON............................................||
|---|---|---|
|2.<br>THE|ORETICAL ISSUES<br>.<br>.|4|
|2.1|Towards an Empirical Model of the earing Auction Rate .<br>.|_5_|
|2.2|Policy hypotheses motivated by auction theory<br>..|9|
|3.<br>DAT|A AND EMPIRICAL METHODOLOGY..<br>.<br>..|12|
|......|..................................................|12|
|3.1.|Cointegration Modelling in Repeated, Sequential Auctions<br>.<br>.12||
|4-<br>ESTI|ATION AND HYPOTHESIS TESTING<br>...................|15|
|4.2|Testing policy hypotheses ....................................|19|
|5.CONCLUS|IONS..|22|
|5.1|Summary of results . ......................................|23|
|5.2|Policy lessons ..................|24|
|REFERENC|ES.............................<br>.|27|
|APPENDIX I|:Econometric methodology. ............................<br>..|38|
|TABLE 1:|LIST OF TABLES<br>Tests for unit roots with structural breaks ..........................|30|
|TABLE 2:|Estimation of the equilibrium auction rate for Zambia, Uganda, Ghana and<br>Nigeria .........-|32|

# LIT OF FIGURES

FIGURE 1 a:b,c,d: Regime shifts and the equilibrium auction price in the SSA auctions .36
**Figure 1:** Sectoral development in GDP (1965 - 98)

<!-- Start of picture text --> 24. Agriculture -<br>24 : .... ..~~~~~~~~~~~~~~~~~~~~~~~. ......<br>23.<br>1965 1970 1975 1980 1985 1990 1995 2000<br>25 - Industry . . ..<br>24,<br>1965 1970 1975 1980 1985 1990 1995 2000<br>25.5 Services<br>25<br>24.< ---------. |<br>1965 1970 1975 1980 1985 1990 1995 2000<br>Source: Banco del Ecuador<br><!-- End of picture text -->

# 3. **Data and Methodology**

# **Data Description**

The data used in this study consists of quarterly data for real GDP in the industrial _(Ind),_ agricultural (Agr) and service _(Ser)_ sectors from 1965 to 1998. The data was provided by the Banco del Ecuador. The three series are depicted in log-levels in Figure 1 and show an increase over the whole sample.

The industrial GDP series is marked by one large jump, caused by the rapid increase in industrial output after the foundation of a national oil industry in 1972. In March 1987 industrial GDP fell temporarily due to a major earthquake, which destroyed the export oil pipeline and halted oil exports for _5_ months. The drop in the agricultural GDP series in 1983 is caused by the adverse impact of the natural phenomena of El Ninlo.

Table 1 presents the contribution of the different sectors in percentage shares of total GDP at various points in time. When comparing the contribution of the sectors to the total GDP across time, it appears that the weight of the agricultural sector declined from 25.8% in 1965 to 17.3% in 1998, while the industrial sector managed to increase its 6
_Connecting Lagging and Leading Regions: The role of labor mobility_ individual’s migration decision, however, this solution is not practical. Instead, we deal with this problem by assuming that any correlation between _X k_ and  _k_ is only with components of _X_ that do not vary over time (i.e.,  _k_ ).

Assuming _E_ [  _X k_  _k_ ]  0 , differencing this expression over time will remove any source of bias. While it is unlikely that this assumption holds perfectly, in practice it is a far better option than simply ignoring the role of correlated unobserved local attributes, and it will likely eliminate much of any potential endogeneity bias.

Practically, we expand the first-stage of the model to include data from two census years, restricting the parameters  ,  to remain fixed over that time-period. We then solve for _K K_ two vectors, <sup>ˆ</sup> _k_ 1,  _k_  1 and <sup>ˆ</sup> _k_ ,2  _k_  1 using an extension of the Berry, Levinson, and Pakes (1995) contraction procedure. Finally, the unbiased estimates of  can be obtained by estimating: where _2.2 Data_ 11
about some management practices. Both the human capital of the manager and the management practices potentially determine the capacity of the business to adjust to the COVID-19 shock, and we therefore include these variables in our specifications. The measures of the human capital of the manager are the years of experience in the sector; whether he or she has a post-graduate degree; whether he or she studied abroad; and whether he or she has worked in a multinational or a large business before. The indicators on management practices in the FAT are whether the business offers incentives to improve performance; the number of key performance indicators (KPI) monitored; the frequency of measurement of KPI; and the time horizon of production targets.<sup>3</sup> We follow ( _18_ ) and combine these four measures into a z-score, which we then include in our linear regressions.<sup>4</sup> Our specifications control for other pre-pandemic characteristics also available in the FAT: the age, size, and sector of the business; whether the business is an exporter; and whether the business is foreign owned.

# **2.2 The COV-BPS data**

The COV-BPS is an initiative of the World Bank Group to track the impact of the COVID-19 pandemic on the private sector across the world ( _1_ ). In Cear ́a-Brazil, Senegal, and Vietnam, the COV-BPS was applied on a sub-sample from the FAT. Combined, the FAT and COV-BPS data offer a representative sample of businesses in each country right before and soon after the COVID-19 shock.<sup>5</sup> Our measure of business performance from the COV-BPS is the reported percentage change in sales in the 30 days prior to the interview relative to the same period of 2019. The COV-BPS

> 3See Appendix A for more details on these questions.

> 4The answers to each question in the management module take values in ascending order starting with 1 for no, none, or never (see Appendix A). We compute the mean and the standard deviation of each indicator in each country (with sampling weights), and use these moments to convert each measure into a z-score. Our management score is the simple average of the four z-scores within a firm.

> 5The FAT sample is a representative sample drawn from the most recent and comprehensive establishment census available in each country (see ( _17_ )). The COV-BPS sub-sample was similarly randomly selected to remain nationally representative for Senegal and Vietnam, and representative at the state level in the case of Cear ́a-Brazil. We provide a description of the sampling strategy in Appendix A. Data collection was conducted by the same agencies that implemented the FAT data in each country. Unless we note otherwise, we do not weight our calculations with sampling weights.

6
the end of the 21st century.

Scenario T: The GHG emissions constraint is introduced. The scenario is illustrative of the range of regulatory uncertainty surrounding global GHG emissions based on IPCC 4AR projections (IPCC 2007a,b), and recent aspirations of a number of countries to incorporate land based GHG mitigation in their climate policies.<sup>20</sup> In this scenario we introduce a maximum target, amounting to a 60% reduction in baseline GHG emissions from petroleum products, crop production and terrestrial carbon uxes by 2100. This corresponds to the upper bound of regulation, aimed at achieving CO2 equivalent concentration (including GHGs and aerosols) at stabilization of 445-490ppm. After the target is introduced in 2025 it rapidly becomes more stringent, reaching the maximum stringency by 2050.

We also consider combinations of scenarios A and E (scenario AE) and scenarios A, E, and T (scenario AET). We assume that all of these alternative scenarios are fully anticipated so that their eect is felt already at the outset in 2005. More detailed information on construction of counterfactual scenarios is provided in the technical appendix, section C.2.

# 4.1 Results of Counterfactual Scenarios

Figures 2, 3, and 4 describe the results of simulations of changes in the optimal allocation of global land-use, GHG emissions, consumption of goods and services that draw on land resources, and consumption of biofuels for scenarios A, AE, and AET. For scenario A, we report changes, which are incremental to the model baseline. For scenario AE, we report incremental changes to scenario A. For scenario AET, we report incremental changes to scenario AE. The results for scenarios E and T alone are available in the technical appendix, tables D.1 and D.2. This section concludes with a comparison of the `perfect storm' (AET) to the baseline in order to understand the combined impact of adverse climate impacts, higher energy prices and stringent climate regulation on land use, and particularly food consumption.

> 20In 2008 New Zealand passed legislation to include commercial forestry sector in the emissions trading scheme. Regulation of other land-use emissions is expected to take place in 2015 (Source: the New Zealand's Ministry of Agriculture and Forestry website: www.maf.govt.nz). In 2010 the European Commission launched a public consultation on whether emissions and removals of greenhouse gases related to land use, land use change and forestry (LULUCF) should be covered by the EU's target of cutting GHG emissions to 30% below 1990 levels by 2020 (Source: the European Commission's website: http://ec.europa.eu/commission_ 2010-2014/hedegaard/headlines/news/2010-09-10_01_en.htm).

25
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_Role of NGOs In Health Delliery Africa Halth Policy Study_ .21The strength of NGOs' health delivery In Swaziland, then, lies not in the coveragc of the services but in the innovative and flexible manner in which they respond to these needs. In many cases, their programs provide a model for government services in, for example, providing inter-sectoral nutritional rehabilitation rather than tackling malnutrition strictly along professional lines. The role of NGOs in Swaziland is not merely to substitute for government services, but also to act as catalysts for community development. By being able to draw on pools of volunteer labor, they mobilize communities and may also help to broaden awareness of health problems.

# _Malawi_

The Private Health Association of Malawi (PHAM) is a coordinating and advisory association of missions of various denominations operating health facilities in Malawi.10 PHAM has three main purposes: 1) to expand and improve church health care fiitdes 2) to run training programs for nurses, midwives and medical assistants; and 3) to implement primary health care programs. Member facilities pay a yearly fee, which supports _50_ percent of the cost of running PHAM; the other half comes from donors. The building of health units is done by the missions themselves, with the help of the overseas church organizations, but PHAM helps advise in the daily running of the facilIties and acts as a liaison between the missions and the government. Non-church-related NGOs or institutions-such as the University of Malawi and a sugar estate-are associate members of PHAM. Until 1981, PHAM was mostly Involved in providing hospital-based services, but since 1982, with support from OXPAM, it has begun to act as a primary health care coordinator as well. With donor support, it is able to offer refresher courses every year. PHAM services are spread throughout Malawi, though the hospitals tend to be more rural than government services. In deciding upon location, PHAM often establishes facilities where there was no previous service. There is no duplication between **PHAM** and government services, which tend to be located at least 15 km apart unless the need for them is greater.

All PRAM facilities charge fees, with the exception of under-five clinics and immunization. They apply a sliding scale, however, to allow the poorest access to their services. Revenue collected through fees is managed at individual units, although they report to PHAM their yearly budget. Foreign exchange comes from donors, for most missions have ties abroad. PHAM facilities procure their drugs from the government central medical supply or directly from donors.

10. This case-study is based on an interview with Mr. Regent Gondwe, Execudve Secretary of the Private Hospital Association of Malawi (PHAM), Lilongwe, June 1989 and PHAM's own literature.
# 4.2 Results at the micro level

# Between- and within- country evolution of inequality

Using the macro results about average income convergence and changes in wage premia, the microsimulation part of the economic framework estimates the impact of the education wave at the household level.<sup>24</sup> The microsimulation results confirm that the world will become more equal by 2030 as it becomes more educated ( Table 5). The (individual-based) Gini index falls from 65.8 in 2012 to 62.6 in 2030, while the Theil-L index is reduced from 90.7 to 76.6. Compared to recent patterns, these results suggest a continuation of the reduction in global inequality. During the great doubling of the global labor force, global inequality decreased by 2.3 percentage points in a 20-year interval from 1988 to 2008 (Lakner and Milanovic 2016). Our education wave scenario shows a comparable reduction of 3.2 percentage point, with the global Gini index falling during the period 2012 to 2030. The Theil-L index also suggests a similar reduction of close to 14 percentage points by 2030. Compared to Lakner and Milanovic (2016), our initial measure of Gini and Theil-L indices are smaller, which may be explained by differences in benchmark year, data sources, country coverage, and the slight improvement in equality when the education wave is measured by 2012.<sup>25</sup> Table5: Global inequality will go down in a more educated world

|Inequality measures|2012|2030- Educa<br>Demographic|tionWave<br>Full<br>simulation|2030 - No<br>Wave|
|---|---|---|---|---|
|Gini index|65.8|65.5|62.6|63.2|
|Theil-L|90.7|91.0|76.6|78.6|
|Theil Decompositions:|||||
|Between regions (%)|51.7|48.0|41.4|41.0|
|Within regions (%)|48.3|52.0|58.6|59.0|
|Between countries (%)|57.2|53.6|49.1|48.6|
|Withincountries (%)|42.8|46.4|50.9|51.4|
|Percentile 75 / Percentile 25|5.5|5.4|6.7|6.6|
|Mean, $(PPP)|416.9|430.3|835.2|827.4|
|Coeff.ofvariation|3.1|3.3|2.4|2.5|

Source: Authors’ calculations based on GIDD projections.

What are the factors explaining the projected reduction in inequality? Using the Theil index, it is possible to decompose inequality between and within groups. The central panel

> 24 The microsimulation exercise is done at the country level using household surveys, but results in this section are aggregated to analyze inequality at the global level. The CGE macro results provide yearly simulations from 2011 to 2030. The microsimulation uses 2012 as a reference year for the harmonized household surveys. Thus, this section uses projections from 2012 to 2030 for the microsimulation exercises.

> 25 The latest year available in Lakner and Milanovic (2016) is 2008 for which the Gini is 70.5.  Our initial year is 2011, for which our Gini index is 65.8. Thus, we focus the comparison on relative changes. Ferreira et. al. (2015) describe the differences across several cross-national inequality databases.

23