rafmacalaba/datause-encoder-data huggingface dataset:
members studying abroad, transfers to travelers undertaking trips, and small trade transactions are often transferred through the same money transfer operators as remittances, and can be difficult to distinguish in the data. Some of these types of transactions are likely to be increasing over time, as the internet facilitates person-to-person trade across borders, and the popularity of study abroad programs grows. Conversations with officials in charge of monitoring remittances also reveal the suspicion that property market booms in some developing countries lead to large surges in measured remittance flows, even though the purpose of these transactions was often for migrants to buy property for themselves. As a result we should not automatically conclude that recorded remittances are necessarily getting closer to the truth in levels over time.

# **4. Growth regressions may have too little power to detect the effects**

Disregard for a moment the previous section, and suppose instead that the estimates are accurate: true remittances have risen as much as the estimates indicate. If that were correct, and those increases caused growth, could we detect that effect in cross-country data? Would feasible hypothesis tests have sufficient power?

Here we first estimate typical standard deviations in the same cross-country data on growth and remittances used above. Table 4 shows these statistics for two different periodizations of the 1990–2010 data: as a cross-section of changes over 20 years, and as a panel of changes during two 10-year periods. (Prior to 1990, remittance data coverage becomes poor.) We then use these estimates as an input to power calculations. We estimate the power of a test of the null hypothesis that, in a regression of Δln(GDP/capita) on Δ(remittances/GDP), the coefficient on remittances is zero. We use the power estimates for linear regression derived by Dupont and Plummer (1998).

Power is very low in a bivariate regression with 20-year periods. The upper portion of Figure 9 shows power estimates for this regression at various sample sizes. For example, suppose that about half of remittances translate directly into additional GDP per capita—an optimistic assumption—so that the coefficient on Δ(remittances/GDP) is 0.5. Even if we had data on 150 countries (more than currently available), in this bivariate setting there is only about a 10% 21
# **B Appendix Figures and Tables**

Figure A.1: Study area <!-- Start of picture text --> This map was produced by the Cartography Unit of the World Bank Group. The boundaries,<br>colors, denominations and any other information shown on this map do not imply, on the part<br>of the World Bank Group, any judgment on the legal status of any territory, or any<br>endorsement or acceptance of such boundaries.<br>IBRD 46408 |<br>FEBRUARY 2022<br>ISLAMABAD<br>BHUTAN<br>PAKISTAN DELHI NEPAL<br>THIMPHU<br>KATHMANDU<br>DHAKA<br>JAUNPUR DISTRICT<br>I N D I A<br>BANGLADESH<br>B a y o f<br>B e n g a l<br>SRI LANKA<br><!-- End of picture text -->

44
|**Table A3: Correla**|**tions between ma**<br>|**in explanatory v**<br>|**ariables**<br>||
|---|---|---|---|---|
||**Overall**<br>**Corruption**<br>**(CI avg.)**|**Petty**<br>**Corruption**<br>**Incidence (CI**<br>**avg.)**|**Petty**<br>**Corruption**<br>**Depth (CI**<br>**avg.)**|**Financially**<br>**Constrained**<br>**(CI avg.)**|
|Financially Constrained (CI avg.)|0.136|0.152|0.147|1.000|
|Initial Employment (logs)|-0.044|0.009|0.012|-0.008|
|Age of firm (logs)|-0.046|-0.059|-0.047|-0.092|
|Manager Experience (logs)|-0.070|-0.097|-0.085|-0.088|
|Exports (proportion of sales)|-0.035|-0.013|-0.012|-0.045|
|Foreign Ownership (% ownership)|0.007|0.040|0.027|0.012|
|Multi-firm (dummy)|-0.024|0.027|0.026|-0.139|
|Female Manager (dummy)|-0.034|-0.007|-0.006|-0.036|
|Power Outage (logs)|0.179|0.227|0.229|0.170|
|Informal Competition (dummy)|0.074|0.044|0.026|0.059|
|Firm Bought Assets (dummy)|-0.022|-0.020|-0.028|0.016|
|Time Tax (%)|0.035|-0.006|-0.012|0.000|
|Courts: Obstacle Severity|0.074|0.084|0.071|0.024|
|Crime Losses (% of sales)|0.104|0.048|0.027|0.038|
|Taxes: Obstacle Severity|0.000|-0.017|-0.028|0.055|
|Labor Laws: Obstacle Severity|0.057|-0.005|-0.019|0.036|

Sample used is the baseline sample as in Tables 1 and 2. CI avg. denotes Country-Industry cell average excluding the own firm as described in section 2.

53
Table 6: Participation in labor market programs (FOSIS), by initial conditions (cohorts 20022006).

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Variable|Hea|d|Spo|use|
|Years after start|2|4|2|4|
||Panel A|: Not emp|loyed before|2002|
|Eligibility (ITT)|0.010***|0.004|0.009***|0.003|
||(0.003)|(0.003)|(0.003)|(0.003)|
|Participation (IV)|0.062***|0.024|0.050***|0.016|
||(0.016)|(0.021)|(0.013)|(0.015)|
|Control Mean|0.008|0.040|0.015|0.023|
|Observations|43,384|19,269|113,299|55,705|
||Panel|B: Emplo|yed before 2|002|
|Eligibility (ITT)|0.002|-0.002|-0.014|0.009|
||(0.002)|(0.002)|(0.009)|(0.007)|
|Participation (IV)|0.014|-0.010|-0.068|0.071|
||(0.009)|(0.010)|(0.051)|(0.052)|
|Control Mean|0.010|0.017|0.028|0.121|
|Observations|149,426|75,439|13,926|6,703|
|P-Value: HA:β<sup>_iv_</sup><br>0 <sup>_>_ β</sup><sup>_iv_</sup><br>1|0.000|0.000|0.000|0.808|

Note: See table 3 for a description of specification used in rows (ITT) and see table 4 for the description of specification used in rows (IV). The coefficient estimate in rows (ITT) is the indicator of eligibility, _Eim_ , and the coefficient estimate in rows (IV) is the indicator of participation in CS, _CSim_ .

The last row presents the p-value for the null hypothesis that the effect for those not employed prior 2002 equals the effect on those employed, H0: β<sup>_iv_</sup> 0<sup>= β</sup><sup>_iv_</sup> 1<sup>, against the alternative that the effect is larger for those</sup> not employed prior to 2002, HA: β<sup>_iv_</sup> 0<sup>_>_β</sup><sup>_iv_</sup> 1<sup>.</sup> Robust standard errors are reported in parenthesis clustered at municipality of residence when eligibility is evaluated. * significant at 10%; ** significant at 5%; *** significant at 1% (the critical values for inference are adjusted for multiple hypotheses testing using the procedure in Algorithms 4.1 and 4.2 of Romano and Wolf, 2005).

32
# Figure 39: State Level Information Deprivation

Information Deprivation - by State - MICS 80% 70% 60% 50% 40% 30% 20% 10% 0%

# Source: Authors’ estimates based  on MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Estimates are based on children aged 5-17.

Figure 40: State Level Information Deprivation a) b) c) <!-- Start of picture text --> Information Deprivation - by State Information Deprivation - by Information Deprivation - by State<br>in North Central - MICS State in North East - MICS in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 5-17 Children 5-17 Children 5-17<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe<br>d) e) f)<br>Information Deprivation - by Information Deprivation - by<br>Information Deprivation - by<br>State in South South MICS State in South West- MICS<br>State in South East - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 5-17 Children 5-17 Children 5-17<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17. Note: Individuals with missing age were dropped from the analysis.

49
68 the technical rate decreases the funding ratio by approximately 10-15 percent. Consequently, the amount of the funding gap would also increase. In 2003, 11.9 percent of all pension funds reported an underfunding. In 2002, this number had been considerably higher, amounting to 20 percent. This improvement can partly be explained by the recovery of financial markets, starting in April 2003. Another reason for this amelioration is selective action that had been taken to correct the funding gap.

The group of registered pension funds is split into two subgroups. The larger part consists of all _pension funds under private law_ and of those pension funds under _public law without a public warranty_ . In 2003, 16.4 percent of this subgroup was underfunded. In 2002, this figure was 28.7 percent. The worst situation in terms of underfunding, however, concerns pension funds under _public law with a public warranty_ . This is not surprising, given the state–financed reinsurance, which covers potential financing gaps. Thus, they have a weaker incentive to prevent a funding deficit. In 2003, more than half of pension funds with a public warranty reported an underfunding.

The financial situation of pension funds from the perspective of members is as follows. In 2003, the fraction of members belonging to a pension fund (without “Collective– Institutions”) with a funding deficit amounted to more than 30 percent. Even though the situation has greatly improved since 2002, where this fraction was almost 50 percent, it remains somewhat critical.

**Table 25  Extent** **<u>of Underfunding</u>**<sup>**61**</sup>

|Pension Funds|2003|2002|
|---|---|---|
||With<br>in %<br>underfunding|With<br>in %<br>underfunding|
|Registered PF:|||
|PF without public warranty|339<br>16.40%|604<br>28.70%|
|PF with public warranty|45<br>54.90%|38<sup>62</sup><br>49.4%|
|Total registered|384<br>17.80%|642<br>29.40%|
|Total PF with underfunding|431<br>11.90%|722<br>20.00%|

Source: 1: Bundesamt für Sozialversicherung (2004a), p.4, T2 2: Bundesamt für Sozialversicherung (2003), p.4, T2 Table 25 provided an idea of how many pension funds have a potential solvency problem. In 2003, the total amount of the funding gap comprising all the pension funds was 25,376 million CHF (16,720 m Euro or 21,884 m dollars). This corresponds to a funding gap of almost 20 percent of the total assets of underfunded pension funds or 6.3 percent of the total assets for all pension funds. The number of underfunded pension funds decreased from 2002 to 2003 and so did the size of the funding gap in absolute terms. The corresponding numbers are reported in Table 26.

> 61  without “Collective-Institutions”

> 62  This figure can not be compared with the one in 2003. This is due to the fact that in 2002 not all pension funds under public law with a funding gap reported their figures.
- 22 -

comfort, as the persistence of high fees has been one of the main criticisms of the Chilean system. These are the levels of fees charged by expensive active/high turnover equity mutual funds in the US. The fees charged by indexed (passive) equity mutual funds, bond funds, and money market mutual funds are much lower than 1 percent of assets, and so are the fees charged by occupational pension funds in most countries.<sup>15</sup>

# **Figure 7: Long Run Total Fees over Contributions for the Average MPF**

<!-- Start of picture text --> 40%<br>37.10%<br>35%<br>30.61%<br>30%<br>25%<br>20%<br>15%<br>10%<br>6.49%<br>5%<br>0%<br>TOT C OP C AM C Past<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Note: real wage growth = 4%, rate of return = 8%, asset management fees = 0.97% of assets and operating fees: 6.49 % of contributions.

Source: Own calculations on HFSA data.

# **Figure 8: Long Run Total Fees over Assets for the Average MPF**

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>1.18%<br>0.97%<br>0.21%<br>0%<br>TOT C OP C AM C Past<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Note: Real wage growth = 4%, rate of return = 8%, asset management fees = 0.97% of assets, and operating fees = 6.49% of contributions.

Source: Own calculations on HFSA data.

15 See Colins (2003), Investment Company Institute (2003), and Rocha (2006).
## II. MEASUREMENT OF THE PUBLIC SECTOR DEFICIT IN ARGENTINA

In the last 15 years many important. changes occurred in the distribution of revenues and expsnditures among different levels of government in Argentina. These changes were due to: i) the transfer of important expenditure items, like education (elementary and secondary) and local transport systems (like the Buenos Aires metro), from the central to the provincial and city governments; and ii) changes in the tax laws and the rules under which tax revenues are distributed among the central and the provincial governments (the Federal Coparticipation Law). Provincial and local governments have no well-defined budget constraints in Argentina, and the distributicn of resources (which are collected mainly by the central governmen is subject to a highly unpredictable pattern. This makes very importait, in the Argentine case, to work on the basis of the consolidated nonfinancial public sector data. Among the studies dealing with the behavior of the consolidated non-financial public sector in Argentina we may mention the stuay by FIEL(198-1) and also Schenone(1987).

In the Argentine case it is also very important to account for the operations of the Central Bank as sources of substantial amounts of revenue (due to inflationary money creation) and no less substantial losses, due to: i, the purchase of international reserves (including those required to service the foreign debt), ii) the offering of swiaps and other "exchange insurance" mechanisms, which have been frequently used to attract short term foreign financing, and iii) losing operations with the domestic financial system (including the bailout of failing financial institutions). In subsection II.2 below we present estimates made by F.I.E.L. of tosses made by the Central Bank of Argentina, which should be considered as components of public sector expenditures.

# **II.1 Consolidated Non-Financial Public Sector**

This subsection presents the official data on the consolidated non-financial public sector ("above the line") expenditure, revenue, and financing needs (deficit), according to the so called "international methodology," as well as the "below the line" financiig flows. The international methodology does not consider the current revenue and expenditure of public enterprises as components of the publir sector's revenue and expenditure; it only computes as such t,geir operating surplusas or deficits.

8
Figure 2: Intervention and survey timing

|Day||+0 days||+1|day|+8|days|
|---|---|---|---|---|---|---|---|
|-2<br>||HH Visi|t and Survey|; Announce|ment of Mon|ey Prize||
|-1||||||||
|0|MK 1,000|MK 25,000|MK 25,000|||||
|1||||MK 25,000|MK 25,000|||
|2||||||||
|3||||||||
|4||||||||
|5||||||||
|6||||||||
|7|Survey 1|Survey 1|Survey 1|||Survey 1<br>|Survey 1<br>|
|8||||Survey 1|Survey 1|MK 25,000|MK 25,000|
|9||||||||
|10||||||||
|11||||||||
|12||||||||
|13||||||||
|14|Survey 2|Survey 2|Survey 2|||||
|15||||Survey 2|Survey 2|Survey 2|Survey 2|
||Activity at<br>home<br>||Cash|||||
||Activity at|||||||
||bank||Direct depo|sit||||

27
# 4 Emigration rates

We count as migrants all adult (25 and over) foreign-born individuals living in an OECD country. However, it is obvious that the pressure exerted by 1,036,000 Indian skilled emigrants (4.3% of the educated total adult population) is less important than the pressure exerted by 15,696 skilled emigrants from Grenada (84% of the educated adult population). A more meaningful measure can then be obtained by comparing the emigration stocks to the total number of people born in the source country and belonging to the same gender and educational category. This method allows us to evaluate the pressure imposed on the labor market in the source country.

## 4.1 Methodology and data sources

In the spirit of Carrington and Detragiache (1998), Adams (2003), Docquier and Marfouk (2006) or Dumont and Lemaitre (2006), our second step consists in calculating the brain drain as a proportion of the total educated population born in the source country. Although our analysis is based on stocks (rather than ‡ows), we will refer to these proportions as emigration rates. Denoting Nt;g;s<sup>jasthestockofindividuals</sup> aged 25+, of skill s, gender g, living in source country i, at time t, we de...ne the emigration rates as In particular, m<sup>i</sup> t;g;h<sup>can be used as a proxy of the brain drain in the source country i.</sup> This step requires using data on the size and the skill and gender structure of the adult population in the source countries. Population data by age are provided by the United Nations<sup>14</sup> . We focus on the population aged 25 and more. Data are missing for a couple of countries but can be estimated using the CIA world factbook<sup>15</sup> . Population data are split across educational group using international human capital indicators. Several sources based on attainment and/or enrollment variables can be found in the literature. As in Docquier and Marfouk (2006), human capital indicators are taken from De La Fuente and Domenech (2002) for OECD countries and from Barro and Lee (2001) for non-OECD countries. For countries where Barro and Lee measures are missing, we predict the proportion of educated using CohenSoto’s measures (see Cohen and Soto, 2007). In the remaining countries where both Barro-Lee and Cohen-Soto data are missing (about 70 countries in 2000), we transpose the skill sharing of the neighboring country with the closest enrolment rate in secondary/tertiary education, the closest gender gap in enrollment rates and/or the closed GDP per capita. This method gives good approximations of the brain drain rate, broadly consistent with anecdotal evidence.

> 14See http://esa.un.org/unpp.

> 15See http://www.cia.gov/cia/publications/factbook.

18
shares include Mozambique and Central African Republic, both with 31% of income flowing to the top 1% – but in both countries these measurements are obtained from adjusted survey data, so are arguably less precise. It is crucial to note, nonetheless, that this type of detailed inequality accounting is not available for the majority of countries, so it is not clear how many of them would fare in comparison with Honduras. Its regional peers like Guatemala, El Salvador and Nicaragua do not have similar estimates – the only country in the region with similar estimates is Costa Rica which registers a much lower 21% share flowing to the top 1%. We also include in the table the estimated top shares for Honduras currently available on the WID platform – the share of the top 10%, at 52%, is not too dissimilar to our results; the share of the top 1%, however, is only 18% or 12 p.p. below our estimates. This further suggests that inequality in other countries in the region, for which detailed accounting exercises have not been performed, is likely much higher than current estimates.

To illustrate the importance of the series of adjustments that leads us to our main estimates using the DINA guidelines, in Figure 5 we present the share of income flowing to the top 1% highest earners measured using three different distributions. First, we show the share of the top 1% highest earners when we use only survey data, without any further adjustments. In that series, the share fell from approximately 15% in the early 2000s to less than 10% by 2019, showing a steady decline in the period and a level of top share three times smaller than our estimates using national account adjustments. We also present the top 1% share using the microdata complementing survey data with tax records, but before any adjustments to national accounts. For recent years, this series roughly doubles the top 1% share from 10% to 20%, but still falls 5 - 10 p.p. short of the estimates using National accounts information. This gap is explained in large part by the discrepancy between microdata and national accounts’ aggregates of capital income.

# **7 Additional Results - Tax panel data**

In this section we explore more in depth the top of the income distribution using tax records. We focus on three dimensions – the importance of undistributed corporate profits at the top; the dynamics and persistence of individuals at the top; and gender gaps among highest earners. For all the analyses in this section, we use the income distribution observed in the tax records, without any adjustments for macroeconomic aggregates or merging with survey microdata. For a more detailed description of of . income levels and composition in tax records see Appendix E 15
Policy Research Working Paper 5249

# **Abstract**

The Government of Cameroon has declared poverty reduction through strong and sustainable economic growth the central objective of its socioeconomic policy. This paper uses available household survey data to assess the performance of the economy with respect to this objective over the period 1996–2007. The authors use counterfactual decompositions based on both the Shapley method and the generalized Oaxaca-Blinder framework to identify proximate factors that might explain differences in observed outcomes over time, across regions and households. The concept of pro-poorness provides a basis for a normative evaluation of these outcomes. The analysis of changes in the size distribution of economic welfare reveals that formal sector employment, access to credit, education, and urban residence are characteristics that bring significantly high returns to households. Employment in smallholder agriculture has a negative impact on welfare across quantiles. Economic growth was accompanied by significant poverty reduction between 1996 and 2001. But poverty barely decreased between 2001 and 2007 due to very weak growth. Over the same period, household investment in human capital took a serious hit. Given the additional finding that the pattern of growth is characterized by urban bias and regional disparity, the overall assessment is that economic growth has been weakly pro-poor in Cameroon. There is therefore a need to re-examine and possibly reform the mechanisms governing the allocation of public resources designed to support individuals’ efforts to improve their standard of living.

This paper—a product of the Poverty Reduction and Equity Group, Poverty Reduction and Economic Management Network—is part of a larger effort in the network to disseminate methods of assessing the poverty and distributional implications of economic growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at bessamannash@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team