rafmacalaba/datause-encoder-data huggingface dataset:
# **Setting and Data**

The data for this study are from the Kagera Region, an area far from the capital and coast, bordering Lake Victoria, Rwanda, Burundi and Uganda. It is overwhelmingly rural and primarily engaged in producing bananas and coffee in the north and rain-fed annual crops (maize, sorghum, cotton) in the south. Relatively low-quality coffee exports and agricultural produce are its main source of income. It is not one the poorest areas of Tanzania, with mean per capita consumption near the mean of mainland Tanzania in 2000. Growth and poverty reduction appears to mirror the rest of Tanzania: real GDP growth was just over 4 percent per year between 1994 and 2004, while poverty in Kagera is estimated to have changed very slightly, falling from 31 percent to 29 percent between 1991 and 2000/01 using the national data.

It is nevertheless an area of early and high HIV-prevalence. Kwesigabo _et al_ . (2005) reported on three population samples in 1987 in districts of contrasting exposure in Kagera, finding in 1987 overall age-adjusted HIV-prevalence in urban Bukoba district of 24.2%, of 10.0% in Muleba district, a medium-prevalence area, and of 4.5% in Karagwe district, a lowprevalence area. Subsequently, this baseline sample was followed further, and prevalence rates appear to have been coming down, both due to mortality and lower incidence. In urban Bukoba, prevalence went down to 18.2% in 1993 and 13.3% in 1996. In the other areas studied, prevalence also declined considerably, to 4.3% and 2.6% in Muleba and Karagwe respectively. Kwesigabo _et al_ . (2005) note that the decline in these areas of different initial HIV-exposure suggests that the epidemic may have been arrested early without necessarily peaking to saturation levels. Nevertheless, and relevant for our study, a rapid decline in prevalence, even without any new incidence of HIV, is only possible due to large mortality in this period.

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**Table 10: Characteristics of importers identified as risky — suits**

|Tax<br>identification<br>number|Declared value (US$)|Unit value (US$)|Taxes (duties and VAT) paid<br>per unit imported (US$)|
|---|---|---|---|
|0000000016|43,120|0.64|0.28|
|0000000017|12,083|0.78|0.34|
|0000000018|6,126|0.58|0.26|
|0000000019|5,754|0.56|0.25|
|0000000020|5,629|1.84|0.81|
|0000000021|2,735|0.76|0.33|
|0000000022|2,112|2.31|1.01|
|0000000023|1,917|0.64|0.28|
|0000000024|1,565|0.59|0.25|
|0000000025|1,506|0.72|0.32|

# **VI. First Lessons and Customs Reforms**

To supplement the risk analysis based on compliance, this study has identified some risky import operations and some risky economic operators. Although we are not yet able to determine what extent we have effectively identified non-compliant operators, we are already in a position to present certain findings: First, by reducing information asymmetry, mirror analysis seems to be a powerful intelligence and investigative tool. While gap indicators aim to provide an estimate of tax losses, ratio indicators aim to identify the type of fraud.

Second, combining mirror analysis with an analysis of declaration patterns is useful to identify and target some high-risk economic operators.

Third, in order to assess the compliance of a declaration, investigators should use all sources of available information, whether internal or external.<sup>38</sup> Abnormal statistics (inconsistent unit values, etc.) suggest fraud. The use of the internal information, that is, using declaration patterns of "competing importers", can help identify non-compliance. Similarly, the use of external sources of information, such as information gleaned from sources other than customs databases, can also reveal bad practices. For instance, based on a comparison of declared unit values with international market prices, we have highlighted probable misclassifications for fertilizers.

Fourth, only effective inspections have a deterrence effect. As highlighted for rice imports, non-compliance may be frequent despite an inspection rate close to 100 percent.

> 38 See Chalendard (2015).

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**Figure 2.6: Year-to-year Employment Changes by Economic Activity** **<u>(in persons)</u>**

<!-- Start of picture text --> Figure 2.a. Agriculture Figure 2.b.Mining and electricity<br>300000 60000<br>40000<br>200000<br>20000<br>100000 0<br>-20000<br>0<br>-40000<br>-100000 -60000<br>-80000<br>-200000<br>-100000<br>-300000 -120000<br>Quarter Quarter<br>Figure 2.c. Manufacturing Figure 2.d. Construction<br>600000 100000<br>400000 50000<br>200000 0<br>0 -50000<br>-200000 -100000<br>-400000 -150000<br>-600000 -200000<br>-800000 -250000<br>Quarter Quarter<br>Figure 2.e. Trade and commerce Figure 2.f. Services<br>900000<br>800000<br>800000<br>600000 700000<br>600000<br>400000<br>500000<br>200000 400000<br>300000<br>0 200000<br>100000<br>-200000<br>0<br>-400000 -100000<br>-200000<br>-600000<br>Quarter Quarter<br>2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>Variation (quarter to quarter) Variation (quarter to quarter)<br>2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>Variation (quarter to quarter) Variation (quarter to quarter)<br>Variation (quarter to quarter) 2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04 Variation (quarter to quarter)<br><!-- End of picture text --> _Source:_ Own calculations using data from ENOE (various years). Figures for the fourth quarter of 2010 are preliminary.

9
with original weights (29.3%), they are still significantly lower than the national average poverty rate (38.2%). This finding suggests that reweighting alone does not fully address the bias in the phone owner sample.

Figure 2 (subfigure 2) shows that the SWIFT models trained with the full sample of EICV5 project the national poverty rate more accurately. Note that in the case of the Rwanda experiment, we develop urban and rural models separately. Applying the SWIFT models to the reference data set leads to a national projected poverty rate of 37.5 percent,<sup>9</sup> which is within the 95 percent confidence interval of the actual poverty rate of 38.2 percent. The details of the models are available in Appendix 1.

Applying the SWIFT models to the phone owner sample, even with original weights, greatly reduces the sampling bias in poverty estimates. The SWIFT models project a poverty headcount rate of 36.6 percent in the phone owner subsample, which is just 1.6 percentage points lower than the national average rate.

Additional reweighting procedures show slight improvements in poverty projections compared to the abovementioned SWIFT poverty projection with unadjusted weights. PSW and non-PSW adjustments produce a SWIFT-based poverty rate of 37.1 percent, which is around one percentage point smaller than the national average estimate (38.2 percent).<sup>10</sup> _Table 2_ displays the summary statistics of all variables involved in this process, namely, variables included in the reweighting procedure, variables included in the SWIFT projection model, and other untargeted indicators not included in either procedure. The weights calculated by PSW-type reweighting techniques produce close estimates for all the aforementioned indicators, and the combination of the PSW and non-PSW adjustments provides incremental improvements.

> 9 The SWIFT model for all countries can be seen in Appendix 1. The simulation results of the SWIFT model are available upon request.

> 10 Adding the non-PSW adjustment (maxentropy), compared to PSW (Lee 2006), has a negligible impact on the SWIFT based poverty projection. The SWIFT-based poverty rate with the inverse propensity score is 37.3 percent, which is the closest to the national poverty rate among all reweighting procedures tested in this experiment. Results are available upon request. Note that the standard errors of all SWIFT-based poverty projections are significantly larger than that of the rural poverty rate estimated from actual consumption data in EICV5 due to the following two reasons: (i) the sample size of the phone owner sample is smaller than the full rural sample of EICV5, and (ii) the SWIFT poverty projections inevitably add prediction errors.

13
Dunning, John H. (1996). “The role of FDI in a globalizing economy,” in C. J. Green and T. L. Brewer (eds.), Investment Issues in Asia and the Pacific Rim, New York: Oceania, 43-64.

- Dunning, John H. (2002). “Determinants of Foreign Direct Investment: Globalization Induced Changes and the Role of FDI Policies,” Background Paper for the Annual Bank Conference on Development Economics held in Oslo, World Bank, Washington, D.C. Dounloadabel at

<u>http://wbln0018.worldbank.org/eurvp/web.nsf/Pages/Paper+by+Dunning/$File/DUNNIN G+1.PDF</u> (August 22, 2007).

International Monetary Fund (2007). Direction of Trade data http://www.imfstatistics.org/dot/.

Kaufmann, Daniel., Aart Kraay, and Massimo Mastruzzi (2007). “Governance Matters VI:

- Aggregate and individual governance indicators 1996-2006,” World Bank Policy Research Working Paper No. 4280, Washington, D.C.

- Larson, Donald F. and Gunnar Breusted, (forthcoming). Will markets direct Investments under the Kyoto Protocol? Lessons from the Activities Implemented JointlyPpilots. _Environmental and Resource Economics_ .

- Larson, F. Donald, Philippe Ambrosi, Ariel Dinar, Shaikh Mahfuzur Rahman and Rebecca Entler (2008). “A review of Carbon Market Policies and Research”. _International Review of Environmental and Resource Economics_ (In Press).

- Lecocq, Franck and Philippe Ambrosi (2007). The Clean Development Mechanism: history, status, and prospects. Review of Environmental Economics and Policy 1(1), 13451.Lecocq and Ambrosi (2007)

- Michaelowa, Axel and Katharina Michaelowa (2007). “Climate or Development: is ODA Diverted from its Original Purpose?” _Climatic Change_ 84:5-21.

- Neumayer, Eric, (2002). ‘Does Trade Openness Promote Multilateral Environmental Cooperation?’ World Economy 25: 815-832.Neumayer, 2002)

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5 Unfortunately, only very few surveys include the necessary information on saving and borrowing for health purposes that allow one to adjust for financing out-ofpocket expenditures through dissaving and borrowing. Rarely do surveys ask respondents in a household that has experienced or is experiencing sickness how their current income compares to their prior or normal income. So, the observations above really serve to remind us that our estimates likely deviate from those we would obtain if we had the necessary data to relate out-of-pocket expenditures to ‘normal’ consumption and ‘normal’ income.

## <u>Previous studies</u>

Table 2 reports results of previous multi-country and global studies. There have been no regional or global studies to date on the dispersion of out-of-pocket expenditures or on the out-of-pocket budget share, and there have been no global studies to date on per capita expenditures in dollar terms, progressivity or inequality in catastrophic expenditures. Moreover, the scope of the regional studies on per capita expenditures, progressivity and inequality in catastrophic expenditures has been limited to the OECD countries in the case of the first two measures and Asia in the case of the third. There have been global studies on catastrophic and impoverishing out-of-pocket expenditures, including two recent large-scale studies. The work reported in this paper on these measures extends these previous studies in a number of ways (for example, we include more recent datapoints, we use the new updated international poverty lines, and we compare results using income and consumption) but we limit our analysis to the most recent datapoint for each country and unlike the two recent studies do not report trends.

# **Challenges in studies of out-of-pocket health expenditures**

Researchers analyzing out-of-pocket health expenditures face several challenges. In this section we review the key ones, in part to help guide future research but also to set the stage for the empirical results presented below. Some are difficult if not impossible to address, such as the inherent differences in the surveys, while others such as computation are easy to harmonize.

## <u>Surveys</u>

Computing the measures in Table 1 requires microdata from nationallyrepresentative household surveys that contain data on out-of-pocket health expenditures as well as total household consumption or income. An invaluable resource for identifying and exploring access to potentially suitable surveys is a microdata catalog, such as those maintained by the International Household Survey Network and the World Bank. These catalogs include ‘standalone’ surveys as well as survey ‘collections’ – see Figure 1.
In the present paper we have used a general interpolation device proposed by Kakwani (1980). This method utilizes, within each income range, a separate continuously differentiable function which exactly fits to the data points. The inequality and poverty measures are then computed by liniing this function. Kakwani's approach requires information on the mean incomes in each income range which is not available in the present case. A modification of the approach is used in this paper.

# S. Trends in Real Per Capita Income in Ukraine

Tables 2, 3 and 4 present information on the real per capita income in Ukraine based on data from the family budget surveys. The nominal incomes were converted to the real incomes by means of the price indices given in Table 1. The real per capita incomes given in the tables are at 1980 prices.

Table 4 giving the growth rates of per capita real inoome shows that the average standard of living in Ukraine increased quite significantly in the late 80s. The 1989-90 period registered a growth rate of 7.4 per cent, but this was eroded in the subsequent periods from 1990 to 1992. In 1990-91, the real value of salary income declined by 7.54 percent and that of collective farm income went down by a massive 30.67 percent. It is intersting to note that government cash benefits to families increased by 45.79 percent. Since government cash benefits are generally concentrated among poorer families, it is to be expected that the distribution of per capita family income would have become more equal dunng the 1990-91 period.

Per capita family income declined by 23.56 percent during the 1991-92 period. This represents a drastic reduction in the average standard of living of the Ukrainian people. Salary income declined by a massive 31.44 percent. Families dependent largely on government transfers suffered the greatest decline in their incomes.<sup>7</sup> Their per capita income fell by 58.33 percent. The substantial income gain made by transfer recipients in the 1990-91 period was completely eroded in the subsequent period. The collective farm income increased, however, by 49.56 percent.

'Note that many families receive wages, smalholding income etc. in addition to government transfers and, therefore, their economic situation may have changed somewhat differently than what is shown by changes in govemment transfers.

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performance related bonuses, transparency in recruitment process and not linking wages to government pay scales despite being an SOE.

Finally, ONEE also performs better (73 percent) than most of its comparators when it comes to using information and technology solutions for system management and consumer interactions. ONEE’s IT systems support recording and resolving incidents and interruptions, distribution management and energy management. It also uses GIS and has an accurate customer database. However, it does not have Advanced Metering Infrastructure (AMI),<sup>66</sup> does not allow for submission and follow-up of customer complaints on its website and does not regularly monitor customer satisfaction. Although in practice, customer satisfaction is undertaken through surveys, carried out by utilities every few years.

Overall, ONEE’s performance on utility management clearly indicates that the utility follows most good practices when it comes to financial discipline, human resources and information and technology. However, when it comes to good practices pertaining to the autonomy and accountability of the Board, ONEE still lacks some of the good practices measured in the index (something seen widely among state owned utilities).

Another aspect of utility governance is that broad stakeholder engagement in policies and decision-making is limited. Unions are seldom involved in the process of reform design. Although the power sector is heavily unionized, the corporatist nature of the relationship between the unions and legacy utilities has resulted in the former’s acquiescence to partial reform efforts. In the mid-1990s, when initial efforts were made to liberalize generation and privatize electricity distribution, unions were offered attractive terms to keep them at bay.

LYDEC for instance, maintained the attractive terms of employment of existing employees including pensions and benefits that are among the best in the public-sector. The unions negotiated an even better bargain from JLEC. Employees who transferred from ONEE’s generation units to JLEC were offered better working conditions and had the option of returning to ONEE after a fixed period if not satisfied with their new working environment. These arrangements were made possible because of the dominant role of _Union Marocaine des Travailleurs_ (UMT) in representing workers in the sector. UMT suppressed dissenting voices with discreet support by ONEE's management and politicians.

Lately, the unions have not been as accommodating of further reforms. A plan to transfer ONEE's distribution activity in the suburbs of Casablanca to LYDEC, announced in 2014 is yet to be implemented because of a strong opposition from UMT with tacit support from ONEE's management. There is also some opposition to transferring some of ONEE's assets to MASEN. UMT may have realized that the sequence of these individual reforms may in the long run threaten the very corporate existence of ONEE as currently constituted, and thus their plight as a union.

> 66 The World Bank’s Clean and Efficient Energy Project aims to help build advanced metering infrastructure (AMI) in Morocco.

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Figure 9: Effects of Removing the Unemployment Insurance System <!-- Start of picture text --> Earnings = 100% Average Earnings=50% Average<br>1 4<br>0 2<br>-0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20<br>-1 0<br>-0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40<br>-2 Cost of formal work (+)Prob working in retirement -2<br>-3 (+)Di ( ) -4<br>-6<br>-4<br>-8<br>-5 -10<br>High cost of working in the<br>formal sector; high probability of work after -6 -12<br>retirement<br>-7 -14<br>Change in Contribution Density (% of Time) Change in Contribution Density (% of Time)<br>Change in Retirement Age (# of Years) Change in Retirement Age (#of Years)<br><!-- End of picture text --> _Source:_ Simulation model.

The effects of FGTS also differ for high and low income workers. For high income workers the main effect is a reduction in contribution densities without a meaningful change in retirement ages (see left panel of **Figure 10** ). Lower contribution densities are not surprising. Indeed, eliminating the program reduces a substantial share of subsidized savings – since contributions are paid by the employer – and therefore incentives to contribute to the social security.<sup>13</sup> We also notice, however, that there is a group of high income individuals for whom contribution densities do not change. The main interpretation is that contribution densities for them are “binding;” reducing them further would make them ineligibility for the highest pension at a given retirement age.

For low income workers we also observe a set of preferences for which a drop in contribution densities occurs without meaningful changes in the retirement age – with or without FGTS they most likely retire late. A majority of individuals, however, choose to retire earlier when FGTS is eliminated (see right panel of **Figure 10** ) without changing much contribution densities. The interpretation is that when loosing FGTS these individuals have fewer incentives to delay retirement and continue benefiting from subsidized savings. Clearly, the additional savings from FGTS can also allow some workers to retire earlier, but the first effect in this case dominates.

> 13 Clearly, FGTS can also induce fake dismissals and promote informal sector work as workers tend to cash savings. But this does not imply reducing the contribution density.  Hence, higher contribution densities can co-exist with a higher turn over rate (which given the long-term nature of our model we have not computed).

27
further details on how we collected baseline and follow-up data.

We were able to re-interview 724 of the 803 individuals in the sample, achieving a 90.2% response rate. An attrition rate of 9.8% compares very favorably to the attrition rates found in other vocational training studies<sup>14</sup> or even in labor market administrative surveys for developed countries.<sup>15</sup> Response rate for the treatment group was 93.3%, while for the control was 87.0%. small in Table 1 shows that this group Although magnitude, difference across treatment groups is statistically significant. We deal with this differential attrition in several ways. First, in Table 3 we show that the sample of non-attriters is extremely similar in pre-treatment observable characteristics to the whole sample. Moreover, we show that pre-treatment covariates remain balanced between treatment groups for the sample of non-attriters: all the differences between treatment and control groups show roughly the same magnitudes and statistical significance when compared to the whole sample. Second, we investigate whether any pre-treatment observable characteristic correlates with the probability that an individual attrits the sample. We estimate a linear probability model in which we regress a dummy that takes the value of 1 if individual attrits, 0 if she remains in sample on treatment status, randomization strata fixed effects, and baseline characteristics.<sup>16</sup> Figure 3 shows the point estimates and confidence intervals for each variable in such regression. In this figure, we can observe that the only variable that significantly predicts attrition is treatment assignment, with no systematic correlation with any other pre-treatment characteristic.

Given that the differential response rate is small in magnitude, that it is not correlated with pre-treatment characteristics, and that the non-attriters sample shows the same balance as the whole sample, our main estimates do not adjust for attrition. Reassuringly, however, in section 4.7 we show that these estimates are robust to adjusting for differential attrition. We do so by implementing bounds by Behaghel et al. (2015).<sup>17</sup>

> 14See Table 1 (pages 132 and 133) in McKenzie (2017) for the attrition rates in 15 other vocational training studies in low and middle income countries.

> 15The attrition rate for the U.S. Current Population Survey, for example, is about 20%.

> 16Baseline characteristics include age, marital status, educational attainment, a dummy that indicates whether the participant has taken a previous coding course, a dummy that indicates whether the participant specialized in a STEM career during her education, employment status, whether the individual was employed in the formal sector, weekly hours employed, and labor earnings.

> 17Since we foresaw this differential attrition, we developed a thorough field protocol that collected information on how many call and email attempts were made for each subject in the sample.

10