rafmacalaba/datause-encoder-data huggingface dataset:
transfers), the outcome is an inadequate provision of services and large disparities in services across localities. Where resources are inadequate, shared responsibility often leads to higher levels of government delegating responsibilities to lower levels of government. This was especially true in China during the long fiscal decline (Wong 1991, 1997; World Bank 2002).

Since 1994, all taxes have been assigned to the central government, “local” government, or shared. At the outset, the Tax Sharing System (TSS) reform of 1994 had aimed to simplify the revenue sharing system by introducing a simple, transparent, and uniform structure for revenue assignments across provinces. The system was introduced with only a few shared taxes – the VAT (75 percent central and 25 percent local) and the resource tax (assigned to either central or local government by type of resource).

In practice, the system of revenue assignments remained very complicated, especially at the subnational levels.<sup>20</sup> In many localities, especially at the township level, “fiscal contracts” were retained, under which the local government either received or remitted a lump-sum amount or a proportion of revenues (Zhou et al. 2005). The TSS simply added a new layer to the division of revenues – in Chinese this is commonly referred to as the “simultaneous implementation of two (revenue-sharing) systems.” In addition, the extension of the TSS to the subnational level began late, and has been implemented differently in different provinces. The current situation is that “local” taxes are extensively shared among subnational levels. At the county and township levels, rural local governments collect a relatively large number of taxes, but draw their revenues mainly from a few taxes.

Nationwide, the VAT, business tax (BT), and the income taxes (enterprise and personal) are the main sources of revenue for all levels of government as well as for both the county and township levels. In 2003 these four taxes combined accounted for 54 percent and 53 percent of total revenues at the county and township levels, respectively. The importance of these taxes suggests that they are the focus of local revenue mobilization efforts.

At the same time, through the 1990s in nearly all provinces the intermediate levels – the provinces and the municipalities – laid claim to a share of the taxes collected in the counties and townships, making it an increasingly uphill battle for the lower levels to increase revenues.

> 20 This is discussed in detail in World Bank (2002, Chapter 2).

15
information on the targeting of SFD programs, but we do have information from the HIECS 2004/2005 at the village level, and so can determine whether SFD spending is directed mainly towards poor villages (even if this is no guarantee that the poor, rather than the non-poor, are the ultimate beneficiaries). Figure 4 displays the relevant information; it shows the cumulative percentage of the population from the poorest to the richest villages and wards on the horizontal axis, while the vertical axis shows the cumulative percentage of SFD spending on the various programs; for each program there is a different “concentration curves”. Although it is not strictly comparable, since it is based on household-level data, Figure 4 also shows a Lorenz curve – the heavy line to the lower right – which tracks the distribution of per capita income in 2004.

<!-- Start of picture text --> 100<br>90<br>80<br>Roads<br>70<br>Microcredit<br>60<br>50<br>Education Income Distribution<br>40 (Lorenz Curve)<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative % of population (poor to rich)<br>100<br>90<br>80<br>Potable Water<br>70<br>Health<br>60<br>Environment<br>50<br>40<br>Waste Water Income Distribution<br>30 (Lorenz Curve)<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative % of population (poor to rich)<br>Cumulative % of SFD spending<br>Cumulative % of SFD spending<br><!-- End of picture text -->

**Figure 4. Distribution of Spending on SFD Programs, Based on Village/Ward-Level Data, 2004-05.** _Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 31 of 36_
- Peters, J., Langbein, J., and Roberts, G. (2018). Generalization in the tropics–development policy, randomized controlled trials, and external validity. _The World Bank Research Observer_ , 33(1):34–64.

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28
# **Table A6: Effects of a Workerʹs Unexpected Illness on the Income of Other Household Members**

|Dependent variable|||Income(cedi|s)||
|---|---|---|---|---|---|
|Other worker ill|21.335|20.612|64.948|2.004|12.878|
||[17.183]|[18.160]|[90.476]|[12.426]|[14.319]|
|Other worker ill X Female|‐25.616|‐17.848|‐68.964|11.491|8.549|
||[21.661]|[23.053]|[113.178]|[19.612]|[23.230]|
|HH Assets at Baseline X Other worker ill||‐10.523<br>[20.826]||||
|HH Assets at Baseline X Other worker ill||24.64||||
|X Female||[24.411]||||
|HH Average Education X Other worker ill|||‐4.450<br>[8.736]|||
|HH Average Education X Other worker ill|||4.417|||
|X Female|||[11.583]|||
|Highest baseline earnings in household||||49.207||
|X Other worker ill||||[40.071]||
|Highest baseline earnings in household||||‐101.552**||
|X Other worker ill X Female||||[45.910]||
|Highest usual hours of work in household|||||24.664|
|X Other worker ill|||||[46.347]|
|Highest usual hours of work in household|||||‐89.162|
|X Other worker ill X Female|||||[53.808]|
|Mean dependent variable|3,948|3,766|3,948|3,948|3,948|
|Observations|116.4|113|116.4|116.4|116.4|

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Assets are measured by the first principal component from an index of household assets including bed, wall clock, watch, player, radio, tv, sewing machine, fan, air conditioning, fridge, freezer, gas stove, shovel, landline telephone, cell, bike, motorbike, car, computer, livestock, farm implements, generator, land; then normalized to have mean zero and standard deviation one. Usual earnings and usual hours of work from reports in baseline survey. All specifications include individual fixed effects. Estimation uses least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
|P117608|2010|2014|1/11/2014|6 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P117608|2010|2014|6/23/2014|7 Closed|Yemen,Republic of|
|P117608|2010|2015|2/2/2015|8 Closed|Yemen,Republic of|
|P117608|2010|2016|9/17/2015|9 Closed|Yemen,Republic of|
|P117608|2010|2016|4/26/2016|10 Closed|Yemen,Republic of|
|P117608|2010|2017|1/2/2017|11 Closed|Yemen,Republic of|
|P117610|2011|2011||0 Closed|Vietnam|
|P117610|2011|2012|9/21/2011|1 Closed|Vietnam|
|P117616|2011|2019|5/7/2019|1 Closed|Central African Repub|
|P117617|2012|2012||0 Closed|Malawi|
|P117617|2012|2013|3/9/2013|1 Closed|Malawi|
|P117617|2012|2014|8/24/2013|2 Closed|Malawi|
|P117617|2012|2014|1/18/2014|3 Closed|Malawi|
|P117617|2012|2015|9/10/2014|4 Closed|Malawi|
|P117617|2012|2015|1/13/2015|5 Closed|Malawi|
|P117617|2012|2015|6/24/2015|6 Closed|Malawi|
|P117617|2012|2016|12/18/2015|7 Closed|Malawi|
|P117617|2012|2016|6/27/2016|8 Closed|Malawi|
|P117617|2012|2017|12/28/2016|9 Closed|Malawi|
|P117617|2012|2017|6/15/2017|10 Closed|Malawi|
|P117617|2012|2018|10/19/2017|11 Closed|Malawi|
|P117617|2012|2018|5/25/2018|12 Closed|Malawi|
|P117617|2012|2019|1/31/2019|13 Closed|Malawi|
|P117635|2013|2013||0 Active|Kenya|
|P117635|2013|2014|1/1/2014|1 Active|Kenya|
|P117635|2013|2015|10/14/2014|2 Active|Kenya|
|P117635|2013|2015|4/15/2015|3 Active|Kenya|
|P117635|2013|2016|12/2/2015|4 Active|Kenya|
|P117635|2013|2016|6/28/2016|5 Active|Kenya|
|P117635|2013|2017|12/28/2016|6 Active|Kenya|
|P117635|2013|2017|6/26/2017|7 Active|Kenya|
|P117635|2013|2018|1/5/2018|8 Active|Kenya|
|P117635|2013|2019|9/13/2018|9 Active|Kenya|
|P117635|2013|2019|3/17/2019|10 Active|Kenya|
|P117635|2013|2019|6/5/2019|11 Active|Kenya|
|P117635|2013|2020|10/17/2019|12 Active|Kenya|
|P117635|2013|2020|4/30/2020|13 Active|Kenya|
|P117644|2010|2020|9/18/2019|1 Closed|Ethiopia|
|P117651|2010|2010||0 Closed|Turkey|
|P117651|2010|2011|1/11/2011|1 Closed|Turkey|
|P117652|2011|2011||0 Closed|Africa|
|P117652|2011|2012|8/27/2011|1 Closed|Africa|
|P117652|2011|2012|4/7/2012|2 Closed|Africa|
|P117652|2011|2013|12/1/2012|3 Closed|Africa|
|P117652|2011|2013|5/23/2013|4 Closed|Africa|
|P117652|2011|2014|1/19/2014|5 Closed|Africa|
|P117652|2011|2014|6/27/2014|6 Closed|Africa|
**Table A6. Electricity average grid emission factor**

||2015|2018|2021|2024|2027|2030|2033|2036|
|---|---|---|---|---|---|---|---|---|
|Electricity grid emission|||||||||
|factor(kt/GWh)|0.142|0.126|0.121|0.127|0.125|0.124|0.125|0.125|

# **Information on MARKAL-Georgia**

**Table A7. Key Data Sources for MARKAL-Georgia**<sup>**11**</sup>

|**Sector**|**Services**|
|---|---|
|Energy Balance|•<br>National Statistics Office of Georgia|
|Natural gas prices|•<br>Georgian Oil and Gas Corporation (GOGC)<br>•<br>Ministry of Energy<br>•<br>Georgian National Energy and Water regulatory<br>Commission<br>•<br>GeorgianNaturalGasTransportationCompany|
|Oil Products prices|•<br>Georgia Oil and Gas Company<br>•<br>Ministry of Finance<br>•<br>International EnergyAgency|
|Coal Prices|•<br>Saknakhshiri Ltd<br>•<br>National Statistics Office of Georgia|
|Biowood prices|•<br>National Environmental Agency<br>•<br>USAID Publication “Potential of Biowood in Georgia<br>andits effective use”|
|Geothermal energy<br>prices|•<br>National Environmental Agency|
|Electricity prices|•<br>Distribution Companies<br>•<br>ElectricityMarket Operator|

> 11 MARKAL -Georgia is the MARKAL model developed under the USAID funded Regional Energy Security and Market Development (RESMD) project.

34
# **Table 5.  Sensitivity to Credit Risk**

(Regulatory Capital to Risk-weighted Assets, percent)

|||**Provisions i**<br>|**ncrease by**<br>|**Migration**<br>|**to NPLs of**<br>|
|---|---|---|---|---|---|
||**Before**<br>**the shock**|**50%**|**100%**|**25% of**<br>**Performing Loans**|**50% of Performing**<br>**Loans**|
|**Peer Group – Large**|18.3|17.1|15.3|18.0|17.8|
|**Peer Group -**|18.7|15.0|11.1|18.1|17.6|
|**Medium**||||||
|**Peer Group - Small**|35.2|32.5|29.6|34.7|34.2|
|**# Banks with CAR**<br>**<12%**|0|11|17|2|3|
|**Share of Assets**|0|20|24.0|5.1|12.3|
|**# Banks with CAR**<br>**<0%**|0|1|4|0|0|
|**Share of Assets**|0|0.99|2.2|0|0|

Source: Central Bank of Kenya data

# **4. Banking Sector Efficiency**

In this section we describe how interest spreads, our proxy for the efficiency of financial intermediation, have evolved over the past ten years in Kenya, and relate those developments to our discussion of the government strategy for the development of the financial sector and the accompanying changes in the legal and regulatory framework discussed above. We then present simple arithmetic decompositions of the interest spreads to explain the factors that have contributed to their relatively high levels and also to their decline over time. We also examine how spread levels and their determinants differ by bank size and ownership type. Finally, we offer regressions that better enable us to test whether the determinants of spreads differ by bank ownership type and if such differences can be explained by the types of activities that different owners pursue.

Headline indicators produced by the Central Bank of Kenya indicate that spreads declined from 1999 to 2003 and have since remained stable (Figure 1). The relatively sharp decline in spreads in 2003 owes much to improvements in Kenya’s fiscal situation and general macro-management, which led to substantial declines in both the volume of government securities issued and the interest rates paid. As government securities became a less attractive investment option for banks, they turned to new lending opportunities, and the competition 10
across urban and rural areas and providing premium subsidies for lower income workers may be necessary to approach full coverage.

With respect to pension program participation, the effect of providing information was also positive and significant for informal sector workers young enough to contribute for sufficient time to receive a full benefit at retirement. Unlike health insurance, pension program participation does not vary with relative premiums, but this likely reflects the fact that the new rural pension program, with low basic benefits, is not perceived to be a substitute for pension insurance under the urban employee pension scheme. Further, and in contrast to the rural and urban health insurance programs, receiving a benefit from the urban employee pension system may not preclude expectations of receiving accrued benefits under the new rural pension as well.

The wage response to the treatment effect offers corroborating evidence that older informal sector workers bargain with employers over wages in lieu of enrollment in the mandated urban employee pension program. While the information treatment increases the likelihood that informal employees more than fifteen years from retirement age will enroll in the urban employee pension, the treatment has no effect on the participation of older workers. A positive treatment effect on wage of 7.2%, however, is consistent with a bargained outcome in which an employer offers a higher wage to informal workers in lieu of contributions to a pension that brings fewer benefits to both. With respect to China’s urban employee pension program, this suggests that formal participation may be increased if workers with fewer than fifteen years in the program are able to draw on both employer and employee contributions at retirement. More broadly, the pension results suggest that while providing program and process information may be a low cost means of increasing enrollment, success in expanding participation in a social insurance program also requires careful attention to incentives embedded in program details.

# **References**

- Ashenfelter, O. (1983). Determining participation in income-tested social programs. _Journal of the American Statistical Association 78_ (383), 517–525.

- Athey, S. and G. Imbens (2017). The econometrics of randomized experiments. In A. V. Banerjee and E. Duflo (Eds.), _Handbook of Field Experiments_ , Volume 1 of _Handbook of Economic Field Experiments_ , Chapter 3, pp. 73–140. North-Holland.

- Bergman, P. L. S. (2015). Parent-child information frictions and human capital investment: Evidence from a field experiment. CESifo Working Paper 5391, CESifo Group Munich.

- Bosworth, B. and S. Collins (2008). Accounting for growth: Comparing China and India. _The Journal of Economic Perspectives 22_ (1), 45–66.

26
the crisis. The “loss” in growth is in comparison to the scenario of no‐crisis for the relevant year, rather than indicating a fall in output from one year to the next.

Interestingly, output growth in the sector “other industries” is expected to be higher during the crisis than in the no‐crisis scenario (Table 1). In 2009 and 2010, the output growth in other industries is expected to be 5.4 and 1.9 percentage points higher in the crisis scenario than in the benchmark scenario. Most likely, this reflects the impact of the stimulus package put in place by the government in response to the crisis, which includes substantial resources for public investment projects and is expected to boost the construction sector.

In addition, the crisis is expected to have a limited impact on remittance flows of $0.8 billion in 2009 and a larger impact of $1.5 billion in 2010 (Table 1). However, even with the crisis, remittances are expected to grow by $1.6 billion between 2008 and 2010, which is about half of the $3.1 billion growth during this period that was projected in the absence of the crisis.

In sum, the macroeconomic impacts of the crisis are estimated to be reasonably large in 2009, with a slight recovery in 2010. The impacts are driven mostly by a sharp contraction in the manufacturing sector. Remittances do not appear to be a significant factor in driving changes in household income in 2009, implying that the effects of the crisis on households are likely to be felt mainly through its impact on employment and labor earnings.

# **3. Proposed approach to simulate poverty and distributional impacts**

We propose to use a microsimulation model that combines macroeconomic projections with pre‐crisis micro data from household and/or labor force surveys to predict income and consumption at the individual and household level under different scenarios, which can then be compared to measure poverty and distributional impacts. Comparisons will be made between different _projected_ scenarios (most commonly a crisis or low scenario and a benchmark or base scenario) rather than a comparison over time (i.e. a comparison between 2006 and 2009 or 2010 in the case of the Philippines). Figure 1 presents a stylized representation of the methodology.

The model focuses on labor markets and migration as transmission mechanisms and allows for two types of shocks: shocks to labor income, modeled as employment shocks, earnings shocks or a combination of both; and shocks to non‐labor income, with special attention paid to international remittances. Shocks can be positive or negative depending on the trends outlined by the macroeconomic projections. In most cases labor income and remittances account for at least 75‐80% of household income. Minimum assumptions are made about other sources, such as capital and financial income or public transfers, as discussed below.

4
The results show that, even after controlling for income per capita, the MENA countries have achieved better child mortality outcomes (all but Iraq are located above the bivariate regression curve) over the past 40 years or so than the African countries (all located below the regression curve). This suggests that differences in performance are due to more than just differences in income per capita. This should not be surprising since countries with similar levels of resources may make different budget allocations to different priorities and with different degrees of effectiveness.

The rest of this paper is organized as follows. We proceed in Section B to a literature survey that helps identify plausible determinants of progress over time in child mortality and school enrollment. In Section C, we develop the empirical strategy to assess the importance of these determinants of crosscountry variations in human development outcomes. The results of our econometric analyses are reported in Section D together with robustness checks of the basic model. In Section E, we explore two extensions of the model that delve deeper into oil-wealth effects. In Section F, we report and discuss some country-specific results of the empirical model that answer the question in the title of the paper in a nuanced fashion. Concluding remarks follow in Section G.

**Figure 3: Income per capita and change in child mortality over 1970-2010**

<!-- Start of picture text --> OMNSAU<br>DZAIRN ARE<br>LBY BHR QAT<br>KWT<br>IRQ<br>NGA<br>GNQ<br>GAB<br>COG<br>AGO<br>0 50000 100000 150000<br>GDP per capita, PPP (constant 2011 international $)<br>100<br>80<br>60<br>40<br>20<br>Percentage change in child mortality: 1970-2010<br>0<br><!-- End of picture text --> 4