rafmacalaba/datause-encoder-data huggingface dataset:
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- Kannan, Prakash & Friederike Koehler-Geib (2011). “Uncertainty and Contagion”. In Robert W. Kolb (editor), _Financial Contagion: The Viral Threat to the Wealth of Nations_ , pp. 21–28. Hoboken, NJ: John Wiley & Sons

- Kashyap, Anil K. & Jeremy C. Stein (2000). “What Do a Million Observations on Banks Say about the Transmission of Monetary Policy?” _American Economic Review_ 90(3) (June): 407–428

- Khandani, Amir E. & Andrew W. Lo (2011). “What Happened to the Quants in August 2007? Evidence from Factors and Transactions Data”. _Journal of Financial Markets_ 14(1) (February): 1–46

- Khwaja, Asim Ijaz & Atif R. Mian (2008). “Tracing the Impact of Bank Liquidity Shocks: Evidence from an Emerging Market”. _American Economic Review_ 98(4) (September): 1413–1442

- Maddala, G. S. & Shaowen Wu (1999). “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test”. _Oxford Bulletin of Economics and Statistics_ 61(S1) (November): 631–652

- Peek, Joe & Eric S. Rosengren (1997). “The International Transmission of Financial Shocks: The Case of Japan”. _American Economic Review_ 87(4) (September): 495–505

- Schwarz, Krista (2010). “Mind the Gap: Distentangling Credit and Liquidity in Risk Spreads”. _Working paper_ , Philadelphia, PA: The Wharton School

- Stiglitz, Joseph E. & Andrew Weiss (1981). “Credit Rationing in Markets with Imperfect Information”. _American Economic Review_ 71(3) (June): 393–410

- Taylor, John B. & John C. Williams (2009). “A Black Swan in the Money Market”. _American Economic Journal: Macroeconomics_ 1(1) (January): 58–83

- Wu, Ji, Alina C. Luca & Bang Nam Jeon (2011). “Foreign Bank Penetration and the Lending Channel in Emerging Economies: Evidence from Bank-Level Panel Data”. _Journal of International Money and Finance_ 30(6) (October): 1128–1156

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|P092154|2008|2012|11/8/2011|7 Closed|Tanzania|
|---|---|---|---|---|---|
|P092154|2008|2012|6/30/2012|8 Closed|Tanzania|
|P092154|2008|2013|1/25/2013|9 Closed|Tanzania|
|P092154|2008|2014|8/18/2013|10 Closed|Tanzania|
|P092154|2008|2014|5/3/2014|11 Closed|Tanzania|
|P092154|2008|2015|11/25/2014|12 Closed|Tanzania|
|P092154|2008|2015|6/30/2015|13 Closed|Tanzania|
|P092154|2008|2016|1/25/2016|14 Closed|Tanzania|
|P092154|2008|2017|9/26/2016|15 Closed|Tanzania|
|P092154|2008|2017|4/26/2017|16 Closed|Tanzania|
|P092154|2008|2018|11/13/2017|17 Closed|Tanzania|
|P092154|2008|2018|6/27/2018|18 Closed|Tanzania|
|P092162|2005|2008||0 Closed|Afghanistan|
|P092162|2005|2009|10/15/2008|1 Closed|Afghanistan|
|P092162|2005|2009|5/29/2009|2 Closed|Afghanistan|
|P092162|2005|2010|11/30/2009|3 Closed|Afghanistan|
|P092162|2005|2010|4/2/2010|4 Closed|Afghanistan|
|P092162|2005|2011|11/9/2010|5 Closed|Afghanistan|
|P092162|2005|2011|1/11/2011|6 Closed|Afghanistan|
|P092190|2004|2008|9/12/2007|1 Closed|Cambodia|
|P092190|2004|2019|5/9/2019|2 Closed|Cambodia|
|P092202|2006|2006|12/27/2005|2 Closed|El Salvador|
|P092202|2006|2006|6/7/2006|3 Closed|El Salvador|
|P092202|2006|2007|3/30/2007|4 Closed|El Salvador|
|P092202|2006|2008|8/8/2007|5 Closed|El Salvador|
|P092202|2006|2008|12/11/2007|6 Closed|El Salvador|
|P092202|2006|2008|6/12/2008|7 Closed|El Salvador|
|P092202|2006|2009|10/15/2008|8 Closed|El Salvador|
|P092202|2006|2009|12/12/2008|9 Closed|El Salvador|
|P092202|2006|2009|5/15/2009|10 Closed|El Salvador|
|P092202|2006|2010|12/19/2009|11 Closed|El Salvador|
|P092202|2006|2010|6/20/2010|12 Closed|El Salvador|
|P092202|2006|2011|2/23/2011|13 Closed|El Salvador|
|P092202|2006|2012|8/10/2011|14 Closed|El Salvador|
|P092202|2006|2012|12/28/2011|15 Closed|El Salvador|
|P092202|2006|2013|7/10/2012|16 Closed|El Salvador|
|P092211|2009|2009|6/25/2009|1 Closed|Yemen,Republic of|
|P092211|2009|2010|11/11/2009|2 Closed|Yemen,Republic of|
|P092211|2009|2010|6/28/2010|3 Closed|Yemen,Republic of|
|P092211|2009|2011|12/27/2010|4 Closed|Yemen,Republic of|
|P092211|2009|2011|6/6/2011|5 Closed|Yemen,Republic of|
|P092211|2009|2012|12/17/2011|6 Closed|Yemen,Republic of|
|P092211|2009|2012|6/24/2012|7 Closed|Yemen,Republic of|
|P092211|2009|2013|12/26/2012|8 Closed|Yemen,Republic of|
|P092211|2009|2014|8/4/2013|9 Closed|Yemen,Republic of|
|P092211|2009|2014|2/21/2014|10 Closed|Yemen,Republic of|
|P092211|2009|2015|9/16/2014|11 Closed|Yemen,Republic of|
as well as the intersection between both. By doing so, it aims to contribute to the literature on intrahousehold allocation between children.

Fourth, insights from this study are directly relevant for policy makers, development practitioners, civil society organizations in Ethiopia, and beyond. The study examines the current impact of public finance on children, and highlights areas where government spending has the largest impact on reductions in child poverty and inequality. Furthermore, the findings build the basis to analyze the distributional effects of future fiscal policies on children, as illustrated by a range of selected policy simulations.

The rest of the paper is organized as follows. Section 2 presents the methodology. Section 3 describes the data. Section 4 presents and discusses the results. Section 5 illustrates the kind of insights that can be generated via policy simulations on the basis of fiscal incidence analyses. Section 6 summarizes key insights and indicates further areas of research.

# **2. Methodology**

# **2.1.  Measuring fiscal incidence**

The analytical framework uses the CEQ methodology (Lustig 2018) as well as its child-specific version CEQ4C (Cuesta, Jellema, and Ferrone 2021) to estimate the distributional impact of fiscal policy on children’s wellbeing. The approach begins with calculating pre-fiscal and post-fiscal income concepts by assigning public transfers, spending, and taxes. Four income concepts are considered: market income, disposable income, consumable income, and final income (Figure 1). The analysis then estimates monetary and multidimensional child poverty and inequality at different income concepts.

In this study, the individual child is the unit of analysis. The construction of most other variables and income concepts closely follows those described in Ambel, Tesfaye, and Yonis (2022). Disposable income is proxied by consumption expenditure in the underlying household survey data. Other income concepts are therefore computed through backward and forward calculations. Individual level expenditure is estimated based on intra-household resource allocation (Belete, Menon, and Perali 2019; Calvi et al. 2020) and equivalence scales (Browning, Chiappori, and Lewbel 2013). The allocation approach of expenditures to household members is based on consumption patterns and the availability of individual-specific information in the data. For example, the 2018/19 Ethiopia Socioeconomic Survey (ESS) collects clothing expenditures for boys and girls, as well as individual expenditures on education and health. Some expenditures, such as alcoholic drinks and cigarettes, are assignable only to adults. Non-assignable expenditures are allocated to each child based on equivalence scales.

5
**Table 2: Characteristics of the Semitsvetik Kindergarten, 2013**

|Gross heated floor area|4,713.1 square meters|
|---|---|
|Capacity|220 people|
|Actual heating system|Single-tube horizontal adjustable heating system connected to the district heat transmission network through an<br>individual heating station built in the basement of the kindergarten. The cast-iron radiators and individual<br>heating station are equipped with automatic temperature controllers. Heat-transfer fluid – water with the<br>temperature ranging between 95 and 70 С.<br>|
|Actual floor heating system|The heated floors are installed in the playrooms on the 1<sup>st</sup>floor, in the swimming pool, as well as in the resting<br>and changing rooms near the swimming pool area. Water temperature is controlled at the individual heating<br>station.<br>Heat-transfer fluid–water with the temperature ranging between 30 and 40 С.|
|Actual ventilation system|Plenum-and-exhaust ventilation system with mechanical forced and natural convection, as well as a local<br>exhaust ventilation near the areas of harmful emissions.<br>Heat-transfer fluid–water with the temperature ranging between 70 and 95 С.|
|Actual lighting system|Kindergarten building is equipped with normal and emergency lighting delivered by general and combined<br>lighting systems. For repair purposes, portable lighting is provided. The main sources of light are fluorescent<br>lamps, compact fluorescent lamps, and incandescent lamps. The lighting equipment is controlled by tumbler<br>switches. The power consumption for lighting is set at 80.3 kW.|
|Gross consumption of electricity|218,843.00 kW-h|
|Gross consumption of thermal energy|860,641.77 kWh|
|Expenditures on electricity|RUR 806,634 (US$25,350)|
|Expenditures on thermal energy|RUR 916,554 (US$28,804)|
|Consumption of thermal energy per m2|182.6 kWh/m2|
|Consumption of electricity per m2|46.4 kWh/ m2|

_Source_ : Data provided by Beloyarski City administration and City Arch company (2013). _Note_ : Exchange rate for 2013 = US$1 to 31.81 Russian rubles 18
The rate of return is calculated at the moment the investment in education takes place. The estimated returns reflect choices facing individuals at different ages (20, 25, etc) of whether to re-enter the education system. Hence, the returns do not simulate the situation of a 20 year-old facing a choice of continuing education now or re-enter the education system at the age of 25, 30 etc. To illustrate the gains from completing an education degree early in life, we computed the net present value of a tertiary education with a constant discount rate of 5 percent—a level above the current world real interest rate. Figure 5 shows that finishing an education early in life yields a higher value.

,,,

**Figure 5 - NPV across age for tertiary students**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10 20 25 30 35 40 45 50 55<br>-20<br>Age of re-entry to the education system<br>Net Present Value of a completed tertiary education (in million Col$)<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000

# **IV-2 HOW INDIVIDUAL  AND EDUCATIONAL FACTORS AFFECT THE RATE OF RETURN; ALTERNATIVE SCENARIOS**

Individual and educational factors significantly affect the rate of return. Below, we outline the main findings from the alternative scenarios. By changing the assumptions behind the baseline case, we can observe the sensitivity of the return to education to: study completion time, part-time work, public subsidies to tertiary education, tuition fees for primary and secondary education, unemployment and tax legislation. Further, we examine the returns by gender. The paragraphs below discuss each factor. The full estimation results are available in Annex table 7.

_<u>Study completion time</u>_ <u>. Foregone income from studying critically affects the rate of</u> return, even though students on average work 86 percent of a full working week. Figure 6 shows how two extra study years decrease the returns to secondary education for a 30 15
# **Figure 3 - Economic Developments and Chairmanship Appointment**

<!-- Start of picture text --> Annual GDP Growth<br>0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>2004 2005 2006 2007 2008 2009 2010 2011<br>Year<br>Annual Employment Growth<br>0.04<br>0.035<br>0.03<br>0.025<br>0.02<br>0.015<br>0.01<br>0.005<br>2004 2005 2006 2007 2008 2009 2010 2011<br>Year<br>States with Chairman States without Chairman<br><!-- End of picture text --> The figure compares the annual GDP and employment growth rate for states with and without a senator serving as chairman.

34
urban areas experiencing the strongest stimulus. The effects are not present for districts that are more than 10 km from the GQ highways, but still within 350 km of a nodal city. This is strong evidence of the GQ system shaping the spatial direction of development around India’s connected cities.

The next three rows document the differential effect compared to these results for districts that are more than 350 km from a nodal city for the GQ system. To identify the response for these hinterland locations, one adds the corresponding coefficients from the fourth through sixth rows to the base effects documented in the first three rows. Looking at the fifth row, many of the important gains for urban areas that we see evidence of around the nodal cities are diminished in Columns 4-6. By contrast, Columns 7-9 show that rural settings that are located on the GQ system but far removed from the nodal districts have an equal or stronger response. Thus, the intriguing spatial finding is that the added connectivity of the GQ upgrades is more impacting urban locations in areas near to the GQ nodal cities, and rural areas in connected districts that are farther away.<sup>4</sup>

# _Urban and Rural Patterns for Unorganized Sector Activity_

Before considering shares of activity, we pause to describe quickly the connection (or lack thereof) between GQ upgrades and the broad development of unorganized manufacturing activity. One effort in this project was to implement the GQ empirical strategy used in Ghani et al. (2012a) in the unorganized sector to compare against the large, rapid effects observed for the organized sector. As described in the prior section, our unorganized sector data stop in 2005, compared to 2007 for the organized sector. We are currently working to extend this time frame to 2010 for both sectors, but even by 2005 the organized sector shows a response in terms of young firm activity and productivity (Ghani et al. 2012a) and sourcing/inventory management (Datta 2011). It is thus useful to quantify whether the unorganized sector has a similarly rapid response.

> 4 It may initially appear that this divergence with respect to distance from a nodal city is simply a consequence of the districts farther removed from a nodal city being more rural overall. It should be noted, however, that these results are estimating proportionate responses off of the base urban or rural activity in each district. Thus, the longer-term degree to which districts are urban or rural has been controlled for in the estimations, with the emphasis instead upon the growth in activity within each type of area.

17
|Arts, entertainment, and recreation|Arts, entertainment and recreation|
|---|---|
|Voluntary and representative organizations|Other services activities|
|Household services|Activities of households as employers, undifferentiated<br>goods and services, producing activities of households for<br>own use|
|Extraterritorial organizations|Activities of extraterritorial organizations and bodies|
|Taxation and budget|Administration of the State and the economic and social<br>policy of the community|
|Multisector laws|-|
|Non-economic laws|-|

## **3.4 Methods for Exploring Legislative Predictability**

Once all the relevant indicators are calculated, both for legislative predictability and contextual variables like sectors, we carry out a range of explorative and descriptive analyses. Combining variables on modifications with contextual variables allows us to measure regulatory predictability and its underlying drivers.

First, we explore legislative predictability across countries, comparing Jordan to a set of different countries. This offers crucial insights into cross-country variability and how different legislative systems operate. The analysis is based on the smoothed hazard risk of legislative modification. Such smooth hazard curves display how the risk of modification changes over time (i.e. months). Second, we deep dive into Jordanian legislative data, by looking at trends to track legislative predictability over two decades. This in-depth descriptive analysis disaggregates the data by year, sector, and initiator. The in-depth analysis starts with the distribution of laws and regulations passed in a given year which takes the absolute yearly count of laws and regulations. Next, the analysis moves to modifications by displaying modifications as percentage of all laws and regulations passed in a given year, followed by modifications as percentage of all laws and regulations passed within 24 months. We further disaggregate modification within 24 months by the providing the percentage share of economic sectors as well as the policy initiator for all laws and regulations that were modified within 24 months.

# 4. LEGISLATIVE PREDICTABILITY IN AN INTERNATIONAL CONTEXT

Uncertainty in policy making is a global issue and legislative predictability concern should not be seen in isolation. Figure 3 provides a set of global comparators with data drawn from six diverse countries, covering both low- and high-income countries, as well different political systems.<sup>4</sup> The purpose of this section is to situate the overall pattern of legislative predictability in the Kingdom of Jordan in a

> 4 The figures are calculated based on the LEGDAT dataset (mid-2023 version). More information on the data and country scope can be found www.globalcorruptionpbservatory.com.

10
impoverishment rate similar to Uganda at 28 percent. In Ghana, fiscal impoverishment was close to 5 percent in 2013. Rates of fiscal impoverishment among those considered poor at post-fiscal income reached 94 percent in 2016/17,<sup>20</sup> second only to Tanzania’s rate (in 2011) at 99 percent.

_Table 4: Fiscal Impoverishment_

|**Country**, survey year|(1)<br>Market<br>income plus<br>contributory<br>pensions<br>poverty<br>headcount<br>(%)|(2)<br>Change in<br>poverty<br>headcount<br>(percentage<br>points)|(3)<br>Market<br>income plus<br>contributory<br>pensions<br>inequality<br>(Gini)|(4)<br>Fiscally<br>impoverished<br>as % of<br>population|(5)<br>Fiscally<br>impoverished<br>as % of<br>consumable<br>income poor|
|---|---|---|---|---|---|
|Ethiopia (2011)|31.9|1.3|32.2|28.5|83.2|
|Ghana (2013)|6.0|0.8|43.7|5.1|76.6|
|Tanzania (2011)|43.7|7.8|38.2|50.9|98.6|
|Uganda (2012)|17.9|0.1|41.3|12.2|67.7|
|**Uganda (2016)**|**22.44**|**3.41**|**43.4**|**22.84**|**92.47**|
|Zambia (2015)|36.0|2.8|55.0|52.6|84.3|

_Note: using the $1.25 PPP [2005] poverty line, and with respect to market income plus pensions._ _Sources: Uganda, 2016/17: authors’ calculations based on UNHS 2016/17; Uganda, 2012/13: Haas and others (2016); Zambia, 2015: de la Fuente and others (2017); all others: Higgins and Lustig (2016)._

# 4.4. Pensions as Government Transfers scenario

In the “Public Contributory Pensions as a Government Transfer” (PGT) scenario, we define our pre-fiscal income as “Market Income”. Pensions are considered to be a government transfer, rather than market income, and therefore included with Disposable Income, when the direct transfers are added. Figure 6 and 7 above shows that at Market Income the poverty headcount is slightly higher than when pensions are included (as at Market Income plus Pensions), but that the Gini coefficient is practically unchanged (0.01 Gini points decline). This means that the Public Contributory Pension system is poverty reducing<sup>21</sup> but does not significantly affect inequality. Thus, when Market Income is taken as pre-fiscal income, the combined effect of taxes and transfers in Uganda also lowers inequality by 3.2 Gini points. In terms of the effect on poverty, under the PGT the increase in poverty is slighter smaller: 2.21 percentage points compared to the 2.31 increase in the PDI scenario.

> 20 In 2012/13, fiscal impoverishment was lower at approximately two-thirds of the poor.

> 21 However, see the discussion at the beginning of this report regarding the “false poor” that the PGT scenario has the potential to identify.  That is, current retirees who receive a large proportion of current income from the public contributory pension system will _appear_ - and count as - poor at the Market Income concept even when those same individuals would be expected to have replaced their public pension income with privately-arranged pension or another old-age income if the public pension system was not available.

25
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As biofuels are not a proper sector in the original GTAP 7.0 database, we modified it in a way that allowed us to introduce biofuels sectors in our CGE model. This was done according to the following procedure. Based on detailed information collected on production, consumption and trade, a total of seven new sectors<sup>5</sup> have been created by splitting existing GTAP sectors. The Splitcom<sup>6</sup> software developed by Horridge (2005) was used to process the splits and keep the global social accounting matrix balanced. In the next step, the energy bundle of the model was augmented in order to integrate the new biofuel and fossil fuel sectors (see Figure 1c). A detailed description of the biofuel module used here can be found in Timilsina et al. (2010).

Land use changes are incorporated into the model via a CET representation of land supply for each country/region and for eighteen Agro-Ecological Zones (AEZs). The land data are based on the GTAP 7.0 database and were derived analogously to Lee et al. (2008). Following Huang et al. (2004) and Banse et al. (2008), we develop a land use module based on a nested structure. In line with Birur et al. (2008), at the top level of this module, total available land area is allocated to forest land, pasture and crop land. At the second level, crop land is further divided into four different categories: rice, sugar-crops, grains and oilseeds, and fruits and vegetables. Finally, the grains and oilseeds category is partitioned into wheat, corn, other coarse grains, and oilseeds (see Figure 1b). This nested structure seems reasonable as, in reality, not all crops stand in direct competition. For instance, rice typically does not compete directly with other crops for available land.

> 5 Corn, gasoline, diesel and biofuels (three ethanol and one biodiesel) sectors are introduced. 6 This software and a detailed description can be freely downloaded at <u>http://www.monash.edu.au/policy/splitcom.htm.</u>

6