rafmacalaba/datause-encoder-data huggingface dataset:
percent of GDP for OECD countries. At 29.1 percent of GDP, China’s overall domestic revenue mobilization in 2018 was above the UMIC average (Figure 10).

However, China collects a greater share of revenues in indirect taxes compared to richer countries. China’s heavy reliance on indirect taxes such as the VAT and its relative underutilization of direct tax instruments such as the PIT make its revenue mix somewhat inconsistent with its level of development. As countries develop, their reliance on broad consumption-based indirect taxes declines. For example, VAT and excise taxes account for 34 percent of total revenue for LMICs, 33 percent for UMICs, and 23 percent in OECD countries. In China, indirect taxes accounted for 35 percent of total revenue in 2018. Richer countries also collect more in PIT. PIT accounts for 2.5 percent of GDP in LMICs, 2.8 percent in UMICs, and 8.5 percent of GDP in the OECD. As share of total revenue, PIT is 21 percent in the OECD. By comparison, only 5 percent of total revenues come from PIT in China.

**Figure 10. Richer countries rely more on direct taxes such as PIT; reliance on indirect taxes such as VAT is higher among poorer countries** _(Revenue as % of GDP)_

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>China 2018 OECD Non-OECD HIC UMIC LMIC LIC<br>VAT Trade Excises PIT Payroll + SSC CIT Other Non-tax rev. Grants<br><!-- End of picture text --> _Source:_ International Centre for Tax and Development. Numbers for China are based on Finance Yearbook of China 2019. _Note:_ The figure shows the composition of government revenue as a percentage of GDP, aggregated by income group. OECD countries form a separate group. Data by revenue type are from 2020 when available or the most recent available year back to 2015. The sample includes 155 economies. CIT = corporate income tax; GDP = gross domestic product; HICs = high-income countries; LICs = low-income countries; LMICs = lower-middle-income countries; OECD = Organization for Economic Cooperation and Development; PIT = personal income tax; CSS = contribution to social security; UMICs = upper-middle-income countries; VAT = value added tax.

Indirect taxes are generally regressive, as the burden of these taxes falls disproportionately on the lower deciles of the income distribution. Indirect taxes are applied on the level of consumption and, because poorer households spend a larger share of their income on consumption (compared to richer ones), indirect taxes paid account for a greater share of their incomes too. Even though informality of purchases plausibly shields some of the lower income 23
**Table 5-1: Simulation steps undertaken to assessment Ukraine's WTO accession.**

**GTAP-7 data Baseline Effect of Ukraine's immediate 2009 accession commitments (IAC)**

**Effect of Ukraine’s final accession commitments (FAC)**

Applied All countries/ **<u>Scenario 1a – IAC:</u>** tariffs for all regions Applied tariffs in baseline 2009 sectors of the projected to cut according to Ukraine’s world 2009 immediate accession economy, according to commitments anticipated in 2009. base data year observed and 2004. projected If WTO commitments are not Ukraine's regional binding for a specific product, the WTO growth rates. corresponding baseline tariff commitments applies. Applied HS6 not included. tariffs for Ukraine **<u>Scenario 1b – IAC + TC:</u>** aggregated to GTAP As Scenario 1a, but in addition it sectors. is assumed that Ukraine improves its productivity in agriculture by 5%.

## **<u>Scenario 2a – FAC:</u>**

Applied tariffs in Scenario 1a are cut further to fulfill Ukraine’s final accession commitments, scheduled to take place by 2013 (earlier for some products).

## **<u>Scenario 2b – FAC + TC:</u>**

As Scenario 2a, but in addition it is assumed that Ukraine improves its productivity in agriculture by 2%.

# **_V.2.3. Aggregation_**

Before a simulation can be carried out, the modeler must make several decisions about key aspects of the model. One of these aspects concerns the level of sector and country/region aggregation. Some aggregation is unavoidable, because standard personal computers are, despite ongoing technical advances, not able to solve the GTAP model with a full disaggregation of 57 sectors and 105 countries/regions. Furthermore, for most applications, the full disaggregation would provide unnecessary detail that obscures key issues. For the scenarios presented below, an aggregation into the 27 countries/regions outlined in Table 5-2 has been chosen. The sectors have been retained at the most disaggregated level possible (57 sectors, see Table 5-3) in order to provide as much information as possible on changes within Ukrainian agriculture and the rest of the Ukrainian economy.

**Table 5-2: The country/region aggregation employed**

|1.|Oceania|15. Bulgaria|
|---|---|---|
|2.|EastAsia|16. Romania|
|3.|South EastAsia|17. Croatia|
|4.|South Asia|18. Albania|
|5.|North America|19. Rest of Eastern Europe|
|6.|Latin America|20. Kazakhstan|
|7.|Iran|21. Kyrgyzstan|
|8.|MiddleEast and North Africa|22. Armenia|
|9.|Sub SaharaAfrica|23. Russia|
|10.|Switzerland|**24. Ukraine**|
|11.|Turkey|25. Azerbaijan|
|12.|Rest of EFTA|26. Georgia|
|13.|Rest of Europe|27. Rest of Former Soviet Union|
|14.|EU_25||

35
|Decision number|||(12.49)|||(12.49)|(18.42)|
|---|---|---|---|---|---|---|---|
|Treatment X||||130.8||130.1|130.1|
|Risk preference||||(117.8)||(118.3)|(118.3)|
|Treatment X|||||0.431|0.413|1.078|
|Social preference|||||(1.03)|(1.03)|(1.27)|
|Social preference X|||||||0.089|
|Decision number|||||||(0.08)|
|Treatment X|||||||-0.102|
|Social preference X Deci|sion number||||||(0.12)|
|Constant|3042.4***|1469.7|1568.5|1440.0|1413.0|1484.5|1561.7|
||(85.03)|(1079.00)|(1079.80)|(1078.20)|(1091.60)|(1091.60)|(1093.50)|
|Within R2|0.420|0.420|0.423|0.420|0.420|0.423|0.423|
|Between R2|0.010|0.081|0.081|0.091|0.082|0.092|0.092|
|Overall R2|0.322|0.339|0.341|0.342|0.340|0.344|0.344|
|P|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level.
further within-country variation. Importantly for our theory, the countries vary in the extent to which SE rights litigation is characterized by decisions with broad applicability beyond the individual case, or by individual cases with individual remedies. We draw on some of this variation to answer questions about the social, economic, political, and institutional conditions that affect the distributive effect of SE rights litigation.

We have two related concerns with the existing literature on the distributive impact of litigation. The first is that too much of the literature – perhaps because it is for the most part based on single country case studies – assumes or implies that all courts have a similar pro-poor potential, and too often presents categorical conclusions for courts in all times and places, regardless of their individual features or social and political context. The second is that the more structural accounts of courts‘ alleged shortcomings miss the role of politics and agency in modifying the impact of structure, underestimating the capacity of social groups and litigants to turn courts to their own purposes. In this paper we try to address these two issues. We offer some hypotheses regarding the characteristics of courts and legal systems that should produce variation in the extent to which benefits extend to the poor, showing that not all courts are the same. We examine how structural constraints that could lead courts to privilege the already privileged might yield to determined mobilization on behalf of the underprivileged. And we examine the actual results of patterns of legal mobilization that brings health care and education issues to the courts for resolution.

> included all available cases at any level after the new constitution of 1998, a total of twelve; and in Nigeria it included federal court decisions going back to 1979 (when the Nigerian constitution first recognized social and economic rights), and all available cases in three selected states (Lagos, Rivers, and Kano), resulting in a total of 46 cases.  With this information it was possible to calculate the number of SE cases reaching courts; the likelihood that plaintiffs succeed; and, on the basis of interviews of judges, advocates, and others involved in the cases, as well as a review of the secondary literature, the level of implementation. This present paper builds on the data on success rates and overall implementation to analyze the distributional effects of the cases.

6
These include land and buildings, machinery and equipment, and personal assets. Unacceptable collateral is often the primary reason for denial of loan to micro and small firms, respectively, in 31% and 39% of cases (World Bank, 2010).

The issue of access to finance has been widely studied for India, especially with regard to significant policy changes it has experienced over the last decades (e.g. Besley et al., 2007; Ayyagari et al., 2013, 2014). Specifically, two of these policy changes include: (i) the expansion of rural bank branches across Indian states during 1977 to 1990 and concomitant changes in financial breadth and outreach, and (ii) the massive financial sector liberalization reforms of 1991.<sup>9</sup> These studies mainly explore the impact of increased financial depth and breadth on poverty (e.g. Burgess and Pande, 2005; Ayyagari et al., 2013) by emphasizing the entrepreneurship channel. Such studies contend that financial deepening primarily facilitates the operations of the self-employed in rural areas and hence leads to a decline in poverty. Ayyagari et al. (2014) however show some limits of financing’s impact for Indian business growth.

Banerjee and Duflo (2014) compare the impact of a directed credit policy change in 1998 on firms that became eligible for such a program but lost eligibility as a result of the reversal of this reform in 2000, relative to smaller firms that were already eligible for the preferential credit before 1998 and remained eligible in 2000. Their work finds no evidence that directed credit was used as a substitute for other forms of credit but was instead used towards more production. In fact, firms that became eligible for this credit had higher rates of growth, but this trend was reversed with the withdrawal of the policy. Their results thus confirm that many Indian firms must have been severely credit constrained, and that the marginal rate of return to capital was very high for these firms.

Access to finance has also been linked to the extent of informality.<sup>10</sup> Following the seminal work by Rajan and Zingales (1998), cross-industry variation in the need for external finance has been

> 9 The Reserve Bank of India instituted a 1:4 licensing policy whereby commercial banks in India had to open four branches in rural unbanked locations for every branch opening in an already banked location. Such a policy led to a significant increase in rural bank branches in financially less-developed states, while the reverse was true after 1990. 10 See Boadway and Sato (2010), Straub (2005) and Beck et al. (2014) for a more detailed exposition. Additionally, La Porta and Shleifer (2014) use World Bank Enterprise Surveys to demonstrate high levels of informality in developing countries. Their findings suggest that operations in 44% of informal enterprises are constrained by

9
> <sup>- 17 -</sup>

**<u>Table 9  Administrative Expense Ratios (Percent of Average Assets)</u>**

||**1995-99**|**2000-04**|**1995-2004**|
|---|---|---|---|
|ATP|0.045%|0.046%|0.046%|
|SP||0.268%||
|LD|0.058%|0.067%|0.083%|
|Total Public|0.048%|0.069%|0.059%|
|LifeInsurance Companies|0.576%|0.557%|0.567%|
|Multi-Employer Pension Funds|0.194%|0.157%|0.176%|
|CorporatePension Funds|0.087%|0.085%|0.086%|
|Total Private|0.442%|0.425%|0.434%|
|GrandTotal|0.348%|0.336%|0.342%|

Source: The Danish Financial Supervisory Authority, LD Pensions and ATP

**<u>Table 10  Investment Expense Ratios (Percent of Average Assets)</u>**

||**1995-99**|**2000-04**|**1995-2004**|
|---|---|---|---|
|ATP|0.011%|0.023%|0.017%|
|SP||0.119%||
|LD|0.073%|0.090%|0.081%|
|Total Public|0.023%|0.040%|0.032%|
|LifeInsurance Companies|0.055%|0.098%|0.077%|
|Multi-Employer Pension Funds|0.153%|0.142%|0.148%|
|CorporatePension Funds|0.145%|0.156%|0.150%|
|Total Private|0.087%|0.113%|0.100%|
|GrandTotal|0.072%|0.095%|0.083%|

Source: The Danish Financial Supervisory Authority, LD Pensions and ATP

**<u>Table 11  Total Operating Expense Ratios (Percent of Average Assets)</u>**

||**1995-99**|**2000-04**|**1995-2004**|
|---|---|---|---|
|ATP|0.056|0.069|0.062|
|SP||0.387||
|LD|0.131|0.156|0.144|
|Total Public|0.072|0.109|0.090|
|LifeInsurance Companies|0.631|0.656|0.643|
|Multi-Employer Pension Funds|0.347|0.300|0.323|
|CorporatePension Funds|0.232|0.241|0.236|
|Total Private|0.529|0.538|0.534|
|GrandTotal|0.420|0.431|0.425%|

Source: The Danish Financial Supervisory Authority, LD Pensions and ATP
<mark>Buri, Sinja; Reitzug, Fabian; Heitmann, Soren; Marinho, Eduardo. 2019. “Do Agent Networks Help to Boost Savings? Effects on Institutional Deposit Mobilization and Customer Saving Behaviour.” IFC and the Mastercard Foundation’s Partnership for Financial Inclusion, mimeo. Washington, DC.</mark> <mark>Brune, L., Giné, X., Goldberg, J., & Yang, D. (2016). Facilitating savings for agriculture: Field experimental evidence from Malawi.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>,</mark> _<mark>64</mark>_ <mark>(2), 187220.</mark> <mark>Bohnet, I. (2007). Why women and men trust others.</mark> _<mark>Economics and Psychology</mark>_ <mark>, 89-110.</mark> <mark>Busso, Matias & Sebastian Galiani, 2019. "The Causal Effect of Competition on Prices and Quality: Evidence from a Field Experiment,"</mark> _<mark>American Economic Journal: Applied Economics,</mark>_ <mark>American Economic Association, vol. 11(1), pages 33-56, January.</mark> <mark>Butter, Inge and Mirijam de Bruijn. 2017. "An Ethnographic Study on Mobile Money Attitudes, Perceptions and Usages in Cameroon, Congo DRC, Senegal and Zambia". World Bank Group, Washington, DC.</mark> <mark>Campbell, John Y.; Howell, E. Jackson; Madrian, Brigitte; and Tufano, Peter. 2011. “Consumer Financial Protection,” Journal of Economic Perspectives 25 (1): 91-114.</mark> <mark>Chamboko, Richard, Robert Cull, Xavier Giné, Soren Heitmann, Fabian Reitzug, and Morne Van Der Westhuizen. 2020. “The Role of Gender in Agent Banking: Evidence from the Democratic Republic of Congo.” Policy Research Working Paper; No. 9449. World Bank: Washington, DC.</mark> <mark>Cole, S., Sampson, T., & Zia, B. (2011). Prices or knowledge? What drives demand for financial services in emerging markets?.</mark> _<mark>The journal of finance</mark>_ <mark>,</mark> _<mark>66</mark>_ <mark>(6), 1933-1967.</mark> <mark>Croson, R., & Gneezy, U. (2009). Gender differences in preferences.</mark> _<mark>Journal of Economic literature</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>(2), 448-74.</mark> <mark>Cull, Robert; Giné, Xavier; Harten, Sven; Heitmann, Soren; Rusu, Anca Bogdana. 2018. “Agent Banking in a Highly Under-Developed Financial Sector: Evidence from Democratic Republic of Congo,”</mark> _<mark>World Development</mark>_ <mark>, 107: 54-74.</mark> <mark>Davidson, N., & Leishman, P. (2012). Building a Network of Mobile Money Agents.</mark> <mark>Drexler, A., Fischer, G., & Schoar, A. (2014). Keeping it simple: Financial literacy and rules of thumb.</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>,</mark> _<mark>6</mark>_ <mark>(2), 1-31.</mark> <mark>Dupas, Pascaline; Green, Sarah; Keats, Anthony; Robinson, Jonathan. 2014. “Challenges in Banking the Rural Poor: Evidence from Kenya’s Western Province.” NBER Chapters in: African Successes, Volume III: Modernization and Development: 63-101; National Bureau of Economic Research.</mark> <mark>Dupas, Pascaline; Robinson, Jonathan. 2013. “Why Don’t the Poor Save More? Evidence from Health Savings Experiments.” American Economic Review; 103 (4): 1138-71.</mark> 21
17

# **Table 4.5: Rural/Urban Pattern of Real Household Per Capita Expenditures by Geographic Region**

|||**RU**|**RAL**||**UR**|**BAN**|
|---|---|---|---|---|---|---|
|**Region**|**1985/86**|**1992/93**|**Percentage change**|**1985/86**|**1992/93**|**Percentage change**|
|Northern|476.58|629.75|32.14|682.63|643.4|-5.75|
|Middle Belt|<br>488.44|766.11|56.85|631.74|732.67|15.98|
|Southern|607.68|936.70|54.14|732.98|931.78|27.12|
|Nigeria|531.28|779.96|46.81|700.35|812.95|16.08|
|Source: NCS,|1985/86 and 1|992/93.|||||

# **4.2 Evolution of Regional Poverty in Nigeria**

This section examines the evolution of poverty between 1985 and 1992. In analyzing the extent of poverty, it would generally be preferable to use individuals rather than household as the unit of analysis. Re-weighting the data according to household size is an alternative, but this makes the assumption that consumption is evenly distributed within the households. Given that there is inadequate information to construct adult equivalence scales, Average per capita expenditure (PCE) is used as the basis for poverty analysis.

The evolution of regional poverty is shown in Table 4.6. In 1992 about 23 million people (36.4 percent) were poor in the rural areas, accounting for about 66 percent of the poor in Nigeria. There was a 13 percentage point decline in rural headcount or the number of poor. However, taking into consideration all below the poverty line in Nigeria, rural areas accounted for about 73 percent of the poor in 1985 and 66 percent of the poor in 1992. In 1985 the depth of poverty was about 30 percent higher in the rural than in the urban areas. This value however, despite the decline in poverty in 1992, had increased to two times the depth in "Other urban" excluding Lagos and almost 17 times the depth in Lagos urban. The severity of poverty in the rural areas in 1985 was three times as high as it was for Lagos. In 1992, the value for rural was over 4 times that of Lagos.

Table 4.7 shows the evolution of regional core or extreme poverty. The table shows that the incidence of extreme poverty increased slightly in the seven year period of this study. The depth of extreme poverty and the severity of extreme poverty also increased during this period. The increase in the severity of extreme poverty was observed to be more in the urban than in rural areas. However, the rural areas continue to contribute the largest share to all the indicators.
affiliate in a developing country. Shares do not add up to 100 because respondents could select up to five of the most relevant reasons.

# **VI. Conclusion and Policy Implications**

To maximize the gains from foreign investments, developing country governments must adopt effective reform strategies, champion reform at the highest political levels, and strengthen interagency coordination. They must also balance the public interest with investor preferences to ensure that the host country truly benefits from FDI. The results of the survey of MNC executives highlight several priorities for policy makers in developing countries seeking to create a conducive business climate for FDI: _Targeting policies and reforms to relevant types of investors can maximize effectiveness and cost efficiency._ While most investors value some characteristics across the board—such as investment protection guarantees and transparency and predictability—some policy variables are more important for certain investor types. Firms involved in efficiency-seeking FDI seem more responsive to incentives. Relatively smaller investors are more sensitive to government support and policy and regulatory uncertainty. Manufacturing firms may be more responsive to business-friendly policies on importing inputs, while services firms are more sensitive to adverse government conduct. These results reinforce the need for targeted policy approaches by governments, keeping in mind the specific types of FDI they wish to attract, retain, and harness for development.

_Addressing policy reforms to attract FDI and offering aftercare services are equally important._ Policy makers tend to focus on attracting FDI through investment incentives, facilitation, and proactive investment promotion. While these are important, investors say that investment protection is even more critical to them, suggesting that government efforts should also aim to encourage investors to stay in the country and expand their operations. Policy initiatives should include strengthening investor protection guarantees, providing proactive investor aftercare, managing grievances, and promoting linkages.

_Predictable government conduct is at least as important to MNCs as countries’ laws and regulations._ Investors cited the importance of transparency and predictability in the conduct of government agencies as the most important among investment climate factors. Investors look not only at policies on paper, but also at implementation and administration of those policies. Implementation weaknesses can include bureaucratic inefficiencies, complex regulations and procedures, and unpredictable or arbitrary government conduct. Addressing these weaknesses can not only attract new investments, but also prevent divestments by existing investors.

24
As can be seen from (1), (2) and (15), the derivatives of _ct_ +20 and _λ_<sup>3</sup> _t_ +20<sup>involvevery</sup> long and messy expressions, which are suppressed here.<sup>2</sup> To close this section, a remark on the (non)-uniqueness of the expectations path is called for. Although the solution to problem (18) is unique for any given set of expectations, only when the solution satisfies (16) is it admissible as an equilibrium under stationary expectations. The imposition of (16) when solving the first-order conditions therefore opens up the possibility that there is more than one pair ( _e_<sup>1</sup> _t_<sup>_, e_2</sup> _t_<sup>)</sup> – that will satisfy them in effect, there may be more than one equilibrium path.

# **3 Calibration**

To determine the parameters and past variables of interest, we need to go back deep into the past century, and since censuses were carried out in 1948, 1962, 1969, 1979, 1989 and 1999, it is natural to choose the time-points, or periods, as _t_ = 10, 20, . . . , 100, . . . , where 100 denotes the year 2000. Morbidity and mortality from AIDS became significant after 1990, so that some aspects of the calibration of the model may be restricted to the period 1910 to 1990. We proceed in two steps.

## **3.1 Step 1: Human Capital and Output**

Eqs. (1) and (3), together with the specialisation of _f_<sup>1</sup> ( _·_ ) to the iso-elastic form ( _e_<sup>1</sup> _t_<sup>)</sup><sup>_ε_,</sup> provide the basis for the first stage of the calibration of the system’s parameters and the historical values of _λ_<sup>_a_</sup> _t_<sup>.The first, preliminary step is to smooth out the considerable</sup> fluctuations in the Kenyan economy. In keeping with the unit time period of a decade, the values of _Yt_ are obtained by forming 5-year moving averages of GDP per caput from the Penn World Tables (hereafter, PWT) and multiplying them by the population

> 2 They are available upon request.

17
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