rafmacalaba/datause-encoder-data huggingface dataset:
|P004151|1990|1994|10 Closed|Korea,Republic of|
|---|---|---|---|---|
|P004153|1989|1989|1 Closed|Korea,Republic of|
|P004153|1989|1990|3 Closed|Korea,Republic of|
|P004153|1989|1991|5 Closed|Korea,Republic of|
|P004153|1989|1992|7 Closed|Korea,Republic of|
|P004153|1989|1993|9 Closed|Korea,Republic of|
|P004154|1989|1989|2 Closed|Korea,Republic of|
|P004154|1989|1990|3 Closed|Korea,Republic of|
|P004154|1989|1991|5 Closed|Korea,Republic of|
|P004154|1989|1992|7 Closed|Korea,Republic of|
|P004154|1989|1993|8 Closed|Korea,Republic of|
|P004154|1989|1994|9 Closed|Korea,Republic of|
|P004156|1992|1992|2 Closed|Korea,Republic of|
|P004156|1992|1993|3 Closed|Korea,Republic of|
|P004156|1992|1994|5 Closed|Korea,Republic of|
|P004156|1992|1995|6 Closed|Korea,Republic of|
|P004156|1992|1996|9 Closed|Korea,Republic of|
|P004159|1991|1991|1 Closed|Korea,Republic of|
|P004159|1991|1992|3 Closed|Korea,Republic of|
|P004159|1991|1993|5 Closed|Korea,Republic of|
|P004159|1991|1994|7 Closed|Korea,Republic of|
|P004160|2015|1991|1 Dropped|Korea,Republic of|
|P004160|2015|1992|3 Dropped|Korea,Republic of|
|P004160|2015|1993|5 Dropped|Korea,Republic of|
|P004160|2015|1994|8 Dropped|Korea,Republic of|
|P004160|2015|1995|10 Dropped|Korea,Republic of|
|P004161|1991|1991|1 Closed|Korea,Republic of|
|P004161|1991|1992|3 Closed|Korea,Republic of|
|P004161|1991|1993|5 Closed|Korea,Republic of|
|P004161|1991|1994|8 Closed|Korea,Republic of|
|P004162|1991|1991|1 Closed|Korea,Republic of|
|P004162|1991|1992|3 Closed|Korea,Republic of|
|P004162|1991|1993|4 Closed|Korea,Republic of|
|P004162|1991|1994|6 Closed|Korea,Republic of|
|P004162|1991|1995|8 Closed|Korea,Republic of|
|P004162|1991|1996|11 Closed|Korea,Republic of|
|P004164|1992|1992|2 Closed|Korea,Republic of|
|P004164|1992|1993|4 Closed|Korea,Republic of|
|P004164|1992|1994|6 Closed|Korea,Republic of|
|P004164|1992|1995|8 Closed|Korea,Republic of|
|P004164|1992|1996|11 Closed|Korea,Republic of|
|P004164|1992|1997|13 Closed|Korea,Republic of|
|P004165|1992|1992|1 Closed|Korea,Republic of|
|P004165|1992|1993|3 Closed|Korea,Republic of|
|P004165|1992|1994|5 Closed|Korea,Republic of|
|P004165|1992|1995|7 Closed|Korea,Republic of|
|P004165|1992|1996|10 Closed|Korea,Republic of|
We also caution users that the aggregate indicators can in some circumstances be a rather blunt tool for policy advice at the country level. We expect that the provision of the underlying data will help users in identifying -- and acting upon -- more specific aspects of governance that may be problematic in a given country. And we also encourage using these aggregate and individual indicators in conjunction with a wealth of possible more detailed and nuanced sources of country-level data on governance in formulating policy advice.

# **References**

- Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobatón (1999a). “Aggregating Governance Indicators.” World Bank Policy Research Working Paper No. 2195, Washington, D.C.

- ——— (1999b). “Governance Matters.” World Bank Policy Research Working Paper No. 2196, Washington, D.C.

- ——— (2002). “Governance Matters II – Updated Indicators for 2000/01.” World Bank Policy Research Working Paper No. 2772, Washington, D.C.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2004).  “Governance Matters III: Governance Indicators for 1996, 1998, 2000, and 2002”.  World Bank Economic Review.  18:253-287.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2005).  “Governance Matters IV: Governance Indicators for 1996-2004.  World Bank Policy Research Working Paper No. 3630.  Washington, D.C.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2006).  “Measuring Governance Using Perceptions Data", in Susan Rose-Ackerman, ed.  Handbook of Economic Corruption.  Edward Elgar.

- Knack, Steven (2006).  "Measuring Corruption in Eastern Europe and Central Asia:  A Critique of the Cross-Country Indicators".  World Bank Policy Research Department Working Paper.

32
The pension industry has been enjoying exceptionally high rates of return on equity (ROE) in many reformed LAC countries (Table 7). This result is not surprising, as the pension fund industry is primarily an asset management business with relatively low capital requirements. Also, declines in operating expenses over time (particularly marketing costs) were only partially passed to members as lower fees. In Chile, for example, after 1997, operating costs significantly fell due to reduced expenditures in marketing services; the decrease in fees was substantially smaller leading to a remarkable increase in PFAs’ ROEs, which reached 51 percent in 2000. ROEs started to fall thereafter partly to absorb an increase in insurance premiums. In El Salvador, a small country with a duopoly market, the ROE in 2005 was as high as 38.8 percent.

**Table 7:** Annual results of pension fund administrator in reformed LAC countries as shares of assets under management, 2000 <u>and 2006 (basis points)</u>

|**Country**|**Fe**|**es**|**Co**|**sts**|**Operatin**|**g Profits**|
|---|---|---|---|---|---|---|
||**2000**|**2006**|**2000**|**2006**|**2000**|**2006**|
|Argentina|428|121|312|104|120|17|
|Bolivia|121|55|137|24|23|58|
|Chile|89|69|60|33|29|36|
|Colombia|n.a.|149|n.a.|166|n.a.|89|
|Costa Rica|n.a.|283|n.a.|253|n.a.|33|
|El Salvador|2,058|141|1,344|74|33|66|
|Mexico|537|189|434|143|103|45|
|Peru|471|118|260|108|212|10|
|Dominican Republic|n.a.|301|n.a.|188|n.a.|114|
|Uruguay|346|94|379|58|-7|36|

Note: n.a. means not available. Source: AIOS (2006).

By contrast, ROEs have decreased in Mexico since 2002 when competition started to increase as a result of regulations based on low cost for automatically assigning workers who do not select a pension fund manager and reduced restrictions on transfers across PFAs. Similarly, the ROE has been declining in Peru since 2004 mostly due to regulatory changes that encouraged the entry of new operators (Figure 6). Hungarian and Polish PFAs have also been able to recover their start-up costs within relatively a few years and to generate high ROEs recently. For example, the ROE of Polish PFAs was 22 percent in 2004 and 24 percent in 2005 (Rudolph and Rocha 2007). Moreover, the average ROEs of pension fund administrators have been higher than the average ROE of banks (except for Mexico), which are subject to stricter capital requirements, manage a more complex business, and bear higher risks (Figure 7).

- 16 -
– **Figure 1** Four types of slumps <!-- Start of picture text --> (a) finished & unchanged trend (b) finished & decelerated trend<br><!-- End of picture text --> <!-- Start of picture text --> Nicaragua (NIC) Switzerland (CHE)<br>1950 1955 1960 1965 1970 1975 1940 1960 1980 2000 2020<br>2<br>4<br>1.8<br>1.6<br>3.5<br>1.4<br>1.2 3<br><!-- End of picture text -->

- **(c)** finished & accelerated trend **(d)** unfinished & negative trend

<!-- Start of picture text --> Philippines (PHL) Madagascar (MDG)<br>1940 1960 1980 2000 2020 1960 1980 2000 2020<br>2 1<br>.8<br>1.5<br>.6<br>1<br>.4<br>.5 .2<br><!-- End of picture text --> _Notes:_ Models refitted using the estimated breaks<sup>�</sup> _tb_ 1 and<sup>�</sup> _tb_ 2 but without the optimal AR( _p_ ) terms to emphasize the trend breaks. The bold vertical lines are at<sup>�</sup> _tb_ 1 and<sup>�</sup> _tb_ 2, respectively. The dashed vertical line indicates<sup>�</sup> _tmin_ . _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

The slump begins in 1958 and the trough is found in 1959. This is a relatively short-lived crisis before the ousting of the Somoza dictatorship ushers in a major slump (and civil war), lasting from 1977 to 1993. Panel (b) shows a finished slump in Switzerland where trend growth decelerates after the second break. In 1974, the Swiss economy is strongly affected by the oil crisis of the mid-1970s, leading to a 8.79% drop in GDP per capita within two years. After a strong recovery, Switzerland enters a low growth regime typical for the high-income economies in Western Europe of the 1980s and 1990s. Panel (c) shows a finished slump in the Philippines occurring in the early 1980s with an accelerated trend after the second break. The Philippines were hit by a combination of external shocks: the second oil crisis, declining export prices, and rising world interest rates, as well as internal shocks caused by a debt 11
Controlling for region, the correlations between SDG gender indicators and both SPI and GDP per capita still hold. There is some indication that countries with worse gender inequality measures fare better in terms of SDG gender indicator reporting.

|**Table 2. Regression**|**of availabilit**<br>(1)|**y of SDGg**<br>(2)|**ender indicat**<br>(3)|**ors on countr**<br>(5)|**y traits**<br>(6)|(7)|
|---|---|---|---|---|---|---|
|SPI Overall Score|0.20***<br>(0.04)||0.43***<br>(0.06)|0.50***<br>(0.06)|0.57***<br>(0.06)|0.60***<br>(0.08)|
|Log GDP per capita|||-5.40***|-5.05***|-4.35***|-4.19**|
||||(0.64)|(0.75)|(1.08)|(1.26)|
|Pop under 1.5 million<br>(0/1)|||-2.95|-3.22|-0.60|-1.23|
||||(2.10)|(2.08)|(2.06)|(2.92)|
|WBL|||0.04|0.02|-0.02|-0.17+|
||||(0.04)|(0.05)|(0.06)|(0.10)|
|UNDP GII|||||15.54*<br>(7.33)||
|OECD SIGI||||||0.10<br>(0.09)|
|_Region (0/1):_|||||||
|Middle East/North<br>Africa||-5.59*||0.95|1.52|-0.63|
|||(2.76)||(2.28)|(2.26)|(3.34)|
|North America||-0.86||-4.96|-2.82|-4.69|
|||(4.80)||(5.33)|(4.65)|(4.99)|
|South Asia||7.14*||8.26*|6.18+|3.04|
|||(3.33)||(3.25)|(3.38)|(3.88)|
|Sub-Saharan Africa||3.52||3.33|1.58|4.08|
|||(2.56)||(2.40)|(2.47)|(3.31)|
|N|163|163|163|163|140|101|
|R Sq.<br>Notes: *** indicates statis|0.13<br>tical significanc|0.15<br>e at 1%, * at|0.42<br>5% and + at 10%|0.51<br>. Constant term i|0.51<br>ncluded.|0.41|

Notes: *** indicates statistical significance at 1%, * at 5% and + at 10%. Constant term included. The WBL has a range of 1-100, with a high score indicating more gender equal laws and regulations. The OECD SIGI SIGI measures the extent of gender discriminatory legislation and restrictive social norms and practices, where a high score indicates greater gender inequality. Likewise, the GII measures poor outcomes for women in regards to reproductive health, empowerment, and the labor market, and a high score indicates greater gender inequality.

# **4. Discussion**

The world’s Sustainable Development Goal (SDG) agenda lays out an ambitious set of indicators to track progress. While the overall coverage of the 231 indicators certainly needs 26
**<u>Table 1: Basic characteristics by farm size group</u>**

||||**Far**<br>|**m size group**<br>|||
|---|---|---|---|---|---|---|
||**0.1-10**|**10.1-50**|**50.1-200**|**200.1-1000**|**1000.1-5000**|**above 5000**|
|**Distribution of farms & area**|||||||
|No. of farms|3,893,083|8,123|176|91|66|19|
|Share of farms (%)|99.7828|0.2082|0.0045|0.0023|0.0017|0.0005|
|Total area in farms|5,543,515|137,680|16,976|48,291|134,661|231,255|
|Share of farm area|90.6932|2.2525|0.2777|0.7901|2.2031|3.7834|
|Total area cultivated|5,543,450|136,816|13,105|15,363|37,279|53,800|
|Share of cultivated area|95.5798|2.3590|0.2260|0.2649|0.6428|0.9276|
|**Distribution of cultivated area**|||||||
|Partial fallow|0.058|0.134|0.613|0.474|0.665|0.399|
|Perennials|0.011|0.027|0.071|0.099|0.049|0.054|
|Annuals|0.920|0.785|0.316|0.427|0.286|0.547|
|Maize|0.317|0.324|0.257|0.091|0.062|0.001|
|Cassava|0.158|0.079|0.014|0.003|0.001|0.000|
|Sorghum|0.061|0.058|0.020|0.000|0.000|0.000|
|Peanuts|0.076|0.093|0.014|0.002|0.001|0.000|
|Rice|0.075|0.004|0.140|0.109|0.002|0.018|
|Sugarcane|0.006|0.001|0.088|0.152|0.401|0.838|
|Millet|0.011|0.010|0.006|0.000|0.000|0.000|
|Pulse|0.163|0.184|0.045|0.040|0.020|0.001|
|Sweet potato|0.017|0.059|0.037|0.032|0.020|0.002|
|Cotton|0.039|0.072|0.117|0.085|0.000|0.000|
|Tobacco|0.007|0.001|0.025|0.011|0.000|0.000|
|Other cash crops|0.022|0.021|0.084|0.243|0.486|0.137|
|Vegetables|0.041|0.096|0.152|0.221|0.008|0.004|

_Source:_ Own computation from 2014 large, medium, and small TIA survey.

16
64. Yield estimates from the crop simulation model were used to calculate total revenue per acre. Volatility and inflation were removed from crop prices by taking a five-year average of prices for the period 1989-93 deflated with the GNP Implicit Price Deflator. Variable costs were based on advice from Colorado State University extension agents. To reflect the cropping pattern in the service area, sugar beets were limited to 7 percent and dry beans to 17 percent of the total irrigated area. Corn silage may be grown on up to 12 percent, and alfalfa on up to 27 percent of the area. The area for corn grain was not constrained. Each crop can be irrigated with any of the five irrigation technologies and receive up to nine irrigations (beans up to eight). These assumptions generated 245 activities for which unit net returns were calculated and included in the economic model.

20
# **Table 5A: T and Rank Tests for Private vs. Public Banks in Non-GCC Region**

Tests are conducted on annual bank-level data for the period 2001-08. Column 5 shows the p-value of a t-test which tests for the equality of means of the groups (allowing for inequality in variance). Column 6 shows the p-value of the Wilcoxon ranksum test which tests whether the groups are samples from populations with the same distribution. Non-GCC countries are Egypt, Jordan, Lebanon, Morocco, Tunisia, and Yemen. State-led countries are Algeria, Libya, and Syria.

|**Variable**|**Private**|**Public**|**Pvalue for t**|**PValue for Rank**|
|---|---|---|---|---|
|Total Assets|2.1E+06|8.2E+06|0.00|0.00|
|ROA|0.92|0.52|0.00|0.00|
|ROE|11.16|7.57|0.01|0.00|
|Net Interest Margin(Total Assets)|2.27|2.25|0.82|0.89|
|Total Interest Income to Assets|5.85|5.18|0.00|0.00|
|Total Interest Expenses to Assets|3.59|2.98|0.00|0.00|
|Non-Interest Income to Assets|1.35|1.14|0.02|0.02|
|Securities to Assets(All Countries)|19.56|20.74|0.40|0.48|
|Securities to Assets(ExcludingLebanon)|13.88|20.23|0.00|0.00|
|OH Costs to Assets|2.00|1.58|0.00|0.00|
|Personnel Expenses to Assets|1.05|1.02|0.64|0.83|
|Employees to Assets|0.05|0.07|0.00|0.00|
|Wage(Personal Expense)|25.60|17.25|0.00|0.00|
|Wage(Overheads)|45.32|30.41|0.00|0.00|
|Loan Loss Provisions to Gross Loans|1.59|2.88|0.00|0.00|

# **Table 5B: T and Rank Tests for Domestic (Private) vs. Foreign Banks in Non-GCC Region**

Tests are conducted on annual bank-level data for the period 2001-08. Column 5 shows the p-value of a t-test which tests for the equality of means of the groups (allowing for inequality in variance). Column 6 shows the p-value of the Wilcoxon ranksum test which tests whether the groups are samples from populations with the same distribution. Non-GCC countries are Egypt, Jordan, Lebanon, Morocco, Tunisia, and Yemen. State-led countries are Algeria, Libya, and Syria.

|**Variable**|**Domestic**|**Foreign**|**P value for t**|**P Value for Rank**|
|---|---|---|---|---|
|Total Assets|2.3E+06|1.4E+06|0.00|0.04|
|ROA|0.89|0.97|0.51|0.09|
|ROE|11.10|11.54|0.71|0.88|
|Net Interest Margin(Total Assets)|2.25|2.30|0.55|0.34|
|Total Interest Income to Assets|6.22|5.30|0.00|0.00|
|Total Interest Expenses to Assets|4.01|3.00|0.00|0.00|
|Non-Interest Income to Assets|1.17|1.62|0.00|0.00|
|Securities to Assets(AllCountries)|23.54|13.51|0.00|0.00|
|Securities to Assets(ExcludingLebanon)|16.70|11.85|0.00|0.00|
|OHCosts to Assets|1.86|2.24|0.00|0.00|
|Personnel Expenses to Assets|1.01|1.19|0.00|0.00|
|Employees to Assets|0.05|0.06|0.00|0.00|
|Wage(Personal Expense)|27.44|21.29|0.00|0.00|
|Wage(Overheads)|49.56|38.24|0.00|0.00|
|Loan Loss Provisions toGross Loans|0.58|0.77|0.01|0.00|

38
prevalence of polygyny are invariant to the agricultural yields reported by households.

Despite the inclusion of all the controls mentioned above, not accounting for internal migration might be seen as another potential threat to the identification of the resilience effect of polygyny. Indeed, it might be argued that internal migration creates a pathway for men who want to have several wives to self-select into communities with a high polygyny prevalence. However, in rural Mali— the setting of our empirical analysis— migration is a rare event. Our data indicate that a mere 0.349% of household heads migrate internally. This evidence has at least two important implications for our identification strategy. First, it assuages concerns about the potential presence of a selection bias, as men are unlikely to self-select into communes with a high polygyny prevalence. Second, even if drought were to determine migration endogenously, this would not affect our estimation results.

We use data from two rounds of Mali’s _Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages (EAC-I)_ , in combination with polygyny data from the _Fourth General Census of Population and Housing_ (2009) and meteorological data. Mali is a bride price country (Corno et al., 2020), with the second highest polygyny prevalence in the world at 34% (Kramer, 2020). These facts make Mali a suitable setting for our hypothesis testing.

We find that drought occurring in the current year and the previous year substanreduces in all affected communes. This result is evidence that rural tially crop yields households in Mali are highly dependent on rainfed agriculture for their livelihoods. Importantly, we find that polygyny—a marriage practice known to undermine women’s health (Bove and Valeggia, 2009; Bove et al., 2014) and increase violence against women (Ebrahim and Atteraya, 2020)— enhances household resilience to this adverse effect a year later following the drought shock. This result is robust to various alternative specifications of the interaction between drought and polygyny.

Exploring the pathways of this resilience effect of polygyny, we find that a year after drought, households living in more polygynous communes are more likely to sell livestock and access credit than those living in less polygynous communes. They are also more likely to hire labor, use improved seeds and chemical inputs, and diversify crop 4
Table A5: Labor force participation by race, immigration status, controlling for the racial <u>composition of the PUMA.</u>

||(1)<br>Males|(2)<br>Females|
|---|---|---|
|Dependent variable|Inthelabor force|Inthelabor force|
|White bornout US|-0.0270***|-0.0847***|
||[0.0014]|[0.0018]|
|Blackborn inUS|-0.0641***|-0.0118***|
||[0.0010]|[0.0011]|
|Blackbornout US|-0.0242***|0.0488***|
||[0.0026]|[0.0029]|
|Asianborn inUS|-0.0353***|-0.0042|
||[0.0033]|[0.0039]|
|Asianbornout US|-0.0731***|-0.0958***|
||[0.0016]|[0.0018]|
|Hispanic born inUS|-0.0370***|-0.0227***|
||[0.0012]|[0.0015]|
|Hispanic bornout US|-0.0421***|-0.0706***|
||[0.0011]|[0.0015]|
|Average whitein PUMA|-0.0263***|-0.0677***|
||[0.0037]|[0.0049]|
|Average black in PUMA|-0.0726***|-0.1200***|
||[0.0040]|[0.0053]|
|AverageHispanicin PUMA|-0.0757***|-0.1129***|
||[0.0040]|[0.0053]|
|Observations|3472846|3649194|
|PseudoR-squared|0.15|0.10|

Robust standard errors in brackets. * Significant at 10%; ** significant at 5%; *** significant at 1%. Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The probit regressions (marginal effects shown) also include 5 year age group dummies, region dummies, education (HS drop out, HS graduate, some college, college and above), indicators for being married, disabled and in school, the number of children under age 5 in the household and the logarithm of total personal income earned by the other household members. The regressions use the sampling weights provided with the IPUMS data.

37
32 marginal. That means that at least a portion of the rate of return accruing to the government comes from infra-marginal profits. One of the great advantages of METRs is that they are designed to measure the tax rate on the marginal project.

The other alternative is to measure average effective tax rates, by which is meant the ratio of tax liabilities to capital income, where capital income is typically adjusted for inflation and true depreciation and may include both equity and interest income before taxes.<sup>17</sup> These will differ from METRs for a variety of reasons. Since they are average rates they will include both the tax collected on marginal projects and that collected on infra-marginal projects. They also measure taxes collected _ex post_ on past investments whereas the METR is that applicable on currently undertaken marginal projects. Thus, average tax rates include windfall gains and losses resulting from unexpected changes in parameters and statutory tax rates. Average tax rates also fail to account for deferred tax liabilities resulting from temporary losses or favorable tax treatment. For these reasons, the average tax rate is not a useful measure of the incentive effects of the tax system. Typically, average effective tax rates are calculated **to** be higher than METRs.<sup>8</sup> We proceed now to summarize the basic theory of METRs and then to consider its application to the measurement of the effect of investment incentives in developing countries.

# A. Measuring Incentive Effects Using METRs

The METR is the amount of taxes collected on the marginal investment, sometimes expressed as a proportion of the rate of return on capital. In absolute terms it is defined to be the difference between the before-tax rate of return on capital rg and the after-tax rate of return on savings _rn._ The task of the investigator is to compute rg and _rn._ The measurement of _rn_ is conceptually straightforward since one can, in principle, observe market rates of return on savings and deduct from them the relevant taxes on savers. However, the measurement of _rg_ is more difficult. To observe it directly one would have to identify the marginal project and measure its rate of return. This would be impossible to do. Instead, what is done is to deduce it indirectly as follows. Using a theoretical model of investment, an expression is derived showing what the marginal product of capital would have to be in order to cover all costs (the _user cost of capital)._ This is converted to a rate of return expression containing the components of cost, 17 Examples of **these** calculations may **be** found in Feldstein, Dicks-Mireaux and Poterba (1983) and **Fullerton,** King, Shoven and **Whalley** (1981).

18 **For** cxample, **see** King and **Fullerton** (1984).