rafmacalaba/datause-encoder-data huggingface dataset:
Page 82

# Human CaRital Indicators

5. To the extent possible, data on human resources should be reported not merely in aggregates and averages, but disaggregated by gender. Ideally, data aggregated by gender should cover:

|i)|edu<br>-<br>-<br>-<br>-|cation<br>numeracy and literacy rates;<br>educational attainment by age;<br>enrollment numbers, rates and trends in<br>primary, secondary,<br>tertiary and vocational<br>levels;<br>school performance;|
|---|---|---|
||-|student/teacher<br>ratios;|
|ii)|dem|ograbhic characteristics|
||-|age at marriage;|
||-|fertility measures such as total fertility|
|||rate;|
||-<br>-|contraceptive<br>prevalence rates and numbers<br>of acceptors by family planning method,<br>education, and rural/urban split;<br>sex ratios by age group;|
|iii)|hea|lth|
||-|life expectancy at birth;|
||-|general mortality and morbidity measures;<br>|
||-|access to health and family planning|
|||facilities- percent of population living|
|||with a certain distance (equivalent to,|
|||say, a half day's travel for the return|
||-|trip) of a facility;<br>births attended by trained health personnel<br>and maternal mortality rates;|
||-|infant and child mortality rates;|
|iv)|nut|rition|
||-|measures of weight for height for boys and|
|||girls;|
||-|incidence of<br>malnutrition (stunting/|
|||wastage);|
||-|low birth weight babies;|
||-|percentage of<br>calorie<br>requirements|
|||satisfied in average diet.|

# Poverty Indicators

6. In addition to basic economic and human resource measures, poverty indicators should include measures of absolute and relative poverty:
(i.e. less farmers per staff member because of more time needed for training and for explaining new adaptation measures to each farmer). A fraction of this additional demand would be attributable to climate change.

Attempts were made to identify institution-specific indicators and assess them, building on the methodology described in UNDP (2002), through a combination of quantitative methods (based on statistical and logical analysis of data involving human resources and organizational activities and budgets), as well as qualitative approaches (such as interviews and focus groups). A detailed description can be found at Givah and Storey (2007). Even though during the research design phase there was solid support for providing necessary data on the part of the managers consulted, at the implementation stage data collection proved difficult. For example, institutions could not provide quantitative indicators of the current gap between capacity and service demand for their main tasks. They do not maintain adequate records on morbidity and absenteeism (this was not foreseen by managers who attended the inception workshop). Similarly, there are no records on most attrition-related expenditures and impacts on performance. Interviews and surveys are often inadequate for producing new data, because HIV/AIDS remains a taboo issue when dealing at the individual level. With a few exceptions, this state of affairs made it almost impossible to quantitatively analyze patterns in staff attrition, particularly given the need to disaggregate by gender, age, educational level and other variables of interest.

Importantly, there was no data allowing for the quantitative attribution of HIV/AIDS causality regarding institutional erosion. For example, for deaths among staff members, there was no data on the seroprevalence status of the deceased – and not even clinical diagnosis of what had caused the death (otherwise it would have been possible to obtain estimates: HIV/AIDS itself is not a lethal disease but a condition that provides fertile ground for opportunistic diseases like tuberculosis to thrive and kill the infected person).

Nonetheless, as indicated by the quote that opens section 2, it is well known that HIV/AIDS is responsible for most of the excess mortality and morbidity experienced among Malawians. Yet other factors may be 18
in 2010—a deep drop in the context of improvements observed in socioeconomic statistics and growth in per capita incomes (see figure 1b).

**Figure 1a: GDP per Capita and Satisfaction with Life, 2008-10**

<!-- Start of picture text --> 8<br>7.5<br>7 ARE<br>6.5 QAT<br>SAU<br>6<br>BHR<br>JOR<br>5.5<br>DZA<br>TUN<br>IRN<br>5 DJI LBN LBY<br>EGYIRQ SYR<br>MAR<br>4.5 YEMWBG<br>4<br>3.5<br>3<br>5 6 7 8 9 10 11 12<br>Log GDP per Capita (2005 US dollars)<br>Figure 1b: Percentage Growth in GDP and Change in Satisfaction with Life (Weighted<br>Averages) in 106 Countries, 2005-10<br>1.5<br>1<br>0.5<br>ARE<br>0 WBG MAR<br>YEM<br>‐3 ‐1 1 3 5 7 9 11 13 15<br>‐0.5 LBN<br>EGY<br>JOR<br>SAU<br>‐1<br>‐1.5<br>Average Annual GDP Growth 2005‐2010<br>Average Life Evaluation<br>Change in Life Evaluation 2005‐2010<br><!-- End of picture text -->

**Figure 1b: Percentage Growth in GDP and Change in Satisfaction with Life (Weighted Averages) in 106 Countries, 2005-10**

_Sources:_ Real GDP per Capita: World Bank Development Indicators; Life Satisfaction: World Database of Happiness. _Note:_ Numbers are weighted averages for 147 countries. Abbreviations: ARE=United Arab Emirates; EGY=Egypt; JOR=Jordan; LBN=Lebanon; MAR=Morocco; SAU=Saudi Arabia; WBG=West Bank and Gaza; YEM=the Republic of Yemen.

3
**<u>Table 9. Synthetic Control Method Detailed Results for All IHC Firms - Median Clearance Time</u>**

|IHC firm #|6-month average<br>treatment effect:<br>change in log<br>hours (�_���<br>��)|Number of donor firms<br>used to construct<br>synthetic control firm|Rank of IHC firm<br>relative to 25 placebo<br>firms|Percentile rank<br>statistic (rank/26)|
|---|---|---|---|---|
|1|-0.049|10|10|0.385|
|2|-0.240|14|5|0.192|
|3|-0.060|8|8|0.308|
|4|-0.822|11|2|0.077|
|5|-0.308|13|3|0.115|
|6|0.496|6|22|0.846|
|7|-0.094|11|12|0.462|
|8|-0.195|8|11|0.423|
|9|-0.783|8|3|0.115|
|10|0.122|10|17|0.654|
|11|-0.104|13|9|0.346|
|12|-0.510|8|3|0.115|
|13|0.183|4|15|0.577|
|14|0.734|5|24|0.923|
|15|0.281|100|21|0.808|
|16|-0.005|5|16|0.615|
|17|0.445|3|23|0.885|
|18|-0.073|3|10|0.385|
|19|-2.779|3|1|0.038|
|20|-0.036|3|13|0.500|
|21|-0.246|7|9|0.346|
|Mean % change in<br>hours|-0.192||Mean percentile rank<br>statistic(�)|0.434|
|Median %|-0.073||||
|change in hours|||||

Notes: Synthetic control firms were constructed with the benchmark specification that includes three pre-treatment values of the outcome variable as part of the set of conditioning variables. The summary statistics at the bottom left of the table are obtained as the mean and the median of the treatment effect across the 21 firms. The statistic at the bottom right of the table �̅ given 21 firms has 5% critical values of 0.377 and 0.623 (based on Appendix table A1 in Dube and Zipperer (2015)).

35
Table 9

# Relative Usefulness of Various Types of Support From Public or Other Non-Profit Agencies to the SMI Garment Exporting Firm in Penetrating Export Markets, by Start-up Size of the Firm

|||Percent|Average|Fl rms||F|i nns||Number|
|---|---|---|---|---|---|---|---|---|---|
|Type of Support.<br>Initial<br>Sizeand<br>Year of First Export||of Flms<br>Using<br>Service|Score of<br>Firms<br>Using|Average<br>Score<br>All|As<br>3|sionino<br>4<br>(Per|a Sc<br>5<br>centag|ore of<br>4 or 5<br>e)|of<br>Firms|
||||Service|Finns||||||
|Information on Export|All|59.6|3.43|2.45|6|10|5|31.9|47|
|<br>Harketing|**c5**|80.0|3.42|2.94|2|3|4|46.7|15|
|Opportunities|5-9|53.8|3.14|2.15|5|1|0|7.7|13|
||10-20|37.5|3.00|1.75|2|||25.0|8|
||>20|83.3|4.00|3.50|1|3|1|66.7|6|
||na|20.0|4.00|1.60|1|||20.0|5|
||To1982|87.5|4.00|3.63|1|2|3|62.5|8|
||1984& on|53.8|3.23|2.20|5|8|2|25.6|39|
|Sent Buyers to the Finn|All|34.0|3.06|1.70|4|5|2|14.9|47|
||**.5**|46.7|3.71|2.27|2|2|2|26.7|15|
||5-9|23.0|1.33|1.A8|||0|0.0|13|
||10-20|25.0|2.00|1.25|1|||0.0|8|
||>20|50.0|3.67|2.33|1|2||60.0|6|
||na|20.0|4.00|1.60|1|||20.0|5|
||To1982|75.0|3.33|2.75|2|3|0|37.5|8|
||1984 & on|25.6|2.90|1.49|2|2|2|50.0|39|
|Organized Fair|All|49.3|3.94|2.45|2|6|7|27.7|47|
|<br>at Home|<5|44.7|4.00|2.34||3|3|40.0|15|
||5-9|35.8|4.00|2.07||2|1|23.1|13|
||10-20|25.0|3.50|1.63|||1|12.5|B|
||>20|33.3|3.50|1.83|1|1||16.7|6|
||na|60.0|4.33|3.00|1||2|40.0|5|
||To 1982|87.5|3.86|3.50|1|2|3|62.5|8|
||1984&on|<br>28.2|4.00|1.B5|1|4|4|20.5|39|
|Facilitated|All|40.4|3.95|2.19|2|6|8|29.8|47|
|Participation|<5|33.3|4.20|2.07|1|2|2|26.7|15|
|<br>in Fair(s) Abroad|5-9|38.5|4.00|2:15||2|2|30.8|13|
||10-20|37.5|4.00|2.13|||2|25.0|8|
||20|50.0|3.00|2.00||2||33.3|6|
||na|60.0|4.33|3.00|1||2|40.0|5|
||To 1982|87.5|3.86|3.50|1|2|3|62.5|8|

41
Table 4: Simulation Results Using World Bank Measure of GW Volume

|Model|Crop<br><br>Sa<br>Co|Grids<br>tisfying<br>nstraints<br>(%)|Grids with<br>Production<br>Gains (%)|Median<br>Gains in<br>Production<br>(%)|Mean<br>Gains in<br>Production<br>(%)|
|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|
|SD|Barley|5_._47|2_._75|29_._57|101_._40|
|SD|Millet|5_._47|4_._31|76_._35|102_._88|
|SD|Maize|5_._47|43_._45|107_._96|184_._96|
|SD|Wetland Rice|5_._47|41_._41|108_._09|126_._08|
|SD|Sorghum|5_._47|18_._22|89_._49|112_._43|
|SD|Wheat|5_._47|8_._05|27_._97|46_._87|
|SRHS|Barley|5_._47|4_._68|33_._28|65_._23|
|SRHS|Millet|5_._47|0_._00|||
|SRHS|Maize|5_._47|57_._93|123_._26|221_._50|
|SRHS|Wetland Rice|5_._47|44_._04|114_._64|135_._14|
|SRHS|Sorghum|5_._47|40_._36|129_._42|242_._49|
|SRHS|Wheat|5_._47|14_._02|53_._08|102_._22|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by World Bank. Each row represents one simulation. Column (1) specifies the choice of model. Column (2) specifies the crop for which the analysis was run. Column (3) presents the fraction of all croplands where the constraints were met. Column (4) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (5) and (6) present the median and mean of the simulated gains in production. All the results presented imposed constraints on groundwater depth _≤_ 7m, an extraction recharge ratio _≤_ 0.7, and aquifer productivity _≥_ 0.05.

28
# **Appendix 1: Variables**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Inflation|Maximum rate of inflation in the last five years|IFS|
|Expected real<br>economic growth<br>|An indicator variable for negative real GDP growth in 2008 and positive expected real GDP<br>growth in 2009|WEO|
|Capital account<br>openness|The extent of openness in capital account transactions (Chinn-Ito financial openness measure)|Chinn and Ito (2008)<br>|
|Deposit insurance|An indicator variable for explicit deposit insurance in 2003|Demirguc-Kunt, Karacaovali and<br>Laeven (2005)|
|Electricity<br>consumption|Kilowatts per hour consumption per capita in 2006|WDI|
|Land line and cell<br>phone users|Number of land line and cell phone users per 100 people in 2008|WDI|
|Absence of violence<br>|Sub-index of political stability / No violence, 2008|WB Governance Indicators|
|Offshore financial<br>center|An indicator variable for offshore financial centers. See source for definition.|Errico and Musalem|
|Deposit rate|Deposit interest rate (%) in 2008|WDI|
|GDP per capita|Gross domestic product per capita in current dollars in 2008<br>|WDI|
|Population density|Total population in 2008 divided by land area (in km<sup>2</sup>)|WDI|
|Branches per adult<br>|Number of branches in 2009 per 100,000 adults (15+)|_Financial Access_database|
|Branches per km<sup>2</sup>|Number of branches in 2009 divided by land area (in km2)|_Financial Access_database|
|Concentration|Share of deposits in the five largest banks|Barth, Caprio and Levine (2004)|
|Registry coverage|max of public registry coverage (% adults) and private registry coverage (% adults) in 2008<br>|DB|
|KYC requirements|An index aggregating the documentation required to open a checking account. See Kendall et al.<br>(2010) for details.|_Financial Access_database|
|KYC exceptions|An indicator variable for having exceptions in KYC requirements for low income individuals as<br>of 2008|_Financial Access_database|
|Low-fee accounts|An indicator variable for regulatory requirements for banks to offer a basic or low fee account to<br>promote access to finance as of 2008|_Financial Access_database|
|Tax incentives|An indicator variable for tax incentive schemes to promote financial access as of 2008<br>|_Financial Access_database|
|Agents (savings)|An index of agent operations allowed in terms of deposit accounts. See Kendall et al. (2010) for<br>details.|_Financial Access_database|
|Postal system (private<br>operator)|An indicator variable for financial services offered by post offices that are provided by private<br>operators as of 2008|_Financial Access_database|
|Credit legal rights|Index of creditor rights in 2009|DB|
|Credit rank|Ranking of countries in terms of the ease of getting credit|DB|
|Credit information|On a zero to six increasing scale measuring the access to, and scope & quality of credit|DB|
|index|information in 2009||
|Road density|Km of roadsper 100 sq. km of land area,5-year averages|WDI|

50
existence<sup>4</sup> . For this paper, we obtained raw survey data from the Doing Business team, for years 2005 through 2010, covering more than 180 countries.

Based on this raw data, we constructed two variables indicating the existence of credit reporting institutions in 2010. The first variable is a dummy that is equal to one if the country has a credit bureau and zero otherwise. The second variable is a dummy that is equal to one if the country has a credit registry and zero otherwise<sup>5</sup> . The Doing Business team defines a credit registry as a publically owned entity that collects information on borrowers and shares it with regulated financial institutions. A credit bureau is a privately owned entity which collects information on borrowers in the financial system and facilitates the exchange of credit among lenders. For the purpose of this paper and the hypotheses stated in section 2, the key distinction is that the credit registry makes reporting of loan information mandatory for banks (in many cases for loans above a certain threshold only), whereas participation in a credit bureau is voluntary. From a broader perspective, credit registries contain primarily information on loans made by regulated lenders and capture a loan at the time of origination. This information is often used for regulatory and supervisory purposes. Credit bureaus tend to contain more repayment information that allows for the tracking of loans and credit risks over time and they also provide additional data processing services, such as calculating credit scores. Panel A of table 1 shows that 54.5 percent of the countries in our sample have a credit bureau and 45.1 percent have a credit registry. Figure 1 further shows that about 74 percent of countries have either institution (26 percent have no credit reporting institution). Low and middle income countries have a relatively higher presence of credit registries while credit bureaus are more common in high income countries (Figure 1).

For countries with a credit bureau, we constructed an indicator measuring the coverage of the information captured in the bureau, by dividing the total loan volume recorded in the bureau by the country’s GDP. We calculated this indicator as an average over the years 2005 through 2010, considering only years when a credit bureau existed during this period, since data is missing for different countries in different years. Panel A in table 1 shows that the average ratio of credit

> 4 In recent years and in very few cases, the survey has covered more than one credit bureau. The bureaus that are covered are typically the largest ones in a country.

> 5 Information on whether a country has a credit bureau or a credit registry is also available on the Doing Business website.

8
# Madhya Pradesh

Figure 5a: Annual Percentage Change in Stamp Duty Revenues and Rates

## **Madhya Pradesh**

<!-- Start of picture text --> 500<br>400<br>300<br>200<br>100<br>0<br>-100<br>Year<br>Tax Rev Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text --> Figure 5b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Madhya Pradesh<br>45000 14.0%<br>40000 12.0%<br>35000<br>30000 10.0%<br>25000 8.0%<br>20000 6.0%<br>15000 4.0%<br>10000<br>5000 2.0%<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rate (%)<br>Rs. Lakhs<br><!-- End of picture text -->

# Maharashtra

Figure 6a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 6b: Stamp Duty Revenues and Rates

### **Maharashtra**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text -->

### **Maharashtra**

<!-- Start of picture text --> 200000 12.0%<br>10.0%<br>150000<br>8.0%<br>100000 6.0%<br>4.0%<br>50000<br>2.0%<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rs. Lakhs Rate (%)<br><!-- End of picture text --> 74
savings‐investment balance; and (b) rules for (non‐equilibrating) payments in the government budget and the balance of payments.

**Table 2.2. Elasticities and base‐year factor data for GEM‐Core applied to macro SAM**

|Item|Private|Government|Nation|
|---|---|---|---|
|Elasticities||||
|factor substitution|0.7|||
|import ‐ domestic output substitution in demand|1.5|||
|export ‐ domestic sales transformation in supply|1.5|||
|TFP with respect to trade openness|0.1|||
|Employment(%)|95.1|4.9|100.0|
|Unemployment rate(%)|||5.5|
|Capital stock(GDP share,%)|180.2|65.0||
|Capital depreciation rate(%)|4.0|2.5||
|Povertyheadcount ratio at$1.90 a day*(% ofpopulation)|||46.2|
|Gini index|||0.428|

*2011 purchasing power parity.

Sources: (a) Elasticities: Authors’ assessment based on literature; for a survey, see for example Annabi _et al_ . (2006, esp. pp. 23‐29 and 30‐31), finding values in the range 0.3‐0.9 for factor substitution and 0.5‐2.0 for the two trade‐related elasticities; elasticity of TFP with respect to trade openness: Dessus et al. (1999, pp. 27‐29). (b) Employment and unemployment: UN (2015), Baddock et al. (2015), and World Bank (2017). (c) Capital stocks: IMF (2017) and authors’ assessment. (d) Depreciation: Agénor et al. (2005, pp. 31 and 54; the authors use these rates for Ethiopia). (e) Poverty and inequality: World Bank (2017).

‐12‐
find that relative to South Africa – the omitted country – all countries except for Namibia have lower levels of financial service use. The differences range from 41 percent in Zambia to 20 percent in Botswana. Individuals in Namibia are as likely to use formal banking services as individuals in South Africa.

The results in Table 10 show that – on average across the 11 surveys – women are more likely to use informal financial services. The effect is also economically large, with women being 3.7 percent more likely to use informal financial services than men. Looking behind the average effect across countries, we find that this is driven by Botswana and the East African countries in our sample, with the effect being especially strong in Kenya (14.7 percent in 2006 and 16.3 percent in 2009). On the other hand, there is no gender difference in the use of informal financial services in Malawi, Namibia, South Africa, and Zambia.

Many of the individual characteristics that explain the use of formal banking services also explain the use of informal financial services. The relationship between income and the use of informal financial services is positive in some but not all countries. In some countries owners of mobile phones are more likely to use informal financial services while in others it does not make a difference or they are less likely (Rwanda and Zambia). Maybe surprisingly, compared to individuals dependent on transfers, employed individuals are more likely to use informal finance in Botswana, Kenya, and Zambia as are self-employed in Kenya and Tanzania (2006), while individuals working in agriculture are only more likely to use informal financial services in Kenya. Rural people in Malawi, South Africa and Tanzania (2006) are more likely to use informal finance, while they are less likely to do so in Tanzania (2009). The relationship between education and the use of informal financial services is not consistent across countries. While in Botswana, Kenya, and Tanzania (2009), individuals with primary education are more likely to use informal financial services than individuals without any formal education, individuals with secondary or tertiary education are not more 24
Table 4: Differences‐in‐Differences Regressions, Eqs. (4) and (5)

||Eq.(4)|Eq.(5)|
|---|---|---|
|�<sup>���</sup>|‐0.64***|‐0.14***|
||(0.029)|(0.033)|
|�<sup>����</sup>|‐0.43***|‐0.09***|
||(0.029)|(0.031)|
|Observations|3,099|2,968|
|R‐squared|0.224|0.073|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 5: Beta‐convergence Analysis

|VARIABLES|(1)<br>Full sample|(2)<br>Below|(3)<br>Above|
|---|---|---|---|
|l_base|‐0.0190***|‐0.0166***|‐0.0200***|
||(0.002)|(0.004)|(0.003)|
|Observations|2,748|552|1,692|
|R‐squared|0.028|0.029|0.024|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 20
66

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