rafmacalaba/datause-encoder-data huggingface dataset:
8 ores, minerals and metals (-7.3 percentage points) and foodstuffs (-5.7 points).<sup>4</sup> Aside from Africa's market share losses for these products, Table 1 shows that they also experienced well below average rates of growth in global trade. World trade in all nonfuel goods increased at a compound annual growth rate of 11.8 percent, yet the corresponding growth rate for the 30 African products was more than six and one-half points lower. _Africa, therefore, sufferedfrom a two pronged problem -- it experienced declining market shares for its maljor exports which, in turn, were of declining relative importance in world trade._ Both factors contributed to Africa's diminished role in world trade.

A question of considerable interest is which countries were primarily responsible for the erosion of Africa's market shares. Was the erosion broad based in terms of the competition, or was one or two groups of countries primarily responsible? For answers, Table 2 examines the 1962-64 to 1991-93 import share changes that occurred for various groups of countries classified by the World Bank in termns of their income level and region (i.e., OECD members, North Africa, low income Asia, Middle East, etc.,). For each of the 30 products these share changes are compared with those for Africa. Perhaps the most surprising finding is that the OECD countries themselves made the largest overall displacement of African exports. Specifically, while Africa's trade shares fell by 11.1 percentage points for these products, OECD shares rose by 9.9 points. Market shares for middle income Asia rose by over 4 percent, while those for other (non-OECD) Europe and central Asia increased by almost the same amount. In contrast, Latin America's trade shares dropped by about 4 points which was about one-third the overall African 4OECD trade barriers probably played a positive role for several products in which Africa increased its market share. For example, the 5.8 percentage point increase for raw sugar (SITC 061.1) was likely the result of increasingly restrictive EEC import controls on other suppliers while African exporters (primarily Mauritius and Reunion which account for over 90 percent of Sub-Saharan exports of raw sugar) had preferential market access and guaranteed market shares under the Lome Convention. In some cases, share changes that initially appeared positive actually have negative implications. For example, most exporters of tin ores (SITC 283.6) developed a capacity to export processed tin products leaving Africa as one of the few remaining suppliers of unprocessed tin. In other words, Africa's market share increase was the result of most other countries shifting to more profitable exports of processed tin and not a real increase in Africa's competitiveness.
– that would indicate that in at least some parts of Indonesia, levels of violent conflict are higher than those reported in PODES.<sup>13</sup> While the figures do not report all conflicts in Indonesia, as long as this underreporting is _systemic_ (that is, fairly equal across regions or characteristics of regions) the data still provide information about relative levels of conflict across regions (e.g., which provinces have higher levels of conflict) thus allowing for consideration of potential factors associated with conflict. If the same questions were asked again in the next PODES (2005/6) it would also tell us whether conflict levels were increasing or decreasing, both nationally and for a given area.

The comparison with qualitative work to-date suggests that while PODES may not accurately represent true levels of conflict across Indonesia, there is little evidence to suggest that the data are biased by particular characteristics of different localities. Under-reporting in East Java and NTT research sites were of similar magnitudes across areas with different majority religions, at different stages of development, and with different levels of conflict. Consistent ratios of conflict relative to conflict with explicit casualties and material damage also suggest at least some common understanding of conflict across cultures. The five provinces with a ratio of reported conflict to _explicitly violent_ conflict (involving deaths/injuries/material damage) of 50 percent or less were spread across three very different islands (Kalimantan, Sulawesi, and Java). A focus of future quantitative work will have to be improved survey design and respondent selection methods to elicit actual levels of bias, as well as to control for different cultural understandings of what defines particular conflict thresholds.

> 13 For example, Welsh’s (2003) study of vigilante killings in West Java and Barron and Madden’s (2003) study of Lampung would suggest levels of conflict higher than the respective PODES provincial rates of 7.9% and 3.5%.

21
**Figure 7.  Demographic Transition in Brazil Crude Birth Rate, Crude Death Rate, and Rate of Natural Increase, 1950-2005**

<!-- Start of picture text --> 50<br>Crude birth rate<br>45<br>Rate of natural increase<br>40<br>Crude death rate<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Year<br>Rate per 1000 population<br><!-- End of picture text --> 33
<!-- Start of picture text --> PIMI vs. Growth per Capita<br>° a<br>Rm °<br>2 @ETH<br>= oe<br>Ew 9 pa oust *RWA ®<br>2o®ShegeLUte mse <<br>:<br><!-- End of picture text --> <!-- Start of picture text --> = a etn SMRPMDGe<br>2<br>©<br>»<br>0 1 2 3 4<br>PIMI Overall Index<br>NOTE: PIM and GOP growth correspond to averages 2007-2010<br>Regression line = 1.47+ 0.78x+«, R-square (%) 4.65<br><!-- End of picture text -->
Figure 1. Index of Agricultural Export Earnings In Constant Prices, **1974-100.** <!-- Start of picture text --> $so<br>160 170 _<br>110<br>10 1+<br>40-<br>S0 - _<br>20<br>10<br>0 , I I - - i I 4 - I I - i<br>1961 1963 1965 1967 1969 1971 1973 1976 1977 1979 1981 1983 1985 1987<br>Year<br><!-- End of picture text --> <!-- Start of picture text -->
- Malawi  - Tanzania  Zimbabwe<br><!-- End of picture text -->

2
review.<sup>11</sup> Loan officers play another important role at the enforcement stage for group liability loans. Depending on the terms of repayment, the loan officer contacts the head of the group and reminds him/her of the due dates of installments. They then typically meet again at the office in the village at some point, and in the case of overdue installment payments, the officer visits the client and negotiates repayment. During the negotiation, the loan officer also visits other members in the group and explains the likely impacts on them for non-repayment. Loan officers rely heavily on their social skills, experience, and prior knowledge of the clients and businesses in performing these tasks. In case of any delay or failure in negotiation, the loan officer is required to report it to the credit committee.

CFPA microfinance also introduced modest financial incentives to its loan officers, who were rewarded for high lending volumes and low shares of delinquent loans. Loan officers receive a flat base wage and a bonus for originating loans that perform well. Delayed installment payments therefore negatively affect their bonuses. The compensation scheme was designed to encourage loan officers to expand outreach, while avoiding loans to riskier clients that would become non-performing.

# 3. Data Sources and Variable Construction

We rely on data from three sources to undertake our analysis. We first use data on the inventory of loan disbursements and overdue repayments from 2006 to 2010. CFPA microfinance has developed a central database that documents the details of every loan disbursement undertaken at all the county branches, including disbursement date, loan size, interest rate, maturity, loan scheme (group guarantee or individual liability lending), purpose of the loan, and the corresponding loan officer. This information is maintained in CFPA’s SQL server from which the inventory of loan disbursements were directly exported. For overdue repayments, scheduled and actual dates of

> 11 We acknowledge that loan officers are not the only influence on mission drift. But to the extent that they have little or no influence on the committee’s decisions, this biases our analysis against finding any significant results, and we find significant associations between loan officers’ backgrounds and the size of their loans in the regressions that follow.

11
# **Taxes and Employment by Firm Size**

More than half of all newly registered and active companies are small businesses. In 2012, 62 percent of newly registered and active companies filed income taxes under one of the two simplified tax regimes (see Exhibits 24 and 25), implying annual turnover of less than RwF 50 million (approximately USD 77,000).

Despite being numerous, the contribution to taxes and employment from small businesses is minor. In 2012, companies filing taxes under one of the two simplified tax regimes contributed only 2 percent of the overall tax declarations and 2 percent of the taxable employment generated by newly registered companies.<sup>14</sup> The remainder accrued to fully‐ fledged taxpayers.

Large taxpayers contributed to almost half of the additional tax receipts and one third of formal jobs. Approximately 30 large taxpayers (see Annex 1 for definition of ‘large’) have been registered. These contributed 45 percent of all tax declarations and 33 percent of all employment generated by newly registered companies in 2012.

Medium sized companies made perhaps the most important contribution, bringing in more than half of the additional tax receipts and two‐thirds of the formal jobs. Medium‐sized companies represent 37 percent of newly registered and active companies. They contributed to 54 percent of the additional tax declarations and 64 percent of the additional employment in 2012.

Given the limited contribution of small firms, explicit formalization drives would not seem cost effective. Efforts for micro and small firms could focus, for instance, on improving the business environment, increasing access to finance, lowering registration costs, and simplifying registration requirements, as the costs of explicit formalization drives are unlikely to be paid back in additional tax revenue.

**Exhibit 16:  New Registrations by Tax Regime**

<!-- Start of picture text --> 8,000<br> 7,000<br> 6,000<br> 5,000<br> 4,000<br> 3,000<br> 2,000<br> 1,000<br> ‐<br>2008 2009 2010 2011 2012<br>Flat amount Lump sum Real regime<br>Number of companies<br><!-- End of picture text --> _<u>Note:</u>_ Refers only to companies registered after 2008 and active as of 2012 (dubbed for simplicity as “Relevant”).

> 14 For a review of evidence of formalization and tax revenue see (Bruhn & McKenzie, 2013).

22
<!-- Start of picture text --> Outlet<br>discharge<br>Authorized<br>Branch canal<br>Actual<br>De-silted<br>0 Distance from head<br>Minor canal<br>Head<br>Head outlets Tail outlets<br>Watercourse<br>Tail = PMIU discharge gauge<br>Distributary<br><!-- End of picture text --> Figure 2: System schematic with discharge gauges kilometers of canals and irrigates about 8.5 million hectacres (Figure 1). From the Indus, Jhelum, Chenab, Ravi, and Sutlej rivers issues a dense network of main canals, branch canals, distributaries, minors, and sub-minors, ultimately feeding 58,000 individual watercourses in Punjab alone (See Figure 2 for a schematic of the canal hierarchy.).

Each watercourse outlet or _mogha_ supplies irrigation to typically several dozen farmers according to a rotational system known as _warabandi_ . Tracing its origins to British colonial rule and to the early development of irrigation in the Indus basin, the institution of _warabandi_ (literally “fixed turns”) embodies a modified principle of equity: to each irrigator in proportion to his cultivated area. As discussed below, adherence to _warabandi_ leads to an efficient allocation of canal water. At each level of the canal hierarchy in this continuous gravity-flow irrigation system, “authorized discharge” is allocated in proportion to cultivable command area (CCA). At the main canal level, irrigation department staff operate a series of gates regulating flow into the off-taking distributaries according to a rotational schedule. However, 7
|Prohibit discrimination on<br>ethnic grounds in the dis-<br>missal of employees?<br>Access to Remedy<br>Does the law allow an emp|Yes<br>loyee to|Criminal<br>Code (1968),<br>Art. 172 Para.<br>1<br>Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Arts. 20–21<br>bringa claim for emp|<br>Yes<br>loyment|Ley Federal del<br>Trabajo (1970),<br>Arts. 3, 133<br>discrimination on ethnicg|Yes<br>rounds:|Code du Travail<br>(2011), Art. 9, Art. 36,<br>Para. 5|Yes|Equal Treatment Act<br>(1994), Secs. 5(1), 8|Yes<br>Employment and Labour<br>Relations Act, (2004),<br>Arts. 7(4)(c), 37(3)(b)(iii)|<br>Yes|Law on Public<br>Employees, Art.<br>30, Decree No.<br>10/2012/QH13<br>Labor Code<br>(2012), Art. 3<br>Para. 7, and 194<br>Para. 6|
|---|---|---|---|---|---|---|---|---|---|---|---|
|In the public sector?|Yes|Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Arts. 50, 71.|Yes|Ley Federal del<br>Trabajo (1970),<br>Arts. 2, 3, 133<br>Ley Federal para<br>Prevenir la Dis-<br>criminación (2003),<br>Art. 43.|<br>Yes|Code du Travail<br>(2011), Arts.9, 41|Yes|Equal Treatment Act<br>(1994), Secs. 10, 12 (2)|Yes<br>Employment and Labour<br>Relations Act, (2004),<br>Arts. 7(4)(c), 37(3).|Yes|Decree No.<br>10/2012/QH13<br>Labor Code<br>(2012), Art. 8<br>Para. 1|
|If there is a law, does it<br>prohibit retaliation by the<br>employer for bringing a<br>complaint or participating<br>in any manner in an em-<br>ployment discrimination<br>proceeding?|Yes|Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Arts. 5, addi-<br>tional provi-<br>sions, Sec. 1<br>(3)|No|No provisions<br>could be located|Yes|Code du Travail<br>(2011), Art. 36, Para. 4|Yes|Equal Treatment Act<br>(1994), Sec. 8(a)|No<br>No provisions could be<br>located|No|No provisions<br>could be located|
|In the private sector?|Yes|Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Arts. 50, 71.|Yes|Ley Federal del<br>Trabajo (1970),<br>Arts.2, 3, 133.<br>Ley Federal para<br>Prevenir la Dis-<br>criminación (2003),<br>Art. 43|<br>Yes|Code du Travail<br>(2011), Arts. 9, 41|Yes|Equal Treatment Act<br>(1994), Secs. 10, 12 (2)|Yes<br>Employment and Labour<br>Relations Act, (2004),<br>Arts. 7(4)(c), 37(3).|Yes|Decree No.<br>10/2012/QH13<br>Labor Code<br>(2012), Art. 3<br>Para. 7, and 194<br>Para. 6|
|If there is a law, does it<br>prohibit retaliation by the<br>employer for bringing a<br>complaint or participating<br>in any manner in an em-<br>ployment discrimination<br>proceeding?|Yes|Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Arts. 5, addi-<br>tional provi-<br>sions, Sec. 1<br>(3)|No|No provisions<br>could be located|Yes|Code du Travail<br>(2011), Art. 36, Para. 4|Yes|Equal Treatment Act<br>(1994), Sec. 8(a)|No<br>No provisions could be<br>located|No|No provisions<br>could be located|
|Does the law:||||||||||||

43 Anti-Discrimination Law and Shared Prosperity
Table 4: Rainfall Shocks, Agricultural Productivity and Employment in large firms

||Log (emplo|yment in m|anufacturing)|L|og(employm|ent in servi|ces)|
|---|---|---|---|---|---|---|---|
||Food &|Other|Transport<br>Const. &||||Other|
||Beverages<br>(1)|Manfact.<br>(2)|other<br>(3)|Trade<br>(4)|Education<br>(5)|Health<br>(6)|Services<br>(7)|
|**Small Cities and**||||||||
|**Towns Sample**||||||||
|Rainfall Deviation|1.769**|2.306**|2.699***|1.215*|2.436***|2.071***|2.189***|
||(2.151)|(2.486)|(3.043)|(1.691)|(3.150)|(3.005)|(3.048)|
|Observations|696|696|696|696|696|696|696|
|Number of Upazilas|232|232|232|232|232|232|232|
|**Rural Sample**||||||||
|Rainfall Deviation|-0.249|-0.108|1.382*|0.189|1.430**|0.758|0.590|
||(-0.362)|(-0.133)|(1.970)|(0.394)|(2.307)|(1.224)|(0.944)|
|Observations|696|696|696|696|696|696|696|
|Number of Upazilas|232|232|232|232|232|232|232|
|Year & Upazila FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Area Characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Rainfall Deviation is defined as the difference between log (current rainfall) and log(30 year average rainfall). The regressions include a number of controls (log of travel time to Capital and/or Port cities interacted with time trend, flood prone dummy interacted with time trend, proportion of household with electricity, log of 1991 population interacted with trend, log of total active labor force, and proportion of labor force with above secondary education, share of urban households. All regressions include year and upazila fixed effects. Standard errors are clustered at upazila level. Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1
- Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198.

- Birdsall, N., A. Kelley, and S. Sinding, eds. 2003. _Demographic Change, Economic Growth, and Poverty in the Developing World._ Oxford, U.K.: Oxford University Press.

- Bloom, D. E., Canning, D., & Malaney, P. N. 2000. Population dynamics and economic growth in Asia. Population and Development Review, 26, 257-290.

- Bloom, D., and D. Canning. 2004. “Global Demographic Change: Dimensions and Economic Significance.” _in Global Demographic Change: Economic Impacts and Policy Challenges,_ proceedings of a symposium, sponsored by the Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming, August 26–28, pp. 9–56.

- Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. 2007. Does age structure forecast economic growth?. International Journal of Forecasting, 23(4), 569-585.

- Bloom, D., D. Canning, G. Fink, and J. E. Finlay. 2009. “Fertility, Female Labor Force Participation, and the Demographic Dividend.” _Journal of Economic Growth_ 14 (2): 79– 101.

- Bloom, D., and J. Williamson. 1998. “Demographic Transition and Economic Miracles in Emerging Asia.” _World Bank Economic Review_ 12 (3): 419–56.

- Dollar, D., & Kraay, A. 2002. Growth is Good for the Poor. _Journal of economic growth_ , 7(3), 195-225.

- Dollar, D., Kleineberg, T., & Kraay, A. 2015. Growth, inequality and social welfare: cross-country evidence. _Economic Policy_ , 30(82), 335-377.

- Eastwood, R., and M. Lipton. 2011. “Demographic Transition in Sub-Saharan Africa: How Big Will the Economic Dividend Be?” _Population Studies_ 65(1): 9–35.

- Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" _American Economic Review_ , 105(10), 3150-3182

- Higgins, Matthew. 1998. “Demography, National Savings, and International Capital Flows.” _International Economic Review_ 39: 343–69.

- Higgins, M., and J. G. Williamson. 1997. “Age Structure Dynamics in Asia and Dependence on Foreign Capital.” _Population and Development Review_ 23 (2): 261–93.

- IMF (International Monetary Fund). 2004. _World Economic Outlook: The Global Demographic Transition._ Washington, DC: International Monetary Fund.

- ______. 2015. _World Economic Outlook: Uneven Growth–Short- and Long-term Factors._ Washington, DC: International Monetary Fund.

25
# **Table 19.  Piecemeal Sensitivity Analysis -- Welfare Impacts as a percent of GDP by Region**

(% change from base year)

||Overall||St.||North-||||||Far|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|average|Moscow|Peters.|Tumen|west|North C|entral|South|Urals|Siberia|East|
|Central Results for reference|4.3|4.7|5.7|3.1|6.2|4.7|4.2|4.7|3.3|4.2|5.2|
|regional investment potential variation<sup>a/</sup>|4.2|5.1|6.0|3.3|5.6|4.0|3.8|4.2|3.2|3.6|4.6|
|esubconsumer = 1.5|4.5|5.0|6.1|3.2|6.1|5.0|4.5|5.0|3.5|4.5|5.5|
|esubconsumer = 0.5|4.1|4.5|5.4|3.0|5.9|4.1|4.0|4.5|3.1|4.0|4.9|
|esubs = 2.0|5.5|5.9|7.3|4.3|7.2|6.1|5.5|6.0|4.3|5.4|6.6|
|esubs = 0.5|3.5|4.0|4.8|2.4|5.2|2.4|3.6|3.9|2.6|3.5|3.9|
|sigmadm = 4|4.3|4.8|5.8|3.2|6.3|4.8|4.2|4.8|3.3|4.3|5.2|
|sigmadm = 2|4.3|4.7|5.7|3.1|6.2|4.7|4.2|4.7|3.3|4.2|5.2|
|etaf = 17.5|4.8|5.2|6.1|3.5|6.7|5.2|4.7|5.3|3.8|4.8|5.6|
|etaf = 12.5|3.7|4.2|5.2|2.7|5.6|3.9|3.6|4.1|2.7|3.6|4.6|
|etad = 10|4.4|5.2|6.1|3.1|6.4|5.0|4.3|4.6|3.3|4.3|5.5|
|etad = 5|4.0|4.1|5.1|3.1|6.0|4.3|4.1|4.5|3.3|4.1|4.7|
|esub = 4|4.3|4.7|5.5|3.2|6.0|4.6|4.2|4.7|3.4|4.3|5.1|
|esub = 2|4.3|4.7|5.5|3.2|6.0|4.6|4.2|4.7|3.4|4.3|5.1|
|esubprimary = 1.5|4.3|4.7|5.7|3.1|6.3|4.7|4.3|4.7|3.4|4.2|5.1|
|esubprimary = 0.5|4.3|4.9|5.7|3.1|5.9|4.8|4.0|4.8|3.1|4.2|5.4|
|etadx = 7|4.3|4.8|5.8|3.0|6.3|4.7|4.3|4.8|3.3|4.2|5.2|
|etadx = 3|4.3|4.6|5.6|3.3|6.1|4.7|4.2|4.7|3.3|4.2|5.1|

Source: Authors' calculations.

a/ We vary etaf by region as follows: Moscow = 20; St. Petersburg = 18.3; Tumen = 17.1; Northwest = 11.3; North = 10; Central = 12.5; South = 11.5; Urals = 14.3; Siberia = 10.8; Far East = 10.8.

## **Notes:**

a. The piecemeal sensitivity analysis employs central values for all parameters (see below) other than the tested parameter and lump sum tax replacement.

b. Hicksian equivalent variation as a percent of the value of consumption in the benchmark equilibrium.

**Parameter Central Definitions of the parameter (see figures, especially figure 4) value for more precise elasticity structure).** esubs 1.25 Elasticity of substitution between value-added and business services esub 3 Elasticity of substitution between firm varieties in imperfectly competitive sectors sigmadm 3 "Armington" elasticity of substitution between imports and domestic goods in CRTS sectors esubprimary 0 Elasticity of substitution between primary factors of production in value added esubintermed 0 Elasticity of substitution in intermediate production between composite Armington aggregate goods esubconsumer 1 Elasticity of substitution in consumer demand etadx 5 Elasticity of transformation (domestic output versus exports) etad 7.5 Elasticity of Russian service firm supply with respect to price of output etaf 15 Elasticity of multinational service firm supply with respect to price of output

51
**Figure 10. Participation Rates in Tertiary Education (Highest Four-Year Participation Rate)**

<!-- Start of picture text --> Finland<br>Italy<br>Netherlands<br>France<br>United Kingdom<br>High Income Average<br>Australia<br>Average<br>Colombia<br>Canada<br>United States<br>Austria<br>Belgium<br>Sweden<br>Mexico<br>Ireland<br>Germany<br>LAC Average<br>Brazil<br>Peru<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45%<br>Participation Rates (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Note: The shown participation rate is calculated as the participation rate of the four year age-group with the highest average participation rate.

As is well-known, the four selected LAC countries have lower participation rates than the high-income countries, 16 percent compared to 24 percent, respectively. The majority of countries’ participation rates are around 20 percent. Among the four LAC countries, Colombia has the highest participation rate in tertiary education in this study with 21 percent. Mexico follows next with 19 percent participation rate. Brazil has 12 percent participation rate and Peru has 10 percent participation. Importantly, this indicator measures participation in a different way than the more traditionally reported gross enrollment rate, which includes students in all ages and all types of tertiary education. Hence, the shown participation rate is lower than gross enrollment.

The limitation of this indicator is that the highest four year participation rate does not reflect the participation of other age groups into tertiary education than the four year agegroup with the highest participation rate. For example, the participation rate of the United States is below the average participation rate. This is because the participation rate only captures the access for the four-year age group with the highest participation. The age distribution of the US student population is broader than other high-income and LAC countries. That is, the second moment of the age distribution of the student population is higher in the US than in other countries. It is the opposite case for the four selected LAC 22