rafmacalaba/datause-encoder-data huggingface dataset:
3 been able to substitute a strong program and strong supportive social legislation for women's and children's rights. In the analysis below, we will examine the relative importance of these factors in the deiermination of contraceptive use and fertility.

During the dLcade between 1978 and 1988, the TFR dropped by one-third. This occurred in part because of the improved education of women, but even within educational categories fertility dropped substantially as well. The TFR among those with no schooling decreased from _7.3_ to 5.1 while the TFR of those with more than primary school, from 5.4 to 3.9. These are almost identical percentage decreases.

The fertility decline in Tunisia has resulted from changes in marriage behavior and increases in contraceptive use. Breastfeeding patterns have changed relatively little. Lapham in his 1970 study attriouted 42 percent of the decline in the crude birth rate in the 1960s (as opposed to the TFR) to changes in marriage behavior and 40 percent to increased contraceptive use. By 1978 the contribution of marriage to suppressing fertility was very important and the average age of marriage was over 21 years, mui;h more like that of Latin America than of Asia or Africa where women spend a much larger proportion of their lives in marriage. Betwveen 1978 and 1988, marriage behavior continued to change in ways to reduce fertility and by the time of the DHS fertility was only 52 percent of what it would have been if women had been married throughout their entire reproductive life' (Rutstein, et al. (in progress)). While in the past marriage has played a major role in reducing fertility, it is unlikely that the age of marriage will increase much further. This and the fact that data were not available to identify the determinants of age of marriage, explain why the analysis of this paper focuses on family planning as a determinant of fertility. Clearly, to help guide other countries in the process of fertility transition, additional work is needed to identify the role that social legislation and economic change played in bringing about this substantial increase in the age of marriage.

Contraceptive use has increased substantially over the period analyzed. The Bongaart's index of contraception has decreased from 75 percent to 65 percent while usage of modern methods has risen from 25 percent to 40 percent and usage of all methods from 31 percent to 50 percent. (UN 1987; Rutstein, et al. (in progress)). The majority of this increase in usage appears to have occurred among the least educated where overall usage rose from 25 percent to 42 percenr.<sup>2</sup> This is consistent with the hypothesis stated above that the strong program has to an extent compensated for slower progress in female education.

I **The** WFS figure was 66 percent, but whether the estimation techniques are exactly comparable is uncertain.

2 Although the more educated have substantially highier usagc than the less educated, (67 percent versus 42 percent) the more educated are much more likely to use traditional methods. The comparable figures for the usage of modern methods is 45 percent and 37 **percent.**
33 trend in elecricity pices was dcrcteristic of the 1980s and shoud be seen in .elation to the ovall energm policy ofthe govenmen **t** o encourage fuel switching from oil to electricity and gas.

_Table 2.1: Malaysta: Commercal Energy Demand, 1984 (000 tons oil equivalent)_

||Ridential<br>and<br>commercia|l Industial|Transpor|Totd<br>t end use|Percet|
|---|---|---|---|---|---|
|Nauralgs|47|$7|-|134|2|
|Perleum (tota)<br>|488<br>|2,545<br>|3,271|6,304<br>|32|
|LPG<br>|_155_|33<br>|-<br>|188<br>||
|Gasoline<br> <br>|-|33|1,892|1,925||
|Keosene & aviaton<br>ofiul<br>|333|24<br>|371<br>|<br>728<br>||
|Diesd<br>|-|1,B93<br>|1,008|2,901<br>||
|Fueloil|-|528|-|34||
|Refinega s|-|34|-|34||
|Col&cd|-|270|-|270|3|
|Elecicity|553|453|-|1,006|13|
|Total|1,088|3,35S|3,271|7,714|100|

_Soarer._ Wod Bank (197 _Figure 21: Malaysia: Enrgy Price Changes in Malaysia (percentage p.a. in ,eal twmns)_ <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>IL 10%<br>IL<br>5%<br>0%<br>-5%<br>-10%<br>1983 1984 1985<br>*0 O Eiectricity Qao Gooin Kosmnej<br><!-- End of picture text -->
|**Data source(s)**|BIS<br>Bloomberg<br>Bloomberg<br>Thomson Datastream<br>Thomson Datastream<br>FDIC Qtr Bank Profle<br>Datastream and GEM<br>Datastream and GEM<br>Fed SLOS/ECB BLS<br>Fed SLOS/ECB BLS<br>World Bank GEM & WDI<br>World Bank GEM & WDI<br>IMF IFS<br>IMF IFS<br>IMF IFS|/EUR, USD/JPY, USD/GBP,|
|---|---|---|
||ting banks on up to 138 developing countries<br> overnight index swap rate diferential<br>and Euro overnight index swap rate diferential<br>d certifcate of deposit rate diferential<br>uropean bank CDS credit default swap indices<br>g loans, banks with foreign ofces<br>(1,1) conditional volatility of 8 assets_†_<br>oefcient of variation of 8 assets_†_<br>k aversion (range: -1 to 1)<br>borrowers from U.S./EU banks (range: -1 to 1)<br>rs<br>omestic product<br> local currency to U.S. dollars|constituent assets: exchange rates for the USD<br> spread.|
||BIS repor<br>Libor and<br>Euribor <br>IBOR an<br>.S. and E<br>utstandin<br> GARCH<br> rolling c<br>nge in ris<br>loans by <br>.S. dolla<br>al gross d<br>PI<br>et rate of <br>g rate|dditional <br> the TED|
|**Defnition and construction**|Total consolidated foreign claims of <br>Quarterly average of daily 3-month <br>Quarterly average of daily 3-month <br>Quarterly average of daily 3-month L<br>Synthetic claims-weighted index of U<br>Allowances for losses as fraction of o<br>Predicted common factor of 1-month<br>Predicted common factor of 1-month<br>Net tightening of lending due to cha<br>Strength of demand change for C&I <br>Gross domestic product, in current U<br>Year-on-year percentage change of re<br>Year-on-year percentage change of C<br>Quarterly average of change in mark<br>Quarterly average of national lendin|X and the computed volatility for 7 a<br>ure, energy, and industrial metals, and|
|**le**|claims<br>IS spread<br>r-Eonia spread<br>D spread<br>bank CDS<br>owances<br>latility<br>latility (uncond)<br>rsion<br>mand<br>owth<br><br>ation<br> rate|comprised the VI<br>ndexes for agricult|
|**b**|n <br>-O<br>o<br>-C<br> <br>ll<br>vo<br>vo<br>ve<br>e<br>gr<br>on<br>ci<br>g|e <br> i|
|**ria**|eig<br>or<br>rib<br>or<br>bal<br>s a<br>et <br>et <br>k a<br>n d<br>P<br>P <br>ati<br>re<br>din|hes<br>rice|
|**Va**|For<br>Lib<br>Eu<br>Lib<br>Glo<br>Los<br>Ass<br>Ass<br>Ris<br>Loa<br>GD<br>GD<br>Inf<br>Dep<br>Len|_†_ T<br>p|

25
million people, but it also includes larger member countries (Jamaica, Lesotho, Namibia and Papua New Guinea) because they share many of the same characteristics of smallness. The World Bank Task Force on small states uses the same threshold as a convenient yardstick for classifying all small states, and only considers sovereign states.

Using the standard of a population below 1.5 million people in 2000, 45 developing countries are small, accounting for nearly one-third of the total number of developing countries. They are home to 20 million people, less than 0.4 percent of the total population of developing countries. They range in size from “micro-states” like Cook Islands, Nauru, Niue, Palau, St. Kitts and Nevis, and Tuvalu (with fewer than 50,000 people each) to Botswana, Gabon, The Gambia, Guinea-Bissau, Mauritius, and Trinidad and Tobago (with more than 1 million people each).

The per capita GNP in these countries also ranges widely, from less than $400 in several African countries (Comoros, The Gambia, Guinea-Bissau, and Sao Tome and Principe); to just $700-1,300 in such countries as Cape Verde, Guyana, Kiribati, Maldives, Solomon Islands, and Tuvalu; to more than $9,000 (The Bahamas, Brunei, Cyprus, Malta, and Qatar). There are small states in every geographic region, but most countries fall into three main groups: 12 states are in the Caribbean region, 14 in East Asia and the Pacific, and 12 in Africa. Of the remaining seven countries, two are in South Asia, two in the Middle East, and three in Europe.

# **2. A NEW DATABASE ON EMIGRATION RATES BY EDUCATIONAL ATTAINMENT**

This section describes the methodology and data sources used to compute emigration stocks and rates by educational attainment and by origin country in 1990 and 2000. In what follows, the term ''country'' usually designates independent states and ''dependent territory'' refers to other entities attached to a particular independent state. Our 2000 data set distinguishes 192 independent territories (Vatican and the 191 UN member states, including East Timor which became independent in 2002) and 39 dependent territories.

Stocks are provided for both types of territories while rates are only provided for independent countries as well as three dependent territories which are treated as countries (Hong Kong, Macao and Taiwan) and one occupied territory (Palestine). Since most Korean migrants to the USA did not accurately report their origin, we cannot distinguish between North and South 4
Table 3: Rural and Urban Inequality: Cross-country Comparison (ISA 2010)

|||Mal|awi|||Tanz|ania|||Uga|nda||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|(A) Rural Residency:|Var.|Gini|_mean_<br>_med._|90<sup>_th_</sup><br>10<sup>_th_</sup>|Var.|Gini|_mean_<br>_med._|90<sup>_th_</sup><br>10<sup>_th_</sup>|Var.|Gini|_mean_<br>_med._|90<sup>_th_</sup><br>10<sup>_th_</sup>|
|Consumption|0.49|0.39|1.3|6|0.49|0.37|1.3|6|0.83|0.48|1.5|10|
|Income|0.99|0.54|1.7|11|1.44|0.61|2.0|21|1.67|0.74|2.6|24|
|Wealth|1.49|0.60|2.0|16|2.95|0.76|4.3|110|2.81|0.75|3.7|52|
|_▷_Land|1.15|0.64|2.1|-|2.25|0.79|4.5|-|2.35|0.81|4.7|-|
|(B) Urban Residency:|Var.|Gini|_mean_<br>_med._|90<sup>_th_</sup><br>10<sup>_th_</sup>|Var.|Gini|_mean_<br>_med._|90<sup>_th_</sup><br>10<sup>_th_</sup>|Var.|Gini|_mean_<br>_med._|90<sup>_th_</sup><br>10<sup>_th_</sup>|
|Consumption|0.59|0.44|1.5|6|0.60|0.40|1.3|7|1.05|0.48|1.4|14|
|Income|1.60|0.71|2.9|22|1.90|0.66|2.3|33|2.01|0.76|2.8|98|
|Wealth|4.52|0.84|6.8|282|4.44|0.94|-|-|4.67|0.80|6.5|369|

_Notes:_ All variables are in current USD. The measures of inequality that we study are the variance of logged variables (i.e., Var.), the Gini index, the mean-to-median ratio (i.e.,<sup>_<u>mean</u>_</sup> _med_<sup>),andtheratiobetweenthetop10%andbottom10%ofthedistribution(i.e.,</sup><sup><u>90</u></sup> 10<sup>_thth_).Theconstructionofthemeasuresof</sup> household consumption, income, and wealth is discussed in Section 2, with further details in the online appendix.
Page 70 180. To take women's needs, functions, responsibilities, and constraints into account, one must begfn at the planning stage in forestry sector work and projects. First, one *.st identify properly the short- and long-term needs of different users -- including different categories of women -- for existing and planned forest resources. This will help identify the extent to which women may or may not benefit from particular interventions and will suggest more promising approaches.

181. Sectoral analysis should look at women in different socioeconomic groups (rural, port, herders, landowners, smallholders, landless, tribal):

- a) As collectors and users of forest products for the household and farming system: fuelwood and fodder worldwide, specific foodstuffs in each country (mushrooms, leafy vegetables, nuts, insects, seeds, starchy roots, spices, cooking oils), silk cocoons, basket fiber, or utensils.

- b) As producers and entrepreneurs -- for example women in Dominican Republic making handicrafts from paim fronds, women in India making bidi cigarettes, Chinese women processing bamboo and Indonesian women processing dyes.

- c) As laborers in nursery operations, soil work and planting; watering seedlings and establishing trees and grasses; protecting trees against foraging animals and illicit felling; and harvesting.

- d) As planners and managers devising rotational systems for forest or grassland management, allocating traditional compost supplies to agriculture, or planting and harvesting household tree stocks.

182. Economic sector work has failed to emphasize women's roles in forestry for lack of swudies that offer clear data on the gender-related aspects of this sector and the economic returns from women's activity. More descriptive information is recently becoming available (Fortmann and Rocheleau 1985, FAO 1987), but no global statistics documenting wt'men's participation exist. As a result: a) women are not as productive as they could be, b) forestry investments are ngt made in areas that are most important to women, and c) expected changes in forest management and industrial development do not occur.

183. In forest-rich countries (such as Indonesia or Brazil), planning tends to center around the timber industry and too little attention is paid to other equally profitable forest products. Planners fail to tap the knowledge of indigenous women (and men) about the value and optimal management of natural forests to provide not only timber but medicinal plants, fibers, resins, gums, dyes, condiments, and foodstuffs. Womer. are major collectors of nonwood forest products (NWFP, usually called "minor" forest produce), but their formal involvement in NWFP collection, marketing, and processing is not consciously addressed. As a result, returns for their work are low, they are often not trained along with men, and they are not recruited as forest contractors, members of forest cooperatives, or managers of forest corporation labor. In the timber industry, they are not given optimal working conditions for plantation, nursery
Second, inequality between voters and state officials can undermine the promise of deliberative institutions. When participation is induced by the state, as in decentralization efforts or community-driven development programs, agents of the state ofen are stuck in the ironic situation of having to act against their self-interest by promoting institutions whose purpose is to undermine their power (Mansuri and Rao, 2012). Terefore, local bureaucrats and politicians may try to undermine these institutions by canceling them (Besley et al., 2005), crowding out meaningful deliberation with bureaucratic announcements, and ignoring voters’ claims and evading their requests (Bhatacharjee and Chatopadhyay, 2011).

Whether and to what extent these forms of dominance — of men over women, of the elite over the poor, of state officials over citizens — affect deliberative institutions in practice is an empirical question, but one that has been challenging to study systematically on a large scale, particularly in real-world, citizen-centered deliberative forums. In this paper, we overcome these challenges by applying Natural Language Processing (NLP, or _text-as-data_ ) methods to an original corpus of village assembly transcripts from rural India to systematically examine variation in the quality of deliberation. In particular, we examine the relationship between deliberative influence and the gender or position (citizen versus official) of a speaker. By using NLP methods, we are able to quantitatively examine not only the relative floor time enjoyed by different types of speakers, but also their ability to influence the topic of conversation (agenda-seting power) and to make claims on state officials (responsiveness of the state).

We find that, despite women’s high rates of atendance in Tamil Nadu’s village assemblies, they are indeed the “silent sex.” Women make up 58 percent of atendees on average, but are responsible for one-third of the available floor time. Moreover, when women do speak on a particular topic, they are significantly _less_ likely than men to elicit a topical or relevant response from state officials — suggesting a meaningful inequality in deliberative influence across the sexes. Importantly, these results hold even if we control for the particular topic that is being raised; that is, for any given topic, a man is more likely to get a response from an official than a woman.

In contrast to our findings on gender inequality, we do not find evidence that assemblies are 3
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The control groups are selected according to similarity with each RBL. Similarity is based on: debtor type, currency, interest type, DSA credit risk measure, similar debt maturity (difference within +/- 60%) and LIBOR rate at the time of agreement (+/- 1%).
informality rate, the average firm size, the number of own-account workers, employees, and micro firms.

The number of people working in each size category (small, medium, and large) and formality is calculated as the result of multiplying the estimated firm size by the number of firms. To ensure consistency, the number of workers for the category of micro firms in each country, both formal and informal, is calculated as the difference between the total number of workers in the formal or informal markets minus the estimated number of workers obtained by the previous multiplication. Because we rely on the WBES distribution for formal small, medium, and large firms in countries for which there are no establishment censuses, we are likely to underestimate the number of workers associated with large firms.<sup>12</sup>

# **4 Results**

## **4.1 Total number of firms and share of workers associated with them**

Our analysis estimates that Africa had approximately 244 million businesses in 2020. The vast majority of these—around 232 million—were own-account businesses, while the remaining 12.7 million were firms employing workers. Table 1 provides a detailed breakdown by size. Excluding own-account businesses, there were 7.7 million micro firms in Africa in 2020, employing 16% of the labor force. Small firms accounted for 4 million businesses, contributing 7% of employment, while medium and large firms totaled 1.1 million and collectively absorbed 15% of the labor force. Overall, the 12.7 million firms employing workers accounted for 38% of the labor force, with the majority — 62% — engaged in own-account businesses.

Table 1: Estimated number of businesses (in thousands) and labor absortion

||(1)<br>Number of Firms|(2)<br>Labor Share(%)|
|---|---|---|
|Micro (less than 5)|7,683|16|
|Small (5-19)|3,997|7|
|Medium (20-100)|933|7|
|Large (100+)|127|8|
|Total Firms|12,741|38|
|Own-account|231,689|62|
|Total Firms + Own-account|244,429|100|

Source: Own calculations.

Note: Micro firms exclude single proprietor and places it within the own-account category

> 12The average size of large firms in WBES is smaller than those obtained from establishment censuses in countries for which data were available.

15
# **Annex supplemental table**

## **Annex Table 1: Estimation results from the base model and alternative model**

||**Base m**<br>|**odel**<br>|**Alternativ**<br>|**e model**<br>|
|---|---|---|---|---|
||Coefficient|Std. error|Coefficient|Std. error|
|**Selection equation parameters**|||||
|Household size|0.103<sup>a</sup>|0.012|0.101<sup>a</sup>|0.012|
|Age of household head|-0.007<sup>a</sup>|0.002|-0.007<sup>a</sup>|0.002|
|Literacy of household head|1.132<sup>a</sup>|0.212|1.049<sup>a</sup>|0.210|
|Credit|0.336<sup>a</sup>|0.062|0.338<sup>a</sup>|0.062|
|Extension|0.881<sup>a</sup>|0.059|0.866<sup>a</sup>|0.059|
|Fertilizer distribution center|0.253<sup>a</sup>|0.061|0.268<sup>a</sup>|0.061|
|Climate|0.002<sup>a</sup>|0.000|0.002<sup>a</sup>|0.000|
|Price risk|-0.081<sup>b</sup>|0.039|-0.084<sup>b</sup>|0.032|
|Wealth|0.122<sup>b</sup>|0.059|0.104<sup>c</sup>|0.059|
|Village Council membership|0.412<sup>a</sup>|0.085|0.422<sup>a</sup>|0.085|
|<br>**Selection-corrected regression parameters**|||||
|<br>Relative price|-14.272<sup>c</sup>|7.816|-14.693<sup>b</sup>|7.182|
|Use of organic fertilizer|9.916<sup>c</sup>|5.867|9.948<sup>c</sup>|5.875|
|<br>Area|-0.897<sup>a</sup>|0.159|-0.895<sup>a</sup>|0.158|
|Household size|0.021|1.131|||
|Age of household head|0.078|0.215|||
|Literacy of household head|24.532<sup>b</sup>|11.313|23.473<sup>b</sup>|11.242|
|Wealth|9.736<sup>c</sup>|5.486|9.944<sup>c</sup>|5.366|
|Weather|-14.246|9.399|-13.889|9.320|
|Time effect|39.847<sup>a</sup>|7.210|40.377<sup>a</sup>|6.936|
|**Error structure**|||||
|Disturbance standard deviation, σ|0.008|0.641|0.008|0.566|
|Correlation between regression andprobit, �|-0.160<sup>a</sup>|0.043|-0.170<sup>a</sup>|0.041|

Note: The superscripts a, b, and c indicate significance at the 1, 5, and 10 percent level, respectively. The model was estimated using LIMDEP version 9.0.

19
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