rafmacalaba/datause-encoder-data huggingface dataset:
Note: Results for all poverty categories and lengths of exposure combined. Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

Next, we look at women's ability to visit people and places without their husband's or other family member's permission, and find a sizable increase in this dimension of autonomy.<sup>22</sup> Still, women's mobility was far from unrestricted: less than half could visit the community center or attend community functions without getting permission first (Table 20).

**<u>Table 20: Percentage of women who can go out alone without permission, 2008</u>**

||_IKP_<br>_participants_|_Non-_<br>_participants_|_Difference_<br>_partic. vs._<br>_non-partic._|
|---|---|---|---|
|To the market|75|72|3|
|To visit friends|68|62|6*|
|To visit relatives|61|52|9***|
|To the local health center|72|65|7**|
|To fields outside the village for work|48|42|6*|
|To the communitycenter/park/plaza in the village|44|38|6*|
|To communityfunctions|36|31|5*|

Note: Results for all poverty categories and lengths of exposure combined. Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

By poverty category, differences are largest for not-so-poor women, whose mobility is restricted more than that of poorer women. Non-poor IKP participants were 10 percent more likely to be able to visit relatives or go to the health center alone than similar non-participants (Table 21). In 2003, the differences between members and non-members were not significant (not reported), confirming the positive impact of IKP on the mobility of better-off women.

**Table 21: Percentage of not-so-poor women who can go out alone without permission, 2008**

||_IKP_<br>_participants_|_Non-_<br>_participants_|_Difference_<br>_partic. vs._<br>_non-partic._|
|---|---|---|---|
|To the market|65|59|6|
|To visit friends|58|51|7|
|To visit relatives|67|57|10**|
|To the local health center|78|67|11**|
|To fields outside the village for work|51|46|5|
|To the communitycenter/park/plaza in the village|44|39|5|
|To communityfunctions|31|31|0|

Note: Results for not-so-poor women, all lengths of exposure combined. Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

In addition to looking at various dimensions separately, we construct an index (as done for instance by Bali Swain and Wallentin (2012) and Deininger and Liu (2012a)).<sup>23</sup> Following Kolenikov and Angeles (2004) and Bali Swain and Wallentin (2012), we use principal

> 22 Unfortunately, there was a change in coding on the mobility variables between 2004 and 2008; we observe improvements under different ways of matching old and new codes.

> 23 We use going to the market, visiting friends and relatives, attending community functions, setting money aside, and disagreeing with husbands; other indicators have many missing answers and would cause us to lose too many observations.

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saltwater trade, transport, fishing potential and amenities potentially persists in importance, much as it was subsequently for European settlement, and to capture this we employ a measure of distance to the coast as calculated by Gennaioli et al. (2013). We further include a measured of ruggedness of terrain from Nunn & Puga (2012).

Table 1 presents the summary statistics at the state level and table 2 at the US county level.

# **3 Historical Innovative Capacity as a Potential Driver of Present Income National Data**

Figure 1 plots our measure of national innovative capacity against GDP per capita, both in 1900.<sup>15</sup> The availability of data means that our effective sample going forward is restricted to the relatively larger countries depicted here. Several facts merit note.

First, there is substantial variance in the stock of engineers that is weakly related to income in 1900. The Northern United States with a density of 160 is the highest in our sample, roughly double the average for the country as a whole, 84, while the US South shows over a third of the engineering density of the North at 60. Lagging as it is, the American South is miles ahead of the Latin American countries who average under 20. What is most striking is that countries that we tend to associate with declining relative position across the previous century, especially Argentina and to a lesser degree Chile and Mexico, show densities below countries of very similar levels of income: Argentina and Chile had roughly the same level of income as the American South, Sweden and Denmark yet had roughly a third of its engineering capacity of the South, and a fifth of the Scandinavian

> 15Maddison does not tabulate a separate series for the American South, but Mitchener & McLean (2003) estimates place the US South roughly 50% below the national average and New England 50% above. Imposing these differentials on Maddison’s data, places the South roughly 15% higher than Spain and the North roughly triple. Clearly, issues can be taken with even Maddison’s Herculean effort, however, the available alternatives do not suggest that the picture would change much. Prados de la Escosura (2000) PPP based estimates with the OECD correlate .89 with those of Maddison, and do not significantly change the level of Spain relative to the US, although they move Portugal up perhaps 40%, now above Mexico but still 20% below Spain.

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A. Sanghi and D. Johnson

# **2.3.1 Kenya exports raw goods and metals to China**

Crude materials such as raw hides and skins are the major exports to China (figure 7). Between 2010 and 2014, exports of crude materials were 55 percent, manufactured goods were 21 percent, food and live animals made up 15 percent, and chemicals and related products were 9 percent of exports to China. In the crude materials category, major exports to China include hides and skins, scrap metals, and sisal; coffee and tea were major exports in the food and live animals category.<sup>4</sup> In general, tea, coffee, sugar and flowers are sources of major foreign exchange earnings for Kenya, but it has managed some diversity in earnings (UNCTAD 2013). Value-added products such as chemicals and plastics have also reached China, a shift from the more common story of oil and resource exports.

<!-- Start of picture text --> 150<br>100<br>Chemicals and related products<br>Crude materials. Ex: raw hides and skins, metal ores<br>Food and live animals<br>Manufactured goods. Ex: plastics, vegetable textile fibres<br>50<br>0<br>Commodity<br>Export Value (USD millions)<br><!-- End of picture text --> Figure 7: Kenya exports metals and hides and skins to China (2010-2014) Source: UN Comtrade database 2015 In figure 8, we show the top products in the crude materials and manufactured goods categories. For crude materials, titanium ores and concentrates exports were the highest, followed by copper, vegetable textile fibers such as cotton, hemp, or sisal, and plastics.<sup>5</sup> Kenya’s exports feature minimal value addition, and the prices of Kenya’s major exports to China are low on international markets. Between January 2010 and January 2015, copper prices dropped 4.62 percent annually, and iron ore prices declined 11.57 percent per year (figure 9). A further 11 percent decline in the price of metals was forecasted by the World Bank Commodities Outlook (2015) because of weak import demand from China and new supplies of metal globally. Titanium and iron ores and copper are important exports to China, and with falling metals prices, Kenya will likely gain little from its current export pattern to China.

> 4Here we use the Standard International Trade Classification (SITC) Rev.4 from the UN Statistics Division 56 digit HS classification

8
11 delete or limit certain preference categories would reallocate visas to other categories and would not reduce the total number of preference immigrants.

Immigrants who are not subject to the numerical limitation primarily fall in two categories -- immediate relatives of U.S. citizens (IR immigrants) and refugees. The immediate relative component of legal immigration has shown a steady annual increase since 1977 (see Figure 2) and the General Accounting Office projects that this component is likely to continue increasing (General Accounting Office, 1988). According to recently released statistics for FY1987, there were 218,575 IR immigrants in that year (Immigration and Naturalization Service, 1988a: Table 4), slightly lower than the 223,468 IR immigrants in FY1986 (Immigration and Naturalization Service, 1988a: Table 7). IR immigration increased at an average annual growth rate of 7 percent from FY1980 to FY1986, before dropping by 2.2 percent in FY1987.

IR immigration depends, to a some extent, on the level of naturalization, since naturalized citizens become immediately eligible to petition their spouses, unmarried minor children, and parents to immigrate. It should be noted, however, that the majority of IR immigrants are petitioned by native-born relatives, not by naturalized citizens. The General Accounting Office found that 64 percent of a sample of IR immigrants in FY1985 were petitioned by native-born petitioners (General Accounting Office, 1988). The majority of Asian IR immigrants, however, were petitioned by naturalized U.S. citizens.

Naturalizations have been increasing very rapidly during the 1980s. The number of persons naturalized increased by 78 percent in just six years, from 157,938 in FY1980 to 280,623 in FY1986 before dropping to 227,008 in FY1987 (see Table 5). There are, however, substantial lags in the naturalization and petitioning process. The GAO found that the average time between arrival of a naturalized petitioner in the United States and the attainment of immigrant status by their exempt immediate relatives in 1985 was 12.4 years. The components of this figure were 8.2 years for the average petitioner to Table 5. Persons Naturalized, Fiscal Years 1980-1987

|Fiscal Year|Persons Naturalized|
|---|---|
|1980|157,938|
|1981|166,317|
|1-982|173,688|
|1983|178,948|
|1984|197,023|
|1985|244,717|
|1986|280,623|
|1987|227,008|

Source: Immigration and Naturalization Service (1988a: Table 47). Note: Approximately 23,000 naturalization cases are missing for FY1984.
**-18-**

education that arise both from the structure of enrollment and that of spending per pupil across levels of education. The steeper is the rise in public spending per pupil as the level of education rises, and the fewer are the opportunities for people to proceed up the educational ladder, the bigger the share of public spending received by the best education in a cohort, and the more inequitable is the distribution of public spending on education.

Figure 10 describes the relation between both indicators of equity and the per capita GNP. It reveals a consistent picture of increasing equity as countries grow rich. The ratio in per pupil spending between primary and secondary education in a country at $200 in per capita income is, on average, only about 0.40 times as high as the corresponding ratio in countries above $10,000 in income. The ratio shows only a moderately tight relation to the per capita GNP, with a **R2** statistic of around 0.37 for the regressions based on both the whole sample and the restricted sample of countries below $4,000 in per capita GNP.

**Figure 10: Relation between selected indicators of equity in public spending on education and the per capita GNP, circa 1993**

<!-- Start of picture text --> (A) Public spending per pupil (B) Cohares 10%A bat e&xated<br>125 S0 - o0<br>0 0 0 00<br>0 0<br>100 00 00 0<br>0 0 0<br>'0 0 00<br>07 4 00 0 100 000 00<br>0 00<br>0 CID ~~ ~~ 0 00 ~~~~~0<br>Cb o 0 0~~~~~~~ 00 ~~~~~~~~<br>50 0 0 0 44 ~ ~~~~~~~~~~~~~~0 00<br>0 00 0 1 20 0 C0<br>0 00 0 0 0' 0 00 OOo<br>0 0~ ~~10<br>200 400 80 10 6 3006 1OODO 20000 200o 1306 3006 I0000 20000<br>Pe capita ONP (log scale) Per capibt GNP (log scale)<br><!-- End of picture text --> Equity measured in terms of the distribution of the public spending by educational attainment also show dramatic differences between rich and poor countries. Among countries at the low end of the income scale, the best educated 10 percent in a cohort receives, on average, 55 percent of the total public spending on that cohort's education, compared with an average of about 13 percent-only slightly more than their population share-for countries at the top end of
The earnings data are also a component of household annual income calculated for the sample given the long survey, along with net returns from sales of food crops, fish, livestock, tapa cloth and mats (from household reports on an average month), income from investments, pensions, and rentals (from reports for the previous fortnight), imputed values for ownproduced or own-captured food consumed by the household (from reports for the previous week), and remittance receipts (from a household-level annual recall).

Household expenditure is measured with a 20 category recall module, with reference periods ranging between one week and six months depending on the source of expenditure. The imputed value of items consumed from own-production is added to the cash expenditure variable to measure total expenditures on consumption. Impacts on income and expenditure are estimated for totals and in per capita terms since household size can change with migration. One category of expenditure collected separately is remittance spending over the previous year, which is captured also for the PAC applicant households in Tonga from questions about transfers given to other Tongan households. The other monetary measures for households in the long survey are the total value of savings and of net worth, while ownership of various durable goods, financial access and dietary diversity is also collected at the household level.

In addition to measuring monetary welfare, the subjective well-being of the principal applicant was elicited. Respondents were asked to imagine a 10-step ladder, where on the bottom step were the poorest people and the top step the richest people, and to state which step of the ladder they were on today. Ravallion and Lokshin (2001) refer to this as an economic ladder question, and note that it leaves it up to the individual to define what constitutes “poor” or not, and captures subjective economic welfare. A similar ladder question was used to ask about social respect. Prior research shows PAC migrants perceive climbing the welfare ladder, if making a retrospective comparison with their pre-migration life, but the comparison with the 12
When evaluating marginal expansions in access to school, the relevant quantities are the returns and costs for the marginal student, not the returns and costs for the average student. In spite of the importance of this topic, there are hardly any estimates of average and marginal returns to schooling in developing countries. Two exceptions using Chinese data are Heckman and Li (2004) and Wang, Fleisher, Li and Li (2011).

Estimating the extent to which returns vary across individuals is also important if we want to understand schooling choices, and their relationship with labor market performance. Studying who is more likely to attend school, and documenting how returns differ between those who attain high and low levels of education, and across individuals more generally, is essential for learning about the main incentives and constraints to school attendance.

We estimate a semi-parametric selection model of upper secondary school attendance and wages using the method of local instrumental variables (Heckman and Vytlacil, 2005). Our data comes from the Indonesia Family Life Survey. Carneiro, Heckman and Vytlacil (2011) use a similar model to estimate the returns to college in the US. Although they examine a different country in a different time period, and a different level of schooling, they also find that the returns to college vary widely across individuals in the US, and that the return to college for the marginal student is well below the return to college for the average student (see also Carneiro and Lee, 2009, 2011).<sup>2</sup> These papers document, across very different environments, how important it is to account for heterogeneity in the returns to schooling. They also show how it is possible to take exactly the same data which is used to estimate a measure of the return to education by instrumental variables methods (IV), and extract much more information from it (allowing us to characterize the heterogeneity in returns across individuals). This can be done using fairly standard parametric methods for estimating selection models, or using a more recent non-parametric approach to the same problem.

Vytlacil (2002) shows that the assumptions underlying standard IV estimates of the effect of a particular program (such as attendance of upper secondary school) are the same as the assumptions underlying a fairly standard non-parametric selection model, and

> 2 There exist also papers which estimate returns for average and marginal student but which account only for selection and heterogeneity given by observable variables (ignoring selection on unobservables). One example is Dearden, McGranahan and Sianesi (2004).

3
# _2.2._ Information from clients' accounts and balance sheet data

Processing accounting data of the clieftt firms is the bread and butter of the banldng business in every country, and the German banking system is not different in that respect. Accounting data are the typical means by which a centralistic institution tries to orvercome the information processing problem referred to aoove. As it is impossibit for a cenuralized agency to gather and process data on the individual decisions facing the decentmlized units one tries to develop summary statistics that make the most different individual cases comparable. As every bank client has to submit a balance sheet, and displays a certain behavior towards the bank with the handling of lines of credit, these items are the natural sources of bank information.

Balance sheet data inherently have a more long-term information content than the handling of clients' aecounts, as they ultimately provide meaningful information on a single client only in comparison across -!ears. Balance sheet data can only provide information in long-term trends, while information from clients' accounts is more rudimentary, but also m,h more timely, and can trigger immediate action.

The accounting data available to the banks are pooled within the banks, a&kd structured with respect to industry, size and legal form of incorporation. First, the information is processed in regional and national headquarters to provide the decision-makers there with summary statistics on the multitude of clients nandled by the branches. Then, the pooled statistics flow back to the branches for the assessment of individual clients in a cross-section of related firms.

Recently, Meyer [31] has come up with a detailed study on the different modes of analyzing accounting data in various banks oi groups of banks. His findings are especially interesting when we examine the roles of the different kinds of banks with respect to their market niches as described above. We have already stated,<sup>4</sup> how the legally independent Sparkassen and cooperative banks have created systems of internal capital markets to help each other. This is no different in the case of analyzing accounting data.

Savings banks and credit cooperatives have centralized their respective systems to process accounting data from the client firms so that each member bank processes its data on a particular client according to a standard format. The study by Meyer [31] also revealed that the savings banks take a leading role in the banking industry with their state of the art of data analysis, and that also the information processing system of the credit cooperatives is extremely refined compared to other banks. We want to offer three potential explanations for this phenomenon.

First and probably foremost stands the fact that small banks have few meaps to diversify. They are virtually by definition limited to a particular region, and are thus more vulnerable to regional shocks than large banks. As regions also have typical industries, this 4See Harm [t8] 3
Historical Perspective. American Enterprise Institute Press.

Varghese, S., Kumar, R., Singh, P. (2019). Aridity and Rural Development: A Case Study of Western Rajasthan. Arid Land Research and Management, 33(2), 123-142.

Vyas, A., Mehta, R. (2018). Traditional Handicrafts and Rural Employment in Rajasthan. Journal of Rural Studies, 42, 156-169.

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**Table 3 (continued)**

|Years:<br>||||19|98|||
|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.070_|0.047|0.050|0.061|0.085|0.101|
|2|20|_0.072_|0.049|0.055|0.067|0.088|0.101|
|2|30|_0.075_|0.051|0.060|0.074|0.091|0.102|
|2|40|_0.077_|0.054|0.066|0.080|0.093|0.103|
|6|10|_0.123_|0.095|0.103|0.120|0.140|0.151|
|6|20|_0.126_|0.098|0.109|0.127|0.143|0.152|
|6|30|_0.128_|0.100|0.114|0.133|0.146|0.153|
|6|40|_0.131_|0.103|0.119|0.139|0.148|0.153|
|11|10|_0.190_|0.157|0.171|0.194|0.209|0.214|
|11|20|_0.193_|0.159|0.176|0.201|0.212|0.215|
|11|30|_0.195_|0.162|0.181|0.207|0.215|0.216|
|11|40|_0.197_|0.164|0.187|0.213|0.217|0.216|
|16|10|_0.257_|0.218|0.238|0.268|0.278|0.277|
|16|20|_0.259_|0.220|0.243|0.275|0.281|0.278|
|16|30|_0.262_|0.223|0.248|0.281|0.284|0.279|
|16|40|_0.264_|0.225|0.254|0.287|0.286|0.279|

Source: computed from results reported in Table 2.
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