rafmacalaba/datause-encoder-data huggingface dataset:
**In countries where mobile banking has taken off, such as Kenya, the Philippines, and Brazil, common characteristics of the interactions between regulators and service providers** have been:

- Willingness to consider non-traditional approaches, observe their performance and adjust rules as necessary,

- Continuous dialogue between service providers and regulators,

- Flexible, proportionate approaches to KYC/AML rules via transaction & balance limits, based on understanding of actual risks, and

- Regulator monitoring and reporting mechanisms to keep authorities informed. (Michaels, 2010)

**In countries where mobile banking products have been allowed to emerge** , financial regulators have tended to focus on whether consumers are making “deposits” when they hand a service provider their money, how to protect consumers, and Anti-Money Laundering rules (in particular Know Your Customer requirements).  The training, performance and liability of agents (for example retailers or airtime sellers) are a particular concern to many regulators in this regard.  There is also a growing regulator focus on competition, shared infrastructure and/or interoperability, and new business models involving different players.

# _Use of Agents_

# **A regulatory structure is needed that allows banks to link with MFIs, telecoms providers,**

**and other non-financial actors.** Banks and other financial service providers, such as insurance companies, can provide services through agents through wholesale financing, service delivery agreements, fee-based services, agent agreements, and other arrangements. Rules on the use of non-bank retail outlets as agents are needed, that allow outlets (agents) to perform cash-in/cashout and other customer interface functions. The recent CGAP-led assessment of branchless banking options in Yemen recommended the following enabling framework for branchless banking through agents (Box 5):

_Box 5. Enabling Framework recommendations for Agents, the case of Yemen_

1. Any legal entity, including MFIs, post office, retail stores, MNO outlets, exchange companies, may be hired by a central bank-licensed institution as an agent to deliver limited financial services

2. Agents may provide most banking services, including bill payments, transfers, deposits, withdrawals, disbursement or repayment of loans, and national wires.

3. The financial institution should remain fully liable for services provided through agents 4. Agents and banks would need to pre-agree customer fees, and communicate those to customers 5. Agents may offer services on behalf of multiple banks, as long as the agent has a separate agreement with each bank.

6. Agents could also be required to implement „know your customer‟ requirements, although potentially with lower level requirements for low volume transactions.

Source: CGAP, 2010 31
Finally, it is interesting to observe the changes of the physical capital coefficients by level of education. In the IV specification for primary and secondary schooling, capital only enters as weakly significant. By contrast, the coefficient for physical capital becomes very significant and alters its long-run elasticity if tertiary education is entered into the estimate.

To the extent that this effect does not merely reflect statistical arbitrariness, a possible interpretation would be that the productivity of physical capital is affected by tertiary schooling. These findings support the conjecture of Romer (1990b) that the level of scientific education should be correlated with the rate of growth and the share of output devoted to investment in physical capital. It should be kept in mind, however, that the reliability of tertiary education data is comparatively poor in Guatemala. Moreover, according to Anderson (2001), low quality and internal inefficiency plague university education. Hence, some care should be taken before drawing too strong conclusions from the observed changes.

# **5.4 Mincerian Human Capital Specification**

An important question is how the effect of schooling at the macro level compares with the microeconomic evidence. The macro returns could be higher because of externalities from education. For example, if post-primary schooling leads to technological progress that is not captured in the private returns to education, or if education produces externalities in the form of the reduction of crime, more informed political decisions, better health and so on. To reconcile the macro effect of schooling with the micro level, Cohen and Soto (2001) estimate the following production function: where _Y_ is output, _A_ total factor productivity, _K_ physical capital, and _HM_ human capital. As first suggested by Bils and Klenow (2000), the micro evidence derived from a log-linear Mincer (1974) formulation can be used to specify the aggregate human capital stock as follows: where _hmt_ is the human capital per worker, _ht_ is average years of schooling and _ψ_ corresponds to the returns to education. This Mincerian approach has become popular in the literature since the work of Bils and Klenow.<sup>26</sup> The specification is a straightforward way of incorporating human capital into the production function in a

> 26 The working paper version was circulated prior to 2000. A caveat here is the missing role of experience.

-37-
20 Uganda's rural sector lost considerable ground during the period up to 1985. Adversely affected by state intervention, civil strife and agricultural price disincentives (through overvalued exchange rates and the implicit taxation of state marketing boards), rural producers retreated into subsistence. The production of cotton, tea and coffee suffered accordingly. From the late 1980s on, government policy changed, dismantling the biases against rural producers. Coffee marketing and exports were liberalized, and direct export taxation was abandoned. Similar measures were taken in the cotton sector. The foreign exchange market was liberalized, leading to real exchange rate depreciation. The weighted real producer price of export crops in Uganda (77 percent of which are coffee) increased by 78 percent between 1989-91 and 1995-97. Decomposition of this increase indicates that changes in the nominal protection coefficient (producer price/border price), changes in the real exchange rate, and changes in the real world price contributed respectively 58, 9 and 11 percent (Townsend, 1999). Over the past decade, agricultural output has recovered, averaging between 4 to 4.5 percent per annum in real terms. And this growth has played an important role in reducing poverty (Appleton et al, 1999).

In sum, economic policy reforms in both Ethiopia and Uganda had significant effects on agricultural markets and the prices farmers received for both food and export crops. At the same time, however, the period witnessed other changes, including rainfall variation. Both Dercon (2001) and Deininger and Okidi (2001) use the panel data to assess how these different changes affected household incomes and consumption, and rural poverty. Focusing on the factors they highlight as key for economic growth and poverty reduction, we then assess the evidence from the other case studies which use either repeated cross sectional regressions (Zimbabwe, Madagascar, Ghana) or simply an extensively documented narrative linking the macro-events to the observed evolutions in household welfare (Zambia and Mauritania).

Dercon (2001) uses panel data from six _rural_ communities in Ethiopia covering the period 1989 to 1995. The change in household real consumption per adult is explained through a reduced form regression model derived from an Oaxaca-Blinder type decomposition. In this approach changes in consumption and poverty can be explained by changes in endowments over time and changes in returns to endowments. The main regressors were changes in real crop producer prices (which Dercon shows to be closely related to the macro-economic and agricultural reforms that were implemented during the period), location (proxied by distance to an urban center), access to roads, private endowments (land, labor and education), and two shock variables, rainfall and ill health. His results are summarized in Table 10.
19 Table 2: Characteristics of different health insurance schemes

|**Characteristics**|**Government Employees**|**Social Security Scheme**|**Universal Health Coverage**|
|---|---|---|---|
||**(CSMBS)**|**(SSS)**|**(UC)**|
|Population Coverage|Civil Servant, pensioners and their<br>dependents (parents, spouse, children)|Formal sector private employee|The rest of Thai population who are<br>not eligible for CSMBS and SSS.|
|**Benefits**||||
|Ambulatory services|Public Only|Public & Private|Public & Private|
|Inpatient services|Public & Private<br>(emergency only for private)|Public & Private|Public & Private|
|Choice of provider|Free choice|Contracted hospital or its network<br>with referral line, registration<br>required|Contracted hospital or its network<br>with referral line, registration required|
|Cash benefit|No|Yes|No|
|Maternity benefits|Yes|Yes|Yes|
|Annual Physical checkup|Yes|No|Yes|
|Prevention Health Promotion|No|Yes|Yes|
|Services not covered|Special nurse|Private bed, special nurse|Private bed, special nurse,<br>eye glasses|
|Copayment|Yes<br>(inpatient at private hospital only)|Maternity,<br>emergency services|30-baht/visit*|
|**Financing**||||
|Source of funds|General tax|Employee & Employer|General tax|
|Financing body|Comptroller General Department,<br>Ministry of Finance|Social Security Office|National Health Security Office|
|Expenditure per capita (in 2006)|8,785|1,738|1,659|
|Per capita tax subsidy (in 2006)|8,785 (plus administrative cost)|579 (plus administrative cost)|1,659 (plus administrative cost)|

Source: Sakunphanit (2006).

* The 30-baht copayment was eliminated in 2006.
assets. Insurance holding companies often are owners of several other stock companies, forming a 'fleet" operation which combines certain central functions such as investment management into a single office.

An international aspect of life insurance operations does exist, although t is limited in magnitude. A few U.S. life companies sell insurance in Canada, Latin America, Europe or Japan in limited quantities. A number of Canadian life companies also market their products in the United States, largely in the states close to the northern border with Canada. In addition, European financial interests based in Germany, France and the Netherlands own or control U.S.-based life insurance companies. While no firm data are available on cross- border sales by "alien" life insurance companies based outside the United States, the actual dollar volume is believed to be quite small within the U.S. total. In general, asset data cited in this study refer to life iusurance companies domiciled in the United States.

Table I

||Summary Data, 1989||
|---|---|---|
||Dollar<br>Amount<br>(in billions)|Percent<br>of GNP|
|Life Insurance reserves|340|6.5|
|Insured pension reserves|738|14.2|
|Other private pension funds|1,050|20.2|
|State and local government<br>employee retirement funds|727|14.0|
|Total non-federal|2,855|54.9|
|Life insurance in force|8,694|167.2|
|Life insurance purchases||Percent<br>of DPI|
|Face amount|1,442|38.7|
|Life insurance premiums|73|2.0|
|Annuity considerations|115|3.0|

The plan of this paper is to first describe the characteristics of life insurance and annuity products offered by U.S. companies, followed by a discussion of the fiscal incentives and tax treatment afforded 2
ers. By varying exogenously aggregate TFP, the model is able to account for many crosscountry patterns on the organization of production, such as lower firm and wage growth and higher dispersion in firm and labor productivity in poor countries.

Porzio (2017) studies the interaction between the allocation of talent and technology adoption. He shows that in poorer countries, where firms have the opportunity to import technologies much more advanced than the locally prevailing vintage, the equilibrium allocation features more concentration of talent and more productivity dispersion across production teams. This prediction is supported by the fact that in developing countries workers with different skills, as proxied by educational attainment, are relatively more segregated across different sectors. The model is also able to account for a sizeable part of the larger productivity dispersion across sectors in poor countries.

This line of research is only in its early stages, and still far from definite conclusions. However, these contribution do suggests that the accumulation and allocation of human capital is a key factor in shaping many features of economic development.

# **6 Conclusions**

Economists have studied the relationship between human capital and economic development through a variety of methodologies. While all the approaches reviewed in this paper come with their own limitations, an overarching consideration of their findings does lead to some general lessons.

Human capital is an important ingredient of economic development. By any metric, rich countries are more abundant in human capital compared to poor countries. The gap is particularly meaningful when the notion of human capital is broadened to go beyond educational attainment, and to incorporate differences in schooling quality and out-of-school investments in skills. Moreover, human capital plays an important role in shaping technology, productivity, wages and the firm size distribution along the process of development.

The cross-country dispersion in these forms of human capital calls for a better understanding of their determinants and implications. Differences in early childhood investments, within-family interactions and institutional features of educational systems might all be important factors behind cross-country gaps in economic performance. Better quantifications and characterizations of their roles are promising avenues for future work.

25
**Table 4: Explaining urbanization level: fixed effects regressions on** levels of urbanization

|Regression|15|16|17|18|19|20|21|22|
|---|---|---|---|---|---|---|---|---|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Ln(Y)<br>(Ln(Y))<sup>2</sup>|0.75**<br>-0.052**|0.52**<br>-0.033**|0.66**<br>-0.045**|1.06**<br>-0.075**|0.91**<br>-0.064**|0.90**<br>-0.064**|0.75**<br>-0.053**|0.68**<br>-0.044**|
|Ln (Yag/Y)|-0.071**|-0.015|_-0.055_|-0.10**|-0.28**|-0.28**|-0.086**|-0.12**|
|<br>Ln(Ymanuf/Y)|0.16**|0.13**|0.19**|0.13**|0.15**|0.15**|0.15**|0.14**|
|Year<br>Africa*pre80|0.017**<br>-1.07**|0.0079**<br>-0.85**|0.016**<br>-0.94**|0.015**<br>-0.94**|0.013**<br>-1.10**|0.013**<br>-1.09**|0.017**<br>-1.11**|0.0071**<br>-1.03**|
|Africa*post80|||||||||
|Africa*pre80*ln(Y)|0.11|0.22**|0.19**|0.078|0.16**|0.16**|0.11*|0.21**|
|<br>Africa*pre80*ln(Y)<br>post80|-0.032<br>-0.089**|0.093<br>-0.026|0.069<br>-0.075**|-0.046<br>-0.068**|0.016<br>-0.099**|0.013<br>-0.097**|-0.033<br>-0.089**|0.064<br>-0.041**|
|<br>Ln(Noeduc)<br>||0.0064|||||||
|(Ln(Noeduc))<sup>2</sup>||-0.00066|||||||
|Ln(secondary)||0.13**||||||0.099**|
|Bmp|||0.0000064||||||
|<br>Cereal||||0.0057|||||
|Ln(Ruw)|||||0.16**|0.16**||0.081**|
|Sivard||||||-0.028*|||
|Poscropsh|||||||0.0039||
|<br>Negcropsh|||||||-0.0091*||
|R<sup>2 </sup>within|.746|.811|.753|.730|.772|.774|.748|.816|
|R<sup>2 </sup>between<br>|.329|.215|.132|.167|.742|.738|.323|.662|
|R<sup>2 </sup>overall|.390|.256|.067|.235|.707|.703|.385|.592|
|N|494|352|450|437|398|398|494|346|

Dependant variable is log of the level of urbanization. Notes: See Table 2.

20
For the empirical analysis, we combine the satellite data on global human settlement with data from two rounds of population census to create panel data sets at the commune level. To estimate the effects of the CIV trade shock on spatial reallocation across Abidjan and non-Abidjan routes, we adopt a triple-difference strategy with location fixed effects. A difficult challenge in analyzing the effects of a large-scale trade cost shock is that, because of general equilibrium effects, it may be difficult, if not impossible, to find a comparison area that remains unaffected (Donaldson (2015)). This is especially challenging in developed countries where the economy is spatially well-integrated by extensive transport infrastructure. The fact that, in many developing countries, a significant part of the economy is effectively in an isolated autarkic equilibrium offers us a plausible strategy; we can use as comparison the part of the economy that remains autarkic both before and after the trade shock. The changes in employment structure and built-up density observed in these comparison areas cannot be due to the trade shock, and thus provide an estimate of the structural changes due to the underlying long-term technological and institutional factors.

The core of our empirical strategy is to compare the DID estimate of the effect of the CIV crisis on the integrated segment of the non-Abidjan route with the corresponding DID estimate for the integrated segment of the Abidjan route, with the respective autarkic areas as the comparison. It is important to appreciate that the triple-difference set-up is used for understanding the spatial reallocation, not for estimating the causal effects of the negative trade shock on a route. An important advantage of our focus on spatial and sectoral reallocation is that the intractable problem of double-counting one faces while estimating the causal effects is not relevant in our context.<sup>4</sup> Using data on a vector of relevant characteristics, we find that the Abidjan and non-Abidjan routes in both Mali and Burkina Faso do not differ significantly in the pre-war period, thus allaying the worry that the shifts in the spatial margins of integration and specialization would have been substantially different across different routes even in the absence of the trade shock caused by the civil war in Cˆote d’Ivoire. The panel data allow us to difference out the timeinvariant characteristics using fixed effects, thus addressing the concern that differences in 4For discussions on the double counting problem, see Redding and Turner (2015) and Donaldson (2015).

3
- In **Spain** , **Ireland** , and **Italy** , all migrants (i.e. groups 1-4) tend to be younger than the nativeborn population.

- In **France** , by contrast, only non-citizens born outside the EU (i.e. group 4) tend to be younger than the rest of the country’s residents.

- Finally, in **Sweden** and the **United Kingdom** , migrants from outside the EU (both citizen and non-citizens) tend to be younger than the rest of the country’s population.

**Educational Background:** Do migrant workers have more or less education than the nativeborn population? It turns out there is no simple answer to this question, but rather that it depends on a number of factors, such as place of origin, migrant sub-group, place of residence, etc. In EU13 countries overall, citizens born in other EU countries as well as in countries outside the EU have a fairly similar educational attainment profile as the native-born population, the exception being that citizens that were born outside the EU are somewhat more likely to have tertiary education (and correspondingly are somewhat less likely to have primary education or below (Table 4).

**Table 4: Breakdown of the Population by Highest Educational Attainment**

|**Population (percent)**<br>**with level of education**<br>**indicated**|**Native-born**<br>**Population**|**1.**<br>**Citizens born**<br>**in other**<br>**EU countries**|**2.**<br>**Citizens born**<br>**outside the**<br>**EU**|**3.**<br>**Non-citizens**<br>**from other**<br>**EU countries**|**4.**<br>**Non-citizens**<br>**born outside**<br>**EU**|
|---|---|---|---|---|---|
|**EU 13**||||||
|Primary and below|24|26|16|32|24|
|Lower secondary|21|19|21|12|21|
|Upper secondary|31|33|30|25|26|
|Post secondary|3|4|5|4|6|
|Tertiary|21|19|28|27|23|
|Total|100|100|100|100|100|
|**EU 4**||||||
|Primary and below|14|20|28|3|2|
|Lower secondary|6|19|8|16|15|
|Upper secondary|62|51|47|59|61|
|Post secondary|3|1|2|3|1|
|Tertiary|14|10|15|20|22|
|Total|100|100|100|100|100|

Non-citizens born in other EU countries, by contrast, have a disproportionately high share of people with either primary or lower education, or with tertiary and higher education (i.e. a more spiked bimodal distribution as compared to other groups). In EU4 countries, by contrast, the foreign-born citizens group has a higher share of people with relatively low educational attainment, while the foreign-born non-citizens groups have a relatively higher share of those with tertiary education as compared to other sub-groups. However, this aggregate picture for the EU13 overall described above conceals considerable diversity at the level of each individual country, as described below (see Annex Table 12):

- In **Austria,** migrants born outside the EU (both citizens as well as non-citizens) tend to have lower educational attainment than the rest of the population; by contrast, those from other EU

10
|**_Primary outcomes_**||||||
|---|---|---|---|---|---|
|Has TPIN|5.4|5.5|4.8|5.4|0.2|
|Has city council license|56.8|58|54|56|0.8|
|Revenue last week winsorized (US$)|245.5|232.5|248.4|248|0.2|
|Revenue last month winsorized (US$)|872.6|841.9|902.1|911.9|0.3|
|Profit last week winsorized (US$)|57.6|58.5|57.2|58|0.0|
|Profit last month winsorized (US$)|201.5|207.5|208.9|206.4|0.2|
|**_Secondary outcomes_**||||||
|Total workers|2|2|2.1|2|0.2|
|Number of days in a work month|25.1|25.5|25.3|25.0|1.5|
|Capital (US$): Fixed Assets|829.2|744.6|1,049.50|1,067.60|2|
|Assets (US$): Fixed Assets + Inventories + Cash|1,683.60|1,554.70|2,124.70|1,936.50|2.8**|
|Proportion of male workers|60|60.8|60.4|58.6|0.3|
|Borrowed in the past|36.2|<br>38.2|35.7|39.4|1|
|Amount of a recent loan (US$)|6.7|28.2|5.6|6.6|0.4|
|Has bank account|58.8|57|56.2|55.8|0.6|
|Has bank account in name of business|2|2.1|1.8|2.4|0.2|
|Uses any account just for business purposes|5|4.4|3.7|4.2|0.7|
|Saves at home|28.7|27|28.2|29.5|0.3|
|ROSCASACCO|6.9|9.9|7.5|8.7|1.1|
|_<br>Banks savings|56|55|53.7|52.8|0.6|
|Saved amount (US$)|237.3|224.5|274.9|223.5|1.2|
|<br>Saved amount at bank (US$)|217.2|196.3|244.9|202.4|1|
|Takes business money whenever for HH|77.5|80.2|78.2|79.3|0.4|
|Identifies benefit(s) of business registration|69.9|73.4|<br>71.8|<br>72.7|0.6|
|Was inspected by municipality before|14.9|15.7|15.1|15.9|0.1|
|Asked for bribe|3.8|4.8|5.2|4.6|0.8|
|Threats to shutdown|99.6|99|99.5|99.3|0.4|
|Confiscation|5.2|5.1|4.9|5.2|0.1|
|Sexually harassed|5.5|4.8|6.5|6.1|0.6|
|Other harassment|9.7|4.4|10.8|9.5|6.3***|
|Provides receipts|15.3|17.1|18.5|19.2|1.6|
|<br>Number of customers|37.0|36.0|34.7|37.4|0.7|
|Tender|4.4|4.8|4.9|4.9|0.1|
|Has written budget|2.8|2.1|1.7|3.3|2|
|<br>Keeps financial records|54.1|52.2|56.1|56.3|0.7|
|Advertises|5.8|3.4|6.1|5.3|1.5|
|Other business activity|14.1|13.3|13.3|14.4|0.2|

30