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34
108 1985; Rosenzweig, 1988).

/yHeck1an and Polachek (1974) confirmed that the logarithmic wage equation fit US male earnings data somewhat better than did the linear age equatlon. They also show statistically that Nincer's (1974) specification of the quadratic in post-schooling **ye#:s** of potential experience **explains** more of the variance in log-wages or log-earnings than does a quadratic in **age.** Rarely has a theoretically based empirical regularity such **as** this specification of the **vage** function **been** replicated so widely, in **so** many parts of the world, and had such an effect on economic thinking about a diverse range of socLal issues.

2WFor example, if more able students self-select theselves into the more educated comparison group, the returns to education could be overstated until a selection-corrected measure of the private returns to schooling is computed (Willis and Rosen, 1979). But this form of selection might not necessarily bias the comt-irison of the directly measured returns of men relative to women, if ability operates in an analogous way to influence who goes to school among both girls and boys. The next section examines evidence on this issue.

1/From the perspective of the early 1960s, this empirical strategy may have reflected a desire to not overstate the returns tc s1hooling, because the concept was novel and controversial. But as indicted below, the resulting differences in empirical methods practiced in the literature has led to reports of women's school returns being deflated for honparticipation in some cases by half or more, and in other reports the estimates are drawn directly
the open model for regulating data. In contrast, there is no country with the limited model that is assessed as having a free internet.<sup>19</sup> Figure 10 concludes this descriptive section by looking at the countries’ domestic institutional quality. Given that economic access to the internet is related to a countries’ independence of regulatory bodies, domestic regulatory institutions in a wider sense be an important factor for trade in digital services. The proxy for this analysis is the indicator from the World Bank Governance Indicators which measures the country’s regulatory quality. Figure 10 shows that countries with greater regulatory quality exhibit higher digital services exports. Countries sharing the conditional model exhibit on average a higher score than countries following the other models, even after excluding EU member states.

Figure 10: Digital services trade, data models, and domestic institutions (2015) <!-- Start of picture text --> Digital Services Trade and data models<br>By regulatory quality<br>100<br>10<br>1<br>.1<br>-1 0 1 2<br>Regulatory quality<br>Open Conditional Limited<br>Per capita exports in 1000 Mln. USD (log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data, World Bank WDI and Governance Indicators. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

# **4. Estimation using the Gravity Model**

This section presents the baseline gravity model used to estimate econometrically whether sharing the same data model is correlated with higher or lower digital services trade.<sup>20</sup> In doing so, the

> 19 The midrange of a partly free Internet is taken up by more or less an equal number of countries having a US or EU model. The Freedom House assesses the openness of the Internet for 65 countries, of which 6 are EU member states. Note furthermore that the Freedom House covers 11 countries which are not categorized for their data realm. Of these, 7 countries are qualified as partly free and 4 as not free.

> 20 Given that the empirical set-up follows a gravity model, which has been extensively used in previous works, the discussion omits how the method for estimating parameters econometrically is consistent with the constraints imposed by standard trade theory. Full details of the gravity model’s solution and characteristics are provided by Anderson _et al_ . (2018; 2015). Their starting point is the familiar structural gravity model

23
# **1. Introduction**

Poorly functioning banking systems impede economic progress, exacerbate poverty, and destabilize economies. Specifically, a substantial literature documents that well-functioning banks accelerate economic growth, which in turn alleviates poverty. Furthermore, there have been an unprecedented number of disruptive banking crises in recent decades. ' The staggering scope of recent crises, coupled with evidence on the beneficial effects of banking systems on economic growth, have generated calls for reforms in bank regulation and supervision. The Basel Committee on Bank Supervision, International Monetary Fund, and World Bank all now promote an extensive list of "best practices" to be adopted by each and every country for the regulation and supervision of banks. There is a strong sense that if only policymakers in countries around the world would implement particular regulatory and supervisory practices, then bank "safety and soundness" would improve, thereby promoting growth and stability.

There is no evidence, however, that the best practices currently being advocated by international agencies are best, or even better than alternative standards, in every country. There is no evidence that successful practices in the United States, for example, will succeed in countries with different institutional and political environments. There is no evidence, moreover, that each regulatory and supervisory practice can be considered as part of an extensive checklist of desirable best practices in which more checks are better than fewer as opposed to considering regulation and supervision as reflecting broad views about the role of government in society.

We draw upon our unique, new database [Barth, Caprio, and Levine (2001 b)] on bank regulation and supervision in 107 countries to examine the relationship between bank regulation/supervision and bank performance and stability. We: (1) assess different broad governmental approaches to bank regulation and supervision and (2) evaluate the efficacy of specific regulatory and supervisory policies. More specifically, we first assess two broad and competing theories of government regulation. Pigou's (1938) classic treatment of regulation holds that monopoly power, externalities, and informational asymmetries create a constructive role for the 'On finance and growth, see Levine (1997). On poverty and growth, see Dollar and Kraay (2000). On the costs of recent crises, see World Bank (2000), Boyd, Kwak, and Smith (2000), and Hoggarth, Reis and Saporta (2001).

1
69 Welfare impacts, defined as the loss in conswner surplus, were equivalent to -1.4 and -2.2 percent of the household income of Z$ 100.

# _Macroeconomic Impacts_

From 1981 to 1984 ihe increase in consumer prices was high compared to the usual inflation rate of around 6 percent during 1973-79. Inflation peaked at 18.4 percent during 1981-82. Howvever, major factors causing inflation were already in effect before the 1983-84 increase in electricity prices was implemented. A number of changes in ihe economy during tis period were responsible for high inflation rate, including the increase in wages and liberal govemment emp!oyment policies (implemented to correct the pre-independence racially-based salaTy and employment structure), increase in govenunent expenditures to expand social programs and rehabilitate the country's infrstructure, increase in domestic credit, and severe drought conditions in 1982 and 1983, which reduced agriculture output by nearly 12 percent and real value-added by over 5 percent between 1981 and 1983. Table 5.6 summarizes ihe selected macroeconomic indicators.

_Table 5.6: Zimbabwe: Selected Macroeconomic Indicators, 1980-85 CZ$ Million)_

||1980|1981|1982|1983|1984|1985|
|---|---|---|---|---|---|---|
|GDP (1980 prices)|3224|3537|3588|3459|3540|3808|
|(Percent growth)|10.7|9.7|1.4|-4.6|2.3|7.6|
|Govermnent revenues|950.9|13645|1789.2|2000.5|2212.8|2618.8|
|(Percentgrowth)|-|43.5|31.1|11.8|<br>10.6|18.4|
|_Change in Price Index(op.a.)_|||||||
|CPI|9.2<br>|14.6<br>|18.4<br>|16.4<br>|12.5|9.9<br>|
||(5.4)|(13.1)<br>|(10.7)<br>|(23.1)<br>|(20.2)<br>|(8.5)<br>|
|Food, weight =20.5 (54.9)|-|13.4|11.4|20.4|20.9|9.1|
|||(12)|(10.5<br>|)(28.5)<br>|(25)|(6.8)<br>|
|Rcnt&rates,<br>weight=19.8<br>(18.4)|-|10.1|26.1<br>|5|4|5.4|
|||(7.2)<br>|(9.5)|(14.7)<br>|(12.7)|(10.3)|
|Fuel & lights, weight=2.9|-|15.9|8.8|4B.5|31|3.1|

**_Note:_** _Figures_ in _brackcs_ are for low-income ups. Fgurs for raft and raes _for_ low-nome groups _mclude_ fuel and _ghts._
focus only on members in the labor force (to ensure more meaningful estimates of the welfare effect of joblessness and other job-related categories). Remaining sample sizes for each country group are reported along with our regression outputs below.

**Table 1. Countries by sub-sample**

||(1)|(3)|(5)|(7)|
|---|---|---|---|---|
|Country income<br>group (based on<br>World Bank / WDI)|High income|Upper middle<br>income|Lower<br>middle<br>income|Low income|
|Countries in sample|Australia<br>Great Britain<br>Canada<br>Finland<br>France<br>Germany<br>Italy<br>Japan|Andorra<br>Brazil<br>Bulgaria<br>Chile<br>China<br>Colombia<br>Cyprus<br>Hong Kong<br>SAR, China|Egypt<br>Georgia<br>Ghana<br>Guatemala<br>India<br>Indonesia<br>Iraq<br>Moldova|Burkina Faso<br>Ethiopia<br>Mali<br>Rwanda|
||Netherlands<br>New Zealand<br>Norway<br>Poland<br>South Korea<br>Slovenia<br>Spain<br>Sweden<br>Switzerland|Iran<br>Malaysia<br>Mexico<br>Peru<br>Romania<br>Russia<br>South Africa<br>Serbia<br>Thailand<br>Trinidad<br>Turkey<br>Uruguay|Morocco<br>Ukraine<br>Vietnam<br>Zambia||

As much as possible, our analysis seeks to capture the ‘pure’ effect of unemployment or job quality. As a consequence most of our estimates rely on individual level regression that control for other personal attributes that may influence wellbeing and personal experiences of unemployment. The underlying estimation model is of the following form: where the dependent variable **_Y_** _ic_ stands for a range of outcomes, such as life satisfaction, self-reported trust or social and political activism (see below). Suffixes _i_ and _c_ denote individuals and countries, respectively.

10
Table 1: Traded Goods Categories and World Expenditure Shares

|Consumer Goods:||Capital Goods:||
|---|---|---|---|
|Good|Share|Good|Share|
|Food|0.30|Transportation|0.04|
|Tobacco & Beverage|0.07|Machines|0.08|
|Clothing|0.10|Agr. Machines|0.01|
|Fuel|0.03|Elec. Equipment|0.04|
|Household|0.06|||
|Appliance|0.01|||
|Medical|0.02|||
|Auto|0.04|||
|Auto Parts|0.05|||
|Recreate - Durable|0.03|||
|Recreate - Nondur|0.02|||
|Government|0.09|||
|Other|0.01|||

traded (import competing or exporting) sector while at the same time increasing the demand for and decreasing the production of nontraded goods. The excess demand for nontraded goods must be rationed with an increase in the price of nontraded goods. The increase is less than the increase in the policy measure since both supply and demand respond to an increase in nonltraded prices.

This relationship introduces a potential bias into the estimation of equation 10. If the average distortion ej increases from a uniform increase in **fij** across goods, then **_e,j_** - cj remains orthogonal to _PNi._ If however distortions are concentrated on some goods, then ai - a would be overestimated for those goods for which **E,,** - f increased and underestimated for the goods for which eij - eJ decreased.

Estimation bias can be avoided by estimating in two stages; since service costs (mostly labor) are predicted to vary systematically with wages across countries, I first regress each of the two nontraded prices (in dollars) on income and income squared per worker (also in dollars) derived from Summers and Heston. The predicted values of these prices are now independent of the error term, and can be used to estimate a, - _d_ in equation 10.

12
closest cities on average much further away for most of the rural population than the closest towns and rural populations often poorer and less educated (Beegle and Christiaensen 2019), the finding that more rural-urban migrants end up in towns than in cities, contributing in the aggregate more to poverty reduction than city migration, does not surprise in light of the regression findings. The importance of distance in understanding the decomposition results is also confirmed by the simulations.

Broadly interpreted, these findings could be seen as supporting the New Urban Agenda (United Nations, 2017), which calls for balanced territorial development policies and plans that strengthen the role of small and intermediate cities and towns, especially since a large share of the global rural population gravitates around small and intermediate urban centers. Globally, 28% of the rural population lives within 1 hour travel time from a city of 1 million or more; 27% within one hour travel time from an intermediate city (250,000-1 million) and 27% within one hour traveling from a town (250,000 or less) (Cattaneo, Nelson, and McMenomy, 2021). In low-income countries, the share of the rural population living within one hour of a town rises to 43%, with another 20% living within 2 hours; 13% live within one hour from an intermediate city and 7% within one hour from a city ( _>_ 1 million). Among the extreme poor, 80 percent are rural (82% in Sub-Saharan Africa) (Beegle et al. 2019). African cities thus face a double burden to act as engines of poverty reduction; they need to become economically dense—not merely crowded, to generate the virtuous agglomeration economies associated with cities in the developed world (Lall, Henderson, and Venables, 2017), and they are also far from where the poor live.

At a minimum, the findings indicate that the role of distance in migration decisions, highlighted in the earlier work on migration, continues to be important empirically, especially in more remote areas, and that it may have been neglected prematurely more generally. The paper did not try to uncover why distance plays such an important role. The aforementioned in-depth life histories with 75 respondents from the KHDS survey (Ingelaere et al. 2018) point among other things to transport costs and the liquidity constraints to overcome this, the socio-cultural affinity with the destination, and the ability to maintain ties with the home community and return home when things go wrong (safety net). Networks can reduce the deterrence of distance (especially over long distances and including deterrence related to socio-cultural affinity) (Lucas, 2001; 2015). It explains their importance in determining 17
demonstrates a regression of likelihood of being ranked higher for RMG priority on each of the three criteria in columns (1) – (3) and the three rankings together in column (4). Regression in column (5) controls for RMG-eligibility status: a dummy equal to 1 if the household is RMG-eligible, and 0 if it belongs to the random sample.

Table 3 reveals several interesting features about the process of village heads’ selection. First, the relationship between priority ranking and each of the three ranking is significant, but coefficients are low, ranging between 0.15 (for land holdings) and 0.22 (for the need for help). Notably, association between the ranking of those who need help most and RMG priority ranking is the strongest: not only is the coefficient the highest, association between these rankings is the only one that remains significant once all three rankings are accounted for in a regression. Village heads appear to rely on some notion of the need of help when deciding on how to prioritize for RMGs.

Second, once we account for RMG-eligibility, none of the three criteria remains significant. Only designation as an RMG-eligible or RS household is significant: being eligible for RMGs is associated with decrease in rank, and consequently higher priority for RMGs in village heads’ perception (please note that households are ordered from 1 to 15, one being the highest priority). This may suggest that village heads fully endorse the selection criterion proposed by PRF: household’s poverty status. An opportunity to prioritize based on a different criterion does not lead to a vastly different result than their selection of RMG-eligible. However, as mentioned earlier, we cannot exclude the possibility of an anchoring effect.

**Table 3: Likelihood of being prioritized for an RMG role as a function of other rankings**

||Priority for<br>RMG|Priority for<br>RMG|Priority for<br>RMG|Priority for<br>RMG|Priority for<br>RMG|
|---|---|---|---|---|---|
|Lowest to highest land holdings|0.1560***|||0.0465|0.0266|
||(0.036)|||(0.038)|(0.028)|
|Highest to lowest likelihood to be<br>hungry||0.1834***||0.0321|-0.0277|
|||(0.036)||(0.047)|(0.037)|
|Highest to lowest need for help|||0.2228***|0.1751***|0.0466|
||||(0.037)|(0.049)|(0.040)|
|RMG-eligible (y/n)|||||-6.2117***<br>(0.420)|
|Village fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|1,245|1,245|1,245|1,245|1,245|

* p<0.1 ** p<0.05 *** p<0.01

Priority for RMG regressions are calculated as highest to lowest priority: the household ranked #1 is the household that should get the highest priority. Standard errors in parentheses. The 3 criteria (land holdings, likelihood to be hungry, need for help) are ranked with #1 as the poorest or worst outcome, and improving in status as the rankings increase until the final slot, #15.

# 6. What information do village heads use in their targeting decisions?

Relatively weak association between RMG priority ranking and the three rankings which may capture different dimensions of poverty begs the question of whether village heads use accurate information in assigning the ranks. To check whether this is the case, we correlate village heads’ ranking and ranking 15
themselves from crime, and also the use of digital technology (Islam et al., 2020). Another significant disparity between female-owned and male-owned businesses is the degree of capital investment. Data from 14 impact evaluations show that the average capital investment by femaleowned firms is more than six times lower than the average for male-owned firm in Africa (World Bank, 2019b). A key question to answer is, therefore, whether highly inclusive digital technology, like mobile money, can benefit women enterprises by increasing their investments.

A recent study across four East Africa economies uncovered a positive relationship between mobile money use and investment by firms (Islam et al, 2018). This study revisits this relationship by exploring whether this relationship varies by gender of the business owner, and also extending the sample of study from 4 economies in East Africa to 16 economies across Sub-Saharan Africa. The analysis is based on a unique firm-level data set with a mobile money module that is implemented using a consistent methodology and the same survey instrument for about 4,700 formal firms across 16 countries in Sub-Saharan Africa. The findings are striking. The positive relationship between mobile money use and investment is confirmed; more importantly, the relationship is largely driven by female-owned firms and is statistically insignificant for maleowned firms. The identification strategy employed is unlikely to obviate all concerns of simultaneity bias between mobile money use and investment given the cross-sectional nature of the data. However, the fact that the relationship is uncovered for female-owned but not maleowned firms attenuates some of the concerns of reverse causality. Furthermore, this study provides external validation for experimental evidence that shows mobile money savings leading to greater capital investments by female-owned micro-business in Tanzania (Bastian et al., 2018) and smallholder farmers in Mozambique (Batista and Vincente, 2020).

We explore potential channels that may convey this effect. Female-owned firms that use mobile money to transact with suppliers are more likely to invest. The importance of supplier relationships uncovered by this paper is consistent with the results of Beck et al. (2018) that find that access to trade credit generates demand for using mobile money as a payment method with suppliers. However, we do not find any significant relationship between mobile money use and supplier credit. Only payments to suppliers specifically undertaken through mobile money have a positive effect on investment by female-owned firms. We also find that female-owned firms that use mobile 3