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40
processing so that recipients in remote areas can receive timely transfers. The banks could also move their services closer to the recipient, through couriers or partnerships with other agents. To improve service, a few private banks have opened branches in migrantdestination countries, and these branches have been able to attract new clients interested in remitting their earnings. Domestically, banks in Sri Lanka are starting to invest heavily into payments system technology. The use of credit cards, debit cards, and the installation of automated teller machines (ATMs) is on the increase (Table 6). In 2004, there were about 40 ATMs per 1 million inhabitants. Although public banks dominate the banking system, most ATMs are owned by private commercial banks (68 percent at the end of 2003).

**Table 6  Total Branches of Commercial and Specialized Banks and Post**

**Offices (2004)**

|Institution|Number of Branches|
|---|---|
|Public commercial banks|618*|
|Private commercial banks|756*|
|Specialized banks|66|
|Rural development banks|196|
|National Savings Bank|112|
|Post offices|4647**|

_*Excludes pawning centers (188) and student savings centers (328) **Includes agency and sub- post offices but excludes estate sub-post offices Source: CBSL Payments Bulletin 2004_ _Hitherto, however, most banks have been investing in proprietary IT platforms for their payments systems with suboptimal gains for the remittance business._ There is very limited outsourcing or sharing of domestic branch networks. Few banks have all branches "on-line" in a network. Paper-based processing of basic transactions has remained a predominant feature in branches and their head offices. Fortunately, this is changing. In 2004, close to 92 percent of ATMs in Sri Lanka were open-access, i.e., they could be used by the holder of an ATM card issued by a bank other than the bank that owned the ATM. But more can be done.

_Like in many other Asian countries, it is unlikely that these electronic networks will reach out in the short or medium term to the poor and the rural areas._ This is not only based on the limited density of the ATM network and their location (mainly in urban and commercial shopping areas). For the banks, ATMs require relatively high initial investments that will have to be recovered from the client operations. Hence, banks are likely to charge fees for cards and electronic transactions creating a threshold for low income users.

# **Sharing Electronic Payments Systems Platforms**

_To maximize, the remittance potential of the financial sector network, there needs to be a harmonization of the payments infrastructure across banks in the country._ As in many developed countries, institutions in Sri Lanka should share their infrastructure platforms and compete on service. For a long time, for example, only a limited number of ATM transactions that 14
indeed produced tangible results; and, more important, Annex 3 provides indications that the RRIs have become more challenging over time, suggesting that the public sector is improving its capability to tackle implementation.<sup>28</sup> <!-- Start of picture text --> Cumulative Number of<br>RRIs implemented<br>300<br>200<br>100<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>-100<br><!-- End of picture text --> Figure 1: Cumulative Number of RRIs implemented between 2006-2012 <!-- Start of picture text --> Percentage of Ministries<br> that have implemented RRIs<br>100%<br>50%<br>0%<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Figure 2: Percentage of ministries implementing RRIs between 2006 and 2012 Indeed, in isolation, an RRI appears to be a very minor blimp in the screen. But when viewed chronologically and globally, RRIs paint a compelling picture of burgeoning capacity.

The key point to make is that Burundi has begun to build its public sector on its own terms through what Andrews (2012) calls problem-driven iterative adaptation (PDIA). Undoubtedly, it will take a long time for the country to become a Singapore and it will confront many bumps along the way. But, because it has adopted a PDIA approach to building capacity, it now seems capable of escaping the dreaded “capability trap.” One might thus consider Burundi’s experience to date a “preliminary test” of the PDIA hypothesis as laid out by Andrews et al. (2012).

# **<u>III. Learning from Burundi</u>**

> 28 The number of RRIs implemented began to decrease in 2011 and continued in 2012 as the program transitioned to the ‘institutionalization’ phase whereby WBI began slowly withdrawing financial and coaching support, and the National School of Administration began to take over the role of training and providing coaches.

11
allow one to simulate the impact of reforms which changed the volatility of prices. For example, the removal of a variable rate tariff is likely to create greater domestic price volatility than previously (along with a change in the average price). A model which incorporated risk could analyse the impact of _both_ these effects upon poverty and vulnerability.

# _Qualitative Approaches_

The above discussion has focused on technical economic methodological issues. However, as WDR 2001 stressed, poverty is about much more than just income and consumption so it is essential to take a multi-dimensional approach to poverty analysis. A multi-dimensional approach does not necessarily mean a qualitative approach; other dimensions of poverty can be quantified and analysed using the same tools as those applied to income and consumption. For example, there is no reason why one cannot report educational level, access to medical facilities, distance from markets, extent of soil degradation etc by decile or plot their cumulative distributions just as is done for income and consumption. Furthermore, several surveys contain subjective measures of wellbeing and almost all surveys contain information on assets and durables enabling the construction and comparison of welfare indices based on these variables (see Sahn and Stifel (2000) for an example using the Demographic and Health Surveys in Africa).

However, aside from the technical literature, there is a vast amount of qualitative literature on poverty and the impact of reform on poverty in developing countries. Such material does not have the advantages of quantitative surveys in terns of providing a statistically representative picture of a whole country. But it can provide enormously valuable insights into the processes of destitution, the structural social relationships which provide the context in which economic activity takes places, the nature and functioning of key institutions, the dynamics of intra-household and community behaviour, the political processes which determine the success of reform, and much more. Of particular importance is the role which qualitative studies can place in uncovering the causal pathways through which reforms may influence different dimensions of wellbeing. For example, if a reform increases the price of a cash crop typically produced by women, but social norms dictate that women are also responsible for household maintenance and childcare then the reforms may simultaneously increase incomes and reduce the welfare of poor women due to the additional pressures on their time. Quantitative household surveys rarely capture such processes making it important to perform analysis using both quantitative and qualitative sources.<sup>2 4</sup> One key resource here is the large volume of participatory material which is now available on many countries. In situations where quantitative surveys are of debateable quality, such participatory analyses can provide key information on the relative

> 24 See (Kanbur, Chambers et al. 2001) for a collection of notes on mixing quantitative and qualitative approaches.

28
Overall microdata accessibility improves by around 50% when we consider international accessibility in addition to national accessibility – from 16/140 to 25/140. Some countries like Iraq and Jordan who had _no microdata openness_ for all data categories when we examined only NSOs website now have _satisfactory data openness_ for some data categories. However, despite these improvements, microdata accessibility in MENA remains poor.

**Table 4: Openness of recent source/survey/micro data on public facing website of MENA NSOs and international microdata libraries.**<sup>**19**</sup>

|**Economy/data category**|**Establishment**<br>**survey**|**Price survey**|**Consumption**<br>**survey**|**Labor force**<br>**survey**|**Health survey**|**Population**<br>**census**|**Economic**<br>**census**|
|---|---|---|---|---|---|---|---|
|Algeria|-|No openness|-|-|-|-|No openness|
|Bahrain|-|No openness|No openness<br>|-|No openness|No openness|-|
|Djibouti|-|No openness|Satisfactory<br>(NSO, WB)|-|-|No openness|-|
|Egypt, Arab Rep.|-|No openness|Satisfactory<br>(NSO)|No openness|Satisfactory<br>(WB, DHS)|No openness|Satisfactory<br>(NSO)|
|Iran, Islamic Rep.|-|No openness|Satisfactory<br>(NSO)<br>|Satisfactory<br>(NSO)|-<br>|Satisfactory<br>(NSO)|-|
|Iraq|-|No openness|Satisfactory<br>(WB)|-|Satisfactory<br>(WB, MICS)|-|-|
|Jordan|-|No openness|No openness|No openness|Satisfactory<br>(WB, DHS)|No openness|No openness|
|Kuwait|No openness|No openness|No openness|No openness<br>|<br>-|-|-|
|Lebanon|-|No openness|-|Excellent<br>(NSO)|-|-|-|
|Libya|-|No openness|-<br>|-<br>|No openness|No openness|-|
|Malta|No openness|No openness|Satisfactory<br>(Eurostat)|Satisfactory<br>(Eurostat)|-|No openness|-|
|Morocco|No openness|No openness|<br>Excellent<br>(NSO)|<br>No openness|-|Excellent<br>(NSO, IPUMS)|-|
|Oman|-|No openness|-|No openness|Satisfactory<br>(WB, MICS)|No openness|-|
|Qatar|-|No openness|-|-|<br>-|No openness|No openness|
|Saudi Arabia|No openness|No openness|No openness|No openness|No openness|No openness|No openness|
|Syrian Arab Republic|-|No openness|-|-|-||No openness|
|Tunisia|-|-|Excellent<br>(NSO)|Excellent<br>(NSO)|Satisfactory<br>(MICS)|No openness|No openness|
|United Arab Emirates|-<br>|No openness|No openness<br>||-<br>|-<br>|-<br>|
|West Bank and Gaza|Satisfactory<br>(NSO)|No openness|Satisfactory<br>(NSO)|Satisfactory<br>(NSO)|Satisfactory<br>(MICS)|Satisfactory<br>(NSO, IPUMS)|Satisfactory<br>(NSO)|
|Yemen,Rep.|-|-|No openness|-|-|No openness|No openness|
|Total economies with some||||||||
|degree<br>of<br>microdata<br>accessibility for each data|1/20|0/20|8/20|5/20|6/20|3/20|2/20|
|<br>category||||||||
|Total surveys/ census with<br>some degree of microdata<br>accessibility on NSO, WB,||||||||
|IHSN, IPUMS, Eurostat and/or||||||||
|DHS and MICS websites.||||25/140||||
|**Legend**||||||||
|-<br>Microdata|Microdata avail|able|Microdata availa|ble|Microdat|a available both n|ationally|
|not collected|nationally only||internationally o|nly|and inter|nationally||

> 19 WB, IHSN microdata library as well as IPUMS, Eurostat, DHS and MICS data was accessed on 8th August 2021.

13
does not lower the tax rate on cash-on-hand, we should see no decline in transfers to others.

**Table 5:** Transfers to Network Members

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Panel A: Likelihood of transfer**|To anyone|To family|To non-family|From anyone|
|Private (vs. non-private)|0.0419|0.0540|-0.00523|-0.0780|
||(0.0464)|(0.0582)|(0.0583)|(0.101)|
||[0.37]|[0.35]|[0.93]|[0.44]|
|Non-private mean|0.784|0.510|0.405|0.314|
|**Panel B: Amount transferred**|To anyone|To family|To non-family|Net amount|
|Private (vs. non-private)|4998.2|3918.2|1080.0|3261.5|
||(2532.5)|(2398.1)|(951.8)|(3244.8)|
||[0.049]|[0.10]|[0.26]|[0.32]|
|Non-private mean|13128.8|9966.7|3162.1|4848.4|

_Notes:_ Data from in-person endline survey, covering transfers in past 3 months (in Phase 2). Observations at worker level. In Panel A, dependent variable is a binary indicator for providing any transfer outside the household to individuals in the given category in Cols. (1)-(3) and receiving any transfer in Col. (4). Panel B dependent variable is the continuous transfer amount sent in Cols. (1)-(3) and the net amount of transfers sent (transfers sent minus transfers received) in Col. (4). Regressions include strata dummies. N=298 Phase 2 workers. Robust standard errors in parentheses.

In Table 5, we examine treatment effects on transfers to individuals outside of the worker’s household. We focus on Phase 2, since the endline survey asked about transfers in the past 3 months to improve recall—covering only this phase of the study. In Panel A, we examine the extensive margin of making or receiving any transfers. We do not find significant changes in either the likelihood of having made a transfer in the last three months (Cols. 1-3) or the likelihood of having received a transfer in the last three months (Col. 4).

In Panel B, we find that workers increase total outgoing transfers to others (Col. 1, 38%, _p_ =0.049). However, the impact on net transfers, while positive, is statistically insignificant (Col. 4, _p_ =0.32). Given that the endline survey was conducted shortly after the Phase 2 unblock date, this suggestive increase in outgoing transfers may reflect a combination of increased transfers from higher cash-on-hand during the intervention period, as well as redistribution from the unblocked savings. This latter force could reflect both altruism, as well as the likely possibility that _τ_ 2—the social tax rate on the blocked savings account—is not zero. Overall, as intended by our design, our results indicate that the income gains achieved by Private group workers did not come at the expense of lower redistribution to others. Rather, they may have led to aggregate welfare gains.<sup>31</sup>

> 31In addition, we find positive (but statistically insignificant) improvements for workers in a measure of subjective well-being. Consistent with the results in Section 7.3, we find no changes in average reported intra-household bargaining power (Donald, forthcoming).

30
The analysis so far has estimated the gains to the financial institution (MRFC) from using fingerprinting to identify new borrowers as part of the process of loan screening. These gains need to be weighed against the costs of fingerprinting. We conduct a benefit-cost analysis of biometric fingerprinting of borrowers. The analysis is most valid for institutions similar in characteristics to those of our partner institution, MRFC, but we have made the elements of the calculation very transparent so that they can be easily modified for other institutions with different characteristics.

Under reasonable assumptions, total benefit per individual fingerprinted is MK475.50 (US$3.28). We consider three types of costs: equipment costs (which need to be amortized across all farmers fingerprinted), loan officer time costs, and transaction costs per fingerprint checked. Summing these costs, total cost per individual fingerprinted is MK209.20. The net benefit per individual fingerprinted is therefore MK266.30 (US$1.84), and the benefit-cost ratio is an attractive 2.27. (Details of this calculation are in Online Appendix G.) For several reasons, this benefit-cost calculation is likely to be quite conservative. First of all, under reasonable circumstances some of the individual costs could be brought down considerably. The cost for equipment units could fall substantially if a fingerprinting function were integrated into equipment packages that had multiple functionalities, such as the hand-held computers that MRFC is considering providing for all of its loan officers. Transaction costs for fingerprint checking could fall due to volume discounts if the lending institution banded together with other lenders to channel all their fingerprint identification through a single service provider (in the context of a credit bureau, for example).

In addition, there are other benefits to the lending institution that this benefit-cost calculation is not capturing. The impact of fingerprinting on loan repayment may become larger in magnitude over time as the lender’s threat of enforcement becomes more credible. We have also assumed that all the benefits come from fingerprinting new loan customers (the subject of this experiment), but there may also be increases in repayment among existing customers who are fingerprinted (on which this experiment does not shed light). Finally, there may be broader 27
- 28 -

Figure 5. Financial Deepening and the Real Interest Rate in Indonesia <!-- Start of picture text -->
-<br>0.4;<br>0 ~  ~5  °_0_r°\°-  76  °7a=0o-°  08  8  .7 .87  48  68 M2Z<br>° ' *1- =~~~~~~~~~~~~~~~~C- RRAT<br>73 74  75  76  77  78  79  80  81  82  83  84  83  86  87<br>MIZ  Ml/GDP~~~~~~~~~~~~~I<br>Daw: I_  dS<br>',IZ  -MI/GDP<br>IN5F  Inflation rate (CPI)<br>RRATE  Oppomtuniy  cost  of money- INF - Deposit rate<br><!-- End of picture text -->

Data:[FS
<!-- Start of picture text --> $2.15 $3.65 $6.85 $10 $20 $50<br>; -2 :: :: :: :: :: /:lL: -2<br>47 0° \ 7 “4<br>\ : : : : / :<br>\: : : : :/ :<br>\: : : : 7] :<br>\: peta a/:<br>\ porrT TINLEY? SO : :<br>0 20 40 60 80 100<br>Percentile<br>— GIC 2019-2020 —- GIC 2020 relative to counterfactual<br><!-- End of picture text -->
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23
# **Data and Descriptive Statistics**

The export data by origin county and destination country in NACE 2 digit product classifications is provided by the Croatian Bureau of Statistics (CBS). The export data covers the years from 2007 to 2012. Only total exports from a specific county to a specific destination are observed, i.e. the port, border gate, exporting firm, or any other transaction level information is unobserved. There are some minor changes in the classifications over the years, for example exports to Montenegro and Kosovo were jointly classified as exports to Serbia in the initial years.

Tables 1-3 present the origin counties, destination country groups, and product categories used for the gravity estimation, along with the relevant abbreviations.<sup>6</sup> After the aggregations, we have 20 counties, 25 destination country groups, and 12 product categories.

Export volumes for all destinations are listed by year in Table 4. Italy, Bosnia Herzegovina, and Germany are the largest destinations for Croatian exports, while West Asia is the smallest in terms of the export volume as shown in Figure 1 which lists each destination country and region according to total export values for 2007 and 2012. Export quantities have been increasing for many destinations over time. The five largest destinations in terms of volume accounted for approximately 68 percent of total exports in 2012.

> 6 All tables are presented in the Annex.

8
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