rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Kenya has undergone a remarkable information and communications technology (ICT) revolution. At the close of the 1990s, less than 3 percent of Kenyan households owned a telephone, and fewer than 1 in 1,000 Kenyan adults had mobile phone service. By the end of 2011, 93 percent of Kenyan households owned a mobile phone.<sup>1</sup> A unique facet of the ICT phenomenon in Kenya has been the widespread proliferation of mobile money. Starting with the M-PESA system launched by Safaricom in 2007 and later joined by other systems, mobile money has become a fixture in the lives of Kenyans, extending a basic form of financial access to a wide population.

Mobile money platforms have evolved since inception and have entered a new phase with the advent of bank-integrated mobile savings products. The first such product, M-KESHO, was launched in March 2010 as a partnership between Safaricom and Equity Bank.

In this paper we examine the mobile savings phenomenon, using data collected in a survey during October and November of 2010. The concept of ―savings‖ on mobile platforms is not well defined, and we begin by putting forward a classification of the existing innovations. We differentiate between ―basic mobile savings‖ and ―bank-integrated mobile savings.‖ Basic mobile savings refers to the simple storage of credit using a mobile system such as M-PESA. Bank-integrated mobile savings refers to systems which include a fuller set of banking services such as interest payments on deposits or overdraft facilities. This is the first study that examines patterns of use of bank-integrated mobile savings in Kenya.

The paper is organized as follows. Section 2 presents findings on the overall prevalence of mobile phone and mobile money usage in Kenya based on the Afrobarometer survey conducted at the end of 2011. Section 3 reviews the existing literature on the broader mobile money phenomenon. Section 4 describes how mobile money works in Kenya and shows the growth of mobile money usage over time. Section 5 describes the data on mobile savings analyzed in this

> 1 These 1999 telephone ownership figures are based on census data, the 1999 mobile phone figures are drawn from the International Telecommunications Union statistical database, and the 2011 mobile ownership figure is based on the 2011 Afrobarometer survey.

2
If the proposed BRI transportation network leads to an average 0.69 shipping day reduction, based on the upper bound estimates from de Soyres et al (2018), our analysis suggests that the improved transportation network can raise BRI countries’ GDP growth rate by 0.09 percentage point through the FDI channel. Across regions, the transportation network is estimated, based on Table 18 and Figure 21, to increase GDP growth rate by about 0.08 percentage point in East Asia & Pacific, 0.04 percentage point in Europe and Central Asia, 0.01 percentage point in Middle East & North Africa, 0.13 percentage point in South Asia, and 0.23 percentage point in Sub-Saharan Africa, as shown in Figure 23.

It is noteworthy that the BRI transportation network can also stimulate growth in non-BRI countries through a spillover effect of the transportation network. For example, non-BRI countries in Sub-Saharan Africa may also benefit from access to ports in Kenya and consequently attract more FDI. Our analysis suggests that such spillover is significant in Africa; non-BRI Sub-Saharan African countries are estimated to see a 3.98 percent increase in FDI inflow with the BRI transportation network and, through the increase in FDI, a 0.13 percentage-point increase in GDP growth.

<!-- Start of picture text --> Estimated effect of the BRI transportation network on<br>GDP growth by BRI region<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>East Asia Pacific Europe Central Middle East South Asia Sub‐Saharan<br>Asia North Africa Africa<br><!-- End of picture text --> Figure 23: The estimated effects of the BRI transportation network on GDP growth by BRI region

# 5. Conclusion

In this paper, we evaluate the patterns, determinants and effects of FDI in BRI countries and the potential impacts of BRI on member countries’ FDI and economic growth through reductions of transportation costs. We first document and compare the historical patterns of FDI around the world including BRI and non-BRI countries and investigate how different types of transportation cost and infrastructure have affected FDI. Based on the findings, we then assess the potential roles of BRI in fostering FDI growth by improving different types of connectivity and infrastructure. Finally, we evaluate the effects of FDI on economic growth and how BRI might affect economic growth through the foreign investment channel. The following main findings emerge from the analysis: 37
Table <u>3. Financial Sector Development and External Finance Dependence</u>

|Dep. Var = Sector’s Share of Total|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Exports|||||
||Export<br>(BACI 19|Share<br>95-2009)|Export<br>(EDD 20|Share<br>05-2010)|
|Private Credit /GDP * EXTFIN|0.000936***<br>(0.000160)|0.000708***<br>(0.000146)|0.00238**<br>(0.00106)|0.00237**<br>(0.00103)|
|Private Credit /GDP *TAN||-0.00255***||-3.04e-05|
|||(0.000443)||(0.00185)|
|Constant|0.101***|0.146***|0.292**|0.292**|
||(0.00309)|(0.00700)|(0.131)|(0.137)|
|Observations|3,832|3,832|2,394|2,394|
|R-squared|0.280|0.287|0.101|0.101|
|Number MENA countries|20|20|5|5|
|Number of Non-MENA countries|119|119|26|26|
|Summary statistics|||||
|Average Private Credit/GDP (MENA)|43.57||49.25||
|Average Private Credit/GDP (non-MENA)|57.49||54.70||
|Average EXTFIN (MENA)|0.239||0.250||
|Average EXTFIN (non-MENA)|0.285||0.258||
|Average TAN (MENA)|0.382||0.32||
|Average TAN(non-MENA)|0.316||0.30||

_Notes: Estimation results from Equation 2. Panel dimension: country-sector. Average EXTFIN and TAN values for MENA and non-MENA regions are simple averages of trade weighted country values. Source: WB staff calculations, BACI, EDD._ Similar to HRR (2006), a relationship is found between trade specialization and the interplay between property rights and asset tangibility (column 2, Table 4). Sectors with high shares of intangible assets are expected to benefit the most from secure property rights, the exact attribute that the GCI indicator captures.

Significant correlations between governance and asset tangibility level of export sectors are also observed when governance is proxied by the GCI Legal Rights index and World Governance indicators (columns 3-8). Considering the legal framework or governance factors of a country to be endowments, it can thus be expected that in countries with well-developed legal frameworks, trade is specialized in sectors having lower shares of tangible assets. When the legal framework is strong, banks are more likely to enter into contracts and less prone to worry about risks associated with default. The quality of a country’s legal framework influences financial sector development as well as the ease of access to external financing.<sup>20</sup>

> 20 La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1998; Jensen and Meckling, 1976; Williamson, 1988; Grossman and Hart, 1986; Hart and Moore, 1990; Hart, 1995

17
but is not significant when used together with the _NRD_ from the EU (column v) or with both those from the EU and North America (column vii). Thus, once the _NRD_ from the EU is included in the regression, the _NRD_ from Japan loses its statistical significance and becomes very small empirically.

The elasticity with respect to _NRD_ from the EU is significant at the 5% level when appearing by itself (column 3) or with the _NRD_ from North America (column vi), and is significant at the 11% when appearing with the _NRD_ from both Japan and North America (column vii). Moreover, given our strong prior of non-negative coefficients, one-tailed tests are warranted and indicate a significance level of less than 10% in column (vii). On the other hand, the _NRD_ from Japan and from North America continue to be non-significant under one-tailed tests in regressions that include the _NRD_ from the EU.

# 5. Interpretation of the Results

The empirical results described above are striking. For all three developing countries, the highest and most precisely estimated elasticity of TFP is the one with respect to _NRD_ from its neighboring OECD region or “natural trading partner.” Thus, our results indicate that North-South trade-related technology diffusion exhibits a strong regional pattern. Why is the impact of _NRD_ from the neighboring OECD region so much bigger than that from the distant regions?

One possibility is that trade between each country in the South and its OECD neighbor involves more than a simple exchange of goods. It is likely to entail more personal interaction, including sub-contracting relationships where firms in the developing country import intermediate goods from firms in the neighbor in the North 10
|||_Table A- 6_|_: Credit constra_|_ined firms and g_|_overnment own_|_ed banks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|
|SOB|0.1725|0.2083*|0.621**|0.592**|0.620**|0.594**|0.621**|0.592**|
||(0.1161)|(0.1173)|(0.293)|(0.292)|(0.294)|(0.293)|(0.299)|(0.294)|
|SOB_MNA|-0.1003|-0.0507|-0.508|-0.550|-0.677|-0.787|-0.672|-0.697|
||(0.2023)|(0.2112)|(1.091)|(1.120)|(0.804)|(0.808)|(0.661)|(0.694)|
|INTER|||-1.076*|-0.940|-1.093*|-0.954|-1.231*|-1.060*|
||||(0.572)|(0.573)|(0.597)|(0.595)|(0.629)|(0.613)|
|INTER_MNA|||1.017|1.056|1.307|1.447|1.404|1.399|
||||(1.769)|(1.788)|(1.375)|(1.377)|(1.060)|(1.075)|
|ROA||0.0192||0.008||||0.012|
|||(0.0300)||(0.029)||||(0.030)|
|LTA||-0.0749*||-0.055||-0.058||-0.068*|
|||(0.0395)||(0.039)||(0.039)||(0.039)|
|lsize|-0.0190|-0.0236|-0.024|-0.026|-0.023|-0.026|-0.021|-0.025|
||(0.0345)|(0.0335)|(0.034)|(0.033)|(0.034)|(0.033)|(0.034)|(0.033)|
|lage|0.1478***|0.1513***|0.152***|0.154***|0.151***|0.153***|0.150***|0.153***|
||(0.0569)|(0.0554)|(0.057)|(0.056)|(0.057)|(0.056)|(0.057)|(0.056)|
|SOE|-0.1306|-0.1047|-0.033|-0.030|0.004|-0.003|-0.017|-0.013|
||(0.1647)|(0.1617)|(0.166)|(0.166)|(0.178)|(0.176)|(0.178)|(0.175)|
|FEM|0.0946|0.1007|0.112|0.114|0.109|0.111|0.102|0.106|
||(0.1091)|(0.1087)|(0.110)|(0.109)|(0.109)|(0.109)|(0.109)|(0.109)|
|Constant|-0.3460*|0.2474|-0.339*|0.099|-0.341*|0.120|-0.341*|0.196|
||(0.1875)|(0.3530)|(0.188)|(0.345)|(0.188)|(0.344)|(0.188)|(0.349)|
|Observations|14,656|14,656|14,656|14,656|14,656|14,656|14,656|14,656|
|R-squared|0.0822|0.0871|0.091|0.093|0.089|0.092|0.086|0.090|

|||_Table A_|_- 7: Firms that i_|_nvest and gover_|_nment-owned b_|_anks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|INV|<br>INV|<br>INV|<br>INV|<br>INV|<br>INV|INV|INV|
|SOB|0.1645**|0.1784**|0.166|0.134|0.149|0.133|0.236|0.207|
||(0.0717)|(0.0739)|(0.179)|(0.178)|(0.179)|(0.179)|(0.184)|(0.181)|
|SOB_MNA|-0.2911**|-0.2355*|0.402|0.555|-0.293|-0.362|-0.359|-0.238|
||(0.1374)|(0.1430)|(0.579)|(0.601)|(0.933)|(0.934)|(0.628)|(0.649)|
|INTER|||-0.004|0.109|0.037|0.125|-0.197|-0.079|
||||(0.337)|(0.346)|(0.352)|(0.359)|(0.376)|(0.372)|
|INTER_MNA|||-1.012|-1.192|-0.009|0.079|0.191|0.040|
||||(0.933)|(0.943)|(1.627)|(1.627)|(1.010)|(1.024)|
|ROA||0.0225||0.025||||0.022|
|||(0.0195)||(0.019)||||(0.019)|
|LTA||-0.0358||-0.038||-0.036||-0.035|
|||(0.0285)||(0.030)||(0.029)||(0.029)|
|lsize|-0.1227***|-0.1252***|-0.123***|-0.125***|-0.123***|-0.124***|-0.123***|-0.125***|
||(0.0230)|(0.0228)|(0.023)|(0.023)|(0.023)|(0.023)|(0.023)|(0.023)|
|lage|0.0354|0.0372|0.036|0.037|0.035|0.037|0.036|0.037|
||(0.0402)|(0.0402)|(0.040)|(0.040)|(0.040)|(0.040)|(0.040)|(0.040)|
|SOE|-0.0229|-0.0023|-0.022|-0.010|-0.027|-0.032|-0.005|0.005|
||(0.1033)|(0.1037)|(0.100)|(0.101)|(0.102)|(0.102)|(0.102)|(0.103)|
|FEM|-0.1172*|-0.1136|-0.117*|-0.115|-0.118*|-0.116*|-0.116*|-0.113|

38
**Figure 112**

<!-- Start of picture text --> Pakistan: Revealed Factor Intensity (RFI) of Exports<br>RFI 1997 RFI 2007<br>12 12<br>8 8<br>4 4<br>0 0<br>Physical Capital Physical Capital<br>0 60000 120000 180000 0 60000 120000 180000<br>12 12<br>8 8<br>4 4<br>0 0<br>Dead Exports by 2007 New Exports in 2007<br>Human Capital<br>Human Capital<br><!-- End of picture text --> Source: Authors’ calculations based on data from UNCTAD.

The product-space analysis indicates what merchandise goods Pakistan exports competitively, and whether the portfolio has undergone a structural transformation over the past decades. Using the tools pioneered by Hidalgo et al. (2007), the product space maps indicate all tradable products at the SITC 4-digit level, with the black dots representing exports in which Pakistan has a Revealed Comparative Advantage (RCA). The hypothesis is that firms that build up competence in producing a certain good can redeploy their human, physical and institutional capital more easily if they seek to produce goods that are “nearby” those that they are producing already. This is predicated on the idea that structural transformations are not smooth movements along a continuum but a messy process beset by market failures. When such market failures are binding, it is harder for firms to hop longer distances without government coordination and support.

In the analogy of Hidalgo et al. (2007) the product/dots are trees that group themselves to form dense and sparse parts of a forest. Location of firms in the denser parts of the forest creates more opportunities for technological upgrading and structural transformation because market failures are less binding when firms have to make smaller adjustments to move to produce “nearby” goods requiring similar capacities. The center of the product-space, for example, is quite dense with better connectedness among industries related to metallurgy, vehicles, machinery, etc. To the bottom right of the product-space lie the more sophisticated electronics and chemical industries. The scattered industries on the upper half are largely agricultural and resource-based. Countries that succeed in transforming themselves over time from producing unprocessed natural or agricultural goods and labor-intensive manufactures (such as footwear 18
deliver each type of care (Ministère de la Santé du Rwanda 1993, 1997, 2003 and 2009). At baseline clinics had about 80 percent of the drugs and equipment necessary to provide curative care services, 78 percent of that needed for deliveries, 96 percent for prenatal care, and 94 percent for immunizations.

Finally, we find no significant differences between the treatment and comparison groups in terms of log total expenditures, allocation of the budget across medical personnel, medical supplies and non-medical purposes, the number of physicians, nurses and other types of personnel, in 2006 before the introduction of P4P in the treatment facilities (Table 3 Panel D). More importantly, there is no difference in mean log expenditures in 2008 between treatment and comparison groups, which supports our interpretation that any differences in outcomes are caused by the P4P performance incentives as opposed to resource differences.

# **c. Provider Skill (Knowledge) and Quality of Care**

A key measure of medical care provider’s skill and capability is their knowledge of appropriate clinic protocol. At baseline, medical providers knew about two-thirds of the prescribed clinical protocol for prenatal care (Table 3 Panel D). Our measure of provider knowledge is the share of the appropriate prenatal care clinical procedures specified in the official Rwandan Clinical Practice Guidelines (CPG) for prenatal care (Ministry of Health Contractual Approach Unit, Rwanda 2006, and Ministère de la Santé du Rwanda 2008).<sup>9</sup> The 24 specific clinical services collected are listed in Table 4. The items cover collecting information about previous pregnancies, medical history, current pregnancy status, physical exams, laboratory tests, and case-risk management.

We used a clinical “vignette” which is a standardized hypothetical patient case with a specific medical history. We presented the vignette to a randomly selected health worker who regularly provides prenatal care and asked the health worker to describe the clinical protocol that

> 9 CPGs are a recommended set of clinical procedures conducted during the prenatal care visit that maximize the probability of good health outcomes based on the clinical literature and expert opinion. First developed in the United States (Field and Lohr 1990), the Rwandan guidelines are based on the US version adjusted to local resource constraints. The Rwandan CPG for prenatal care includes medical and pregnancy history questions, physical examinations, lab tests and follow-up procedures (Table 4).

16
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 28: In a pure DC reform for only new entrants, replacement rates* , for non switchers are higher than for switchers****

<!-- Start of picture text --> 120.0%<br>100.0%<br>80.0%<br>60.0%<br>40.0%<br>20.0%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>non-sw itchers switchers<br>Replacement rate<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of the group average wage; ** Comparison of replacement rates in Reform I.2 for civil servants – switchers with non-switchers. **Source** : WB calculations based on PROST.

- 61 -
25 recorded GDP in 1 9 9 0 -9 1IA' Even though their CNEA shock was, to some degree, self-inflicted,L9' when it came, the East European countries saw little alternative to an economic opening involving compreh3nsive price and trade liberalization and swift moves towards current convertibility. Non-CMEA trade already accounted for half of total trade in the more open of these countries, however.

These factors are relevant to Mongolia also, and its experience offers further insights into how they interact to speed or slow reforms.

(i) Organizational and Institutional Caoacitv. Market infrastructure and the institutional capacity for a market economy is particularly inadequate in Mongolia, especially relative to the more open of the East European countries. This has constrained the speed of reforms in many ways. Although the need for radical reform was widely appreciated early on, there was not a widespread awareness of what radical reform actually meant, even within the government. This limited the extent to which the actual reforms can be said to have been supported by a broad consensus, and almost surely contributed to inconsistencies between reform plans and their implementation.

(ii) Political Factors. The urge to transform the economy was initially led by a reform minded group within and outside of the MPRP who realized that with the collapse of the Soviet Union Mongolia would not be able to sustain its economy. As Soviet aid halved in 1990, the Mongolian government began to move away from the official ideology of the MPRP, which still continued official adherence to socialism through 1990 and 1991. In fact, a law passed in January 1991 banned political parties from operating in government organs and required all government officers, including the president and vice president, to drop their party 18/ There is no rigorous way of disentangling the effects of the several factors that have contributed to Eastern Europe's economic contraction. This is a "best guess" estimate based on the outcome of a World Bank-IMF conference on the Macroeconomic Situation in Eastern Europe in July, 1992.

19/ Following political changes, East European countries moved as fast as possible to strengthen ties with the West and reduce those to the USSR. This included a rejection of the CMEA system, despite the high short-run costs.
In many countries, ALMP programs operate on a very large scale. An issue that was not addressed in the evaluation studies is to what extent large government-sponsored training programs crowd out private training. Additionally, it is possible that the benefits observed for program beneficiaries are at the expense of nonbeneficiaries, because of displacement, substitution effects or lower wages. For example, a wage subsidy might encourage a firm to hire a particular worker rather than some other worker. These questions warrant more investigation to more fully assess of the effectiveness of these programs on the general population.

Our review of microlending programs found mixed evidence on their effectiveness and on the benefits of gender-targeted lending. For example, Pitt and Khandker‟s (1998) evaluation of a program in Bangladesh finds substantial benefits of targeting microcredit to women relative to men, as measured by effects on consumption, labor supply and children‟s school-going. However, Roodman and Morduch‟s (2009) replication and extension of their analysis indicates that the results are highly sensitive to changes in the specifications used in estimation, questions the validity of some of the instruments and finds estimates to suggest impact estimates in the opposite direction.

The de Mel, Duresh, McKenzie and Woodruf (2007) study also raises questions about the rationale for gender targeting, because the female-owned enterprises exhibited much lower rates of return on capital (given through grants rather than credit) than male-owned enterprises in their study of Sri Lankan microenterprises. Female borrowers also tended not to come from the poorest families, so that gender targeting may have been at odds with poverty targeting. The evidence for Pakistan (Hussein and Hussein (2003)), however, shows that when loans were not targeted at women, they tended to go to men and even when they were targeted at women, male relatives tended to appropriate them. Even so, targeting them toward women appears to have affected their bargaining power within the household. The degree to which gender targeting makes a difference to how funds are used and to female empowerment is likely to depend on the country context.

One of the studies reviewed by Kaboski and Townsend‟s (2007) uses a theoretical model to study the relative effectiveness of microcredit (non-gender targeted) versus cash transfers as a way of increasing the consumption and utility of the poor. Interestingly, they show that even people who did not avail themselves of the credit benefited from it, in the sense of having been able to better smooth their consumption, knowing that credit was available. They also conclude 59