rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>5: Determinants of land productivity</u>**

||**No tenure or**<br>**’**|**Tenure but no agro-**<br> **’**|**All variables**|
|---|---|---|---|
|Total number of trees (Log)|**knowledge vars**<br>0.137***|**ecological vars**<br>0.118***|0.059***|
||(7.77)|(6.62)|(3.22)|
|Soil conservation dummy|<br>0.264***<br>|<br>0.244***<br>|<br>0.163***<br>|
||(4.31)|(4.03)|(2.75)|
|Knowledge of land law at the hh level (index)||<br>0.183*|<br>0.192**|
|||(1.88)|(2.00)|
|Unconditional transfer rights||0.014<br>|0.008<br>|
|Conditional transfer rihts||(0.69)<br>0001|(0.39)<br>-0008|
|g||.<br>(0.05)|.<br>(0.27)|
|Freehold and_mailo_tenure||<br>0.462***|<br>0.096|
|||(4.19)|(0.86)|
|Freehold and_mailo_occupant/tenant||-0016|0016|
|||.<br>(0.14)|.<br>(0.14)|
|Parcel area in acres (Log)|0.431***|<br>0.431***|<br>0.370***|
||(13.03)|(12.86)|(11.17)|
|Log of parcel area squared|-0.012<br>|-0.029<br>|-0.037**<br>|
||(0.66)|(1.55)|(2.08)|
|Household size (Log)|<br>0.110*|<br>0.134**|<br>0.121**|
||(1.91)|(2.34)|(2.21)|
|Value of purchased seeds (Log)|<br>0.135***<br>|<br>0.125***<br>|<br>0.148***<br>|
||(2.94)|(2.78)|(3.42)|
|Value of purchased chemical fertilizer (Log)|<br>0.172<br>|<br>0.163<br>|<br>0.170<br>|
||(0.55)|(0.53)|(0.58)|
|Value of purchased pesticides (Log)|0.091<br>|0.094<br>|0.030<br>|
||(0.60)|(0.64)|(0.20)|
|Value of hired labor (Log)|0.134***|0.135***|0.174***|
||(2.80)|(2.87)|(3.82)|
|Household head's age|-0.036***<br>|-0.029***<br>|-0.026**<br>|
||(3.21)|(2.61)|(2.46)|
|Head's age squared|0000***|0000***|0000**|
||.<br>(3.23)|.<br>(2.66)|.<br>(2.51)|
|Head's education in years|0.020**<br>|0.020**<br>|0.017**<br>|
||(2.51)|(2.53)|(2.17)|
|Female headed household|0049|-0013|-0009|
||.<br>(0.60)|.<br>(0.16)|.<br>(0.11)|
|Current value of household assets (Log)|<br>0.052**|<br>0.031|<br>0.022|
||(2.44)|(1.46)|(1.10)|
|Distance from house|0.001<br>|0.003<br>|0.002<br>|
|Good land quality|(0.33)<br>-0.001*<br>|(0.98)<br>-0.001<br>|(0.72)<br>-0.000<br>|
||(1.71)|(1.41)|(0.61)|
|Poor land quality|0068|0068|0065|
||.<br>(1.18)|.<br>(1.20)|.<br>(1.18)|
|Flat land|-0.147|-0.194**|-0.163*|
||<br>(1.62)|<br>(2.19)|<br>(1.91)|
|Steeply sloped land|-0.097*<br>|-0.042<br>|-0.007<br>|
|Swamp/Wetland|(1.69)<br>-0186*|(0.73)<br>-0107|(0.13)<br>-0162|
||.<br>(1.72)|.<br>(1.01)|.<br>(1.58)|
|Distance to the nearest district town in miles|0.728***|0.732***|0.851***|
||(3.00)|(3.07)|(3.72)|
|Electricity accessible|-0.014*<br>|-0.029***<br>|-0.021***<br>|
|Banana on parcel|(1.93)|(3.81)|(2.85)<br>-0.003<br>|
|Root crops on parcel|||(0.04)<br>0.380***<br>|
|Fruit crops on parcel|||(5.62)<br>0.447***<br>|
|Vegetables on parcel|||(7.59)<br>0.443***|
||||<br>(4.55)|
|Observations|1378|1377|1377|
|<br>R<sup>2</sup>|<br>0.38|<br>0.41|<br>0.46|

Absolute value of z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% Constant and dummies for use of different inputs as well as irrigation dummy included but not reported.

26
- 39 -

on urban traffic conditions. These data indicate that the application of 'borrowed' estimates (such as Newbery;s Tunisian estimates of the MTC) may lead to gross<sup>mistakes and</sup> erroneous policy recommendations. In Ghana's case, urban congestion costs are estimated to be only one half of the damage costs (they are only one quarter if one adopts the higher estimates of the damage costs). However, rapid urbanization and motorization, on the one hand, and improved road standards, on the other hand, may change the relative importance of the two problems in the future (as was shown in Tunisia). Recent research of road-user charges in LDCs has emphasized the damages externality, playing down the importance of congestion. This may have been the right research policy in the past, but it may have to be reevaluated in the future.

Finally, to estimate average road user costs and to weigh the importance of damage and urban and interurban congestion costs for the various types of vehicles one has to know the distribution of the vehicles' annual km between urban and interurban roads. These data<sup>are</sup> not collected routinely (as, for example, are interurban road traffic counts). Their collection requires special surveys. These surveys are lacking in Ghana as in most other SSA countries.

The most important lesson relates to the feasibility of road-user costs studies. In this study the sensitivity of the results to different assumptions was strongly emphasized, but this is not unique to a study of RUC in LDCs or sub-Saharan Africa; it applies as well to the developed countries (as demonstrated by Newbery's study). More important is the fact that this study, with all its inaccuracies and reservations, was at all feasible.

Embarking on this study, Ghana was chosen as an example of a data-poor country. This impression was dispelled. Though in a few cases (mentioned above) the data are missing, very often the problem was the existence of several conflicting sources of data. Most of these sources did not exist a few years ago and have been established as part of the transport rehabilitation program. A lot of work has to go into the consolidation of these data sources, and to assure the continuation of the information collection process, but the Ghanaian experience (although, perhaps, still rare in SSA) proves the importance and feasibility of information gathering as part of any major transport program.
Table 3: Discrete time transition model of reported days in customs - <u>policy</u> data excluded<sup>_a_</sup>

||**Days (Im**|**ports)**|**Days (E**|**xports)**|
|---|---|---|---|---|
|**Variable**|**Spec1**|**Spec2**|**Spec1**|**Spec2**|
|**Basic controls**|||||
|lngdppc|-0.725|0.01|-0.549|-0.036|
||(0.825)<sup>_b_</sup>|(0.925)|(0.856)|(0.952)|
|lngdppc2|0.052|-0.028|0.044|-0.006|
||(0.050)|(0.057)|(0.052)|(0.059)|
|lngdp|0.152|0.131|0.096|0.046|
||(0.090)*|(0.116)|(0.096)|(0.123)|
|lnsqkm|-0.236|-0.158|-0.113|-0.035|
||(0.081)***|(0.091)*|(0.084)|(0.094)|
|landlocked|-0.204|-0.729|-0.210|-0.471|
||(0.271)|(0.297)**|(0.274)|(0.298)|
|island|0.557|0.295|0.493|0.210|
||(0.319)*|(0.347)|(0.339)|(0.365)|
|wto|0.938|-0.218|1.247|0.571|
||(0.265)***|(0.346)|(0.286)***|(0.346)*|
|oecd|1.651|1.062|1.408|0.681|
||(0.464)***|(0.521)**|(0.492)***|(0.541)|
|**World Governance **|**Indicators**||||
|Corruption||0.243||0.295|
|||(0.339)||(0.363)|
|Efectiveness||0.677||0.454|
|||(0.470)||(0.482)|
|Stability||0.259||-0.081|
|||(0.188)||(0.199)|
|Regulatory quality||0.847||0.441|
|||(0.363)**||(0.353)|
|Voice||-0.082||0.219|
|||(0.218)||(0.243)|
|Ephron’s Pseudo-R2|0.1579|0.2324|0.1651|0.2201|

Notes:<sup>_a_</sup> Parameter estimates from a discrete-time transition model assuming a logit specification for the transition. There are no missing data among the independent variables.

> _b_ standard error is reported in brackets below estimated parameter, *** indicates 1% significant, ** 5% significant and * 10% significant.

20
<!-- Start of picture text --> GIN CHL<br>SWZ<br>MEX<br>AGO ZAF<br>BWA TZA UGA MRT<br>URY PANBWA EGY<br>NIC<br>PAN SWZARGNAMCOLBRA CRI<br>BRA<br>NAM CRI GTM ECU ECUPERGTMSLV<br>MEX AGOMAR<br>BOL PRY<br>SLV RWA PRY<br>URYCHLARGCOL HND EGY KEN MRTBOLNICHND<br>PER<br>ZAF<br>KEN<br>MDG<br>MAR GIN UGA TZARWAMDG<br>.1 .2 .3 .4 .5 0 .5 1 1.5 2<br>Data Data<br>R−squared=.3505665524954171 R−squared=.1007588272129979<br>1<br>.6<br>.8<br>.4<br>.6<br>Model Model<br>.4<br>.2<br>.2<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> (a) Tax evasion (b) GDP per worker<br><!-- End of picture text --> Figure 9. Tax evasion and GDP per worker - Data vs the model predictions across - Alternative calibration 38
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE these three countries. Subscribers who belong to one of these networks can use their mobile handset in the others. When in one of these countries, users do not pay for incoming calls and are charged local rates for outgoing calls. In Burundi, none of the operators allows international roaming, partly because Burundi does not share any strategic investors with Kenya, Tanzania, or Uganda. Burundi, however, offers oneway roaming, which allows users from other countries to use their phones in Burundi. MTN in Rwanda has preferential roaming with MTN in Uganda. Ethiopia, on the other hand, has relatively few roaming arrangements.

**Table 4.2 . Benchmarking ICT in EAC member states**

||Burundi|Kenya|Rwanda|Tanzania|Uganda|Ethiopia|Sudan|
|---|---|---|---|---|---|---|---|
|ACCESS||||||||
|Coverage of mobile network (% of population)|60|92|81|69|97|10|80|
|Mobile telephone subscribers (per 100 inhabitants)*|5.3|48.5|12.7|32.7|23.3|2|24.1|
|Internet subscribers (per 100 inhabitants)|0.01|0.04|0.03|0.09|0.03|0.03|0.11|
|Broadband subscribers (per 100 inhabitants)|0.0|0.05|0.03|0.00|0.01|0.00|0.11|
|International Internet bandwidth (Mbps)|4|885|31|200|344|245|705|
|Landline penetration|0.4|0.7|0.2|0.4|0.5|1.2|1.6|
|PRICES||||||||
|Prepaid mobile monthly price basket (US$)*|NA|7.36|6.16|9.5|9.32|2.9|2.9|
|Price of fixed telephone monthly price basket (US$)|2.5|19.4|7.8|11.3|13.0|1.7|4.5|
|Price of a three-minute call to United States (US$)|2.4|1.8|1.3|0.7|1.4|3.3|1.3|
|Price of 20-hour Internet basket (US$)|52|82|85|148|58|15|29|

Source: Ampah and others 2009.

* Denotes data from 2008/09. All other data are from 2006/07.

**Table 4.3 . GSM roaming in East Africa**

||Burundi|Kenya|Rwanda|Tanzania|Uganda|Ethiopia|Sudan|
|---|---|---|---|---|---|---|---|
|Burundi||||||||
|Kenya|X||X|P|P|X|X|
|Rwanda|X|X||X|P|||
|Tanzania|X|P|X||P|X|X|
|Uganda|X|P|P|P||X|X|
|Ethiopia|X|X|X|X|X||X|
|Sudan||||||||

_Note_ : P = preferential (cheaper tariffs and free incoming calls when using same group network—such as Zain in Kenya, Tanzania, and Uganda, and MTN in Rwanda and Uganda—and typically available for prepaid subscribers). X = available but with regulator roaming charges and typically limited to postpaid subscribers. _Source_ : Derived from Ampah 2009 and others.

The East African Regulatory Post and Telecommunications Organization (EARPTO),<sup>2</sup> an EAC regulatory body, appears to be less active than similar subregional organizations, but it has made some progress. Although the agency led the drafting of an EAC interconnection policy, agreements on border frequencies and common standards still tend to be bilateral, and there do not appear to be any model laws or regulations. Even for approvals of equipment types there does not seem to be any regional agreement.

> 2 http://www.cck.go.ke/earpto_issues/

49
# **_Administration of the MAP_**

During the evaluation period, the MAP was administered by the former State United Social Insurance Fund (SUSIF). Each individual in an eligible family received a medical assistance card with a common family ID and a specific individual ID. Beneficiaries who presented their cards at government health facilities were exempted from official user fees. The SUSIF also managed the payments of the Universal Benefit Package (UBP) for the general population.

In August 2007, SUSIF was restructured and its purchasing function of health services was transferred to the Health and Social Programs Agency (HeSPA), a new legal entity subordinated to the MoLHSA. The HeSPA is currently managing the MAP nationwide except in Tbilisi and Imereti regions.<sup>5</sup>

# **_Benefit Package_**

Since the introduction of the MAP in June 2006, the benefit package has been rapidly evolving, making evaluation of the package difficult. Three different packages have been offered: an initial one from June to December 2006, a less generous one from January 2007 to September 2007, and a new, most generous package from September 2007 to the present. For the review period of this paper (June-December 2006), the benefit package covers the following:<sup>6</sup>

- a) Acute out-patient and in-patient treatment;

- b) Planned in-patient service, except expenses for aesthetic surgery, cosmetic treatment, sanitary-resort treatment, sexual disorder, infertility, treatment abroad, sexually transmitted infections, HIV, and hepatitis C;

- c) Out-patient examinations prescribed by the doctor that cost no more then 200 GEL per beneficiary a year;

- d) Reimbursement of baby delivery costs; and

- e) Services envisaged by the Child Medical Assistance, oncology diseases diagnostics and treatment, and referral components of the State Program for Inpatient Assistance for the Population.

The process is different for planned in-patient services and acute inpatient service. In the _planned_ cases, providers have to submit an authorization form to SUSIF, and it takes about two to three weeks before scheduling; in the _acute_ cases, authorization is not needed and patients can simply present their medical assistance card to providers. Despite the lists of procedures SUSIF includes under these two broad categories, there is leeway for providers and patients to pursue their collective interests and classify procedures as acute cases when needed.

> 5 For details, see next section “The trend of Multi-payers under the MAP.”

> 6 Description of the MAP is dated May 28, 2007 (Source: MoLHSA),

5
_h_<sup>_grad_</sup> , equal to 98.<sup>13</sup> The functional form for the _zt_ shock is as follows: where _h_<sup> ̃</sup> _t−_ 1 is a measure of past cumulative number of classes completed, with _h_<sup> ̃</sup> _t−_ 1 = ln( _h_<sup> ̄</sup> _t−_ 1) for every _t >_ 1, and _h_<sup> ̃</sup> 0 = 0 for _t_ = 1 _._ The terms associated with _d_ 1 allow the shock distribution to differ in year 1, when _d_ 1 = 1. The shock also depends on an _iid_ component, _νt_ , drawn from the uniform distribution _U_ (0 _,_ 1). The functional form in (13) ensures that _zt ∈_ (0 _,_ 1) for any combination of parameter values and for all _h, θ_<sup> ̃</sup> _∈_ R. Importantly, all the parameters in (13) affect the mean and variance of _zt_ . In Section 7.3 below we discuss the effect of _h_<sup> ̃</sup> _t−_ 1 and _κθ_ on this mean and variance at our specific parameter estimates.

We parameterize the probability of dropping out as where _δ_ ( _t, θ, y_ ) is a year-, ability- and income- specific fixed effect, and _h_<sup> ̃</sup> _t_ measures cumulative performance over all periods, including the current one. Evaluating _p_ ̃<sup>_d_</sup> ( _t, ht, θ, y_ ) at _π_ = 0 yields the “exogenous dropout probability”—the dropout probability for students of a given type in a given year if they had accumulated no classes. It is “exogenous” because it is independent of effort. For example, low-income, low-ability students may have a high exogenous dropout probability in year 1—perhaps due to lack of parental guidance to navigate college—yet a lower one in subsequent years.

The model’s full parameter vector is Θ = (Θ<sup> ̃</sup> _,_ **_ξ_** _,_ **_δ_** ), where is the vector of parameters common across individuals. Vector **_ξ_** _J×_ 1 contains type-specific unobserved preferences for college, _ξj_ (see (9)) and **_δ_** ( _J∗_ 8) _×_ 1 contains exogenous dropout probability fixed effects, _δ_ ( _t, θj, yj_ ), for the _J_ types and 8 years (see (14)).

# **6.2 Computational representation**

## **6.2.1 Student types**

To build the empirical distribution of ability and income for school graduates, Φ( _y, θ_ ), we start from Appendix Table A1, which classifies 2005 high school graduates by ability quintile and income bracket. We refine this table to work with ability deciles rather than quintiles, for a total of fifty student types. To construct values for _θ_ , we start from the distribution of standardized Saber 11 test scores and normalize them between 0 and 1.<sup>14</sup> Our _θ_ values are the 5th, 15th, ...95th percentiles from the normalized scores. We calculate the _y_ value corresponding to each

> 13We set this requirement to 98 rather than 100 because we observe students who graduate with slightly fewer than 100 classes, perhaps due to measurement error.

> 14 Let _sts_ denote the standardized test score. The normalized _sts_ is thus equal to ( _sts −_ min( _sts_ )) _/_ (max( _sts_ ) _−_ min( _sts_ )) _._

19
|**Table 8. Annual Surplus and To**|**tal fund**|**in Tra**|**nsition**|**Account**|**and Pi**|**llarIa**||
|---|---|---|---|---|---|---|---|
||1995|2000|2010|2020|2030|2050|2100|
|Without coverage expansion||||||||
|Revenues minus Pension Payments<br>in transition account<br>astof covered wage bill|(12)|(8)|(8)|(3)|(1)|(2)|(3)|
|Funds in transition acct+Pillar<br>I<br>as % of current covered wage bill|(6)|(30)|(36)|(38)|(43)|(33)|0|
|as % of 1995 covered wage bill|(6)|(41)|(96)|(145)|(221)|(318)|0|
|With coverage expansion||||||||
|Revenues minus Pension Payments<br>in transition account<br>ast<br>of covered wage bill|(13)|(6)|(2)|(1)|0|1||
|Debt of transition acct+ Pillar I<br>as % of current covered wage bill|(8)|(19)|(7)|6|13|22|0|
|as % of 1995 covered wage bill|(8)|(37)|(35)|59|194|634|0|

a numbers in parentheses are deficits or debt.

**44**
(1), 2.2). The effect on college enrollment is insignificant for age group 16-18 and highly significant for age group 19-22 and 23-25. These results lend further credit to our main finding that manufacturing FDI inflows promote local school enrollment. In Appendix 3 (Table A5 and A6), we further show that domestic market-oriented FDI and high-skill FDI have a larger effect on college enrollment, the effect is larger for age group 19-22 and 23-25.

Table 13. FDI inflows and local school enrollment: by age group

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Panel A:|16-18|Panel B:|19-22|Panel C:|23-25|
|Dependent var|∆Highschool|∆College|∆Highschool|∆College|∆Highschool|∆College|
|FDI_shock|2.200***|-0.0468|1.041***|0.689**|1.131***|0.981***|
||(0.551)|(0.132)|(0.369)|(0.289)|(0.317)|(0.300)|
|L.FDI_empshare|-0.0595|0.0209|-0.0991**|0.106*|0.0316|-0.0113|
||(0.0551)|(0.0293)|(0.0402)|(0.0562)|(0.0328)|(0.0348)|
|Observations|607|607|607|607|607|607|
|R-squared|0.839|0.497|0.847|0.678|0.721|0.589|
|Prov-Year FE|Y|Y|Y|Y|Y|Y|
|Lagged city|||||||
|characteristics|Y|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level school enrollment by student age group. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 6.4 School enrollment by gender

Lastly, we test how the effect of FDI shock on school enrollment varies by gender. We calculate high school and college enrollment for boys and girls separately based on population censuses. The results are reported in Table 14. FDI shock has a positive impact on high school and college enrollment for both boys and girls. The effect on high school enrollment is larger for girls while the effect on college enrollment is higher for boys. High-skill FDI shock has a larger effect on female college enrollment than on male (Appendix 3, Table A7 and A8).

Table 14. FDI inflows and local school enrollment: by <u>gender</u>

||(1)<br>Panel A: F<br>|(2)<br>emale<br>|(3)<br>Panel B:<br>|(4)<br>Male<br>|
|---|---|---|---|---|
|Dependent var|∆Highschool|∆College|∆Highschool|∆College|
|FDI_shock|1.500***|0.414**|1.123***|0.613***|
||(0.380)|(0.194)|(0.351)|(0.185)|
|L.FDI_empshare|-0.0530|0.0442|-0.0662*|0.0168|
||(0.0353)|(0.0397)|(0.0348)|(0.0282)|
|Observations|607|607|607|607|
|R-squared|0.828|0.682|0.770|0.652|
|Prov-Year FE|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|

30
Policy Research Working Paper 4987

# **Abstract**

Although there is broad agreement that the way that health care providers are paid affects their performance, the empirical literature on the impacts of provider payment reforms is surprisingly thin. During the 1990s and early 2000s, many European and Central Asian countries shifted from paying hospitals through historical budgets to fee-for-service or patient-basedpayment methods (mostly variants of diagnosis-related groups). Using panel data on 28 countries over the period 1990–2004, the authors of this study exploit the phased shift from historical budgets to explore aggregate impacts on hospital throughput, national health spending, and mortality from causes amenable to medical care. They use a regression version of difference-in-differences and two variants that relax the difference-in-differences parallel trends assumption. The results show that feefor-service and patient-based-payment methods both increased national health spending, including private (out-of-pocket) spending. However, they had different effects on inpatient admissions (fee-for-service increased them; patient-based-payment had no effect), and average length of stay (fee-for-service had no effect; patientbased-payment reduced it). Of the two methods, only patient-based-payment appears to have had any beneficial effect on “amenable mortality,” but there were significant impacts for only a couple of causes of death, and not in all model specifications.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department investigating health service financing and delivery. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at awagstaff@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figure 3: Growth contributions of different policy innovations**

<!-- Start of picture text --> Percentage points<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>1985-1989 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014 2015-2019<br>Trade and FDI Macro Infrastructure Finance Political Stability Demography<br><!-- End of picture text -->

## _Note:_ FDI = foreign direct investment.

Second, the era of large growth contributions in the early 1990s started with the effects of recovery from a financial crisis. Bangladesh’s banking system was technically insolvent by the late 1980s (Laeven and Valencia 2013).<sup>19</sup> In response, a national commission proposed far-reaching structural changes in 1987, many of which were implemented as part of a three-year International Monetary Fund financing facility that was signed that year. These included the privatization of two major public banks (Uttara Bank and Pubali Bank), the licensing of private commercial banks, and the introduction of back-to-back letters of credit that catalyzed RMG exports. Recovery effects from the financial turmoil were reflected starting in the early 1990s and have also been observed after banking crises in other economies (such as after the Turkish Banking crisis 2000/2001). Further reforms focused on interest rate deregulation, partial restructuring of the banks’ operational procedures, introduction of capital adequacy standards, and removal of the lending interest rate band and deposit ceiling.

Third, infrastructure contributed consistently to growth. For example, the opening of the Bangabandhu bridge in 1998 connected Dhaka to northern Bangladesh. The contributions from infrastructure declined in the most recent periods as well but remained by far the largest contributor to growth over the past two decades. Infrastructure investment tends to contribute to growth during construction and after completion due to the services it provides. In line with this, ongoing

> 19 Four banks accounting for 70 percent of outstanding credit had a nonperforming loan rate of 20 percent.

-18-