rafmacalaba/datause-encoder-data huggingface dataset:
21 An important lesson from international experience is that standard financial ratios (such as return on equity and return on assets) are not reliable indicators of the economic soundness of agricultural credit institutions because of the endemic subsidies (such as replenishment of equity, cheap loans, and rediscounts from the central bank) that prop up these ratios. Thus institutions can continue to enjoy a good public image even though their sustainability is in serious doubt. To evaluate the true health of a financial institution, a subsidy dependency index (SDI) is used. The SDI shows by how much the current interest rate on agricultural loans would have to increase to truly cover the cost of lending, assuming all incoming funds and services were charged market rates.<sup>5</sup> Several financial intermediaries were reassessed using the SDI. Thailand's BAAC performed the best, showing an SDI of 26 percent in 1988 (a 26 percent incrcase in the on-lending rate from 11.9 percent to 15 percent would permit BAAC to become self-financing) . The Jamaican Agricultural Credit Bank is the other extreme. Even though the bank looked very hLalthy on conventional financial criteria, the SDI index had a value of 312 percent in 1991 (nominal interest rates of 10 percent would have to be raised to 41.2 percent for the institution's financial viability).

The SDI for the ADBP was 31.4 percent in 1988 (table 2.5). It fell to 25.5 percent in 1990 and then increased to 36.2 percent in 1992. It feDl in 1990 because ADBP raised Rs. 5.6 billion on the interbank market, which reduced the amount borrowed at a subsidized rate. The interbank borrowing was partly repaid in 1991 and fuly repaid by June 1992 through increased borrowing at a concessionary rate from the SBP. This increased the SDI in 1992. The implication of this movement in SDI is that the interest rate that the ADBP should have charged on its lending to eliminate the subsidy was 15.5 percent in 1988 and 16.6 percent in 1992, compared with the actual rates of 11.8 percent and 12.2 percent that the ADBP charged.

**Table 2.5.** ADBP Subsidy Dependency Index (in percentage)

||1988|1989|1990|1991|1992|
|---|---|---|---|---|---|
|SDI|31.4|29.6|25.5|31.6|36.2|
|Rate charged onloans|11.8|11.9|11.9|12.5|12.2|
|Rate needed to eliminate subsidies|15.5|15.4|14.9|16.5|16.6|

# **Source:** Authors' estimates

5 See J. Yaron (1992), Assessing Development Finance Institutions: A Public Interest Analysis. World Bank Discussion Paper 174.
Melville, B.F; lawrence, 0; Williams, M; Archer, E; Fraudcis, V; & Collins, L. "Feeding Practices of Infants, and Beliefs of Mothers in Western Jamaica." West Indian J of Medicine 36(2) 1987:68-72.

Mitchell, M. Faith "Popular Medical Concepts in Jamaica and their Impact on Drug Use." **'Ihe** Western J of Medicine 139(6) 1983:841-847.

Monteith, RS; Warren, CW; Stanziola, E; Urzua, RL; & Oberle, MW. "Use of Maternal and Child Health Services and Immunization Coverage in Panama and Guatemala." Bull PAHO 21(1) 1987:1-15.

Morrison, JC "Sickle-Cell Disease." IN Queenan & Hobbs (eds) Protocols for High Risk Pregnancy Ordell, JJ: Medical Eoornmics, 1987.

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Papiernik, E & Vial,M "Problemes actuels de la prevention de la prematurite." Med Hyg 2 1979:1658.

Poddar, D; Maude, G.H; Plant, M.J; Scorere, H; & Sergeant, G.R. "Pregnancy in Jamaican Wcmen with Homozygous Sickle Cell Disease: c Fetal and Maternal cutcome." British J of Obstetrics and Gynecoloy 93(7) 1986:727-732.

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Reynolds, J "Final Report on the Pretest of the Trinidad and Tobago National Family Planning Programme Follow-Up Study." New York: International Institute for the Study of Human Reproduction, 1970.

Roberts, _SW_ & Sinclair, S. Women in Jamaica New York: KIt Press, 1978.

Rosenfield, A. & Maine, D. "Maternal Mortality-a neglected tragedy. Where is the M in MCH?a The Lancet 1985:83-85.

Royston, E & Lcpez, AD. "On the Assessment of Maternal Mortality." World Health Statistics Ouarterly 40 1987:214-224.

Seaga, Edward "Jamaica's Primitive Medicine."Tomorrow Ouarterlv: Review of Psychical Research 3 1956:70-78.

Segree, Winsome. "Health Problems of Women in Jamaica." (Plenary Address, First International Congress on Women's Health Issues, Halifax, Nova Scotia, October 4, 1984.) Health Care for Women International 6 1985:225-238.

26
results. The coefficient on whether the firm has bank credit remains negative and statistically significant—although the point estimate is larger in absolute value (see column 2).

As an additional robustness check, and to partly control for firm heterogeneity, we add some additional control variables to see whether this appears to affect our results. The additional variables are similar to those that Brown et al (2011) used to explain access to credit in the some of the same countries. They are: (i) a dummy variable indicating that the owner is female; (ii) a dummy variable representing whether the firm has audited accounts; (iii) a variable representing the business environment that the firm faces (the average of the firm‘s answers to perception questions on tax rates, tax administration, business licensing, and corruption) and (iv) a variable representing the number of competitors that the firm has in local product markets.<sup>28</sup> Including the first three variables reduces sample size from 1779 to 1726 observations. The point estimate for the credit variable becomes smaller (in absolute value)—although it remains larger than before we included the performance variables. Moreover, it remains statistically significant at conventional significance levels. The estimate suggests that firms with bank credit are about 4 percentage points less likely to die than firms without bank credit.

Because only manufacturing firms were asked about competition, sample size falls considerably when the competition index is included—from 1726 observations to 666. Despite this, the coefficient remains statistically significant and is slightly larger than before. The estimate suggests that firms with bank credit are about 8 percentage points less likely to die than firms without credit.

> 28 Brown et al (2011) include two additional variables in their analysis that were not included in the 2008 BEEPS data set.  The two variables are: (i) percent of firm earnings received through a bank account and (ii) the percent of working capital financed with retained earnings.  We omit these because as noted above, there were no equivalent variables in the 2008 data set.  Although the second variable was not available in the 2008-09 data set, we include a different proxy for the firm‘s need for external financing in the analysis—a dummy variable indicating that the firm did not apply for a loan in 2007 because it had no need for external financing.

23
appears to have a roughly equal impact on large and medium companies,' falling less heavily on small registered firms. As the patente is invariant with profit or sales (and tax agents have been reluctant to reduce assessments during the crisis), it represents a significant fuxed cost of staying in business for many firms.

VAT: Cote d'lvoire's value-added tax (VAT) is assessed at different rates for different items. The simultaneous existence of several rates and insufficiently precise definitions of the different categories creates strong incentives for entrepreneurs to pressure the government to classify their products in less taxed categories -- and the likelihood that large, better connected enterprises will fare better than smaller ones. Interviews suggested that the classification of a commodity could make or break its profitability, and even large international companies sometimes had products crushed by inappropriate classification, hence a high VAT rate.

Given the limitations in private accounting practices and public monitoring capacity, precise administration of the VAT or a profits tax would be nearly impossible. Since most SMEs keep at best a partial set of books, it is not rare for them to be subject to the VAT even when their book keeping is inadequate to the taslk The common solution belies the whole concept of the VAT: the tax inspector assesses an estimated VAT which then becomes a negotiable but "sticky" tax liability.

Imor Taxes: As a result of efforts to effectively devalue the real exchange rate, the import tariff averages 70%.4 However, the rate varies widely and there are a variety of fees (e.g. for storage) and penalties that can be assessed by the customs service. As a result, customs officials are possessed of extraordinary discretion which, according to a variety of entrepreneurs and officials, is broadly abused.' The resulting process is not only inordinately expensive, but quite time-consuming and unpredictable. Large, specialized importing companies have professionals who deal only with customs. The difficulty of the process is particularly important for mid-sized firms that attempt to export: their managers simply do not have the time and energy to obtain exemptions for inputs used in export products.

High import tariffs (needed, in part, to compensate for the overvalued currency) are having the ultimate Lafferian effect: they so increase the incentives for smuggling that they not only reduce < The CECPS survey shows it averages about 1% of turnover.

". This figure may be tariff VAT inclusive. A study of scheduled tariff in 1989 suggests an average rate on manufactured goods of 34%, with a range from 0 to 151%. Sucb a range creates a substantial rent potential for those with discretion over the classification of goods.

4 When a dozen microentrepreneurs in Bouake were asked zheir first priority for removing one source of government interference in business, they unanimously pointed to custom duties and hassles.

If _35_
contributors to total employment (54.6%). However, if we were to consider both small and medium firms (SMEs) together, we find the median employment share across our sample to be 45.45%, so comparable to large firms. Previous studies such as Ayyagari, Beck, and DemirgucKunt (2007) have also found SMEs to be significant contributors to aggregate employment although they define SMEs as firms with less than 250 employees and use data from a different database.<sup>11</sup> The median summary statistics across the 104 countries are also plotted in Figures 1 and 2. Figure 1 shows that it is the large firms (>99 employees) that are the largest contributors to total employment (54.6%). Small firms contribute 16.5% and Medium firms contribute 27%. We get similar patterns if we were to use mean values in each size bin rather than median shares across the 104 countries.

Table 1 also shows that we find similar results when we look across income groups<sup>12</sup> or geographic regions. Figure 2 plots the median statistics by income groups. Across income groups, establishments that employ more than 99 people have the largest employment shares, ranging from 44.2% in low income countries to 60.6% in upper-middle income countries. This is consistent with the US evidence in Haltiwanger, Jarmin, and Miranda (2010a) who find that large (and mature firms) in the US have the largest share of employment though they also find relatively little employment in large young firms. We do not focus on size-age breakdowns since our surveys are not stratified by firm age.

> 11 The SME database in Ayyagari, Beck, and Demirguc-Kunt (2007) only covered the formal labor force in manufacturing.  The sample of 54 countries in Ayyagari, Beck, and Demirguc-Kunt (2007) were mostly rich developed nations and thus differ greatly from the developing country sample in the Enterprise Surveys (ES). Of the 54 counties for which SME250 share of manufacturing labor force is reported in Ayyagari et al. (2007), only 30 countries overlapped with the ES database. When we include the 44 countries for which we have additional data from sources other than the ES, we find the SME250 Manufacturing measure in our data to be significantly correlated with that in Ayyagari et al. (2007).

> 12 We only have 9 high-income countries in our sample – Croatia, Czech Republic, Estonia, Hungary, Latvia, Poland, Slovak Republic, Slovenia, and The Bahamas

10
I I I − ~~(~~ (C IppllCCAAI IIII I , CII I TTppLLCCrr C III<sup>ss</sup> )/1000 ) (15) ∆CC = TTppLLCCrr C II UUIIppi<sup>ss</sup> ∗CCppIIUUIIpprr I pps rrppIIUUCC∗<sup>rrIIWWppIrr</sup> Where MMC I CCIIQQrrII I is the total cost differential associated with overstaffing in utility TT<sup>II,WWppI</sup> CCIIQQrrII I TT<sup>II,WWppI</sup> _s_ ; are the reported labor costs of utility II _s_ ; are the reported number of ppllCCAAI II employees in utility ∆CC _s_ ; _o_ is the efficient ratio of employees per 1,000 II customers; TTppLLCCrr ccCCI IIC<sup>II</sup> I I is the reported number of water service consumers of UUIIpps utility _s_ in IBNET database; and ppIIUUIIpprr ICppsI<sup>rr</sup> rrppIIUUCC I is the reported number of wastewater service consumers of utility CCIIQQrrII I TT<sup>II,WWppI</sup> _s_ in IBNET database.<sup>IIWWppIrr</sup> CCIIQQrrII I TT<sup>II,WWppI</sup> Based on this equation we estimate the cost and tariff differential associated with inefficient levels of employment for each utility in IBNET:

## Where TT

## **3.7.3 Full Tariff**

Once we have computed the cost differential associated with water losses and staffing levels, we calculate the full tariff for each utility in IBNET, adding these two elements to the efficient tariff computed earlier for accounting for inefficiencies: I (17) Where is the full tariff. TTFFCCl = TTEEi iicciiI II + TTllCCI + TTEEIIppllCCAAI II FFCCl As previously discussed, we allocate the tariff differential due to excessive losses to the water tariff, while TT As previously discussed, we allocate the tariff differential due to excessive losses to the water tariff, while the tariff differential due to overstaffing is distributed across the water and sanitation tariffs proportionally based on the respective asset bases.

# **3.8 Estimating subsidies for utilities listed in IBNET**

Once we have computed the efficient and full tariff for each utility’s service in the IBNET database, we quantify the implicit subsidies at the firm level. Strictly speaking, the subsidy is the difference between the efficient tariff and the effective tariff received for each service by the utility, which can be approximated by the average revenue. Formally: (18) Where is the subsidy, or difference between efficient and effective tariff; and UUEEi iicciiI II = TTEEi iicciiI II −CCIIAARR is the average revenue. EEi iicciiI II UU CCIIAARR This estimate, however, does not account for inefficiencies found at the utility level. This means that the efficient tariff would allow only an efficient utility (i.e., with losses of 15 percent and five employees per 1,000 customers) to cover the economic costs of providing service. If, following the hidden cost approach, we want to consider the actual efficiency level of each utility, we need to use the full tariff estimated in section 3.7.3. Formally: 12
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Wireless technology is also having an impact on the fixed line market; the incumbent operator provides fixed wireless services using CDMA technology, accounting for some 40 percent of all fixed lines in service.

But the full benefit of being connected to the international submarine cable has been muted owing to Benin Telecom’s monopoly over the gateway: Although prices are cheaper where there is access to submarine cable, they are even lower when there is a competitive international gateway. For instance, the price of a minute call to the United States could be further reduced from the current $0.40 (table 15).

**Table 15. Submarine cable and competition, ICT prices, 2008**

|US$|**Peak 1 minute call within**<br>**region**|**Peak 1 minute call to U.S.**|**Monthly Internet ADSL (256**<br>**kbps)**|
|---|---|---|---|
|Without submarine cable|0.97|0.96|266|
|With submarine cable|1.07|0.63|89|
|Monopoly on international gateway|1.65|1.11|109|
|Competitive international gateway|0.45|0.28|65|
|_Source_: AICD analysis.||||

## **Challenges**

Benin needs to further reform its ICT sector. The incumbent Benin Telecom remains fully government-owned. The liberalization of the mobile market has not been straightforward, with the government withdrawing the licenses of operators who did not pay a retroactive increase in license fees. The operators eventually accepted the terms of the increase, and their licenses were reissued. The ATRPT was only recently established, and its provisional status creates uncertainty for further market expansion.

Incomplete liberalization and poor infrastructure led to the stagnation of the landline market, which remains under state monopoly control. In the absence of regulation, the price of a landline connection has not declined; it is higher than the average for comparable low-income countries (table 14). Benin Telecom SA is facing major challenges stemming from its strong dependence on the state, which complicates its procurement procedures. Unlike fixed line operators in other countries, Benin Telecom SA does not benefit from a favorable investment code.

# Financing Benin’s infrastructure

To meet its most pressing infrastructure needs and to catch up with developing countries in other parts of the world, Benin needs to expand its infrastructure assets in key areas (table 16). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting targets. The targets can be modified or delayed as needed to achieve financial balance.

34
<!-- Start of picture text --> Figure 17: Correlation between the number of days to establish a foreign subsidiary and firms reporting<br>licensing/permits as a major constraint<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0<br>Foreign firms identifying licensing/permits as a major constraint (%)<br>Days to start a foreign-owned firm<br><!-- End of picture text --> The Pearson correlation coefficient is 0.348, highly significant beyond the 1% level _Source_ : _FDI Regulations_ database 2012; Enterprise Surveys 2012 The _FDI Regulations_ database covers 17 fragile and conflict situations (FCS) economies worldwide. Sustainable FDI normally requires political stability among other factors. Other things being equal, improved economic and political conditions will contribute to attracting strong and diversified FDI inflows. Box 3 highlights the main findings related to sectoral restrictions as well as procedural implications for setting up a greenfield foreign investment in FCS economies.

**Box 3:** **_Starting a Foreign Investment Across Sectors in_ Fragile and Conflict Situations (FCS) economies**

<mark>Attracting investment is crucial for the development of an economy; this is particularly true in countries where conflict may have discouraged investment. Several challenges continue to impede FCS economies from fully</mark> leveraging their potential and attaining better, if not optimal, private sector development.

<mark>Seventeen FCS economies are covered by the</mark> _<mark>FDI Regulations</mark>_ <mark>analysis namely: Afghanistan, Angola, Bosnia and Herzegovina, Burundi, Chad, Côte d’Ivoire, the Democratic Republic of Congo, Haiti, Iraq, Kosovo, Liberia, Nepal,</mark> Sierra Leone, Solomon Islands, Sudan, Syria and Yemen.

<mark>When looking at foreign equity ownership restrictions, save for few exceptions, notably in Sub-Saharan Africa, the FCS economies covered tend to be among the world’s most open economies to foreign ownership, Côte d’Ivoire, Sierra Leone, Burundi, Chad, Afghanistan, Bosnia and Herzegovina, Haiti, Kosovo, the Republic of Yemen and</mark> 25
If existing behavior lies at the root of current global and local environmental and socioeconomic threats, then massive behavioral change is required to address these looming problems (World Climate Summit, 2010). Behavioral and social change, in addition or conjunction with technological change, is thus a crucial component of any green growth strategy. A better understanding of how and why people decide to do what they do as they consume, preserve, or exploit resources or otherwise make choices that collectively impact the environment has important and far-reaching consequences for the predictive accuracy of more sophisticated models, both of future states of the world and of the likely impact of different growth strategies and potential risk management strategies.

The prevailing characterization of human decision making in policy circles is a rational economic one. Rational economic men and women update their beliefs objectively based on new information and compute the costs and benefits of different actions, to select the action that provides optimal returns to them, with no limitations in information availability or processing capacity. Reliance on the assumptions of rational choice excludes from consideration a wide range of factors that affect how people make decisions and that therefore need to be considered in predictions of human reactions to environmental conditions or proposed policy initiatives. In addition, a more complete and more fully descriptive understanding of decision processes provide powerful tools for policy design that complement legal or economic instruments or may lead to more effective implementation of such policy instruments, helping people, groups, companies, and governments towards decision that are in their own long(er)-term interest as well as contributing to greater public welfare.

# **Typical vs. behavioral-economic taxonomy of green growth target behaviors**

Discussions of green growth initiatives typically divide the action space into resource sectors (with categories like agriculture, energy, and water) or into categories of action that can be taken by either policy makers (capacity building, education, climate change adaptation or mitigation) or members of the general public (transportation, housing, heating/cooling, or food decisions) that will lead to a more sustainable use of natural or social resources.

In contrast, psychology and behavioral economics approaches to green growth initiatives distinguish actions along more abstract dimensions, differentiating for example between interventions that target antecedents of decisions (e.g., commitment, information, or goal-setting) and those that target consequences of decisions (e.g., feedback, rewards) (Abrahamse et al., 2005). Other authors distinguish between promotion-focused actions that add positives (e.g., creating green energy technologies) vs. prevention-focused actions that remove negatives (e.g., reducing CO2 emissions) as different motivational systems are involved (Higgins, 2005), or between automatic behaviors (e.g., electricity use habits) vs. reflective and deliberate actions (e.g., the decision to switch to a green electricity 4
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