rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3**

**Donor scores and rankings (by sub-index and overall, for 2007)**

|||||**Sub-**|**indexes**||||**Overal**|**l index**|**_Avg rank_**|**_N o. of_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Donor nam e**|**Select**|**ivity**|**Align**|**m ent**|**Harm on**|**ization**|**Speciali**|**zation**|||**_difference of_**|**_indicators_**<br>|
||**Score**|**Rank**|**Score**|**Rank**|**Score**|**Rank**|**Score**|**Rank**|**Score**|**Rank**|**_sub-indexes_**|**_w ith data_**|
|African Dev.Bank|0.505|11|-0.359|28|-0.193|26|1.095|5|0.310|13|14.0|15|
|Asian Dev.Bank|1.837|2|0.800|2|0.305|14|2.382|1|1.784|1|6.5|15|
|Australia|0.122|13|-0.042|22|0.708|5|0.253|8|0.348|12|9.3|18|
|Austria|-0.307|21|-0.654|33|-0.484|31|-0.317|28|-0.641|34|6.5|18|
|Belgium|0.104|14|0.161|17|-0.191|25|-0.416|29|-0.130|21|8.8|18|
|Canada|-0.357|23|-0.241|26|-0.416|29|-0.187|21|-0.421|29|4.5|18|
|Czech Republic|-0.727|28|-0.754|36|-1.150|36|-1.016|38|-1.283|37|5.0|14|
|Denmark|1.158|5|0.711|4|1.015|2|0.064|14|0.985|3|6.2|18|
|EBRD|0.960|6|-0.077|23|0.129|18|-0.555|34|0.083|16|14.8|15|
|EC|-0.612|26|-0.466|31|0.384|13|0.212|10|-0.160|23|12.7|17|
|Finland|0.035|17|0.447|9|0.574|9|-0.419|30|0.233|15|11.8|18|
|France|-0.908|33|0.180|15|-0.200|27|0.116|12|-0.226|24|12.5|18|
|GAVI Alliance|.|.|0.752|3|0.862|3|0.145|11|0.861|5|5.3|9|
|Germany|-0.452|24|0.407|11|0.205|17|-0.198|22|0.026|17|7.3|18|
|Global Fund|-0.652|27|0.213|14|-0.094|22|0.019|16|-0.137|22|7.5|15|
|Greece|-0.828|31|-0.535|32|-0.859|34|-0.602|35|-0.975|35|2.3|18|
|Hungary|-0.753|30|-1.623|38|-0.604|32|1.492|2|-0.558|31|18.3|17|
|IDB|0.007|18|0.044|20|0.263|16|0.873|6|0.412|9|7.3|15|
|IFAD|0.886|7|0.676|5|0.669|6|0.232|9|0.831|7|2.2|15|
|IMF|1.161|4|0.099|18|0.089|19|1.114|4|0.787|8|9.8|12|
|Ireland|0.099|15|1.453|1|1.145|1|-0.238|23|0.919|4|13.3|17|
|Italy|-0.486|25|-0.403|29|-0.159|23|-0.115|20|-0.406|28|4.8|18|
|Japan|-0.349|22|0.475|8|-0.894|35|-0.278|24|-0.333|27|13.8|18|
|Korea|-1.128|36|-0.035|21|-1.409|37|-0.768|36|-1.114|36|8.0|15|
|Luxembourg|0.675|9|-0.427|30|0.552|10|-0.517|33|0.024|18|15.3|18|
|Netherlands|1.332|3|0.655|6|0.605|8|-0.022|18|0.838|6|7.8|18|
|New Zealand|0.573|10|0.070|19|-0.459|30|-0.311|27|-0.090|20|11.3|18|
|Norway|0.380|12|0.387|12|0.280|15|-0.298|25|0.247|14|7.0|18|
|Poland|-1.098|35|0.161|16|-0.843|33|1.441|3|-0.046|19|18.8|14|
|Portugal|-1.018|34|-1.562|37|-2.208|38|-0.044|19|-1.718|38|10.0|17|
|Spain|0.086|16|-0.289|27|-0.066|21|-0.308|26|-0.233|25|6.3|18|
|Sweden|0.691|8|0.356|13|0.061|20|0.088|13|0.383|11|6.0|18|
|Switzerland|-0.063|19|-0.122|25|-0.187|24|-0.495|32|-0.317|26|6.7|18|
|Turkey|-1.447|37|-0.101|24|0.388|12|-0.887|37|-0.640|33|14.7|13|
|United Kingdom|-0.143|20|0.421|10|0.765|4|0.023|15|0.396|10|8.8|18|
|United Nations|-0.833|32|-0.700|34|0.621|7|-0.484|31|-0.479|30|13.7|15|
|United States|-0.751|29|-0.731|35|-0.331|28|-0.004|17|-0.634|32|9.2|18|
|W orld Bank<br>Note: Sub-index sc|2.301<br>ores are n|1<br>ormalize|0.600<br>d in stan|7<br>dard de|0.522<br>viation un|11<br>its with|0.623<br>a mean of|7<br>0.|1.291|2|5.0|15|

Source: Authors’ calculations
## IV. ESTIMATING OPENNESS-PERFORMANCE REIATIONSHIP

This section develops the estimating equation and reports the estimation results. In line with the existing empirica **l** iterature we use the augmented production function approach to estimate the openness-performance relationship for Sub-Saharan Africa. We use two estimation methods: the traditional cross-section estimation using period-average and period change data and the fixed-effect estimation using pooled annual data with country dummies to capture unobserved country-specific differences. The estimations are carried out for all Sub-Saharan African countries on which data is available i.e., 27 countries. The regressions on Sub-Saharan African are subjected to sensitivity tests by adding a macro-economic policy variable in the regression, as well as compared with results on non-Sub-Saharan Africa.

# Framework for Estlmaflon

We assume a country's production to be characterized by the following aggregate production function.

# Q = Q{(K L); Al (1)

where Q is real aggregate output and K and L denote capital and labor inputs respectively. WA" is the productivity or efficiency parameter which for the purpose of this study is assumed to be a function of the trade regine (TP) or the degree of openness. Thus Q = Q{(K,L);A(TP)l since A = **A(TP)** (2) _15_
# **Table 9: Impact of household welfare indicators and their variation during the 2008 food crisis on the**

**evolution of the CD4 counts of HIV/AIDS patients in Mozambique. OLS Regressions.**

|**VARIABLES**|**Variation of**<br>**cd4**|**Variation of**<br>**cd4**|<br>**Variation of**<br>**cd4**|**Variation of**<br>**cd4**|
|---|---|---|---|---|
|**Income variation**|0.0140**<br>(0.00688)||||
|**Income level in 2007**|0.0257**<br>(0.0116)||||
|**Consumption variation**||-0.00773<br>(0.0140)|||
|**Consumption level in 2007**||0.0151<br>(0.0200)|||
|**Food consumption variation**|||-0.0533||
||||(0.0354)||
|**Food consumption level in 2007**|||-0.000264<br>(0.0380)||
|**Weight-for-age z-score variation**||||6.022|
|||||(22.24)|
|**Weight-for-age z-score in 2007**||||10.81|
|||||(27.37)|
|**Constant**|-363.0|-334.8|-419.2|446.7|
||(368.1)|(356.1)|(348.9)|(484.6)|
|**Observations**|186|172|172|75|
|**R-squared**|0.302|0.276|0.306|0.416|

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Controls not shown but included in the regression: individual characteristics (age, sex, marital status, education), biological variables (months between the two CD4 counts, value of the lowest CD4 count, on-treatment indicator, time since initiated treatment), health facility and province indicators.

28
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure 3:  Population, Labor Force, Urban Population 1960-2002**

<!-- Start of picture text --> C ote d'Ivoire: Population and Labor Force<br>18<br>16 Labor force, total<br>Population, total<br>Urban population<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br>Ye a rs since inde pe nde nce<br>Millions<br><!-- End of picture text -->

**Finally, Cote d’Ivoire’s growth record also compares poorly with main comparator groups of countries, suggesting that country-specific rather than regional factors are behind this performance.** Using real per capita growth data for 1993-2002,  we compared Cote d’Ivoire’s growth with that of several other “peer groups” or benchmark sets of countries<sup>3</sup> . Cote d’Ivoire grew more slowly than developing countries worldwide, and slower than both sub-Saharan African countries and West African countries. Cote d’Ivoire also grew more slowly than most of the countries in the West CFA-franc zone, and slower than most countries in a set of African countries experiencing conflict during 1993-2002. Cote d’Ivoire’s poor long-term growth cannot be dismissed as “typical” of either developing countries or African countries, and is not explained by the recent conflict.

# **2. The Macroeconomic and Social Impact of the Crisis 2002-03**

**The brief 2002-03 civil conflict**<sup>**4**</sup> **came on top of the two decades of declining per capita real GDP and rising poverty.** As such, the conflict alone does not explain the secular economic decline since late 1970s; it aggravated the already unfavourable longterm economic trends. In this section, we briefly discuss the short term impact of the crisis, focusing mainly on the social consequences. After the 1994 devaluation, which triggered gains in competitiveness and four years of rising per capita income and modest gains in poverty reduction, in 1999-2000, the Ivorian economy was almost simultaneously hit by a large terms of trade shock and political turmoil. Real per capita

> 3 See the Annex for details.

> 4 For details, see World Bank (2003).

4
promise of a higher rate of progress against poverty. Attribution to reforms _per se_ is clearly problematic. However, revisiting our earlier findings with these new data offers at least a clue as to whether the reform process has accelerated, or decelerated, India’s progress against poverty. We also use these data revisit the results on the urban-rural composition of growth from Ravallion and Datt (1996) in the light of the extra 15 years of data for the post-reform period.

For the purpose of this study, we have derived a new and consistent time series of poverty measures for rural and urban India over the period 1951 to 2006. This is based on consumption distributions from 47 household surveys conducted by the National Sample Survey Organization (NSSO); beginning with the 3rd round for August to November 1951, we use distributions up to the 62nd round for 2005/06. This series significantly improves upon the most widely-used time series on poverty measures in India to date.<sup>21</sup> The pre-1991 data also differ in some respects to the data set we constructed in Ravallion and Datt (1996), as noted below.

Some of the early NSS rounds (in particular rounds 4 to 12) had survey periods that were considerably shorter than a year. We opted to aggregate some of these rounds to broadly conform to a year-long survey period. The estimates for rounds 4 and 5, 6 and 7, 9 and 10, and rounds 11 and 12 were thus pair-wise aggregated using the number of survey months covered by the round as weights. For instance, the headcount index for combined rounds 6 (for MaySeptember 1953) and 7 (for October 1953-March 1954) is 5/11-th of the headcount index for round 6 plus 6/11-th of the headcount index for round 7. Thus, with these combined rounds, for the full period from 1951 to 2006 our data set has 43 observations.

Following now well-established practice for India and elsewhere, a household's standard of living is measured by real consumption expenditure per person. The underlying NSS data do not include incomes, though it can be argued that current consumption is a better indicator of living standards than current income. Nonetheless, there are various "non-income" dimensions of well-being that this measure cannot hope to capture, and we say nothing here about how responsive these other dimensions may be to growth.

While the NSS surveys are highly comparable over time by international standards, there is a comparability problem in the rounds since the early 1990s. While most of the surveys have

> 21 Prior to Ravallion and Datt (1996), past work on poverty and growth in India had relied on poverty measures presented in Ahluwalia (1978) giving estimates of poverty measures for rural areas only for 12 rounds spanning 1956-57 to 1973-74. This was extended to add one round (1977-78) in Ahluwalia (1985).

10
**Table B1. Simulations – Introduction of FUNPI and sensitivity to international price (** **_ε=0.75_ )**

|**Policy**|**(I) Base**<br>**Scenario - no**<br>**FUNPI**|**(II) = (I) +**<br>**FUNPI**|**(III) = (II) +**<br>**max intn'l**<br>**price**|**(IV) = (II) +**<br>**average**<br>**intn'l**<br>**price**|
|---|---|---|---|---|
|_International Price (USD/ton)_|799.88|799.88|1350.00|950.00|
|_Trade taxes (USD/ton)_|93.50|199.88|199.88|199.88|
|_International Price net of taxes (USD/ton)_|706.38|600.00|1150.12|750.12|
|_Intermediate Price (USD/ton)_|515.67|426.66|891.45|552.42|
|_Farm-gate Price (USD/ton)_|430.82|351.00|769.62|463.83|
|_Exporters + cashew processors profit (USD/ton)_|75.72|67.20|119.29|81.85|
|_of which, exporters (USD/ton)_|72.29|61.78|114.00|76.54|
|_of which, cashew processors (USD/ton)_|352.13|441.14|780.73|534.76|
|_HHI exporters and cashew processors_|493|510|453|487|
|_Traders profit (USD/ton)_|41.45|34.66|69.21|44.23|
|_HHI traders_|256|256|256|256|
|_Quantity (tons)_|163255|140000|252265|172551|

**Table B2. Simulations – FUNPI and complementary** **<u>policies (</u>** **_ε=0.75_** **<u>)</u>**

|**Policy**<br>|**(I) Base**<br>**Scenario - no**<br>**FUNPI**|**(II) = (I) +**<br>**FUNPI**|**(V) = (I) + FUNPI +**<br>**10% reduction in**<br>**exporters costs**|**(VI) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in traders costs**|**(VII) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in exporters and**<br>**traders costs**<br>|**(VIII) = (I) + FUNPI**<br>**+ 46% reduction**<br>**in exporters and**<br>**traders costs**|**(IX) = (I) + FUNPI**<br>**+ 2 efficent**<br>**exporters**<br>**(**|**X) = (I) + FUNPI +**<br>**33 efficent**<br>**exporters**|
|---|---|---|---|---|---|---|---|---|
|_International Price (USD/ton)_|799.88|799.88|799.88|799.88|799.88|799.88|799.88|799.88|
|_Trade taxes (USD/ton)_|93.50|199.88|199.88|199.88|199.88|199.88|199.88|199.88|
|_International Price net of taxes (USD/ton)_|706.38|600.00|600.00|600.00|600.00|600.00|600.00|600.00|
|_Intermediate Price (USD/ton)_|515.67|426.66|439.55|425.95|438.89|487.40|444.35|515.87|
|_Farm-gate Price (USD/ton)_|430.82|351.00|362.54|355.49|367.15|430.85|366.85|430.99|
|_Exporters + cashew processors profit (USD/ton)_|75.72|67.20|63.83|67.53|64.14|50.59|61.83|25.46|
|_of which, exporters (USD/ton)_|72.29|61.78|58.68|62.16|59.03|46.50|56.76|21.41|
|_of which, cashew processors (USD/ton)_|352.13|441.14|428.25|441.85|428.91|380.40|423.45|351.93|
|_HHI exporters and cashew processors_|493|510|489|512|490|406|559|269|
|_Traders profit (USD/ton)_|41.45|34.66|35.65|33.44|34.40|33.00|36.02|41.47|
|_HHI traders_|256|256|256|256|256|256|256|256|
|_Quantity (tons)_|163255|140000|143439|141340|144803|163265|144714|163305|

39
# **1.  Introduction**

Debt relief has formed a highly-visible part of aid to low-income developing countries over the past decade. In 1996 the World Bank and International Monetary Fund (IMF) launched the Heavily-Indebted Poor Countries (HIPC) Initiative which has over the past decade provided $59 billion in nominal debt service reductions to a set of 30 eligible countries, mostly in Africa, on public debt owed to bilateral and multilateral creditors. In 2005 the G8 governments agreed to pay off the remaining debts owed to IDA (the International Development Agency, or soft-loan window of the World Bank) by 19 countries that have "graduated" from the HIPC debt relief process, resulting in a further reduction of their total debt outstanding by $37 billion. Prior to these high-profile efforts, the Paris Club group of bilateral official creditors has been providing concessional restructurings to low-income countries since the late 1980s. The accompanying rhetoric has been as expansive as the large sums of money involved. UN economic adviser Jeffrey Sachs has argued that "no civilised nation should try to collect the debts of people who are dying of hunger and disease and poverty".<sup>1</sup> World Bank president Paul Wolfowitz heralded the latest round of debt relief with the statement that "leaders in 38 countries around the world will no longer have to choose between spending to benefit their people and repaying impossible debts".<sup>2</sup> Despite its prominence, surprisingly little is known about the causes or consequences of debt relief. As we describe in more detail in Section 2 below, a basic problem is that existing data on debt relief from standard data sources do not provide reliable estimates of the value of debt relief. In order to fill this gap, we have in a companion paper (Depetris and Kraay 2005) developed a new set of estimates of debt relief, and we are refining these further in ongoing work. The absence of reliable data has also limited our understanding of the effects of debt relief. In Depetris and Kraay (2005) we used these new estimates of debt relief to perform a systematic analysis of the effects of debt relief in low-income countries, and found little evidence of its purported benefits.<sup>3</sup>

> 1 Financial Times, July 6, 2004.

> 2 Speech to 2005 Annual Meetings of World Bank and International Monetary Fund.

> 3 See also Easterly (2002) and Arslanalp and Henry (2004, 2005) for discussions of HIPC debt relief.

1
|Country|1961-65|1966-70|1971-75|1976-80|1981-85|1986-90|1991-93|
|---|---|---|---|---|---|---|---|
|Netherlands|x|x|x|x|x|x|x|
|Niger||x|x|x|x|x||
|Norway|x|x|x|x|x|x|x|
|Pakistan|x|x|x|x|x|x|x|
|Paraguay|x|x|x|x|x|x|x|
|Peru|x|x|x|x|x|x|x|
|Philippines|x|x|x|x|x|x|x|
|Portugal||x|x|x|x|x|x|
|Rwanda||x|x|x|x|x|x|
|Senegal||x|x|x|x|x|x|
|Singapore||||x|x|x|x|
|South Africa|x|x|x|x|x|x||
|Spain|x|x|x|x|x|x|x|
|SriLanka|x|x|x|x|x|x|x|
|Sudan||||x|x|x||
|Swaziland||||x|x|x||
|Sweden|x|x|x|x|x|x|x|
|Switzerland|x|x|x|x|x|x|x|
|Thailand|x|x|x|x|x|x|x|
|Togo|||x|x|x|x|x|
|Trinidad and Tobago||||x|x|x|x|
|Tunisia|x|x|x|x|x|x|x|
|Turkey||x|x|x|x|x|x|
|United Kingdom|x|x|x|x|x|x|x|
|United States|x|x|x|x|x|x|x|
|Uruguay|x|x|x|x|x|x|x|
|Venezuela|||x|x|x|x|x|
|Zaire|x|x|x|x|x|x*||
|Zambia||x|x|x|x|x|x|
|Zimbabwe|||||x|x|x|

Note: * indicates that observations are available when investment data are not included.

# A. Data sources

1. Data on the level and growti rate of per capita real GDP is calculated from the World Bank's real GDP and population data.

2. Data on total real imports and exports come from the World Bank.

3. Data on the ratio of real government consumption to GDP come from the World Bank.

4. Inflation rates are calculated using the IFS's CPI data.

5. Data on M2 and CPI come from IFS. Statistic is [M2/(end of year CPI)]/[GDP/(average year CPI)] 30
**Figure 1.1. Trends of Consumption per capita and Gini Coefficients, RLMS 1994-2015**

<!-- Start of picture text --> 9.5 .5<br>9 .45<br>8.5 .4<br>8 .35<br>Total consumption per capita<br>Ginni for consumption per capita<br>7.5 .3<br>Gini coefficient<br>Log of consumption<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are based on total household consumption per capita using the repeated cross sections for each year. Household consumption is the total of non-durable consumption, including food, alcoholic and nonalcoholic beverages, tobacco products, clothing and footwear, gasoline and other fuel, rents and utilities, services (such as transportation, repair, health care services, education, entertainment, etc. without lumpy expenditures on housing construction, refurbishment and ritual services) and durable consumption. All consumption measures are converted to a monthly base. To keep the consumption variable consistent over time, we exclude expenditure categories that became available after 1994. All numbers are deflated with December to December regional CPIs and weighted with population weights.

60
By mapping activities depending on their climate transition risk, and by matching firms’ climaterelevant non-financial information into the financial information about the financial instrument of the firm (e.g., equity holdings, bonds, loans), the CPRS allows the quantification of transition risk exposure of the portfolios of investors’ portfolios, and the computation of scenario-adjusted risk measures.

The CPRS methodology is replicable and comparable across portfolios and jurisdictions, and is available at different levels of disaggregation, depending on the level of disaggregation of information about the energy technology profile of the activity that is relevant for the transition (i.e., the type of energy or electricity that the activity uses, for instance, electricity production out of coal). At their most aggregate level, we obtain CPRS-main, which include CPRS1-fossil fuels, CPRS2-utility, CPRS3-energy efficiency, CPRS4-housing, CPRS5-transport, CPRS6-agriculture.

The CPRS classification is open access<sup>31</sup> and is recognized as a best practice from European financial authorities,<sup>32</sup> which applied them to assess investors’ portfolios exposure to climate transition risk. It has recently been updated into the CPRS-granular, considering firms’ revenues from energy technologies, and mapped into the IAM variables used by the NGFS scenarios. Finally, the CPRS is it is also fully compatible with the EU Taxonomy for the share of green of activities of firms, thus allowing for the consideration of firms with multiple business lines (e.g., energy and utility companies).

The importance of using standardized, science-based taxonomies for “green” and “high-carbon” activities is particularly relevant for central banks. Indeed, it would allow them to (i) compare their current portfolios exposures to those under low-carbon investment strategies; (ii) assess the impact of greening financial portfolios in terms of the composition of the real economy, prices and distributive effects; (iii) assign risk weights to assets, depending on whether they are high or lowcarbon; and (iv) calculate the investment risk profile in a forward-looking way, across climate mitigation scenarios (e.g. the NGFS ones), thus affecting the eligibility criteria.

# **3.3.2 Enabling conditions for the implementation of GFSI**

We can identify the following set of general enabling conditions that would foster the effectiveness of all GFSI.

> 31 The CPRS classification can be downloaded at: https://www.finexus.uzh.ch/en/projects/CPRS.html

> 32 These include the European Banking Authority, The European Insurance and Occupational Pension Authority, the European Securities and Markets Authority, the European Central Bank, the Swiss National Bank and the Austrian National Bank. In the context of EMDEs, CPRS have been already applied by the Caribbean Development Bank to its project portfolio in the Caribbean region, by Monasterolo et al. (2018) to the energy infrastructure projects of the China Development Bank and Import-Export Bank of China outside the Chinese borders, and by Roncoroni et al. (2021) in the climate stress test of Mexico.

20
# **Table 5. Shapley Decomposition of Circumstances for Opportunities**

(Including ―ethnicity and conflict variables‖ as proxies for impact of civil conflict)

|Inequalitydecomposition|Electricity<br>(%)|Sanitation<br>(%)|Clean<br>water(%)|Attendance<br>(6–11 years)<br>(%)|Attendance<br>(12–15<br>years)<br>(%)|Start<br>primary<br>school on<br>time(%)|Finish sixth<br>grade<br>(%)|
|---|---|---|---|---|---|---|---|
|Urban rural/region|63|58|47|26|24|30|31|
|Gender of the child|1|1|0|7|16|2|10|
|Gender, age, and education of the<br>head|18|18|25|30|22|33|27|
|Children and presence of elderly|3|1|14|3|2|3|6|
|Household income quintile|11|12|7|9|7|11|10|
|Violence, displacement, and loss<br>during crises|0|0|1|2|3|2|1|
|Ethnicity|5|10|9|24|26|19|15|
|Total<br>_Source:_Household survey 2008; authors’ c<br>_Note_: Conflict-relatedquestions were availa|100<br>alculation.<br>ble onlyfor the 20|100<br>08 survey.|100|100|100|100|100|

# **Box 5. Shapley Decomposition: Identifying How Each Circumstance “Contributes” to Inequality**

Following Barros et al. (2009), the inequality of opportunities can be measured by the penalty ( _P_ ) or by the dissimilarity index ( _D)._ The value of these two measures, where _P_ is just a scalar transformation of _D_ , is dependent on the set of circumstances considered. Moreover, these measures have the important property that adding more circumstances always increases the values of _P_ and _D_ . If there are two sets of circumstances _A_ and _B_ , and set _A_ and _B_ do not overlap, then , and alternatively . The impact of adding a circumstance _A_ is given by: Where _N_ is the set of all circumstances, which includes _n_ circumstances in total; _S_ is a subset of _N_ that does not contain the particular circumstance _A_ . _D_ ( _S_ ) is the dissimilarity index estimated with the set of circumstances _S_ . is the dissimilarity index calculated with set of circumstances _S_ and the circumstance _A_ .

One can define the contribution of circumstance _A_ to the dissimilarity index as: _Source:_ Adapted from World Bank African Report on the HOI (forthcoming) 24
# **Mobile Money and Investment by Women Businesses in Sub-Saharan Africa**

Asif Islam<sup>1</sup> and Silvia Muzi<sup>2</sup> JEL: J16, E22, G2, L25, O16 Keywords: gender, mobile money, financial development, investment, firms, Africa

> 1 Office of the MENA Chief Economist, MNACE, World Bank, Washington DC. Email: aislam@worldbank.org

> 2 Enterprise Analysis Unit, DECIG, World Bank, Washington DC. Email: smuzi@worldbank.org