rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8312

# **Abstract**

Financial management information systems are a sine qua non in public financial management and play a foundational role in the execution of the budget. Recognizing their potential contribution to fiscal discipline, the strategic allocation of resources, and operational efficiency, significant time and resources have been invested by the World Bank and other development institutions into such systems across the world. However, the reform of financial management information systems tends to be complex, and the evidence base of causal effects and mechanisms is thin. This study develops a framework that outlines the various steps involved in reform that illustrate how change is expected to happen. Three major dimensions were identified: (1) diagnostic phase, (2) systems development lifecycle, and (3) coverage and utilization. The paper argues that reaching the financial management information systems production frontier requires optimization across these dimensions, and that a programmatically coherent approach is required to realize fully the expected improvements in budget management. The study identifies a set of lessons on the various stages that are mapped against the framework by triangulating findings from a systematic review of the financial management information systems literature, field-based project-level evaluations and protocol based case studies, and a comprehensive desk review of the World Bank financial management information systems project documentation.

This paper is a product of the Independent Evaluation Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at moritzpiatti@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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
# ANNEX

## DATA

For the cross-country, sector-level comparative analysis of manufacturing output, the INDSTAT 4 2009 Revision 2<sup>23</sup> and INDSTAT 4 2012 Revision 3<sup>24</sup> datasets from the United Nations Industrial Development Organization (UNIDO) are used. The two UNIDO datasets were combined to create a database representing 84 sectors (4-digit NACE)<sup>25</sup> from 134 countries for the time-period 1977 to 2009. The analysis is supplemented with data from the World Development Indicators,<sup>26</sup> and the World Bank’s _Enterprise Surveys._ The list of countries included in the UNIDO dataset, the average length of the panel per country, the number of observations per country and the number of sectors included in the data is listed in Table A1, below.

### **Table A1: Panel Statistics**

|**COUNTRYNAME**|**AVERAGELENGTH**<br>**OF THEPANEL**|**NO. OFOBS. **|**NUMBER OF**<br>**SECTORS**|
|---|---|---|---|
|**Afghanistan**|6.8|31|4|
|**Albania**|4.7|332|63|
|**Algeria**|0.0|40|40|
|**Argentina**|10.0|339|81|
|**Armenia**|10.3|685|62|
|**Aruba**||0||
|**Australia**|21.6|1539|77|
|**Austria**|22.2|1584|76|
|**Azerbaijan**|15.5|1192|76|
|**Bahamas**|7.7|194|38|
|**Bahrain**|0.0|39|39|
|**Bangladesh**|14.0|911|78|
|**Belarus**|1.0|14|7|
|**Belgium**|14.3|932|68|

> 23 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=8&Lg=1</u>

> 24 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2&Lg=1</u>

25 NACE is the acronym used to designate the various statistical classifications of economic activities developed since 1970 in the European Union (EU). NACE provides the framework for collecting and presenting a large range of statistical data according to economic activity in the fields of economic statistics (e.g. production, employment, national accounts) and in other statistical domains. This classification was designed to delineate broad economic categories, into large economic classes of commodities, distinguishing food, industrial supplies, capital equipment, consumer durables and consumer non-durables. It is broadly used to stand for sectors. The higher the number of digits for the NACE, the more detailed the sector; from the most aggregate to the least, the classifications are organized by Section, Division, Group and finally Class. The analysis here is at the 4-digit NACE level; namely at the Group level. For more information, see <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27&Lg=1.</u>

> 26 <u>http://data.worldbank.org/data-catalog/world-development-indicators</u>

15
**Meyer, Klaus** . 1998. Direct investment in economies in transition.” New Horizons in International Business series. Cheltenham, U.K. and Northampton, Mass.: Elgar.

**Miller, Jeffrey.** 2006. “Evaluation of Mass Privatization in Bulgaria.” William Davidson Institute Working Paper 814.

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**Munich, Daniel, Jan Svejnar, and Katherine Terrell.** 2005. “Returns to Human Capital under the Communist Wage Grid and During the Transition to a Market Economy.” _The Review of Economics and Statistics,_ 87(1): 100-23.

**Orazem, Peter F., and Milan Vodopivec.** 2004. “Do Market Pressures Induce Economic Efficiency? The Case of Slovenian Manufacturing, 1994-2001.” World Bank Policy Research Working Paper 3189.

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**Pivovarsky, Alexander.** 2001. “How Does Privatization Work?: Ownership Concentration and Enterprise Performance in Ukraine.” IMF Working Paper 01/42.

**Pivovarsky, Alexander.** 2003. “Ownership Concentration and Performance in Ukraine’s Privatized Enterprises.” _IMF Staff Papers_ , 50(1): 10-42.

**Plane, Patrick.** 1997. “Privatization and Economic Growth: An Empirical Investigation from a Sample of Developing Market Economies.” _Applied Economics_ , 29(2): 161-78.

**Roland, Gerard.** 1994. “On the Speed and Sequencing of Privatization and Restructuring.” _The Economic Journal_ , 104(426): 1158-68.

**Sabirianova Klara, Jan Svejnar, and Katherine Terrell.** 2005. “Foreign Investment, Corporate Ownership, and Development: Are Firms in Emerging Markets Catching Up to the World Standard?” William Davidson Institute Working 734.

**Salis, Sergio.** 2006. “Evaluating the Causal Effect of Foreign Acquisition on Domestic Performances: The Case of Slovenian Manufacturing Firms.” William Davidson Institute Working Paper 803.

38
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The second set of data, the Targeting data set _,_ is also a census-like data set for refugees living in Chad. The main aims of this census are to fill knowledge gaps on refugee livelihoods and the levels and differences of vulnerability in refugee households, and to categorize refugees into wealth groups for cash, food, and livelihood assistance. In addition to categorizing refugees, the Targeting data set aims to identify factors that can enable self-reliance. This data set is based on a mixed methods approach, including qualitative and quantitative methods. The first step is the use of focus groups with refugee leaders, women’s organizations, and youth associations, to identify the wealth characteristics and key challenges specific to age and gender. Next, a sample survey is carried out across camps to confirm the wealth characteristics that were identified by refugees in the first step. Based on the outcomes of the first two steps, a quantitative survey that is designed to capture wealth characteristics is administered to all the refugee households. <mark>The T</mark> argeting data set <mark>includes all Sudanese, Central African, and Nigerian refugees living in Chad. The data were collected between June 17, 2017 and July 15, 2017 and cover 19 refugee sites and refugees living in nine host villages</mark> **<mark>.</mark>** After the data collection, a statistical model, which takes into account household welfare, is used to classify households into four socioeconomic groups (very poor, poor, average, and better off). For the variables that are relevant for this study, this data set contains demographic variables (household size, gender, age, country of origin, and region of residence), variables for asset and animal ownership, and variables reflecting coping strategies. As for the ProGres data set, the Targeting data set does not contain information on consumption or expenditure. However, it does contain information on wealth.

The last set of data is the Post-Distribution Monitoring (PDM) data set, which is from a sample survey that covers similar themes as the Targeting data set. The PDM data set, which was collected in 2017 by the WFP, aims to provide better understanding of how refugees use food assistance and contains data on consumption and expenditure. The sampling design is a two-stage stratified random sample, where the first stage includes the selection of camps and the second stage the selection of households. Beforehand, the different camps are stratified in three zones: <mark>(i) North East (Ourecassoni, Amnaback, Iridimi Touloum), (ii) Centre-East (Goz Amir, Djabal, Gaga, Teguine, Bredjing, Farchana), and (iii) South (Amboko, Dossey, Gondjé, Belom, Moyo) (Map 1).</mark> In addition, the sampling takes into account the kind of humanitarian assistance that is provided to refugees (in-kind, food voucher, or cash). The survey includes two <mark>consumption aggregates</mark> 8
**Table 3: Migration Patterns by Education and Age**

|||Males<br>|||Females<br>|||Total<br>||
|---|---|---|---|---|---|---|---|---|---|
||Stayers in<br>Mexico|Movers<br>within<br>Mexico|Recent<br>migrants<br>to the US|Stayers in<br>Mexico|Movers<br>within<br>Mexico|Recent<br>migrants<br>to the US|Stayers in<br>Mexico|Movers<br>within<br>Mexico|Recent<br>migrants<br>to the US|
|Schooling (%)||||||||||
|No schooling|14.1|10.5|8.0|16.0|11.0|9.6|15.1|10.7|8.6|
|1 to 4|27.9|19.7|5.3|26.8|19.5|5.6|27.3|19.6|5.4|
|5 to 8|28.9|25.5|32.1|29.2|28.2|29.6|29.1|26.9|31.1|
|9|12.9|16.4|12.4|12.3|16.6|11.3|12.6|16.5|11.9|
|10 to 11|4.0|5.1|7.3|3.9|5.2|6.2|3.9|5.1|6.9|
|less than 12|87.8|77.2|65.1|88.2|80.5|62.2|88.0|78.9|64.0|
|12|5.2|8.7|24.6|6.0|9.5|24.8|5.6|9.1|24.7|
|13 to 15|2.4|4.2|6.1|2.4|3.7|7.4|2.4|3.9|6.6|
|more than 15|4.6|9.9|4.2|3.4|6.3|5.6|4.0|8.0|4.7|
|Age|31.2|28.6|28.3|32.2|27.8|30.7|31.7|28.2|29.2|
|N|4,008,334|159,712|694,206|4,288,508|172,192|461,990|8,296,842|331,904|1,156,196|

**Source:** For migrants to the US, the 2000 US Census and within Mexico, the 2000 Mexican Census.

29
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE each account for a further one-quarter of current capital expenditure. Power and transport capture almost 90 percent of the resources allocated to operating expenditures. The figures for ICT operations likely changed significantly in 2007 with the privatization of Kenya Telekom. Private participation in ICT accounts for almost 90 percent of all private participation in the country’s infrastructure; in power, private participation is conspicuously small.

# **Table 11. Financial flows to Kenyan infrastructure, average 2001 to 2006**

<u>US$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|Information and communication technologies|44|36|0|0|449|485|529|
|Irrigation|2|0|0|0|0|0|3|
|Power (trade)|274|125|59|0|13|197|471|
|Transport (basic)|242|84|114|11|22|232|474|
|Water supply and sanitation|12|34|97|2|23|155|167|
|Total|575|278|271|13|507|1,069|1,644|

Source: Derived from Foster and Briceño-Garmendia 2009.

O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

Kenya’s current spending on infrastructure spending is also quite substantial as a share of GDP. Kenya spends 9 percent of its GDP on infrastructure, about the same as other nonfragile low-income countries (figure 13). Although this represents a substantial effort, it translates into just $22 per capita per year in infrastructure spending. And although Kenya invests as much in infrastructure as other lowincome countries in Africa, the pattern of its investments is different. Kenya’s public sector invests substantially more in power but less in ICT, water, and sanitation than its African peers. For power, transport, and WSS, the pattern of finance is similar to the peer group, with a mix of public and donor finance. In the case of ICT, however, there are notable differences. Kenya relies more heavily on the private sector for ICT investments than does the peer group (figure 14).

24
Table 2. Descriptive Statistics: Control Variables

||N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|_Panel A. Individual Controls (in Afghanistan)_||||||
|Age|5,592|33.65|15.66|14.00|98.00|
|Male [=1]|5,592|0.49|0.50|0.00|1.00|
|Student before migrating [=1]|5,592|0.31|0.46|0.00|1.00|
|<br>Employed [=1]|5,592|0.34|0.47|0.00|1.00|
|Friends in Tajikistan(before moving) [=1]|5,592|0.44|0.50|0.00|1.00|
|_Panel B. Household Variables (in Afghanistan)_||||||
|Household size|5,592|6.70|2.61|1.00|25.00|
|Own an enterprise in Afghanistan [=1]|5,592|0.05|0.22|0.00|1.00|
|<br>Own a dwelling [=1]|5,592|0.53|0.50|0.00|1.00|
|Dwelling contract: own it [=1]|5,592|0.01|0.09|0.00|1.00|
|Dwelling contract: pay rent or lease [=1]|5,592|0.98|0.13|0.00|1.00|
|Dwelling contract: consent with the owner [=1]|5,592|0.01|0.10|0.00|1.00|
|Dwelling: individual apartment or house [=1]|5,592|0.92|0.27|0.00|1.00|
|Dwelling: shared apartment or house [=1]|5,592|0.08|0.27|0.00|1.00|
|<br>Dwelling: temporary shelter, shack or tent [=1]|5,592|0.00|0.04|0.00|1.00|
|<br>Income from wage labor (Somoni)|5,592|29,628|73,163|0.00|1,555,000|
|<br>Income from self-employment (Somoni)|5,592|16,779|47,019|0.00|800,000|
|<br>Income from livestock or crops (Somoni)|5,592|1,593|16,205|0.00|300,000|
|Income from handcrafted products (Somoni)|5,592|317|2,452|0.00|60,000|
|<br>Income from remittances (Somoni)|5,592|2,711|13,513|0.00|350,000|
|Income from a loan (Somoni)|5,592|20,353|516,832|0.00|20,000,000|
|Number of refrigerators in the HH.|5,592|1.06|0.57|0.00|11.00|
|<br>Number of washing machine in the HH.|5,592|1.03|0.55|0.00|10.00|
|<br>Number of AC in the HH.|5,592|0.62|1.20|0.00|40.00|
|Number of fan in the HH.|5,592|1.63|1.25|0.00|10.00|
|Number of stoves in the HH.|5,592|1.46|1.13|0.00|15.00|
|Number of bread oven in the HH.|5,592|0.78|0.64|0.00|8.00|
|Number of gas heaters in the HH.|5,592|0.78|0.93|0.00|19.00|
|<br>Number of microwaves in the HH.|5,592|0.38|0.59|0.00|8.00|
|Number of radios in the HH.|5,592|0.48|0.80|0.00|22.00|
|Number of TVs in the HH.|5,592|1.66|1.00|0.00|15.00|
|Number of computers in the HH.|5,592|1.07|0.94|0.00|12.00|
|Number of bicycles in the HH.|5,592|0.80|0.91|0.00|6.00|
|<br>Number of motorbikes in the HH.|5,592|0.12|0.36|0.00|2.00|
|Number of cars and other vehicles in the HH.|5,592|0.62|1.06|0.00|44.00|
|Number of mobile phone in the HH.|5,592|3.12|1.96|0.00|50.00|
|<br>Sqm plot of land in the HH.|5,592|12.26|387|0.00|20,000.00|
|<br>Number of livestock or farm equipment in the HH.|5,592|4.37|83.28|0.00|2,000.00|
|<br>Number of place of business in the HH.|5,592|0.06|0.31|0.00|4.00|
|<br>Number of business assets in the HH.|5,592|0.07|0.78|0.00|30.00|
country. The prevalence rates at the individual and household (HH) levels are calculated using the formulas: the formulas: P P P =<sup>N</sup> where c denotes a country. cc<sup>NPoPPaaNNPPrraa wrrh ooNNnnccrrwwoonnPaawwowwccNNPPrrddcc</sup> PPnnccPP P rrPP cc TToorrP ppooppNNP rrwwoonn H P P P =<sup>N</sup> cc PPnnccPP P rrPP N N P o<sup>NPoHHooNNaaPPhooPPawrrh PPrr PPPaarr oonnPP PPaaNNPPrr wrrh ooNNnnccrrwwoonnPaawwowwccNNPPrrddcc</sup> cc hooNNaaPPhooPPa

# **2.4 Human Development Indicators**

We compare human development indicators across groups by functional difficulty status to establish the size of the gap that may be associated with disability, i.e. the disability gap or inequalities associated with disability. Results are presented in graphs and tables, often in appendix tables. Double disaggregation tables by disability and a demographic characteristic (sex, rural/urban, age group) are available from the authors upon request (Appendices A to C). In tables, the disability gap and its statistical significance is typically noted in a separate column. Statistical significance is based on a t-test (*, **, and *** at the 10%, 5% and 1% levels respectively). As indicators reflect achievements (e.g. employment population ratios, asset ownership) and deprivations (unemployment rate, food insecurity, exposure to shock), positive/negative differences will not consistently reflect a disability gap in achievement/deprivation indicators, respectively. We also use and graph the ratio for a given indicator by dividing the indicator for persons with disability with the indicator for persons with no disability. This is often referred to as a relative rate or as a parity index (e.g. UNESCO 2018, OECD 2003). For instance, for the relative employment rate, a relative employment rate at or near one reflects a similar situation of persons with and without disabilities in terms of the share who are employed.

The indicators under study are listed in Table 2.2. To compile this list of indicators, we started from the list of SDG indicators of the Inter-secretariat Working Group on Household Surveys (2018) in a Report to the 49th Session of the UN Statistical Commission (doc CN.3/2018/7) which showed that approximately one third of the SDG indicators are or can be compiled from household surveys. From this list of indicators, we selected those indicators that might be found in general household surveys or censuses, since health or other topic specific surveys are beyond the scope of this study. We selected and were able to produce estimates for those in Table 2.2 (e.g. unemployment rate, out of school rates for children in the household).

We then added indicators that are known to be particularly suited to assess the situation of persons with disabilities and could be estimated with some of the data sets above: employment population ratio, economic insecurity (food insecurity, exposure to shocks) and indicators that may reflect the extra costs of living with disabilities for households (poverty status once health expenditures are removed from total consumption expenditures), as well as material well-being 9
the period 1992-96. We can thus pool the data across countries. This increases the number of observations and therefore the power of our tests. We specify the following one-factor fixed effects model with ownership dummies to explain differences in risk-taking behaviour among banks and across countries where g denotes one of the five countries, _Rfg_ is our risk-taking estimate for bankf in country g, _Dfg_ are ownership dummies, and _Efg_ is normally distributed with mean zero and variance _a2_ Instead of an overall constant, model (9) has a country effect for each of the five countries _(ag)._ As explanatory variables we include the market share of bank assets in 19926, the growth in the ratio of loan loss provisioning to loans during the period 1992-96 and the growth in short-term borrowing during the period 1992-96. The market share of banks assets in 1992 is included as an initial condition to control for a possible size effect. It is expected that small banks are likely to take more risk than large banks in their efforts to become big as well. The growth in the ratio of loan loss provisioning to loans is included to correct for a possible discrimination by the regulator of banks in imposing provisioning rules. The growth in short-term borrowing is included because high short-term debt was a key factor behind the financial difficulties of many banks, and is therefore expected to be positively linked to bank risk-taking. To avoid multicollinearity we will suppress one of the ownership dummies.

# **6. Empirical Findings**

The DEA efficiency results are obtained from applying model (1)-(5) to estimate a 'grand frontier'. As noted before, the 'grand frontier' approach implicitly assumes that there are no changes in technology, regulation, and market environment during the period 1992-96 that affect bank efficiency. Since the sample of banks is large enough not to put too many banks on the efficiency frontier, we decide to use the VRS results.

For the period 1992-96, our DEA efficiency estimates (see Appendix, Table 5) show that for all five East Asian countries bank efficiency did not decrease significantly. For Indonesia, the Philippines and Thailand we see a substantial increase in efficiency, and Korea and Malaysia

> _6_ The asset market share of bankf in countryj is defined as the amount of assets of bankf in 1992 divided by the total amount of assets of all banks in countryj in 1992.

17