rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8689

# **Abstract**

Public financial management enables government to implement policy. Financial management information systems are a central element of PFM in that they facilitate government financial transactions and subject them to rigorous budgetary controls. Therefore, the adequate use of FMIS systems supports the implementation of fiscal rules and provides the basis for holding the executive accountable for implementing the budget. Yet, the integrity of expenditure transactions and effectiveness of budget controls is rarely assessed. This paper explores the meaning of adequate use of FMIS systems and points to data analytics techniques that can shed light on how the use FMIS systems affects expenditure data integrity and effectiveness of budget controls. This methodology is applied to Pakistan and Cambodia, which reveals two very different problems. In Pakistan the analysis of the transactions profile suggests that a large share of the budget is not subjected to FMIS internal controls. In Cambodia the majority of the budget is channeled through the system, but the method exposes very large advances to commercial bank accounts that are subsequently drawn upon but outside the system. Both of these raise concerns about the effectiveness of the FMIS system. Deploying data analytics techniques can help assess whether FMIS systems serve form or function. Judging a system by its use is informative about governments’ revealed preferences in expenditure management. This paper puts the spotlight on the foundation of expenditure data and advocates for a transparent, methodical and evidenced based approach to FMIS deployment and expenditure management reform.

This paper is a product of the Governance Global Practice. 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://www.worldbank.org/research. The authors may be contacted at mpiatti@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
> **Figure** 4.6

<!-- Start of picture text --> Shares of FDI Flows to East Asia for ASEAN and China<br>'o-<br>40-<br>30-<br>AC ~ ~~ ~ ~~~~~~2r~ ~<br>1,'1<br>1'0<br>10H<br>1990.95 1996 1997 1998 1999 2000 2001<br>COASEAN CICNnD<br><!-- End of picture text --> Source Compilted from UNCTAD, World Investment Renort 2002. Annex B 1 303-306 Over the course of this period, ASEAN's share of inflows to East Asia steadily declined from 38 percent (annual average for 1990-95) to a low of 8 percent m 2000 Thls recovered slightly in 2001 to 14 percent On the other hand, China's share of East Asian inflows rose from an average of 44 percent for 1990-95 to over half by 1998 Thereafter, it declined for two years, havmg lost share to Hong Kong (particularly m 2000), but rose to reach 49 percent mn 2001, close to its earlier peak ASEAN countries were able to easily attract FDI in the 1980s because of their relatively high degree of openness in a market comprismg fewer recipients than there are today In the 1 990s, competition for EDI increased markedly **5I** Furthernore, the cnsis of 1997-98 had a severe impact on flows to the ASEAN economies, but not on flows to China. Some countnes have been slow to recover from that shock The Philippines is the only ASEAN country in which 2001 mflows reached the peak levels of the 1990s.

> China's potential to attract large amounts of FDI has caused considerable concem among the ASEAN countries Its accession to the WTO has made it more attractive to TNCs worldwide and lower costs there have become a major reason for TNCs in industnal countnes to move production to China Malaysia is one country that has ,reportedly lost electronics-related inflows to China 52 In 2001, Malaysia's tnflows 'dropped to only $554 million after two years of inflows of nearly $4 billion (See Table S 10 **in** appendix ) This has been attributed to a loss of flows that have gone to China

> instead, particularly in electronics production, which m the past has played an important

> role in Malaysia's economy 53 Capital mvestment applications for electronmcs projects to be located **in** Malaysia dropped from 18 6 billion rmggit in 2000 to 7 3 billion nnggtt in

> 2001.'54 For China, foreign affiliates' share of exports in technology-intensive industnes

22
where the subscript, _−j_ , indicates all economies other than _j_ . As is the case for the normalized expressions (6) and (7), (8) ranges from 0 to 1, with lower values indicating greater dispersion. Table 6 reports the three concentration indices (6)–(8) for selected period averages, with growth polarity computed from the same four permutations in Table 3. It is clear that, regardless of the underlying polarity measure chosen, concentration has fallen over time. Importantly, this has occurred over the relatively short period spanned by the sample considered. This overall decline is well captured in Figure 2, which plots three alternative concentration measures corresponding to the benchmark aggregate growth polarity measure, computed from real GDP growth rates and excluding migration flows.

Table 6: Concentration indices, selected period averages, by real and PPP-adjusted GDP growth rates<sup>*</sup>

||_H_<sup>_∗_</sup>|_T _<sup>_∗_</sup>|_G_<sup>_∗_</sup>|_H_<sup>_∗_</sup>|_T _<sup>_∗_</sup>|_G_<sup>_∗_</sup>|
|---|---|---|---|---|---|---|
|||**Real**|_without _|_migration_|**PPP**||
|1984–88|0.00583|0.01284|0.12603|0.02287|0.04682|0.25557|
|1994–98|0.00504|0.01064|0.09900|0.01995|0.03733|0.20204|
|2004–08|0.00334|0.00751|0.08268|0.01640|0.03294|0.19090|
|||**Real**|||**PPP**||
||||_with mi_|_gration_|||
|1984–88|0.01667|0.02288|0.17114|0.03513|0.05369|0.27763|
|1994–98|0.01059|0.02088|0.14169|0.02682|0.04824|0.22984|
|2004–08|0.00569|0.01261|0.11994|0.01726|0.03574|0.21274|

* Authors’ calculations, using data from the World Development Indicators. Index generated from first principal component of (1)–(5), variously excluding and including (4). Real GDP and PPP-adjusted indicate growth rates calculated from GDP data in real 2000 U.S. dollars and constant 2005 international dollars. _†_ Migration flows are calculated for 1985, 1995, and 2005, respectively, the years where data are available.

# **5.2 Toward a multipolar world**

What do the changing polarities mean for the distribution of economic influence in the global economy as a whole? To the extent that growth polarity is an accurate measure of such influence, the results in the previous section suggest that multipolarity increased steadily through the end of the Cold War, fell during the final decade of the 20th century, and finally rose again in the first decade of the 21st century; indeed, over the past decade, the world has attained some of the most diverse distributions since 1968.<sup>22</sup>

> 22The sharp decline in the early 1970s deserves some comment. This fall is a function of several factors. Most crucially, the industrial economies underwent major recessions resulting from the first oil shock in 1973 (reinforced

23
**Table 9 Robustness tests**

|VARIABLES|I|II|III|IV|V|VI|VII|
|---|---|---|---|---|---|---|---|
|NetJobCr|-0.163<br>(0.084)*|-0.154<br>(0.082)*|-1.163<br>(0.366)***|-0.202<br>(0.081)**|-0.151<br>(0.084)*|-0.160<br>(0.083)*|-0.143<br>(0.084)*|
|Log(labor)|0.068|0.066||0.076|0.069|0.070|0.069|
||(0.005)***|(0.005)***||(0.005)***|(0.005)***|(0.006)***|(0.006)***|
|Age|0.001|0.001|0.002|0.001|0.001|0.001|0.001|
||(0.000)***|(0.000)***|(0.001)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Foreign|0.242|0.235|0.283|0.229|0.229|0.240|0.218|
||(0.017)***|(0.017)***|(0.032)***|(0.018)***|(0.017)***|(0.017)***|(0.018)***|
|ExternalFin|0.001<br>(0.000)***||||||0.001<br>(0.000)***|
|Innovate||0.096<br>(0.012)***|||||0.084<br>(0.011)***|
|Log(Wage)|||0.057<br>(0.016)***|||||
|NonProdWorke|r|||0.133<br>(0.023)***|||0.116<br>(0.023)***|
|Capital City|||||0.069<br>(0.016)***||0.051<br>(0.016)***|
|>1Billion|||||0.071||0.053|
||||||(0.028)**||(0.026)**|
|250K-1 Billion|||||0.026||0.019|
||||||(0.016)||(0.016)|
|50K-250K|||||0.000||-0.005|
||||||(0.012)||(0.012)|
|Multi-plant||||||0.003|-0.005|
|||||||(0.013)|(0.013)|
|Observations|13845|13823|1663|13659|13845|13834|13627|
|R2(p)|0.227|0.233|0.242|0.229|0.228|0.224|0.242|

Robust standard errors clustered by country, sector, year are in parentheses. In the regressions, I control for sector, survey year, and country fixed effects. (I) controls for use of external finance for investment, (II) controls for past innovative experience of the firm, (III) controls for log of wage rate, (IV) controls for ratio of non-production workers to all workers,(V) controls for population of the city that firm is located,(VI) controls for being part of a multi-plant enterprise, (VII) includes all controls. Coefficients for the probit regressions show the marginal effects at their mean values. *** p<0.01, ** p<0.05, * p<0.1.

27
analysis proceeds in three steps. In Section 5.1, we analyze how correlations between displacement status and political engagement change over time within Uganda. In Section 5.2, we use matching models (Ho, Imai, King & Stuart 2007) to grapple with non-random patterns of displacement and the context of displacement in the Philippines. Finally, in Section 5.3, we again use matching models to grapple with potential selection effects in Iraq.

# **5.1 Uganda**

We analyze two waves of repeated cross-sectional data in Acholi, Uganda. We plot the trends according to our survey data in Figure 8. We collect two measures of empowerment consistently across the two survey waves: Relationship with the Community and Contribute to Events in Community. As such, we restrict our analysis in Uganda to those variables.

We plot aggregate trends in these two measures across the survey waves in Figure 8. In general, the trend-line improved between the survey waves, while the number of respondents who reported ever being displaced declined modestly. In aggregate, respondents in Uganda consistently report strong perceptions of their relationships with their communities, limiting the variation available to understand how experiences of displacement are associated with these relationships. There is important variation in whether respondents feel they are able to contribute to events in their communities both within and across survey waves, however.

To explore how the relationship between individual experiences with displacement and empowerment vary over time, we run a set of four logistic regressions, which we plot in Figure 3b. The sampling is consistent between surveys, but we do not re-sample the same people (therefore it is not a panel), we therefore run separate models for the 2007 and 2010 data. In the top models, we use a binary indicator for whether respondents indicate that they have either “Good” or “Very Good” relationships with their communities as our dependent variable. In the bottom two models, we use a binary indicator for whether respondents agree with the statement that they are able to contribute to events in their communities. In all four models, the independent variable of interest is whether the respondent self-reports ever being displaced. Each model controls for Witnessing Violence, Mental Health, Sex, Age, Education, and Monthly Income.

The results are mixed. Results for the relationship with community are insignificant across both survey waves.<sup>16</sup> The results for perceptions of the ability to contribute to the community, however, are statistically significant but in different directions between waves. In 2007, experience with displacement is negatively correlated with respondent perceptions of being able to contribute to their 16As shown in Figure 8, there is limited variation in perceptions of relationships with the community within the Acholi region, and this overwhelming positive perception is stable across survey waves.

17
|res and determinants<br>dard deviation<br>3.15<br>3.10<br>3.04<br>13.23<br>1.30<br>2.17<br>0.95<br>28.44<br>0.12<br>41.22<br>0.48<br>0.18<br>0.40<br>0.07<br>0.06<br>0.04<br>2.03<br>sed in Table 8.|
|---|
|cipation measu<br>Median<br>Stan<br>iod 1999-2015<br>4.40<br>3.71<br>5.85<br>999-2015<br>20.64<br>11.44<br>23.42<br>-0.12<br>25.47<br>0.40<br>45.30<br>0.00<br>0.59<br>0.00<br>od 1999-2015<br>0.06<br>0.05<br>0.03<br>14.08<br>mating sample u|
|Table A5: Summary statistics on country-sector GVC parti<br>Number of observations<br>Average<br>Summary statistics based on country-sector current year in per<br>EORA backward GVC participation (log)<br>14387<br>4.57<br>EORA forward GVC participation (log)<br>14387<br>3.96<br>Exports (log)<br>14387<br>5.94<br>Summary statistics based on country 3-year lags in period 1<br>High-skilled labor share (%)<br>14387<br>22.02<br>Capital / labor (log)<br>14387<br>11.24<br>Rents from resources / GDP<br>14387<br>23.51<br>Rule of law index<br>14387<br>0.09<br>Internet use<br>8871<br>32.88<br>Summary statistics based on sector<br>Skill intensity<br>13319<br>0.37<br>Capital intensity<br>13319<br>60.50<br>Natural resource intensity<br>14387<br>0.37<br>Contract intensity<br>13319<br>0.58<br>Technology intensity<br>8243<br>0.20<br>Summary statistics based on country-sector 3-year lags in peri<br>Average output tari¤<br>14387<br>0.07<br>Average input tari¤<br>14387<br>0.06<br>Average market access tari¤<br>14387<br>0.04<br>Number of FDI projects<br>3743<br>13.80<br>Note: Summary statistics are based on the esti|

8
SYS-REVIEW/HI-LMIC

- (3) Controlled before-and-after studies or difference-in-differences; the pre- and postintervention periods for study and control groups should be the same, and the choice of the control site should be similar in terms of socioeconomic characteristics and/or should have no major differences in the baseline

- (4) Regression studies that consider the probability of selection into treatment through the instrumental variable method

- (5) Qualitative studies focused on exploring the impact of health insurance and meeting a checklist

However, no qualitative studies that explored the impact of insurance were found.

# **<<B>>** _Search Method_

A number of electronic databases<sup>1</sup> were searched using keywords related to health insurance, health care, and low- and middle-income countries. This search yielded 4756 references, including numerous duplicates and studies detailing general health issues in low- and middleincome countries. We filtered by titles and abstracts to reduce the number of relevant studies to 64. Of these, 35 were related to the impact of SHI on low- and middle-income countries. Inclusion criteria were met in 24 studies. Further examination found that five studies used poor identification strategies when measured against the standard methods recommended for impact evaluation studies (Imbens and Wooldridge 2009).

# **<<B>>** _Summarizing the Results_

It is difficult—and, more important, misleading—to aggregate the outcome measures that we found into some form of meta-analysis. This difficulty arises for three reasons: (1) many of the

> 1 Databases from the 1950s to September 2010 were searched: the Cochrane EPOC group Specialised Register and Library (3. 2010), MEDLINE, EMBASE, ECONLIT, ISI Web of Knowledge, CAB Abstracts, CENTRAL, DARE, ELDIS, and IDEAS as well as websites from the World Bank, the World Health Organization, and the U.S. National Bureau of Economic Research. Expert opinions and searches in key journals yielded findings of additional studies published before July 2011.

8
and stable prices of wheat and rice for citizen’s is to pay attention to farm productivity. This is among the most urgent policy focus for South Asian governments.

The scope for productivity improvements is clear from figure 19, but this can be seen more specifically from the productivity comparisons of the two major food crops, wheat and rice. The trends in productivity improvements in South Asia and global comparators for wheat and rice per hectare of land cultivated are shown in figures 20 and 21.

**Figure 20. Wheat Productivity Trends**

<!-- Start of picture text --> East Asia<br>2008<br>European Union<br>2000<br>North America 1990<br>1980<br>Pakistan<br>1970<br>India<br>0 1 2 3 4 5 6<br>MT/Hectare<br><!-- End of picture text --> _Source: USDA database_

**Figure 21. Rice Productivity Trend**

<!-- Start of picture text --> North africa<br>EAP<br>EEC<br>2008<br>North america<br>2000<br>Sri Lanka 1990<br>1980<br>Pakistan<br>1970<br>Nepal<br>India<br>Bangladesh<br>0 1 2 3 4 5 6 7<br>MT/Hectare<br><!-- End of picture text --> _Source: USDA database_ Focusing on land productivity is particularly important in South Asia where land endowment is likely to emerge as a binding constraint. Regarding wheat, the two major South Asian wheat producing countries (India and Pakistan) achieved substantial gains in productivity between 1970 and 2000, but faced stagnation since then. Productivity 28
**Figure III.17**

<!-- Start of picture text --> Short-Term Debt, June 1997<br>(as a percentage of total res rvss)<br>250<br>200<br>1SO<br>*r 100a - ____ ____<br>0<br>8<br>Source: Bank for Intematonal Settlements, IMF International Financial Statistics.<br><!-- End of picture text -->

**Growing Investor Concerns** Against this backdrop of growing vulnerability, developments in 1996 and early 1997 led to growing concerns on the part of investors. First, Thailand saw a sharp deterioration in export performance. After having averaged over 14% in real terms during 1990-95, Thailand's export growth turned negative in 1996. At the same time, Thailand saw its real exchange rate appreciate by about 10% during 1996. The bulk of this appreciation was due to an appreciation of the nominal effective exchange rate, which, in turn, reflected fluctuations between the US dollar (which Thailand was in effect targeting) and the currencies of Thailand's other trading partners, notably Japan. Using competitor weights, the appreciation in Thailand's real exchange rate was even larger at 15 percent. Moreover, the fact that the other East Asian countries also saw an appreciation in their real exchange rates after 1995 and a concurrent slowdown in the growth of their exports, led to investor concerns regarding the region's competitiveness generally.

Export growth-which had been very strong during the 1990s in the other South East Asian countries as well-decelerated sizably in 1996, albeit much less than in the case of Thailand. Malaysia saw a slowdown in export growth from an average of 14% per annum during 1990-95 to 5% in 1996, and Indonesia saw a slowdown from an average of 8% per annum to 5% (Figure III.18). In case of Korea, exports slumped in 1996--in large part due to a decline in terms of trade as a result of a glut in the global electronics markets that resulted in a sharp fall in prices, but were growing at a rate of 15% in mid-1997. And export receipts during the same period increased by about 20% in case of the Philippines.

43
- 30 -

obvious danger in this connection. However, it has to be recognized that simultaneous equation models have not so far yielded satisfactory econometric results.

The policy conclusions that can be drawn from the examination of cross-sectional studies are particularly unreliable as regards the role exercised by government through budgetary and regulatory policy. This unreliability is no doubt attributable simultaneously to lack of suitable indicators, government intervdntion, heterogeneity of the samples in terms of the countries' reactions to this intervention, and the diversity of the modes of transmission that can be envisaged.
Policy ReseaRch WoRking PaPeR 4787

# **Abstract**

This paper studies the relation between institutional investors and capital market development by analyzing unique data on monthly asset-level portfolio allocations of Chilean pension funds between 1995 and 2005. The results depict pension funds as large and important institutional investors that tend to hold a large amount of bank deposits, government paper, and short-term assets; buy and hold assets in their portfolios without actively trading them; hold similar portfolios at the asset-class level; simultaneously buy and sell similar assets; and follow momentum strategies when trading. Although pension funds may have contributed to the development of certain primary markets, these patterns do not seem fully consistent with the initial expectations that pension funds would be a dynamic force driving the overall development of capital markets. The results do not appear to be explained by regulatory restrictions. Instead, asset illiquidity and manger incentives might be behind the patterns illustrated in this paper.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to understand financial development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at craddatz@worldbank.org and sschmukler@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