rafmacalaba/datause-encoder-data huggingface dataset:
# **Overview**

**What is an FMIS and what is its purpose?** A government’s capacity to manage its public finances is central to its ability to deliver services. Financial management information systems (FMIS) are among the basics that facilitate this as they “support management of public sector budgetary, accounting, treasury, and public debt management processes as well as generate corresponding reporting documents” (Uña and Pimenta 2015, p.282). FMIS systems are a set of automation solutions that allow government finance and accounting staff to carry out their day-to-day operational tasks. This enables them to plan, prepare and approve budgets, approve and verify commitments, issue payment orders and payments, monitor and report on financial resources collected, and develop appropriate resource allocation and borrowing strategies. As such, the potential of such systems to improve budgetary management outcomes in terms of fiscal discipline, allocative efficiency, and operational efficiency is widely recognized by the literature and practitioners alike. Recognizing the potential benefits, the World Bank invested significant resources into the development and implementation of such systems around the world.

**Motivation and Scope.** FMIS projects tend to be complex and prone to a wide range of implementation challenges. Despite the relevance of FMIS systems to expenditure management the literature on causal effects and mechanisms from FMIS on budgetary outcomes is relatively thin and the quality uneven. The aim of this study is to identify practical issues that may help practitioners during the design, implementation, and operationalization of FMIS systems for the achievement of improved results, such as a reduced variance between authorized and actual expenditure, timely in-year accounts and annual financial reports, and improved information available to the legislative.

**Data and Methods.** This study draws on a systematic review of the FMIS literature, field based project-level evaluations and protocol based case studies, and a comprehensive desk review of the World Bank FMIS project documentation. Through a triangulation process of the various sources, findings were identified and mapped against a theoretical framework. Case studies were used to inform causal mechanisms on what worked, why, and under what circumstances. The convergence of conclusions across sources and the at times axiomatic nature of arguments addresses concerns of external validity of the findings.

**An FMIS Framework.** A theory of change was developed from the literature that visualizes the various steps involved in FMIS implementation, which is important as it illustrates how change is expected to happen. It also visualizes how the various

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# **References:**

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indices representing the different impacts of governance: for example, Mauro (1995) use these measures to create an index of governance instability while Knack and Keefer (1995) select some indicators as being of greatest relevance to the security of private property and the enforceability of contracts.

Expanding on surveys designed and implemented by Brunetti, Kisunko and Weder (1997), the World Business Environment Survey (WBES) of the World Bank surveys firms in more than 75 countries, inquiring about corruption, the quality of government-provided services, and the predictability of laws and policies. Kaufmann et al. (1999) provide a methodology for constructing indices of perceptions on corruption, by aggregating governance indicators from numerous sources including surveys of enterprises and households.

These subjective governance indicators have attained wide usage in the growth and development literature. Its application when discussing the impact of reforms, however, should be very careful. First, ratings may be affected by the expert's knowledge of recent economic performance. Second, these measures typically represent conditions facing foreign or large investors, not necessarily those confronting most domestic investors. Subjective risk evaluations remain only partial indicators of the institutional environment that can affect economic performance.

An alternative to subjective ratings was proposed by Clague et al. (1999). They suggest using the proportion of M2 within the banking system, an objective measure not subject to contamination by the expectations of surveyed country experts. The justification for this measure is that individuals will hold a larger proportion of their financial assets in the form of currency in environments where third-party enforcement of contracts is unreliable. Money lent to financial institutions (i.e. bank deposits) is less safe where contracts cannot be relied upon. The ratio will increase where governments better enforce and respect contracts and private property rights. The main limitation of the CIM is that, at best, it only measures the tradeoff between holding assets in currency or bank deposits. Moreover, other elements affect this decision rendering its comparability across countries dubious (e.g., inflation).

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Downstream spillovers arising from policy reform and foreign participation in the services sectors are qualitatively different from those arising from foreign direct investment in manufacturing industries. Disruption in the provision of services can result in large delays in production and product delivery, high information costs and an inability to invest in potentially profitable new activities. There has not, however, been much empirical analysis of the downstream effects of services reform, and the few existing studies have focused on specific services sectors, usually banking.<sup>4</sup> Rajan and Zingales (1998) show that financial development increases growth. They weight industries by dependence on outside financing (as estimated from US data) and find that firms which are more dependent on external financing gain more from financial development than other firms. Bertrand, Scholar and Thesmar (2004) demonstrate that banking deregulation in France in 1985 led to improved productivity in manufacturing firms. Entry and exit rates increased following liberalization, suggesting that less productive firms had been protected by the easy access to credit allocated to large firms by the previously nationalized banking sector. Productivity effects were particularly strong in banking-dependent sectors. Aghion and Schankerman (1999) identify channels through which infrastructure and institutions affect entry and exit. They generate a Dixit-Stiglitz model to demonstrate that infrastructure investment increases the probability of entry by low cost firms and discourages entry by high cost firms. Thus, infrastructure development is likely to improve economic performance if it reduces transactions costs thereby increasing competition and fostering Schumpeterian “creative destruction.” The present paper is most closely related to Arnold, Javorcik and Mattoo (2011) which uses firm-level data to show that increased foreign participation in services provision led to improvement in manufacturing productivity in the Czech Republic in the period 1998-2003. The current paper studies the more complex and dynamic Indian context with new data on and measures of services reform. Furthermore, while the previous paper considered the services sector as a whole, in the present paper, by separating the liberalization measures into measures for banking, telecommunications, transport and insurance services, we are able to identify the impact of key reforms in individual sectors. Finally, in contrast to the previous paper, we distinguish between the implications of services liberalization for domestic and foreign manufacturers.

> 4 There is some work on the economy-wide effects of services reform. Mattoo, Rathindran and Subramanian (2006) show that services liberalization leads to higher levels of economic growth.  Eschenbach and Hoekman (2006) find similar evidence for Eastern Europe.

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The free standing terms trade and investment variables also show propensity to flip sign in all cases suggesting that they were capturing the initial wealth/liquidity now captured by initial wages and unemployment. Minus this effect the trade and investment variables have a more powerful deterrent effect than destination an attractive effect. The fact that all relative trade and investment variables retain an effect outside of the contemporaneous labor market variables may reflect that there is an independent disincentive effect to migrate, perhaps through expectations of future growth. In all cases, the possible endogeneity between local labor market variables and the location of trade/FDI is left largely unexplored due to lack of credible instruments. Hence, we treat the results as suggestive that part of the impact of these variables on migration works through the labor market.

As an example of the implied magnitudes, figure 3a presents the calculated elasticities with response to an increase in FDI by state for both the reduction in “push” or out migration from the state and increase in “pull” from the other states. Though in theory, the liquidity effect identified in the maquila and export cases could dominate and cause an increase in “push” migration, in the FDI case the coefficient is small and insignificant, and push is clearly reduced from all states with elasticities averaging .02. Pull elasticities are substantially higher averaging around .05 but reaching .1 in the case of Colima, Zacatecas and Campeche. A doubling of FDI in these states leads to a 10% increase in migrants attracted to the state and a 2% decline in outflows.

To see whether these magnitudes are important, figure 3b calculates the impact of a 10% rise in each states’ FDI on the magnitude of push and pull migration divided, as a measure of scale, by total out migration to both the US and other Mexican states. What is clear is that both effects are of non –trivial magnitude. The reduction in push migration lies between 1-3% of total migration while pull migration can range from under .5% to again over 10% in the three states mentioned above. Inflows of FDI potentially have substantial impact as a mechanism for affecting migratory flows.

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**Table 5: Sensitivity Analysis for DPT, Upper Middle Income Countries, IV with country fixed effects**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||DPT rate (log)|DPT rate (log)|DPT rate (log)|DPT rate (log)|DPT rate (log)|DPT rate (log)|
|Convention on the Rights of the Child-yes/no (CRC)|0.052<br>(2.79)**|0.056<br>(2.90)**|0.058<br>(2.95)**|0.006<br>(0.16)|0.087<br>(3.98)**|0.026<br>(1.51)|
|GDP it i tt 2000 US dll l|-0.12|-0.121|-0.049||-0.118|-0.067|
|per capa (n consan oars, og)|(2.92)**|(3.00)**|(1.91)||(2.88)**|(1.78)|
||0.018|0.019|0.02||0.021|0.008|
|ICRG-Bureaucratic Quality|(2.62)**|(2.80)**|(3.00)**||(3.07)**|(1.24)|
|Polity2x|-0.008<br>(2.28)*||||||
|Population Density (log)|0.533<br>(0.19)|0.741<br>(0.27)|0.046<br>(3.22)**||1.644<br>(0.59)|0.02<br>(0.01)|
|Total Population (log)|-0.172<br>(0.06)|-0.386<br>(0.14)|0.011<br>(0.92)||-1.21<br>(0.43)|0.168<br>(0.07)|
|YEAR|0.004<br>(2.59)**|0.004<br>(2.62)**|0.007<br>(6.32)**|0.005<br>(3.19)**||0.003<br>-1.86|
|Democracy||-0.004<br>(2.12)*|-0.003<br>(1.69)||-0.004<br>(1.94)|-0.003<br>(1.55)|
|GDP per capita (in constant 2000 US dollars, log) (Lag)||||-0.106<br>(2.62)**|||
|Log DPT Immunization Rate (Lag)||||||0.372<br>(10.05)**|
|B-spline on [1989,1995)|||||-0.044<br>-1.87||
|B-spline on [1995,2000)|||||-0.048<br>(2.45)*||
|B-spline on [2000,2006)|||||-0.029<br>-1.8||
|ICRG-Bureaucratic Quality (Lag)||||0.001<br>(0.11)|||
|Democracy (lag)||||-0.002<br>(0.91)|||
|Log Population Density (Lag)||||0.63<br>(0.19)|||
|Log Total Population (Lag)||||-0.332<br>(0.1)|||
||-2.482|0.134|-9.005|<br>-1.836|19.047|-5.339|
|Constant|(0.07)|(0)|(4.48)**|(0.04)|(0.54)|(0.16)|
|Observations|610|614|614|580|614|611|
|Number of group(countryc)|52|52|52|52|52|52|

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Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) Brunei, Cambodia, Malaysia, Myanmar, New Zealand, Singapore, and Thailand, have just one tariff schedule that is on offer for all other members, while others have variations by partners.<sup>6</sup> The level of protection applied by RCEP members in intra-RCEP trade differs, due to the different previous agreements in place. For some countries, such as Korea, China, Lao PDR, and Japan, the average tariff applied to RCEP partners is around the same as the tariff applied to countries outside RCEP (Figure 3). In a few cases, the average tariff applied to RCEP countries is even higher than the tariff applied to non-members. This is the case of Australia, New Zealand, and Indonesia. Cambodia, Thailand, Philippines and Malaysia apply a significantly lower protection to RCEP imports than to non-members. Implementation of chapter 2 (trade in goods) in the RCEP agreement is expected to level out these differences.

Figure 3. Average tariff applied by RCEP countries. Trade weighted average <!-- Start of picture text --> 5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Korea China Laos Australia- Vietnam Cambodia Indonesia Thailand Japan Philippines Malaysia<br>New<br>Zealand<br>RCEP ROW<br><!-- End of picture text --> Source: Own elaboration with data from MacMap and Comtrade.

Note: Brunei, Myanmar and Singapore are aggregated into the Rest of Southeast Asia region.

The highest level of protection in RCEP is found in the agriculture and food sectors, considering tariffs applied to both RCEP countries and to the rest of the world (Figure 4). The trade-weighted average tariff applied to imports of agricultural and food products from the rest of the world is 11.7 percent, and 7.3 percent to imports from RCEP countries. In the agriculture and food sectors, Meat products and Crops have the highest protection levels: 21.9 percent and 6.6 percent respectively to imports from RCEP. The most protected manufacturing sectors within RCEP are Motor vehicles and parts (9.4 percent trade weighted tariff); Wearing apparel and leather (7.1 percent); Nonmetallic minerals (3.7 percent); and Textiles (3.6 percent). Trade in Extractive products face zero or near-zero tariff rates in the region.

> 6 A more detailed discussion  of the agreement can be found at http://asiantradecentre.org/talkingtrade/rcep-a-first-look-at-the-texts and the full text of the agreement, together with the tariff commitment schedules, can be retrieved from https://www.dfat.gov.au/trade/agreements/not-yet-in-force/rcep/rcep-text.

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Table 1: Summary statistics and response rate for energy management questions.

|Question|Scoring|Mean|Std Dev|Med|p25|p75|Score = 0|Responses|
|---|---|---|---|---|---|---|---|---|
|(1) Did the establishment<br>monitor energy consump-<br>tion?|0 - No or don’t know, 1 - Yes|0.568|0.495|1.0|0.0|1.0|8,431|19,511|
|(2) How often did the estab-<br>lishment monitor its energy<br>consumption?|0 - Don’t know or no monitoring,<br>increments of 0.125 from annu-<br>ally (0.125) to hourly (1)|0.253|0.245|0.3|0.0|0.5|8,493|19,518|
|(3) Was an external energy<br>audit completed?|0 - No or don’t know, 1 - Yes|0.265|0.441|0|0|1.0|8,146|11,087|
|(4) Did the establishment<br>have targets for energy con-<br>sumption?|0 - No or don’t know, 1 - Yes|0.276|0.447|0|0|1.0|14,120|19,511|
|(5) What was targeted?|0 - Don’t know, refuse to answer,<br>or no targets, 0.5 - Quant or exp<br>only, 1 - Both quant and exp|0.220|0.382|0|0|0.5|14,199|19,513|
|(6) Did the establishment<br>adopt energy management?|0 - No or don’t know or refuse to<br>answer, 1 - Yes|0.308|0.462|0|0|1.0|13,494|19,511|
|(7) Did the establishment<br>adopt any measures to en-<br>hance energy efciency?|0 - No or don’t know or refuse to<br>answer, 1 - Yes|0.334|0.472|0|0|1.0|12,987|19,511|
|(8) Were any measures de-<br>veloped by establishment?|0 - No or don’t know or refuse to<br>answer, 1 - Yes|0.252|0.386|0|0|0.5|12,987|19,516|
|(9)<br>What<br>was<br>the<br>pay-<br>back period vs. non-energy-|0 - Did not implement, 0.25 -<br>Don’t know, 0.5 - Shorter, 0.75 -|0.106|0.269|0|0|0|12,987|15,214|
|efciency measures?|Equal payback, 1 - Longer||||||||
|E Mgmt Average||0.287|0.292|0.167|0|0.5|6,540|18,621|

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to the fact that a smaller _fraction_ of _post-ultrasound_ births preceded by a firstborn girl are now female, where as the conception effect is driven by the change in the number of births that are preceded by a firstborn girl.

The number of excess postnatal female deaths in a year equals: where _EFMFG_ refers to the difference between the probability of death by age 5 among children preceded by a firstborn girl and a firstborn boy.

The change in the number of excess postnatal female deaths can thus be written as: The change in the number of excess postnatal female deaths can also be decomposed into three components: the “behavioral effect” refers to the EFM decline due to, say, better postnatal health investments in girls in the post-ultrasound period; the “mechanical sex-selection effect” is driven by the decrease in the _fraction_ of _post-ultrasound_ births that are preceded by a firstborn girl; and the “conception effect” that reflects the change in the _number_ of births that are preceded by a firstborn girl itself.

The table below uses the aforementioned formulae to calculate the number of births preceded by a firstborn girl during the pre-ultrasound (in column (1)), during the late diffusion period (in column (2)), and the change in the number of missing girls as the difference between columns (1) and (2) in column (3). _EFMFG_ in column (1) is the coefficient of _Firstborn girl * Female_ and in column (2) is the coefficient of _Firstborn girl * Female * Post2_ from column (4) in panel B of Table 3. During the pre-ultrasound period, the fraction of females in births preceded by a firstborn girl was 47.9 percent. Ultrasound access decreased this fraction by 1.8 p.p..<sup>64</sup> We calculate<sup>_<u>N</u>_</sup> _N_<sup>_<u>F G</u>_</sup> from the entire sample since the pre-ultrasound sample of births is likely to be underreported due to recall bias. The number of births in column (1), _N_ , is the 1995 figure from UN Statistics Division. The number of mothers, _m_ , is obtained as General Fertility _<u>N</u>_ Rate<sup>usingthegeneralfertilityrateestimated</sup> from NFHS-2 by Retherford and Mishra (2001) as 131.53 per 1000 women. ∆ _NFG_ is estimated to

> 64This number is derived from a regression specification similar to the one estimated by Bhalotra and Cochrane (2010).

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Research. Retrieved from http://files.eric.ed.gov/fulltext/ED509683.pdf. Accessed December 16<sup>th</sup> , 2016.

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