rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.Diagnosing the Transaction Ecosystem: The Cases of Pakistan and Cambodia**

This study draws on transactions data from Pakistan and Cambodia to investigate actual FMIS budget coverage and determine the transactions profile to help interpret how data from actual system use can guide expenditure management reform.

## **3.1. The Pakistan Case**

### Background

The process of improving fiscal and financial reporting in Pakistan was initiated in the early 1990s after observations by both the World Bank and the IMF, and a diagnostic study under- taken by the Auditor General of Pakistan (AGP), that the (then manual) accounting and reporting system did not meet adequate standards for either financial or fiscal reporting. Notably, both accounting and auditing were directed by the Auditor General of Pakistan (an anomaly from pre-1947), budget reports by the Ministry of Finance (MOF) were not fully reconciled with accounting reports by the AGPR and provincial AGs, the Chart of Accounts (COA) was not compliant with IMF Government Finance Statistics (GFS) standards for fiscal reporting, and neither financial nor fiscal reports were timely or reliable. To meet these challenges, the Pakistan Audit Department (PAD) launched the PIFRA Program in 1995 with World Bank support. Under this program two projects have been executed, PIFRA I and PIFRA II. PIFRA I was completed in 2005, and PIFRA II in 2015.

As part of the project, a major mission critical country-wide integrated system, the Financial Accountability and Budget System (FABS), has been implemented to spearhead the reforms and assist the government in the functional processes associated with financial accounting and budgeting at the federal, provincial and district levels.

### **FABS functionality includes:**

- Budget preparation: assists in compilation of budgets, prints the budget book and associated reports, maintains a record of initial budgets, revisions, and budget releases.

- Budget execution:

- Records commitments, receipts of goods and services;

- Ensures expenditures are in accordance with
|No|Yes|
|Observations|18170|18170|
|Notes: Robust standard errors clustered by firm in parentheses. *** p<0.01, ** p<0.05, * <br>year effects. Firms controls are: log employment; average age of workers; average tenure; <br>share of workers with less than high school completed; share of workers with exactly hi<br>workers with more than high school completed; average log wage; share of white workers; <br>of skill levels 1 to 4.|p<0.1. All regre<br> male to female <br>gh school comp<br> and share of w|ssions include<br> workers ratio;<br>leted; share of<br>orkers in each|
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
, Baez et al. (2015) looked at the consequences of Tropical Storm Agatha (2010) on household welfare, finding that consumption per capita fell by 12.6 percent and poverty increased 5.5 percentage points (an 18 percent increase).

> 4 Calculation of authors based on information from EM‐DAT: The international Disasters Database.

> 5 Dell, Jones and Olken (2014) provide an excellent review on the new climate‐economy literature and summarize results on different socioeconomic outcomes (e.g., health, conflict, economic growth, among others).

2
|share of population discrimated against|||-2.682<br>(1.665)|
|refugees per 1000 population|||0.00178<br>(0.00322)|
|no refugees|||0.196|
||||(0.350)|
|Observations|5,834|4,012|4,190|
|Number of countries|211|160|156|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Table 11 present results of a poisson regression with an exposure variable capturing global flows in OECD sample. Fitted values of “fragile peace” is coming from a regression of “fragile peace” (i.e. a dummy that takes a value if a year is peaceful, within five years of the last conflict and reverts back to conflict within the next five years) on “share of the population excluded from political power”, “share of the population that is discriminated” and “refugees per 1000 population”. “Peace” is a dummy that indicates when a country is not in conflict, i.e. violence intensity is above a threshold of 0.008 battle-related deaths per 1000 inhabitants. “First five years of recovery” is a dummy that indicates if the end of the most recent conflict has been less than five years ago. Covariates used in the three specifications are gathered from several sources: “polity score” and “strong executive constraints” are provided by Polity IV dataset; GDP data are provided by Penn World Tables; “share of the population that is excluded from political power” and “share of the population that is discriminated” variables are provided by GROWup dataset; information about “refugees” is provided by UNHCR dataset. “No refugees” is a dummy that takes a value of 1 if the yearly stock of refugees in a given country is
The consumption patterns under home-based work and lockdowns have changed with consumers decreasing spending on services requiring human interaction.<sup>3</sup> We assume that the demand for the former category of services drops by 15 percent.

- _Fourth,_ we introduce a food supply shock, to simulate a post-harvest loss in food producing sectors. The measures put in place by many governments to contain the spread of COVID-19 can have a disruptive effect on the availability of agro-food products to consumers.<sup>4</sup> Therefore, to take into consideration this disruption of the logistics of food distribution, we assume that the cost of agricultural and food commodities production increases by 3 percent.<sup>5</sup>

- _Fifth_ is the shift toward communication technologies in the service sectors and away from transportation activities, particularly air transportation. To represent the shift from (air) transportation toward communication services used by service sectors we decrease by 20 percent a share of air transport activity in the cost structure of services production and increase the share of communication input to the services cost structure by 20 percent.

- _Finally,_ we introduce a reduction in global oil prices (in 2020 relative to 2019) by increasing the supply of oil. We further assume that in the post-2021 period oil prices would rebound to the 2019 levels in the next several years and exceed 2019 prices by 2030 – in line with International Energy Agency (IEA) price projections (IEA 2020). All these shocks are implemented for the 2020 simulated year together with the GDP target consistent with the GEP 2021 forecast (World Bank 2021).

Our baseline scenario also includes implementation of the trade frictions between the United States and its trading partners (mainly China) implemented in the model via increases in import tariffs based on the detailed tariff line data from the CARD database (Li 2018). We assume that these trade policy shocks stay in the model throughout the entire simulated timeframe, i.e. until 2030. We also include all
- <mark>Sheahan, Megan, and Christopher B. Barrett. (2017). "Ten striking facts about agricultural input use in Sub-Saharan Africa."</mark> _<mark>Food Policy</mark>_ <mark>, 67: 12-25.</mark>

- <mark>Snijders, Tom AB, and Roel J. Bosker. (1994). "Modeled variance in two-level models."</mark> _<mark>Sociological Methods & Research</mark>_ <mark>, 22(3): 342-363.</mark>

- Stifel, D. and Christiaensen, L. (2007) “Tracking Poverty over Time in the Absence of Comparable Consumption Data”. _World Bank Economic Review_ , 21, 317-341.

- Tanzania’s National Bureau of Statistics. (2011). _Basic Information Document—National Panel Survey 2010-11_ .

- Tarozzi, Alessandro. (2007). “Calculating Comparable Statistics from Incomparable Surveys, With an Application to Poverty in India”. _Journal of Business and Economic Statistics_ 25, no. 3:314-336.

- UNESCO-UIS/OECD/EUROSTAT. (UOE). (2020). _Data collection on formal education— Manual on concepts, definitions and classifications_ . Montreal/ Paris/ Luxembourg.

- United States Census Bureau. (2017). Current Population Survey, Imputation of Unreported Data -

- Items.  Accessed on the Internet on May 24, 2021 at <u>https://www.census.gov/programs surveys/cps/technical-documentation/methodology/imputation-of-unreported-data-items.html</u>

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returns is to compare the GDP growth, investment ratios and efficiency of investments of the different types of borrowers in the region.

**Figure 20. Growth, investment and efficiency of investment across types of borrowers in Sub-Saharan Africa, 2012-17**

<!-- Start of picture text --> Real GDP Growth Investment Efficiency of Investment<br>6 30 25<br>5 25<br>20<br>4 20<br>15<br>3<br>15<br>10<br>2<br>10<br>1 5<br>5<br>0<br>Light Moderate Heavy 0 0<br>Light Moderate Heavy Light Moderate Heavy<br>Percent, 2012-17 (group median)<br>Percent of GDP, Avg. 2012-17 (group median)<br>Percent per year, 2012-17 (group median)<br><!-- End of picture text --> _Source: World Bank, World Development Indicators. Elaboration: AFRCE Staff estimates._

**Figure 20** reports the median rate of GDP growth, total investment (as percentage of GDP) and the efficiency of investment (as proxied by the growth multiplier of investment). It shows that heavy borrowers exhibit the highest investment coefficients with a median of 28.4 percent of GDP as opposed to the 26.3 and 20.9 percent of GDP coefficients displayed by moderate and light borrowers’ rates of investment. However, heavy borrowers show the lower returns for that higher investment coefficient —as captured by the growth rate of GDP. Total output of heavy borrowers grew at an average annual rate of 3.1 percent. Note that the GDP growth rate of light and moderate borrowers totaled 4.2 and 4.9 percent per year. These findings imply that heavy borrowers might be financing greater investment; however, the efficiency associated to that spending is significantly lower than that of the other groups of borrowers within
also the inflation risk premium and liquidity premium. A key advantage of the swap rate is that, unlike for break-even inflation rates, liquidity has a limited impact on its movements (Grothe and Meyler 2018). Both types of market-based measures have the advantage of being available at very high frequencies, which may help policy makers develop an understanding of how inflation expectations are formed and may be calculated at a wider range of forecast horizons than is possible using surveys. However, swap markets in EMDEs are typically insufficiently developed to allow such a measure to be reliably extracted. Therefore, central banks in several large EMDEs (for example, Brazil, Chile, Colombia, Mexico, Peru, Poland, the Russian Federation, South Africa, Thailand, and Turkey) typically derive their market-based measures of inflation expectations from inflation-indexed government bonds (Sousa and Yetman 2016; De Pooter et al. 2014).

Differences between survey-based and market-based measures. In terms of the level of inflation expectations, those derived from surveys of professional forecasters are not systematically higher or lower than market-based measures (Figure 2). However, professional forecasters’ inflation expectations tend to be close to central bank inflation forecasts, as has been shown for New Zealand and the United States (Coibion and Gorodnichenko 2015). In addition, the volatility of professional forecaster-based expectations tends to be lower than that of market-based expectations. During periods of market stress, break-even inflation rates can be particularly volatile because “flight-tonecessarily true of firms’ inflation expectations. The latter may be more important for actual inflation, because firms may incorporate expected marginal costs into their product prices.

5
average decline of 1 percentage point yearly, whereas the incidence of moderate poverty changed from 29.1 to 11.7 percent for an annual average decline of 1.9 percentage points. In the case of income inequality, changes in the Gini coefficient show a declining trend, although they were not statistically significant between 2006 (0.499) and 2013 (0.491), and it was until 2015 that inequality registered a significant reduction (0.482).<sup>5</sup> In such context, this paper applies a comprehensive tax-benefit incidence analysis to estimate the effects that public social spending, hand in hand with the tax system, exert on poverty and inequality indicators in Chile using household-level data and administrative records for 2013. Specifically, the analysis presented in the next sections evaluates the concentration and incidence of several fiscal instruments in Chile —including direct and indirect taxes, contributory and non-contributory pensions, direct transfers, indirect subsidies, and in-kind government transfers in the form of health and education— to address five questions. First, who bears the tax burden and receives the benefits from social spending? Second, are fiscal interventions in Chile equalizing? Third, are they poverty-

> 1 The Plan AUGE (Universal Access to Explicit Guarantees), now called GES (Explicit Guarantees in Health), guarantees the coverage of 80 diseases by the public National Health Fund (FONASA) and the private health system (ISAPRE).

> 2 This program was introduced to replace and extend the benefits of _Chile Solidario_ .

> 3 This rate of change was calculated using the OECD social expenditure database (OECD, 2016a).

> 4 In 2015, a multi-dimensional poverty measure was officially introduced to assess non-monetary deprivations of households. This measure considers four equally-weighted dimensions, each measured through three indicators: education (school attendance, years of schooling and underachievement), health (child malnutrition, access to the health system, and medical care), labor and social security (access to social security, employment
## _Source: Authors' estimates using datafor GLSS I and GLSS3._

While comparisons between the two surveys are somewhat hampered by the apparently more liberal definition of migration in 1988-89 survey, comparing the relative proportions among those who did migrate may still be possible. In both years there is very little migration from rural to urban areas-most migrants from rural areas went to other rural areas. What is even more interesting is that rather more than half of the migrants from urban areas went to the rural sector, and only a negligible fraction went to the capital city. Ghana had a remarkable experience among developing countries in actually showing "deurbanization" in this period. Overall the 1988-89 survey found that 63.7 percent of the population was rural, but in the 1991-92 survey this proportion had gone up to 65.2 percent.

The recent evidence on migration flows thus shows that, in the groups which are most generally prone to migrate, the income levels in town, in real terms are not perceived to be higher than in the rural areas. It is consistent with the finding that the incidence of poverty is increasing and is probably higher in Accra than in the rural economy in 1992.

# 7. **Conclusions and Recommendations**

# 7.1 **The Future of Labor Absorption in Ghana**

The slow-down and, perhaps reversal, in the rural-to-urban flow of labor is symptomatic of a basic shortcoming in the economic recovery of Ghana-viz., the inadequate growth of the productive sector in the non-agricultural economy. The rate of growth of GDP in real terms, although slowing down a bit in the 1990-95 period, has still been reasonably adequate at an average of 4.3 per cent. (It had been fully one percentage point higher in the 1985-90 period). But much of this growth has been fueled and led by the services sector. According to the National Accounts this sector has surpassed agriculture as the major contributor to GDP-since 1992 its share has exceeded 46 per cent. There are several positive
<!-- Start of picture text --> 16,000,000<br>9.8% below 28% below 74% below<br>poverty line 15x PL 3.5x PL<br>14,000,000 (Py<br>12,000,000<br>10,000,000<br>2oS<br>2 8,000,000<br>2<br>£<br>6,000,000<br>4,000,000<br>2,000,000<br>0 807,678 1,615,355 2,423,033<br>Monthly household per capita consumption (IDR)<br><!-- End of picture text -->
will refer to BPL households and these vulnerable APL groups jointly as _Vulnerable Groups_ (VGs). SBM’s administrative data (see Section 3.2) suggest that overall in rural India, 63% of households were recorded as not having a toilet at 2012-13 SBM baseline. Most of these households were classified as either being BPL (46%) or vulnerable APL (49%), yielding an overall share of 60% of officially subsidy eligible households in rural India at SBM baseline. This study focuses on households residing in two districts of Maharashtra, Latur and Nanded. Whereas the share of subsidy eligible households in Maharashtra was lower than the national average (i.e. 52%) - driven by a higher baseline toilet coverage (45%), baseline toilet coverage in our study districts was particularly low. On average (including MF clients and nonclients), 72% of rural households were recorded as not having a toilet at SBM baseline and, adding to this the VG condition, almost 70% of all rural households in our study area were eligible for an SBM subsidy.

# **2.3 Sanitation micro-credit intervention**

In this setting our implementing partner, a large MFI active in five states in India, pioneered micro-credit for sanitation. It started offering the new loan product to its exclusively female clients in February 2015 (shortly after the launch of the SBM) in five of their branches, covering rural and semi-urban areas in the districts of Nanded and Latur in Maharashtra.<sup>7</sup> Our estimate of the proportion of clients of our MFI

> 6As per the SBM guidelines, states have the authority to introduce variations in the subsidy provision, in particular to allow households to request a share of the subsidy amount prior to the completion of their toilet. This share is meant to be small though and only to be provided when the household can show that significant works have already been completed (e.g. by having dug a pit).

> 7Outside
**_ES9_**|10.36|10.31|||||
|**_ES16_**|12.11|12.55|||||

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling, (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
much more strongly in favor of theories in which superior information at decentralized levels is a result of greater control rights.<sup>25</sup>

# **Management Matters**

The impact of those control rights can be mitigated by organizational incentives (Aghion and Tirole, 1997). Recent empirical evidence has shown that management affects the distribution of assets across firms such as human capital (Bloom and Van Reenen, 2007; Qian et al., 2015; Bender et al., 2018) and physical capital (Bloom and Van Reenen, 2010). It therefore seems plausible that it would have substantive impacts on the distribution of information.

The diversity in management practices across Ethiopia’s public organizations, and specifically in terms of how highly information is valued as an organizational asset, can be interpreted as variation in the rewards to officials of acquiring information. Organizations that value the use of information, captured by the ‘Management Practices: Monitoring’ index, create a high relative reward for obtaining information. In environments in which organizations value information, captured in the ‘Monitoring’ index, we should expect to see lower errors. Conversely, management practices that raise the alignment of the principal and agents’ interests may lead to a strategic substitution of effort in acquiring information.

We can extend our analysis in (1) to include measures of management practices and assess the consistency of these ideas with our data. To do so, we broaden the variation across which we make comparisons by switching from district fixed effects to regional fixed effects. Our reasoning is that since management practices vary at the organization-level, within a district we constrain the variation to be across 5 organizations. By extending our estimating variation to the region, we are able to capitalize on variation across an average of 122 organizations while still constraining the comparisons of errors within a restricted geographic and institutional environment. We also follow standard practice in studies of the World Management Survey indices, and include ‘noise controls’, which aim to soak up aspects
every period _t_ as life expectancy for cohort _j_ , j , rises, but schooling is the most readily observable and consistent measure of human capital investment LLEE

> 11 <mark>The survey fixed effects give us an estimate of average wages net of fixed factors such as the home currency; the time unit used for wages, whether hourly, weekly, monthly or annually; country-specific fixed effects; and inflation. The wages are presented on a logarithmic scale.</mark>

16
treatment effect on social attitudes and perceptions. This outcome is a standardized index of the following outcomes: out of 3 closest friends, how many are Syrian refugees; out of 3 people you exchange advice with, how many are Syrian refugees; do the children in this household have Syrian refugee friends (=1); do the children in this household share recreational spaces with Syrian refugee children (=1); how comfortable would you be accepting the marriage of a friend or loved one to a Syrian refugee (1-5); how comfortable would you be accepting a Syrian refugee as a neighbor (1-5); what is the net effect of Syrian refugees on Jordanian society (1-3); are Syrian refugees hardworking or lazy (1-7). Economic attitudes and perceptions is a standardized index of the following outcomes: an indicator =1 if the respondent listed “hosting Syrian refugees” as one of the most important challenges facing Jordan (options not read); an outcome with higher values for responses associated with beliefs that Syrian refugees pay more in taxes than Jordanians; an outcome with higher values for responses associated with refugees having a positive effect on the economy.Policy preferences in a standardized index of the following outcomes: less belief that Syrian refugees should be forced to live in camps; more belief that refugees should have the right to work outside camps; more belief that refugees should be able to attain Jordanian citizenship; more support for unrestricted work permits for refugees; more support for integrated classrooms with Syrians and Jordanians; more support for refugee right to enter/exit camps freely; support for housing assistance for Syrian refugees; belief that the international community should spend more money supporting refugees.Altruism to Syrians reports how many JD out of 5 the respondent allocated to a charity supporting syrian refugees. The other two options were allocating to a charity supporting low-income Jordanians or keeping for self. **Panel B** reports the two significant subcomponents of the social attitudes and perceptions index. **Panel C** reports