rafmacalaba/data-use-mentions-extended huggingface dataset:
ALLOCATION AND MANAGEMENT OF PUBLIC SPENDING

# The Importance of Budget Process and Presentation

20. Managing public finances In SSA has become typically an exercise in crisis control. In a crisis atmosphere, horizon **s** horten and there is a tendency also to narrow the group of effective decision takers. In the long run, however, better public expenditure management requires a more public process. Indeed, the process of planning may be at least as valuable as the budget/planning documents that are Its visible product. The opportunities for educating opinion about economic alternatives, for building consensus, and for spreading responsibilities for public expen#lture choices more widely are lost If the process Is always conducted in secrecy and in haste.

21. The task of rehabilitating budgetary systems may be long and difficult, especially where current dilapidation is particularly extensive. **Tt** may be some time, for example, before government accounts can be restored to the point of providing a reliable benchmark for each year's budget exercise. At the same time some Improvements are available that are relatively simple and straightforward to Implement. The way information is presented to (ostensible) decision makers Is often a case in point.

22. Thus, for example, budgets (and Annual Plans - see Box 4) that are presented to parliaments and published are often remarkably uninformative documents. A simple shortcoming (though it may on occasion be less an oversigh **t** han a reflection of the state of monitoring and accounting records) Is the failure to show previous years' actuals and/or future yeaLs' projections alongside the current year's estimates. This is particularly common where projects In the development budget are concerned. Estimates commonly give a so-called "one-year time-slice" of a proJect without indicating its fund!'.g requirements in subsequent years or revealing its previous expenditures (or if these are shown, the period covered may be unspecified or Inconsistent among projects). Very often projects exist for yeaxs without more description in
**Decoupling and recoupling**

(2000-Q3 2010) period, and assuming for simplicity that trend growth remained stable within each sub-period, the specification is simple enough:

- !!" !"!!" !

<!-- Start of picture text --> ! ! !!!!" !"!!!!! ! !!!!" !"!!!!! ! !!"#$ ! !!!!" !"!!!!"#!! ! !!!!" !"!!!!"#!! ! !!"#$<br>(1)<br><!-- End of picture text --> a regression of the annual q/q growth rate of country _i_ ’s cyclical output (relative to a log linear GDP trend) on the G7 and Chinese cycles, based on quarterly data, identifying the late period (2001-Q3 2010) with an interacting dummy. In passing, such a specification also allows us to estimate the long-term growth convergence (the EM “alphas”) as the constant of the regression.

Note that we deliberately exclude China from the EM sample. For sheer size and growth dynamics, China represents a class in itself that, if grouped with other EM, could bias the conclusions of this exercise (as it does, most notably, in the typical analysis of EM aggregate output). Indeed, it remains a global driver that should be treated as an additional exogenous force behind EM growth and, thus, be included as an additional control, as we do here.

**Table 2** summarizes the results. The first thing to note is the fact that emerging economies display mostly “positive alphas” relative to the G7 group: a median 2.5% growth differential (Column 4) per year over the whole period, roughly in line with the back-of-the-envelope illustration in **Figure 5** . Is
drastic (from .087 in column 2 to .079). Third, industry fixed effects seem to matter somewhat for our main results in the sense that, controlling for these causes the estimated coefficient value of paternity leave variable to increase from .068 (column 1) to .083 (not shown, significant at 1% level). However, controlling for all the other variables discussed above does not matter much, with the estimated coefficient value of the paternity leave variable declining only modestly to .074 (column 8). Last, as expected, _Women workers_ is higher and significantly so (at 5% level or less) for firms with a woman owner, top woman manager, presence of foreign owners and exporting activity of the firm. Similarly, some of the macro controls such as GDP per capita, share of women’s population and secondary enrollment ratio also show positive correlations with _Women workers_ but these correlations are not too robust perhaps due to the high correlation between the various macro variables.

# **Conclusion**

Using firm-level data for 53 developing countries, we find a strong positive relationship between the provision of paternity leave and women’s employment. A number of issues remain to be answered. For example, does the provision of paternity leave affect women’s employment equally or differently across all types of firms and developing countries? Are paternity and maternity leave substitutes or complements for women’s employment? Why do some countries have paternity leave and others do not? We hope that future research will help answer these and related questions.

6
|.011|.63|.65|<br>.68|<br>.70|<br>.72|
|Gasoline<br>|.04|1.71|1.58|<br>1.41|1.33|1.29|
|Public<br>transportation<br>|.03|1.13|1.12|<br>1.11|1.10|1.10|
|Telegraph-telephone|.003|2.25|1.96|<br>1.67|1.54|1.47|
|Alcoholic beverages|.017|1.17|1.14|<br>1.11|1.10|1.09|
|Electricity|.02|1.64|1.52|<br>1.41|1.36|1.32|
|Education expenses|.042|1.24|1.20|<br>1.16|1.14|1.13|
|Rent and dividends|.05|1.61|1.54|<br>1.45|1.39|1.35|
|Wages|.30|1.54|1.52|<br>1.42|1.37|1.34|

s' Income is defined as total expenditure plus consumption of home produced goods per capita.

Source: C6te d'Ivoire Living Standards Survey, World Bank, 1986.

Mass11#4/Welfare Dominance **_&_** the Design of Excise Taxation in the Cote D'1voire/Wlhirsk:ab/5/18/88
# **1 Introduction**

Using data collected in cooperation with two e-commerce platforms – Chaldal which operates in Bangladesh and Daraz which is present in several countries in South Asia, including Bangladesh, Nepal, Pakistan, and Sri Lanka – this paper investigates whether engagement with e-commerce is linked to increased sales and productivity gains for informal firms in South Asia.

This question is relevant for this region where a stubbornly high share of informality is a key development issue, and where the recent expansion of e-commerce is often portrayed as an opportunity to expand market access for informal firms. Excluding agriculture, three-quarters of South Asia’s workers were informal around 2017, the highest share among developing regions. And, in terms of their number, the share of informal firms is even higher, reaching 99 percent in India according to data from the Annual Survey of Industries and the National Sample Survey (NSS) of Unincorporated Enterprises of 2015-16.<sup>1</sup> Remarkably, these shares have not changed much in the last two decades, World Bank (2020). In contrast, even if still comparatively small, the e-commerce sector has been growing rapidly. In 2015, the share of online sales in retail sales was only 1.6 percent in India and 0.7 percent in Bangladesh as compared to more than 15 percent in China and the United Kingdom.<sup>2</sup> However, reports in the business media and by industry associations suggest it is growing fast. For example, in 2021 e-commerce’s share of retail sales in India had already reached 5 percent and is expected to expand at 20 percent annually in the next few years.The COVID-19 crisis seems to have accelerated this rise, though it remains to be seen if the momentum will be sustained: according to the e-Commerce Association of Bangladesh, total e-commerce revenues increased by 70-80 percent in the space of a few months during 2020.<sup>3</sup> The expectation that the digital economy can have broad
Figure 2: Food Insecurity Measures Over Time <!-- Start of picture text --> Ethiopia Malawi<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0<br>0<br>Nigeria<br>100<br>80<br>60<br>40<br>20<br>0<br>Mild Food Insecurity<br>Moderate Food Insecurity<br>Severe Food Insecurity<br>Jun/Jul19 May20 Jun20 Aug20 Sep20 Oct20 2019 Jun20 Jul20 Aug20 Nov20 Dec20 Jan21 Mar21 Apr21 May21<br>Aug/Sep18 Jan/Feb19 Jun20 Aug20 Nov20<br>Percent Reporting Food Insecurity<br><!-- End of picture text --> _Note_ : The figure shows the percentage of households experiencing mild, moderate, and severe food insecurity in each round of available data. In Nigeria, the pre-outbreak surveys ask respondents food security questions in both a post-planting (labeled “pp”) and a post-harvest (labeled “ph”) survey.

25
Most migrants (40 percent), regardless of their origins and destinations, migrate to accompany someone. Marriage is the second most frequent reason for migration in any direction; with the exception of rural to urban migration where 26.6 percent migrants move to seek work.

Table 7 – Direction of Migration and Reasons

|||Direction o|f Migration||
|---|---|---|---|---|
|Reason of Migration|Urban-Urban|Urban-Rural|Rural - Urban|Rural - Rural|
|For work only|18.8|16.3|26.7|17.5|
|Education|1.3|0.3|0.7||
|Marriage|33.3|33.9|23.4|28.1|
|Accompany|40.9|40.2|42.1|45.2|
|Others|5.7|9.5|7.2|9.2|
|Total|100|100|100|100|

Source: Authors’ calculations using Labor Force Survey Most men migrate either to work or accompany another migrant (more than one-third each). Employment is the reason behind migration from rural to urban localities (53 percent) as well as from one rural area to another. Women mostly migrate because of marriage or to accompany a migrant. The percentage that migrates for work is negligible (see Tables A3 and A4 in the appendix).

# **Internal Migration – Labor Mobility**

Those who migrate to work are mostly males (97.8% of migrants to work are males while 2.2% are females), and the distribution of these migrants by level of education is multimodal (Table 8): the biggest fraction is composed by illiterate workers (26%) and starts decreasing gradually with the level of attainment until it reaches the technical secondary level (25%), and then
across economic activities.<sup>2</sup> The database is based on a uniform definition, a global data source, and a standardized protocol to include supplementary information from complementary sources beyond ORBIS (e.g., business registries, central depositories, central oversight bodies, MoF) (See Annex 3). This database is more comprehensive when compared to former or parallel efforts since it covers over 80 countries doubling the size of countries compared to the OECD coverage or quadruples the sample size of the IMF databases, focuses on developing countries, expands on the firmlevel indicators beyond financial performance, covers all relevant economic sectors including natural monopolies and financial sector that are excluded in the IMF or EU databases, and does not trim the sample by any threshold on size (See Annex 1). The global BOS database provides unique country and firm-level information for research and policy interventions on areas such as the presence of SOEs by economic sector, the economic performance of SOEs, government ownership structures, and governance.

The main contributions of the global BOS database go beyond existing efforts expanding the coverage. First, the global BOS database builds on a harmonized definition that can be applied systematically across countries and separate from country-specific definitions that hinder comparability. Second, it not only covers direct linkages between a company and a government, but also identifies and spells out all indirect ownership linkages allowing to trace state participation throughout multiple levels (e.g., subsidiaries of subsidiaries of companies with state participation). Third, it allows to trace state participation in domestic markets but also across borders.<sup>3</sup> Fourth, the database captures the ability of the state to influence markets not only through majority owned companies, but also by lowering the

> 1 https://blogs.imf.org/2020/05/07/state-owned-enterprises-in-the-time-of-covid-19/ (Gaspar, 2020).

> 2 The database not only captures the conventional and traditional SOEs that are often linked to majority control of the state, but
use at village level is not the most appropriate indicator to measure market power.

_External effects of informality:_ The significant and negative coefficient on the share of a field’s area not mapped in the cadaster suggests that informality generates negative externalities as registered parcels in fields that include unmapped parcels fetch lower lease rates. If, as seems plausible, informality is associated with higher costs of concluding or enforcing contracts, such lower lease rates are a real economic cost rather than a transfer.

Based on the point estimate, a program to map all the country’s agricultural land and thus reduce the level of unmapped but cultivated land from the current level of 5% to zero would imply a 1.7% increase in the lease rate. In other words, had such a program been implemented by 2021, it would have increased annual rental income received by landowners by US$ 8.71 million (118*0.017*4.34), implying a net present value of close to US$ 100 million. To the extent that it would cost less to map all agricultural land, a program to do so and, rather than charge the full cost to owners, recover them through a fee that takes into account the external effects from informality, may be more appropriate and effective.

Although registry data is only available for about 10% of our sample (table 5), it allows to consider other forms of informality, beyond checking the robustness of earlier estimates. Coefficients on policy reforms (10% for the short-term effects of lifting the moratorium and up to 8% for allowing participation of legal entities), positive effects of local competition (estimated coefficients of number of transaction in village 16
included in the simulations, using a sub‐grid channel network set‐up (Neal, Schumann, and Bates 2012). In channel flow was estimated to be 0.5*bankfull Q; rivers were estimated to be at 50% channel capacity.

Coastal simulations under future climate conditions were undertaken using the latest projections of global mean sea‐level rise, outlined in the Fourth Assessment Report (AR5) and Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC 2014; IPCC 2007). Estimates of sea‐level changes were taken and used to directly perturb the boundary conditions used in the simulations under current conditions. In order to incorporate uncertainty, simulations were undertaken for a range of projected changes, represented here as Low, Medium and High sea‐level rise (SLR) projections ( Table 1). The simulations are all conducted assuming that no flood defenses are in place; clearly flood defenses are not represented in the available terrain data. Therefore, these simulations should be considered as an upper bound of flood risk in the country.

The possibility of including a storm surge intensity component to the future projections was also discussed, but there are significant uncertainties around quantifying storm intensity changes that would preclude any reasonable modeling being undertaken (Seneviratne et al. 2012). It also seems that changes in surge extremes are going to be largely driven by sea‐level rise (Lowe et al. 2010). That being said, there are studies attempting to quantify changes in storm surge intensity; Lin et al. (2012) reported in a study focused on the North Atlantic, that in some cases changing storm surge intensity was comparable to changing sea‐level rise. Such changes would effectively double change in hazard intensities presented here. As of yet, we have not included this in the simulations due to the uncertainty.

# **9.2. Appendix 2. City‐case: HCMC**

# **9.2.1.
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The CAR’s infrastructure spending needs, at 17.5 percent of GDP (using 2009 GDP), are among the average level for Central African countries and lower than comparable fragile states. Capital investment would absorb around 10 percent of GDP, somewhat less than what China invested in its infrastructure during the mid-2000s. Spending needs for O&M account for around 7 percent of the CAR’s GDP (figure 16).

Spending needs in the transport sector are mainly driven by needs in the road sector, in particular in capital expenditure. Whereas road needs account for $158.2 million per year, needs for the air transport sector are only $1.5 million per year (table 16).

**Figure 16. Infrastructure spending needs in the regional context**

<!-- Start of picture text --> As percentage of GDP<br>SSA<br>Fragile states<br>Low income countries<br>Resource-Rich<br>Middle income countries<br>DRC Congo, Dem. Rep.<br>Niger<br>Congo, Rep.<br>Chad<br>CAR<br>Rwanda<br>Sao Tome and Principe<br>Seychelles<br>0 10 20 30 40 50 60 70<br>% of GDP<br>Capex O&M<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009. _Note:_ O&M= operations and maintenance; CAPEX = capital expenditure; GDP = gross domestic product; SSA = Sub-Saharan Africa; the CAR = Central African Republic.

To ensure regional connectivity, the CAR must have access to the Port of Douala. National connectivity is provided by a transport network that links each country’s provincial capitals and cities with a population of at least 25,000. High capital expenditure on transport is justified by substantial upgrades of the road sector
- BLS. (2011) Counting Green Jobs: Developing the Green Technologies and Practices (GTP) Survey. Conference paper: Stang, S. and Jones, C. Section on Survey Research Methods : JSM 2011

- ____. (2012a) Occupational employment and wages in green goods and services: November 2011. U.S. Bureau of Labor Statistics. News release: September 28, 2012

- ____. (2012b) Green technologies and practices: August 2011. U.S. Bureau of Labor Statistics. News release: June 29, 2012.

- ____. (2013a) Green Goods and Services survey: results and collection. U.S. Bureau of Labor Statistics. Monthly Labor Review. September 2013.

- ____. (2013b) The Green Goods and Services Occupational survey: initial results. Warren, Zack. U.S. Bureau of Labor Statistics. Monthly Labor Review. January 2013.

- ____. (n.d.). Measuring green jobs. U.S. Bureau of Labor Statistics (BLS). https://www.bls.gov/green/#definition (accessed on 06/03/2022)

- Bontadini, F.; Vona, F. (2021) Anatomy of Green Specialization: Evidence from EU Production Data, 1995-2015. Sciences Po OFCE Working Paper, n° 21/2020

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- BPS (2014). Klasifikasi Baku Jabatan Indonesia (KBJI) 2014. Kementerian Ketenagakerjaan dan Badan Pusat Statistik.

- Consoli, D.; Marin, G.; Marzucchi, A.; Vona F. (2016): Do green jobs differ from nongreen jobs in terms of skills and human capital? In: Research Policy, 45(5), 1046- 1060.

- Dierdorff, E.;
is formally employed at least one month (col. 1); percent of months she is formally employed (col. 2); and average monthly wage (col. 3). Average monthly wage is computed over the months worked; it is zero if the individual does not work formally at all. Regressions are weighted using WBSCPS sampling weights. All specifications include noise controls as well as fixed effects for graduation year, state, and field. They also control for PCA scores for the following: student body characteristics (based on survey data), student characteristics (based on individual-level data, including proxies for previous labor market experience), program characteristics (based on survey data), HEI characteristics (based on survey data), and peer characteristics (based on individual-level data, including proxies of peers’ previous labor market experience). See Appendix 1 for the list of the variables included in each index. Standard errors clustered at program level are in parenthesis. Significance levels: * p<0.1, ** p< 0.05, *** p<0.0 91
Pastureland has a significant negative impact on wages, with a 10% increase linked to an annualized wage decrease of about 2.57% (coefficient of -0.219, significant at the 1% level). This may stem from the lower labor intensity of livestock farming compared to crop farming and the seasonal nature of pastoral activities, often reliant on family labor.

# 5.3 Estimates for Food Stress and Food Crisis Risks

The logit models show strong predictive performance, correctly classifying 69.14% of food stress cases and 87.89% of food crisis cases. These results highlight the importance of climatic and economic variables in shaping food security and demonstrate the models' ability to capture complex dynamics. The predictive power also points to their potential use in targeting and monitoring efforts.

To estimate the population at risk, we multiplied the population of each period by its predicted probability of food stress and food crisis. These estimates were rescaled by fitting a linear regression model, using the share of the population exceeding IPC levels 2 and 3 as the dependent variable and the population-weighted probabilities as the predictor. The rescaled values were then used to estimate the population at risk over time.

## **Geographical Variation in Modeled Risks**

Tables 4 and 5 provide a detailed breakdown of the probability of food stress and food crisis across Afghan provinces, along with provincial demographic data (NSIA, 2021) to capture population exposure to these risks. Overall, while food stress is widespread, food crisis risk is more concentrated but significant in certain areas.

Table 4 shows the probability of food stress, indicating moderate levels of food insecurity. Most provinces fall into higher risk categories, with 28 of 34 provinces showing an average probability of food stress above 50%, representing over 28 million people (89% of the country's population). Notably, 12 provinces have probabilities between 61%-70%, and two provinces exceed 71%, exposing large populations to chronic food stress. The distribution highlights widespread