rafmacalaba/data-use-mentions-extended huggingface dataset:
those that are not reached will continue to be listed in the bank of possible phone numbers to be tried each month, and the same household is potentially contacted for a number of survey rounds.<sup>10</sup> Regardless of the exact numbers of households in the database of phone numbers, in each survey 20 percent of responses are from new households and the remaining responses are from households surveyed at least once before.

The survey first collects information regarding a household’s location, and whether the household has been displaced. If the household is currently displaced, the survey inquires about the month and year of displacement and the origin governorate.<sup>11</sup> Given that households are followed over time, there are households that are displaced more than once, and also households that return to their homes following one or more initial displacements. We then merge the time and location of households with conflict data from the Uppsala Data Program (prior to 2016) and from the Armed Conflict and Event Data (ACLED) (2016 and later).<sup>12</sup> We match the violence data before, during, and after the time of displacement in the origin district.

The survey further collects a full food security module, including the number of times households in the week before the survey have consumed from important food groups, the number of times households have resorted to five common food coping strategies, and whether the household received food assistance in the past 30 days. From these data, we calculate the household Food Consumption Score (FCS) and the reduced Food Coping Strategies Index (rCSI). A higher FCS and a higher number of days consuming particular food items are associated with better food access; and a lower rCSI and a lower number of days relying on negative food coping strategies are associated with better food access.

The entire survey between August 2015 and December 2018 includes 18,078 separate households.<sup>13</sup> This analysis focuses
would buttress the argument that funds should be set aside by current populations to cover benefits received in the future.

- Some revenue sources are of a non-recurring and capital nature (asset disposals, auctioning of licenses, resource royalties and death duties).  One could argue that receipts should be included in a capital budget and be used to reduce the need for debt finance (Boothe 1993).

The application of “capital” to budgets of governments is therefore not a simple manner and judgement is needed to classify expenditures as public capital. Given that this exercise could involve value judgements, a concern could arise that far too many public expenditures might be classified by governments as capital in order to justify debt financing as later discussed in the next section on fiscal rules. In part, this is related to motives for debt financing of capital as discussed above.

# **<u>4. Survey of current practice</u>**

# **4.1 Fiscal rules**

Fiscal rules and capital budgeting has impacted on a number of practices – European fiscal targeting, golden rules for debt accumulation, public infrastructure agencies and public-private partnerships. Below, a brief review of the issues related to these practices is provided.

_Europe and the SGP._ The Maastricht Treaty contains a provision requiring member states to avoid running “excessive” deficits, whether or not they have adopted the euro. A protocol to the treaty specifies in turn that members’ fiscal stance is to be judged by two criteria: whether the budget deficit is less than 3 percent of GDP, and whether total government debt exceeds 60 percent of GDP. If the Council determines a deficit is excessive, there is a procedure to encourage its elimination. The Council may issue warnings and impose deposit requirements and, eventually, fines.

The SGP corresponds to the provisions of the Maastricht excessive deficits procedure, but it clarifies the terms, introduces monitoring procedures, and it gives the Council greater teeth in the event of violations. Under the SGP, deficits
business only, 11 percent own a farm business only, and the rest own both. An estimated 26 percent of the pooled sample households do not own a business of any type.

**Table 2. Distribution of household business ownership (household business definition)**

||Share (%)|Observations|Countries|
|---|---|---|---|
|Owns a business|73|3008|11|
|Farm business|11|3008|11|
|Nonfarm business|42|3008|11|
|Farm & Nonfarm business|20|3008|11|
|Does not own a business|26|3008|11|

Source: Own calculations based on HFPS-Wave 1.

Notes: Urban observations only. Countries include Chad, Ethiopia, Madagascar, Malawi, Nigeria, Sierra Leone, Somalia, South Sudan, Sudan, Zimbabwe.

# **These patterns are similar when using the household head definition to identify household**

**enterprises** . Table 3 presents the distribution of respondent employment type from wave 1 of the HFPS from 11 countries.<sup>27</sup> Most respondents are self-employed, from which we infer that they own or work in a household enterprise (79 percent). An estimated 58 percent the sample are selfemployed in non-agriculture sectors, similar to the estimates using the household business definition. The remaining observations correspond mainly to wage employees (19 percent).<sup>28</sup>

**Table 3. Distribution of Urban Household Enterprises (Household Head Definition)**

||Share(%)|Observations|Countries|
|---|---|---|---|
|Self-employed/Family agricultural business|21|2,812|11|
|Self-employed/Family non-agricultural business|58|2,812|11|
|Wage employee|19|2,812|11|
|Seasonal or Temporaryworkers|
a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

34
# **2. Effective Rates of Protection in Egypt: Cost-share Data Sources and Previous Studies**

Effective rates of protection are measures of the impact on an industry’s or economic sector’s net returns due to border protection. Nominal tariffs are relevant for consumer welfare and the analysis of patterns of consumption. ERPs are relevant for analyzing the impact of commercial policies on incentives for resource allocations. Without making normative assessment of the desirability of interventions in the form of tariffs and subsidies, ERP estimates aid in understanding the effects of these policies on the incentive framework for production, and so provide a quantitative measure of economic signals guiding investments and factor use. In calculating effective rates of protection (ERPs) of industries for any country, there are two technical decisions to make. First, what is the most appropriate level of disaggregation to use? Second, based on the disaggregation level, how does one incorporate non-tariff barriers? Given the information available at the present date for Egypt, the following calculations of ERPs focus mainly on the effects of tariffs and energy subsidies, but ERPs are also presented that adjust for the estimated effects of NTBs for activities where estimates are available.

The question regarding the level of aggregation is addressed using two sources of costshare information at different levels of aggregation. The most detailed cost share data available were obtained for a study by Cassing, et al., which was done for a larger Nathan Associates project on tariff reforms in Egypt for US AID, completed in October 2000. (Hereinafter we will refer to this as the Nathan study.) Although ERPs were reported in this study, the cost share data on which the ERPs were based are reported in greater detail by Nabila Al Iskandarani.<sup>1</sup> These cost-share data cover 20 specific industries in the private sector and 17 industries in the public sector. A list of these industries and their intermediate inputs are found in Annex Table 1. These industries are identified by their 4-digit ISIC code (rev. 3
**Table** 2

Average Fiscal Ratios for Africa and Developing Countries, 1978-82

||Africa_<br>(perce|Developing<br> <br>Countries<br>ntage)|
|---|---|---|
|Share of Central Governmen.t<br>Expenditures<br>and Lending<br>Minus Repayment in GDP|25-|25|
|Shareof Capital<br>Expenditures<br>in Expenditures<br>and Lending<br>Minus Repayments|24|19|
|Share of**Taxes**in GDP**|17|17|
|Share of International Trade<br>**Taxes**in Revenue|22++|16|
|Share of Nontax Revenues in<br>Revenue|12-|21|
|Share of Grants in Revenue|3++|2|

* Sub-Saharan Africa plus Morocco, South Africa and Tunisia. Since the averages are weighted by (1980) GDP, they are heavily influenced by South Africa, Nigeria, and Morocco. The pluses and minuses indicate the probable direction of change in the average if these countries were also omitted.

** Calculated as (Revenues/GDP) minus the sum of (Nontax Revenues/GDP) and (Grants/GDP).

Source: IM, Government Finance Statistics Yearbook, 1985.

35
<mark>hit by a major disaster, dependent firms experience an average drop by 2–3 percentage points in sales growth after the event. Given that suppliers represent a small share of firms’ total intermediate inputs, they conclude that these estimates are strikingly large.</mark> The vulnerability of firms depends on their sector and their position in the supply chain. Colon et al. (2019) show that, after the 2016 Morogoro flood in Tanzania, the supply chain amplification of disasters is higher for nonprimary products. Agriculture products, which as primary products are less dependent on other supply chains, are less affected by disruptions than secondary products (such as processed food), which rely on the supply of primary agricultural products. <mark>Mainly due to a lack of data, most research on supply chain propagation of disaster effects has focused on industrialized countries.</mark> Firms have a range of coping measures to mitigate losses, speed up recovery, and smoothen expenses to ensure business continuity. This study uses survey data to estimate whether Tanzanian firms implement similar coping measures to those identified by Dormady et al. (2017) in a case study of firms affected by Hurricane Sandy in the United States. The most common coping mechanisms relate to a firm’s decisions about its capital, assets, labor, inputs, and production technology (figure 2). But a lack of access to credit, inadequate governmental support, or limited cash can make certain coping measures unaffordable or inaccessible, particularly for informal and small firms (Rentschler et al. 2019a).

**Figure 2: Coping measures firms can use to mitigate the adverse effects of infrastructure disruptions**

|**Capital**|**Labor**|**Inputs**|**Technology**|
|---|---|---|---|
|- relocate|- hire/fire|- switch suppliers|- adjust|
|- replace<
US (Chinese) tariffs. Our second proposition shows that the estimated tariff elasticities of exports jointly identify: i) whether a country’s exports substitute or complement the US or China; and ii) whether it operates along downward- or upward-sloping supply curves.

We implement the empirical analysis on global bilateral HS6-level trade data. We first estimate product-level export responses from exporters other than the US or China assuming common tariff elasticities across countries.<sup>2</sup> The first takeaway is that, on average, bystanders increased their exports to the US, barely changed their exports to China, and _increased_ their exports to the rest of the world in products with higher US-China tariffs. So, while the US and China taxed each other, the average country increased its global exports in targeted products relative to untargeted products. Therefore, the trade war created net trade opportunities rather than simply shifting trade across destinations.

This initial approach assumes common tariff elasticities across countries, but these elasticities may vary by exporter, importer, sector, and size of the trade flow, as implied by the model.

> 1See Amiti et al. (2019), Cavallo et al. (2021), Fajgelbaum et al. (2020), Flaaen et al. (2020), Flaaen and Pierce (2019), and Waugh (2019), among others. Fajgelbaum and Khandelwal (2021) review the research that has examined the economic impacts of the trade war.

> 2Consistent with other findings in the literature, we find that the US and China reduced bilateral exports in products with larger tariff increases.

1
not driven by the 72 countries sharing 18 regional input-output tables, we estimate the baseline specification on the sample of 60 countries that have country-specific input-output tables; column 2 displays the results. With less than half of the total number of observations, aid to transportation, energy, and banking services maintains the positive significant relation with manufacturing exports whereas aid to ICT and business services is no longer positive; however it is not statistically significant.

A different way to verify the sensitivity of the input-output data in our estimates is to use the same input-output table for all countries. We replace services intensities computed from GTAP input-output tables with intensities obtained from the total input requirement table from Argentina.<sup>10</sup> The estimates are shown in column 3 of Table 4. The main results hold: aid to transportation, energy, and banking services is positively associated with higher levels of manufacturing exports. However, in this case, aid to business services becomes positive and significant whereas the coefficient on aid to ICT services remains statistically insignificant.

As mentioned in the data description, CRS aid disbursements coverage has been improving over time. As a way to check for measurement error in aid disbursement data we estimate the baseline regression using only the last three years of available data. Column 4 of Table 4 displays the results. Aid to all service sectors displays a positive relation with manufacturing exports; however, aid to banking is not statistically significant. Notice that the magnitude of the coefficient on business services increases substantially. This could be due to the concentration of more resources into this type of services in the last few years as shown in Figure 2.

As a final robustness check, we estimate equation (1) using only the sample of countries that have export data for every manufacturing sector and every year. This sample consists of 117

> 10  http://www.indec.gov.ar/

13
who.int/tobacco/resources/publications/ru_tfi_tb_tobacco_factsheet.pdf.

- ———. 2017a. “WHO Tobacco Fact Sheet.” WHO, Geneva. http://www.who.int/mediacentre/factsheets/fs339/en/.

- ———. 2017b. “GATS, Global Adult Tobacco Survey; Fact Sheet: Russian Federation 2016.” May 18, WHO, Geneva. http://www.who.int/tobacco/surveillance/survey/gats/rus_factsheet_2016.pdf?ua=1.

- World Bank. 1999. “Curbing the Epidemic: Governments and the Economics of Tobacco Control.” Report 19638 (May), Development in Practice Series, World Bank, Washington, DC.

- Wynder, E. L., and E. A. Graham. 1950. “Tobacco Smoking as a Possible Etiologic Factor in Bronchiogenic Car Cinoma: A Study of Six Hundred and Eighty-Four Proved Cases.” _Journal of the American Medical Association_ 143: 329–36.

15
information on the sampling distribution of our bound estimates, so that we have more clarity on their precision. Second, a potential extension to our analysis is to dive deeper into the chronic nature of poverty by creating a synthetic panel for three rounds. For example, we could investigate the crosssectional surveys of 2012, 2014, 2016, which would give estimates for the proportion of poor people over a longer or shorter time interval.

Naturally, our analysis would benefit from the inclusion of more recent data, specifically the latest available HIS 2019 and upcoming HIS 2022 survey data. This would enable the investigation of both the effects of the Covid-19 pandemic and longer-term trends in poverty and income mobility. This would be useful for future policies, including the implementation of the recently released Twelfth Malaysia Plan for 2021–25 and the preparation of the Thirteenth Malaysia Plan that will cover 2026–2030. As in many other countries, the pandemic has had a severe impact on the lives and livelihoods of Malaysians. It is very likely that some of the progress in poverty reduction has been reversed, and that the likelihood of poverty exit 33
# **2.1. Evolution of Per Capita Income and Inequality**

Table 2.1 presents a summary of the distribution of per adult equivalent<sup>7</sup> expenditure based on the 1996, 2001 and 2007 household surveys conducted by the National Statistical Office. All these surveys follow the sampling frame of the 1987 population census. The samples are stratified and the 1996 survey has the smallest sample size with 1,728 observations 36 percent of which represent the rural sector. The National Statistical Office (2002) has noted this under-representation of the rural areas in the 1996 household survey. For the other two surveys, the sample size is 10,992 observations for 2001 and 11,391 observations for 2007.

On the basis of the means reported in the second column of table 2.1, we find that (see table 2.2) the average per adult equivalent expenditure grew 5.4 percent per year over the period of 1996-2007 in nominal terms. Looking within sub-periods, the mean per adult equivalent expenditure grew by about 9 percent per year between 1996 and 2001, and by about 2.5 per year between 2001 and 2007. In real terms, these average rates of growth fall respectively to 1.9 percent, 4.1 percent and 0.5 percent. National account statistics tell a different story. The real per capita GDP is believed to have grown only by 1.57 percent per year between 1996 and 2001, and by 0.57 percent between 2001 and 2007 (National Statistical Office 2002, 2008).

Table 2.2. Growth in Average per Adult Equivalent Expenditure in Cameroon

||(1996-2007)|
|---|---|
|Period|Average Growth Rate (percentage)|
||Nominal<br>Real|
|1996-2001|9.0<br>4.1|
|2001-2007|2.5<br>0.5|
|1996-2007|5.4<br>
back into the labor force (World Bank 2018a). In assessing whether growth is jobless or not, it is therefore important to distinguish between the short- and long-run effect of economic growth on employment growth (World Bank 2018a).

Estimating the short-term relationship between economic growth and employment rates has been the subject of a vast literature in advanced economies. This short-term relationship has often been interpreted in connection with Okun’s Law, which posits that when growth accelerates above potential, the unemployment rate falls below its natural level (Lee 2000, Ball et al. 2017). In advanced economies, the consensus is that rapid economic growth reduces unemployment rates in the short term, while slowdowns are associated with increases in unemployment. The literature is much scanter in developing countries, partly due to data limitations. Ball et al. (2016) conclude that the relationship in developing countries is only half as strong as in advanced countries and varies considerably across countries. The weakness of the relationship in these countries is not surprising as many are too poor to afford being idle and unemployment is hence a poor indicator of the situation in the labor market.

Instead of analyzing unemployment, we correlate quarterly economic growth with quarterly changes in employment. Some sources, such as the Sri Lanka LFS, are explicitly designed to generate quarterly data. For other sources, the month of the interview can be relied upon to disaggregate annual data by quarters. This approach can be used for Bangladesh, India and Pakistan. Combining genuine quarterly employment data with constructed breakdowns by quarter, 123 observations on quarterly changes in employment can be generated. As for GDP data, both India and Sri Lanka publish quarterly series, whereas Bangladesh and Pakistan do not. For the latter two countries we used the Denton method to interpolate annual GDP with quarterly industrial production. The Denton method imposes the constraint that the interpolated series adds up to the annual GDP totals.<sup>5</sup>

> 5  The Denton method aims to achieve consistency between time series related
to ensure consistent measurement.

Agents’ jobs involved several sales-related activities, including assisting customers via online chat and phone. Each agent worked the chat interface six weekdays per month on average. On days when agents responded to chats, they spent 2.9 hours on the online sales interface with customers, engaging in approximately 8 unique chat conversations per day. The average chat lasted 22 minutes and contained 73 words. The sales agents did not all work during the full study period for institutional reasons, although they all worked a majority of the time.

> 14 We only know customers’ approximate locations—we do not have access to any customer demographic data. However, estimates from other sources suggest that in 2019 nearly 50% of digital buyers in Africa were female (Statista, 2019).

> 15 We restrict our sample to observations with five or fewer previous purchases as some users may have accessed the site using non-unique locations—e.g., public areas or businesses—and hence their purchase records were not well-linked. This restriction retains 98% of observations.

8
), defined as the probability of rejecting at least one true null hypothesis.

Our second way of reporting results focuses on individual outcomes that can be regarded as important on their own. These are selected within the broader list of variables from which we compute the indexes, and we single them out because they are key to the overall message of Shuga (e.g., “you should wear a condom”; “having concurrent partners can be risky”; etc.) or they capture specific messages that are strongly emphasized in certain episodes (e.g., “a young boy should be allowed to play football”; “you have to take a second HIV test after 3 months”; etc.). For our coeffi cients of interest, we report both ‘naive’standard errors corrected for clustering at the location level, and FWER-adjusted p-values —that adjust for multiple hypothesis testing—based on 10,000 replications.

# 4.2 Data and descriptive statistics

## Sample

In total we interviewed 5166 main study participants at baseline and 4986 at follow up.<sup>13</sup> Since our conditional specification includes the lag of the dependent variable, our working sample consists of the individuals for whom we have both rounds of data, i.e., 4986 observations.

> 13 We performed power calculations using the Nigeria DHS 2008 and determined that a sample of 64 individuals per location, or 5120 individuals in total, half male and half female, would enable us to detect a change of between 0.15 − 0.20 standard deviations in our main outcomes of interest. Updated power calculations using our baseline data showed an improved minimum detectable effect of 0.12 − 0.17 standard deviations.

17