rafmacalaba/data-use-mentions-extended huggingface dataset:
following the promised tariffication of many nontariff agricultural trade barriers following the UR, but such is not the case. The reason is that governments agreed to allow countries to set their bound tariff at excessively high levels so long as they promised at least existing levels of imports to come in at low tariffs. That triggered the use of so-called tariff rate quotas (TRQs). TRQs add considerable complexity to modelling empirically even the domestic impacts of agricultural trade policies and their reform, let alone their trade effects. Again, they are better handled in the new Version 6 GTAP database.

Fourth, an additional complication for modelling agriculture is that many countries impose quarantine restrictions and even bans on imports of farm products. Hence even if the bound rates and TRQs had been correctly modelled, the results may still overstate what would actually happen following tariff cuts and TRQ expansions if those quarantine restrictions begin to bite. It will be a long time before we have a comprehensive usable data base showing the extent of protection afforded by these and other technical barriers to trade, even though such barriers may be even larger than that due to the bound tariff on numerous farm products. This problem will escalate as and when food safety process standards (e.g., for GMOs) become more widespread, since the concept of ‘like product’ will come under challenge. Similar protective effects result from technical barriers to trade on non-farm products (Baldwin 2001). The Uruguay Round’s Sanitary and Phytosanitary Agreement and Technical Barriers to Trade Agreement have only gone a small way towards disciplining the abuse of these forms of trade protection.

Fifth, safeguards may be applied under certain circumstances such as import surges. That may well be how the US and/or EU respond when the present voluntary export restraints on textile and clothing trade are removed under the UR Agreement on Textiles and Clothing at the end of 2004 (or a few years later for China and possibly other WTO accedants such as Vietnam). That possibility has to be kept in mind when modelling
mechanism, to some degree or another, to involve local communities in the management process and create awareness of MPA purposed and objectives.<sup>7</sup> MPAs, often with technical and financial assistance from NGOs, also promote alternative income generating activities (AIGA) to reduce pressure on fisheries resources. A broad range of activities fall under these _AIGA_ strategies, some of which are alternatives to fishing (beekeeping, tree planting, farming, direct employment in MPA, etc), while others are alternatives to improve fishing practices, such as through fishing gear exchanges or loans to acquire specific fishing equipment to allow offshore fishing. We expect involvement and awareness of MPA activities, and participation in AIGA to positively influence household fishing gear choices.

# <u>Other control factors</u>

The survey design included _control_ villages that were located outside of the MPAs included in the study. In Kilwa, since there is no MPA, all villages are considered control villages. This provides a comparison group to evaluate whether MPAs have an impact on household fishing gear choice through other means which are not captured in the questions posed in the household questionnaire.

We also examine the impact of household choice of fishing gear on household welfare. If destructive fishing gear types are more productive, then one would naturally expect that households’ motivation to use such gears is the realization of a higher income. We use per capita consumption as a measure of household welfare rather than income, however, as extensive experience with household surveys has shown that income data are often more susceptible to measurement error and show more variance, due to saving and consumption smoothing behavior (Deaton, 1997). We follow the standard specification adopted in many poverty assessments, and which has been previously used in a study of rural poverty in Tanzania (Narayan and Pritchett, 1999). In this framework, consumption is determined by the households endowments of human (H), natural (N), social (S), physical (K), and
is reduced due to the employment guarantee, the share of total inputs allocated to each of the crops approaches the ratio of the deterministic scenario. Interestingly, this effect holds independently of whether credit constraints reduce total input allocation or not.<sup>17</sup>

# **3 Data**

When estimating the insurance effect of the NREGS, one must take into account considerable variation in the quality of implementation of the program across states (Dutta et al., 2012). The section above highlighted the importance of households’ expectations about future income streams. Therefore it seems plausible to observe insurance effects only in states in which the demand for employment has been sufficiently met, already in the early years of program implementation.

Given these considerations, the model specified above is tested using the Young Lives Survey (YLS) data for Andhra Pradesh. Andhra Pradesh is particularly suited to studying the question of interest because it is one of the best performing states in India in terms of the number of workdays generated per household and meeting the demand for work (Dutta et al., 2012). Regarding outreach, only Chhattisgarh, West Bengal, Madya Pradesh and Rajasthan reached higher proportions of rural households in the financial year 2009/10.<sup>18</sup> The YLS data set covers 3,019 households living in six different districts, 17 subdistricts (blocks) and 87 villages. The selection process of districts for the YLS ensured that all three geographical regions were surveyed, as too were the poor and non-poor districts of each region, such that the YLS is broadly representative of the population of Andhra Pradesh (Galab et al., 2011).<sup>19</sup> Three rounds of interviews have been conducted

> 17C.f. appendix A for a detailed derivation of this result.

> 18At the same time, Andhra Pradesh has been a forerunner in terms of innovative approaches to the implementation of the NREGS. First, it has a lot of experience with performing
_Figure 10: Population estimates for Syria_ <!-- Start of picture text --> 30,000,000<br> 25,000,000<br>POP CENSUS<br> 20,000,000<br> 15,000,000<br>HNAP MNM<br> 10,000,000<br>CONFLICT STARTS<br> 5,000,000<br> -<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>CBS - HNAP Prospects 2010 Prospects 2015<br>Prospects 2019 Prospects 2022<br><!-- End of picture text --> _Source:_ WB staff calculations based on World Population Prospects (UNDESA), HNAP and CBS _Note:_ Population projections based on medium fertility scenario _Population data from the 2022 revision of the World Population Prospects (which is included in WDI) will be used for the poverty projections._<sup>_29_</sup>

- 4.2 Distribution neutral poverty projections: Sensitivity analysis

This section presents the results of distribution neutral poverty projections for Syria. As the country was re-classified as low-income country in 2018, estimates have been computed using both the lower-middleincome countries international poverty line ($3.65 in 2017 PPP) and low-income countries international poverty line ($2.15 in 2017 PPP).

For an analysis of the sensitivity of poverty projections to underlying assumptions, we present results obtained using baseline poverty estimates from all years available (2003, 2007 and 2009); growth estimates based on per capita GDP in constant terms and per capita GDP in current LCU adjusted using CPI; and using either a unit pass-through or a “conflict-adjusted” pass through (Box 2).<sup>30</sup> Figure 11 show poverty projections results at the Lower income countries international poverty line (in 2017 PPP), using different poverty baselines and different NA aggregate growth with a unitary pass through
# **3. Data**

Fiscal incidence analyses such as those presented in this study rely on integrating two sources of data. First, administrative data mainly provide key insights into public revenues and expenditures, but can also provide information on subsidy schemes, transfer systems and users of public services. Second, household survey data is crucial in identifying individuals, both as taxpayers for different kinds of taxes and as users of publicly funded services. The next subsections present details on these datasets used in this study.

# **3.1.  Administrative data: Taxes and the child-relevant budget in Ethiopia**

Administrative data used in this study include the following: (i) public revenue and expenditure data for the 2018/19 fiscal year and regional education and health spending from the Ministry of Finance, (ii) school enrollment information from the Ministry of Education, (iii) kerosene subsidy from the Ethiopian Petroleum Supply, and (iv) wheat subsidy from the Ethiopian Trading Businesses Corporation.

Table 2 shows Ethiopia’s tax revenues in 2018/19. Revenue collection was equivalent to 13.5% of GDP; 43% of those through direct taxes (mainly business profit tax followed by personal income tax) and 57% through indirect taxation. The last two columns show the tax burden per child. Domestic indirect taxes are the most important followed by personal income taxes and business profit tax.

_Table 2: Annual tax revenues, share of GDP and per child burden, 2018/19_

|T|Gove<br>|rnment tax reven<br>|ue<br>|Tax burde|n per child<br>|
|---|---|---|---|---|---|
|ax category|ETB (in<br>millions)|Share of tax<br>revenue (%)|Share of<br>GDP (%)|ETB|Share of tax<br>burden (
1570|1.7|0.19|---|Encuesta Nacional de Hogares, 2019;<br>ILO SIALC; BloombergNewswire|
|Indonesia (Brinkman et al, 2017)|70.1|2913897|-0.2|---|0.158|National Labour Force Survey, 2020;<br>Statistics Indonesia of the Republic<br>of Indonesia; BloombergNewswire|
|Bangladesh (Spier et al, 2020)<br>Notes: Missing recent data for averag|59.4<br>e real wage growth|12016<br>for Ghana, we|3<br>averaged valu|0.25<br>es across other|0.37<br>countries. Coun|Labour Force Survey, 2017;<br>Bangladesh Bureau of Statistics<br>try currencies were the following:|

Notes: Missing recent data for average real wage growth for Ghana, we averaged values across other countries. Country currencies were the following: India (INR), Malawi (MWK), Mozambique (MZN), Ghana (GHS), Peru (PEN), Indonesia (IDR), and Bangladesh (BDT)
Existing sources of publicly available data are rather limited with respect to these three criteria. We resorted to the LIS Cross-National Data Center in Luxembourg (http://www.lisdatacenter.org/), which allowed us to process data from four countries (Italy, Germany, France and Switzerland), while a fifth country was obtained from accessing the original provider (United Kingdom – <u>https://www.understandingsociety.ac.uk/).</u> The surveys we have used are therefore the following:

- **Italy:** Survey on Household Incomes and Wealth (SHIW), collected by the Bank of Italy – 11 surveys, covering the period 1993-2014 (information on parental background is not available before the starting date – originally consisting of 112,690 individuals, which reduces to 107,846 when considering non-missing information.

- **Germany:** German Socio-economic Panel (SOEP) – 11 surveys, covering the period 1984-2013 – originally including 156,338 individuals, then reduced to 133,467 in case of non-missing information.

- **France:** Household Budget Survey (HBS), conducted by the Banque de France) – 6 surveys, covering the period 1978-2005 – originally consisting of 97,306 individuals, declining to 89,119 when missing information is excluded.

- **Switzerland:** Swiss Household Panel (SHP) – 6 surveys, covering the period 1999-2014 – originally consisting of 43,102 individuals, which then decline to 31,273 valid observations.

- **United Kingdom:** starts as British Household Panel (BHPS), replaced after 2009 by the Understanding Society-Household Longitudinal Survey (UKHLS) – considers 24 waves over the period 1991-2014 – originally consisting of 434,253 individuals, which then decline to 308,625 valid observations.

Our selection rules include individuals aged 25-80 with a positive disposable income, harmonized according to the LIS procedure (variable DPI).<sup>7</sup
Where the _ds_ are dummies for the 27 sub-district*firm density strata used in the block level randomization, and the standard errors are clustered at the block level. The first five rows show variables obtained from the listing survey, while the remainder show variables from the baseline survey. Out of 48 coefficients shown, 5 are significantly different from zero at the 10 percent level, and only one at the 5 percent level, which is in line with what would be expected by pure chance.

In contrast, since the inspector block firms were not interviewed at baseline, randomization for this group required randomizing among all firms originally listed. Our original plan called for assigning half the firms listed in these blocks to receive the inspection treatment, and half to be indirect inspector firms. However, this was not feasible with the existing number of inspectors, and so we instead had to randomly choose only one-quarter to be inspector firms. This resulted in 577 firms getting assigned to the inspector treatment. The remaining firms in this block are then all indirectly inspected, but budget constraints required us to randomly choose 593 of the indirectly inspected firms as the sample we would work with for follow-up surveys. The right control group for these firms consists of listed firms in the control blocks. Recall we already have a sample of 689 firms in the control blocks which had answered the baseline survey. We therefore randomly choose an additional sample of 709 control block firms which had not answered the baseline survey, and assigned them also for follow-up. We then reweight the control group sample to take account of the fact that we have all firms which answered the baseline, and only a sample of those which did not, in this group.

The first three columns of Table 3 then compare the inspector and indirectly inspected firms to this weighted sample of control firms, in terms of the characteristics available from the listing survey. Although this offers far fewer variables for checking balance than is possible for the communication and free cost treatments, the results are still reassuring, with
_Brazilian spatial labor dynamics Page 17_

**Table 6. Variables and Sources**

A. Variables used in the Wage Equation and their sources:

|**Variable**|**Source**|**Instrument(s)**|**Source**|
|---|---|---|---|
|Teacher qualification in 1991|IBGE - Census|Exogenous||
|Years of schooling in 1991|IBGE - Census|Exogenous||
|Total precipitation|DECRG - IE|Exogenous||
|Government with Accountability|IBGE - MUNIC|Exogenous||
|Delta employment|IBGE - Census|Employment rate in 1991<br>Soil quality<br>First and third principal components of<br>monthly rainfall|IBGE - Census<br>DECRG - IE<br>DECRG - IE|
|Transport cost to São Paulo|NEMESIS|Transport cost to São Paulo in 1968|NEMESIS|
|Transport cost to nearest State<br>Capital|NEMESIS|Transport cost to nearest State Capital in<br>1968|NEMESIS|
|||Dependency ratio in 1991<br>Illiteracy rate in 1991|IBGE - Census<br>IBGE - Census|
|Delta government transference|IBGE - Census|Repetition rate in 1991<br>Proportion of elderly persons in 1991<br>Population in 1991<br>Proportion 5-15 over 15-55 yrs old in 1991|IBGE - Census<br>IBGE - Census<br>IBGE - Census<br>IBGE - Census|
|||Delta education market potential|DECRG - IE|
|Delta Market Potential|DECRG - IE|Urbanization rate in 1991<br>Population density in 1991|IBGE - Census<br>IBGE - Census|
The various policy uncertainty measures have some similarities but also differences to another measure of global economic policy uncertainty (EPU) in Davis (2016). The global EPU and the policy uncertainty measures in this paper show peaks of uncertainty in the early 2000s, during the GFC, and the pandemic. Fiscal policy uncertainty reflects rising uncertainty during the European debt crisis of 2012, in the mid-2010s, and during the pandemic in line with EPU peaks. Differences are also evident. Most notably, the global EPU suggests that policy uncertainty rose earlier, in 2018-19, than fiscal policy uncertainty before both reach new highs in 2020. Several factors could explain the differences across these measures. First, this paper uses a measure of policy uncertainty as in Jurado et al. (2015) which is fundamentally different from proxy-measures like the EPU. Second, measures of uncertainty are specific to the fiscal or monetary policy measure and not general policy uncertainty as in Davis (2016). Notable events in the EPU such as the European immigration crisis in 2015, the Brexit referendum, and political turmoil in several countries do not reflect as heavily in the unforecastable part of government spending or interest rate policy which is the focus of this paper. Third, as in Jurado et al. (2015), peaks in uncertainty occur far more infrequently compared to the EPU but have larger and more persistent correlations with economic activity.

While there is no global monetary policy uncertainty measure to compare this paper’s results to, Husted et al. (2020) provide some guidance on the similarities and differences for the United States. Overall, the correlation between the newspaper-based measure of monetary policy uncertainty and in this paper is weak although there are similar spikes: elevated uncertainty can be seen in both measures in the early 2000s and following the COVID-19 pandemic. The measure in this paper, however, better reflects monetary policy uncertainty embedded in the Primary Dealers survey or implied volatility of options on one-year swap rates. Notably, monetary policy uncertainty is highest during the GFC in
community. Certainly, a percentage of those reporting that microcredit was unavailable in their community might also simply be unaware of its presence, given that some form of MFIs had reached 85 percent of all depositors and borrowers in SSA by 2009 (CGAP and MIX, 2011). When taken together, these numbers point to exceedingly low levels of awareness.

Sparse evidence from the literature also points towards awareness as part of the problem. In an intervention aimed at urban microenterprises in Sri Lanka, de Mel, McKenzie and Woodruff (2011) find that providing more information about the loan product on offer by a regional development bank doubled the proportion of firms receiving a loan. There are a couple of things worth bearing in mind about this intervention: first, the information session was combined with a decrease in the bureaucratic requirements for the loan, so the impact of information alone cannot be isolated. More importantly, the intervention entailed providing more details about the loan to clients who already knew about its availability. The SSA problem, on the other hand, centers on low awareness about the existence of MFIs and the availability of microloans as such. However, as suggested by Beck et al. (2011) financial literacy programs for households and enterprises might be able to address some of the nonfinancial constraints to borrowing, particularly in rural areas.

Another fundamental challenge that might be keeping MFIs from making deep inroads into SSA, is that borrowing from formal financial institutions, in general, is very low (Figure 3). These numbers are based on supply-side data collected by the International Monetary Fund (IMF) directly from Central Banks around the world on the number of loan accounts. SSA shows very low loan penetration; for example, the number of loan accounts normalized by population is less than 10 percent, with the exception of a number of countries in Southern Africa. However, there is a great degree of country-level heterogeneity in this borrowing rate – the numbers range 13
Real GDP growth|Real GDP growth is<br>lowered in years t+2 and<br>t+3<sup>2/</sup>|B2: Exports growth|Export value growth lower<br>in t+2 and t+3<sup>2/</sup>|
|B4: Exchange rate<br>depreciation|One-time 30 percent real<br>depreciation in t+2|B6: Exchange rate<br>depreciation|One-time 30 percent real<br>depreciation in t+2|

1/ For additional details see Painchaud and Stučka (2011). 2/ Growth is set at its historical average minus one standard deviation.

Overall, we observe that average liquidity indicators are only marginally affected by shocks for the sample as a whole and for the different groups, despite the fact that small deviations from the baseline tend to be protracted, especially in the case of debt service to revenue.<sup>14</sup> As a result risk ratings on average are not sensitive to liquidity shocks. Further, in the situation pre-shock breaches are on average short lived and small in size (Annex Table 7C), leading to few cases of high risk of debt distress.

On the contrary, solvency indicators show sensitivity to shocks, leading to more protracted and larger breaches on average. In the public DSA shocks lead to protracted deviations from the baseline in general, although the size of the changes in debt-to-GDP ratios varies across groups. The depreciation shock has a lesser effect in general, on average, while shocks to the primary balance (A2) and real GDP growth (B1) affect the Frontier and HIPC groups more. Real GDP shocks are the main source of vulnerability of Fragile and Oil Exporting countries (Annex Table 7A).

Similar effects are found for solvency indicators in the external DSA, although the effect on breaches vis-àvis the threshold is more significant on the duration rather than the size of the breach (Annex Table
remains weak in a number of developing countries, inferior to what may be considered the minimal threshold for take-off (around 30 percent). Progress made with mobile phone technology can lead to rapid improvements in connectivity, however it does not solve the necessity for greater internet penetration – something which remains quite low in most developing countries. Infrastructural needs for innovation in developing countries are, however, not limited to telecommunications. Road and other transport infrastructure are of primary importance, as well as sanitation, water, and other systems.

# **_Innovation systems_**

As a consequence of this overall problematic environment, innovation systems<sup>8</sup> in developing countries are poorly constructed and are very fragmented. On the enterprise side, generally a large number of micro-enterprises operate in the informal economy, and a more or less important number of foreign-based firms, which tend, however, to be disconnected from the rest of the economy.

> 6 For a convincing demonstration of the correlation between educational levels and innovation performances, see W. Maloney, presentation at the ECA KE forum Budapest, March 2004.  Innovation performances are measured by patent deposited in international systems.

> 7 On this notion of functional literacy – which means a capability to orient oneself in various walks of life – see OECD, and notably the Adult Education Surveys.

> 8 The innovation system is defined as the set of organizations (firms, universities, public laboratories, etc.) and their linkages through which innovation processes develop.  For an in-depth analysis of innovation systems in developed countries, see Managing Innovation Systems, OECD, Paris 2000.

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