rafmacalaba/data-use-mentions-extended huggingface dataset:
builds on a global dynamic CGE model i.e. the multi-regional input-output (MRIO) version of the ENVISAGE model, and the global microsimulation framework Global Income Distribution Dynamics (GIDD). The ENVISAGE-GIDD top-down assessment approach allows for the analysis of global development and structural transformation, incorporating the complex interactions of productivity differences at the country, sector, or factor level, shifts in demand as income changes, demographic and skill dynamics in factor markets, and changes in comparative advantage. This application includes an extension of the standard modeling framework by incorporating MRIO tables that distinguish between imports of intermediate, final, and investment goods to better capture the nature of trade typical for GVCs. The CGE model relies on the GTAP 10 Data Base with a 2014 reference year and runs until 2030. The analysis covers 27 sectors and 21 countries/regions.

The CGE tool goes hand-in-hand with the GIDD microsimulation tool which translates the CGE results into implications for poverty and income distribution. This includes impacts on employment and wages of female and male workers. The GIDD simulations are based on a Global Micro Database, which covers over 80 percent of the global population and GDP. It includes harmonized household surveys for 130 countries. This study presents scenarios of globalized or segmented worlds and analyzes the implications of various shocks, such as future trade policy changes, disruptions in production, and climate mitigation policies. Their impacts on patterns of international trade, including GVCs, along with their implications for poverty and distribution, are the key questions addressed.

4
on the poorest countries – is easily understood. It is also consistent with the readily grasped “do no harm” principle (more on that below).

The health targets in the MDGs were to reduce by two-thirds the under-five mortality rate and by three quarters in the maternal mortality ratio. These are complex indicators, and the numerical quantities associated with good outcomes on these measures, are, for most people, not intuitive. For instance, it is

> 5 The share of GNI target may well have played a role in motivating an increase in ODA in some countries, such as the Norway, Sweden, United Kingdom, and Ireland, in the period from 2000 to 2011. But it is not clear, yet, whether public support for increases in ODA has consolidated. For instance, Hudson and VanHeerde-Hudson (2012) find that OECD public support for development aid, as measured by Eurobarometer surveys, rapidly fell back to 1990s levels after peaking in run up to the 2005 Gleneagles Summit.

11
collected by Rama and Artecona (2000). It includes 44 indicators on policies and outcomes related to labor markets in 121 countries since the 1950s. A more recent attempt of providing cross-country information on labor market policies is given by Djankov et al. (2003). These data set is quite comprehensive in terms of country and thematic coverage; however, it lacks time-series variation as its observations correspond to the early 2000s only.

**_3. Outcome Indicators._** We consider general and specific outcome indicators of labor market reform.  General indicators assess outcomes from the perspective of the overall functioning of labor markets, while specific indicators measure closely the immediate impact of particular policy changes.

Among general indicators, we should look for measures of improved adjustment flexibility of labor markets to aggregate and industry-specific shocks and for indications of reduced labor market segmentation. Two broad outcome indicators are the rates of unemployment and underemployment. These data are readily available from national or labor statistics; they are difficult to compare across countries but, with enough care, are useful for overtime comparisons. More precise measures of flexibility, although more difficult to obtain, are given by sectoral-, gender-, and age-specific data on labor force participation, worker turnover rates, average time between jobs, and labor force dropout rates (for an application to several Latin American countries, see Micco and Pagés 2002). Regarding labor market segmentation, a “quantity” approach would measure the share of the labor force in the informal sector, defined as the segment of the economy that does not comply with labor-market regulations (see Loayza 1996). A “price” approach would compare wages and total compensation between similarly qualified workers across different firms (in terms of size and age, for example) in comparable industries and sectors.

Specific indicators measure the immediate outcomes of policy changes. In fact, we can group these outcome indicators using the same taxonomy as the one used for policy indicators. On hiring and firing of workers, the best indicators are turnover
in the construction of consumption aggregates, prices are used to make consumption data comparable across space and time. Following Deaton and Zaidi (2002), the harmonization of consumption data is done by scaling consumption values by a Paasche index of prices. Finally, finding a price increase in primary units where demobilized ex-combatants are numerous would corroborate the hypothesis that positive spillovers of the DDR program are due to a local economy effect.

We distinguish two sets of prices. On the one hand, the reference price matrix, denoted _p_<sup>0</sup> , is a 47×1 vector such that each line is a reference price for one of our 47 consumption products. This matrix is common for all households and both periods of time. We used the 2010 panel survey as the reference year. For each one of the 47 products, the reference price was computed as follows. First, the most widely used unit of measurement was selected by considering the 44
3%|2.27|9.7|
|Finance|390|1%|0.25|1,700|2%|0.33|24.9|
|Personal, cultural, &<br>recreational|190|1%|0.69|770|1%|1.20|20.2|
|Intellectual property|260|1%|0.25|740|1%|0.14|14.9|
|Insurance|320|1%|0.88|580|1%|0.48|9.8|

Source: IMF, Balance of Payments Statistics.

62
household debt between (i) domestic and foreign currency loans and (ii) variable and fixed interest rate loans. The same data source is used to assess changes in interest rates and exchange rates. The weights are determined by aggregate central bank data and the shocks that households are exposed to are incorporated using random sampling techniques; the methodology is described in the next section. Also, the unemployment data reported by the statistical office and the same random sampling techniques are used to assess a household’s vulnerability to income shocks.

# <u>Shocks and Vulnerability</u>

To carry out vulnerability exercises, two factors must be taken into account: the type and size of the economic shocks to which households are exposed and the criteria that should be used to tag a household as vulnerable; the same criteria is used both before and after an economic shock.<sup>7</sup> Regarding economic shocks, four different types of shocks are explored: (i) an increase in the interest paid on loans issued with variable interest rates, (ii) a devaluation of the exchange rate that impacts debt service on foreign currency denominated (or foreign currency indexed) loans,

> 6 In the 2008 HBS, there are very few cases where information on loan amounts is missing. As to income data, only five households do not report current income components, but they nevertheless do not belong to the cluster of households with debt holdings. For a comprehensive description of the 2008 HBS refer to Croatian Bureau of Statistics (2010).

> 7 The shock simulation for HBS data includes, following Beer and Schürz (2007), an arbitrary threshold on total debt service— this paper uses a 35 percent threshold. The shocks are applied as does in existing literature, such as Holló (2007), Żochowski and Zajączkowski (2008), Johansson and Persson (2006), and Vatne (2006).

5
|100.00%|
|Dummy variable (minutes_≤_25)|10.00%|30.04%|0.00%|100.00%|

_Notes:_ This table reports summary statistic of the main variables. Panel A presents the statistics for the outcomes of interests: workers’ informality rates from the Economic Census in 1999 and residents’ informality rates from the Population Census in 2000. Panel B for the different definitions of the treatment group that includes: the euclidean distance, the network walking distance, a dummy variable whether the centroid of the ageb is within buffer zone of 2000 meters to the new stations, and a dummy variable whether the centroid of the ageb is within a 25 minutes walking range.

Table B3: Results: Census tract characteristics 1999 and 2000 vs. Treatment

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome:|ln Income|High Skill Share|Occupation share|Informality Rates|
|_Ti_|-0.026***|-0.032***|-0.012***|0.026***|
||(0.009)|(0.008)|(0.002)|(0.007)|
|Observations|3,193|3,193|3,193|3,193|
|R-squared|0.299|0.205|0.332|0.125|
|Distance controls|X|X|X|X|
|State FE|X|X|X|X|

_Notes:_ This table reports the results of a regression relating census tract characteristics with a dummy variable whether the centroid of the census tract is within a 25 minutes walking range. The first column reports the results for the log of income, the second column for
efficient of _Firstborn girl_ in Table A.10 is negative and often significant suggesting that FG families consume significantly less per capita than FB families even when dowry is not a consideration. This could be because FG families have more children than FB families and thus have more household members. But the interaction term is never significant, implying that there is no differential impact of expected dowry on per capita food, non-food, as well as total consumption expenditure.

**Labor supply.** The birth of a first child can affect parents’ time allocation in the following ways. As the permanent dowry-related income shock due to firstborn sex is a pure lottery and does not change the reward or wage from working, there is no substitution effect. The income effect of future dowry implies that, in the absence of credit constraints, FB parents should increase leisure (decrease labor supply) and FG parents should increase labor supply. However, if the household is credit constrained, current labor supply may not decrease for FB parents despite higher permanent income. If FG parents are income-constrained, they may also increase labor supply in an attempt to supplement their income to finance the higher savings needed for the future dowry expense. We focus on father’s labor supply in this paper as 89 percent of the mothers in our data set report being a housewife as their primary occupation.<sup>37</sup>

REDS provides information on the number of days worked each year between 1986 and the year of survey for each household member. We construct a panel data set of fathers’ labor supply from this information and estimate the effect on father _i_ from caste _c_ in state _s_ in year _t_<sup>_′_</sup> and whose first child was born in year _t_ using the following specification: where _Lit′_ denotes the number of days worked in year _t_<sup>_′_</sup> ; _Postt′>t_ equals 1 if _t_<sup>_′_</sup
the pricing of Federal Reserve payment services in the early 1980s, the central bank's share of the check processing market was around 33%. After pricing, the4 Federal Reserve' **s** hare fell to around 25%. The remaining shares alf comprised of on-us items (<sup>3 0</sup> %) plus correspondent banks and local check clearing houses (45%). With the advent of interstate bank branching in 1997, U.S. banks will become larger and their offices more geographically dispersed. This will increase the proportiop of on-us checks and further reduce the check processing market share of the Federal Reserve.

In the long-run, the Federal Reserve will increasingly become the check processor of "last resort"; it will be used by only small banks for the purpose of offsetting the market power of large correspondent banks. As its market share dwindles, due to the growth of on-us checks, reciprocal _One widely quoted example of circuitous routing at the time concerned a check deposited at a bank in Hoboken, New Jersey, and drawn on a bank only 100 miles away in Sag Harbor, New York. This check '...passed through eleven banks, traveled about 1,500 miles, and was in transit about eleven days...' (Spahr,_ 1926, _p. 105). Almost every businessman, the primary user of checks at the time, had a similar horror story to tell about their ability to collect checks in a timely fashion._

**_44_** _On-us checks are those that are deposited at the same bank they are drawn on and so are processed internally._

_The last survey of U.S. checks was for 1979, before Federal Reserve payment services were priced. Thus these market share figures are estimates._ 46 _With the full implementation of nationwide banking and branching, the central bank's check processing market share may fall to 13 % to 18 % (Berger and Humphrey, 1988)
.85)|(0.38)|(0.07)*<br>|(0.53)|(0.56)|(0.56)|

Note: P-values are in parentheses. ADF = augmented Dickey-Fuller unit-root test; EMDEs = emerging market and developing economies; PP = Phillips-Perron unit-root test.

A. Results for the full sample of 47 countries, using data for 1995-2016.

B. Results for 24 advanced economies, using data for 1995-2016. C. Results for 23 EMDEs, using data for 1995-2016. The null hypothesis of a unit root is rejected at significance levels of *** p < 0.01, ** p < 0.05, * p < 0.1.

39
distribution is not the same as the population distribution, we expect this should not affect the comparisons between the relative importance of productivities vs. preferences for different assumptions on the elasticity parameters.

# **5.2 Baseline Estimates**

Using our baseline estimates from Chetty (2012), _ξz_<sup>_c_=</sup><sup>_ξ_</sup> _z_<sup>_u_=</sup><sup>_ξ_</sup> _h_<sup>_c_=</sup><sup>_ξ_</sup> _h_<sup>_u_=0</sup><sup>_._15,wecan</sup> recover the function _G_<sup>_−_1</sup> (log( _z_<sup>_∗_</sup> ) _,_ log( _h_<sup>_∗_</sup> )) using the inverse Jacobian from Proposition 4.2. Applying _G_<sup>_−_1</sup> (log( _z_<sup>_∗_</sup> ) _,_ log( _h_<sup>_∗_</sup> )) to the observed distribution of incomes and hours worked from the ATUS yields a value of ( _n, α_ ) for each individual in our sample. However, this distribution of productivities and preferences ( _n, α_ ) is not easily interpreted. Towards understanding the role productivities and preferences play in driving income inequality, we will construct the counter-factual income for each individual if (1) everyone had the same productivity or (2) everyone had the same preferences. Comparing these measures with actual income will help us understand the extent to which inequality is due to productivities vs. preferences.<sup>29</sup> First, for all individuals ( _n, α_ ) we will calculate _zn_<sup>_CF_</sup> 0<sup>=</sup><
from the FinScope survey show that, compared to other neighboring countries, access to financial services in Tanzania is notably low. In 2009, 56 percent of households had no access to any banking or savings/credit services at all in any form despite the sharp growth in credit lending to households in recent years (Figures 4.1 to 4.3).

46. Reforms in the financial sector have had only marginal impact on improving household access to financial services overall. While formal access to finance increased by less than 4 percentage points (from 9 percent of the population in 2004 to 12.4 percent in 2009), the proportion of financially excluded households increased from 54 percent to 56 percent, respectively (Figure 4.1). Among HEs, access to banks was limited to only about 14 percent of them. Of those without bank access, about 80 percent were in the urban areas and 90 percent in the rural areas (Table A1.13). Lack of bank access was attributed by respondents mainly to economic reasons, such as not having a regular income or employment, or having very little income. However, this also reflects the low access of Tanzanian households to cheap financial services. The high bank charges prevalent among commercial banks, the only ones which can take deposits, dissuade HEs from using their services. Most HEs reported in 2009 that they depended on informal sources, such as rotating savings scheme (e.g., merry-go-round or ROSCA), family or clan, and welfare groups, rather than on formal and semi-formal sources of finance (Table A1.14). This implies that ten years after the National Microfinance Policy (NMP) was created (2001) and twenty years since the liberalization of Tanzania‟s financial sector, government interventions to increase access to financial services (including through microfinance), especially for Tanzania‟s rural population, remain largely ineffective.

**Figure 4.1:  Formal and Semi-formal Access13 Lags, Tanzania and Other East African Countries,
shock on small firms (Bartik et al., 2020; Humphries et al., 2020; Apedo-Amah et al., 2020; Cirera et al., 2021; Brucal et al., 2021).

# **3. Data and Methodology**

# **3.1 Data**

To analyze the role of informality in the context of the COVID-19 crisis, we use data from the World Bank BPS. This survey has collected data for thirty-three months for over 150,000 firms in 87 countries. The BPS survey instrument includes questions that have focused on the different shocks that firms suffered in the aftermath of COVID (change in sales, uncertainty, expectations, supply shocks, financial fragility), the adjustment mechanisms they had to implement (employment adjustments, digitalization, and innovation efforts), as well as the amount and characteristics of the public support received.<sup>5</sup> 5It also contains detailed information about firm-level baseline characteristics such as firm size, sector, age, gender of the owner, indebtedness, access to credit, and pre-COVID sales, among other indicators. For details on the first three waves, see Apedo-Amah et al. (2020) and Cirera et al. (2021).

6
**Figure 1:** Sovereign bond yields following Mozambique’s hidden borrowing

<!-- Start of picture text --> 2,800 Mozambique<br>Restructuring re-enters<br>of market debt debt distress<br>2,400<br>WSJ reports<br>on and IMF<br>confirms<br>2,000 existence<br>of hidden debt<br>1,600 Mozambique<br>signals intent<br>to restructure<br>market debt<br>1,200 Issuance<br>of market<br>and hidden<br>800 debt in 2013<br>400<br>0<br>Dec 2013 Apr 2014 Aug 2014 Dec 2014 Apr 2015 Aug 2015 Dec 2015 Apr 2016 Aug 2016 Nov 2016<br>(in basis points)<br>Sovereign bond yield spread<br><!-- End of picture text --> _Sources:_ Wall Street Journal (2016, April 3) _“Tuna and Gunships: How $850 Million in Bonds Went Bad in Mozambique”_ , Reuters (2016, April 23) _“IMF says Mozambique has over $1 bln of hidden debt”_ , J.P. Morgan (2024). _Notes:_ The figure plots the EMBI+ spread for Mozambique from December 2013 until December 2016. EMBI+ spread in basis points.

racy of the country’s debt statistics (see Figure 3).<sup>8</sup> At the beginning of 2020, the onset of COVID-19 and the accompanying capital outflows from emerging markets exacerbated the country’s debt challenges. In March 2020, Zambia’s ministry of finance sought advice from banks on the restructuring of USD 11.2 billion of foreign debt.<sup>9</sup> The news sent Zambia’s bond spread surging and ultimately pushed the country into default when the government missed a USD 42.5 million coupon payment
Ministry of Finance and National Planning (2009) Financial Report for the Year Ended 31<sup>st</sup> December 2008, Republic of Zambia: Lusaka Ministry of Finance and National Planning (2008) Financial Report for the Year Ended 31<sup>st</sup> December 2007, Republic of Zambia: Lusaka Ministry of Finance and National Planning (2007) Financial Report for the Year Ended 31<sup>st</sup> December 2006, Republic of Zambia: Lusaka Ministry of Health (2009) Annual Health Statistical Bulletin 2008, Republic of Zambia: Lusaka.

Ministry of Health (2001) National Strategic Plan 2001-2005, Republic of Zambia: Lusaka Sahn,D. and S. Younger (2000) Expenditure Incidence in Africa: Microeconomic Evidence. Fiscal Studies, vol. 21, no. 3, pp. 329–347.

Fiszbein, A. and N. Schady (2009) Conditional Cash Transfers: Reducing Present and Future Poverty. World Bank. Washington DC.

Republic of Zambia (2011) Sixth national development plan 2011 – 2015: Sustained Economic Growth and Poverty Reduction, Available at: siteresources.worldbank.org/INTZAMBIA/Resources/SNDP_Final_Draft__20_01_2011.pdf.

van de Walle, D. 1998. "Assessing the Welfare Impacts of Public Spending." _<u>World Development</u>_ 26(3): 365–79.

World Bank, 2012a, Social Protection Databases. Available at: <u>http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTSOCIALPROTECTION/EXTSAFET YNETSANDTRANSFERS/0,,contentMDK:22986320~menuPK:8117027~pagePK:210058~piPK:21 0062~theSitePK:282761,00.html</u> World Bank, 2012b, Data Indicators. Available at: http://data.worldbank.org/ World Bank. 2011, "Zambia: Country Economic Memorandum." Policies for Growth and Diversification. 2004. Report No. 28069-ZM.

World
We find households cope with flood and local rainfall variability in different ways. For example, nonhead working members and the heads of a household are less likely to be in the same occupation, in the same sector, or both in agriculture if the household lives in the flood prone Upazila (Table 2 Part A). This means households are likely to use sectoral diversification to cope with flood risks. On the other hand, households more likely to diversify between self and wage employment to cope with local rainfall variability.

Part B shows alternate results of the same set of estimations using weather station interpolated measures of local rainfall variability. We find that weather station interpolated measures are not always statistically significant. This may be due to the less sophisticated interpolation methods used in our station based measure of local rainfall variability as compared with the data from CRU. Thus we focus on the results from the CRU data in the rest of the paper.

One possible reason for the insignificant relationships between occupational focus and local rainfall variability may be because “normal” flood may reduce the dependency on local rainfall in crop cultivation. Thus, households living in flood prone Upazilas may use normal flood water as a substitute for rain and irrigation water. If this is correct, households in flood prone Upazilas would face different sets of risks as compared with the households in non-flood prone Upazilas.

To test this hypothesis we estimate separate models for non-flood prone Upazilas and flood prone Upazilas. As expected, we find households in non-flood prone Upazilas use sectoral diversify as well as self versus wage employment to cope with local rainfall variability (Table 2 Part C). On the other hand, since “normal” flood insulates households from local rainfall variability risks in flood prone Upazilas, households there do not need to use sectoral or employment diversity to cope with local rainfall variability (Table 2 Part D).

The results presented in Table 2 indicate that availability of water from “normal” flood where present may reduce the ex ante risks from local rainfall variability. Thus,