rafmacalaba/datause-encoder-data huggingface dataset:
total for UCB and Stanbic in 2000. On that basis, the government has clearly been successful in its desire to sell UCB to private interests that would maintain an extensive branch network.

# **_III. Bank-Level Analysis_**

# **_A. Profitability_**

Figure 1 offers yearly profitability data from 1996 to 2004. From 1996 to 2001, return on assets (ROA) is calculated three ways: for Stanbic only, UCB only, and for a composite of those two banks (by summing their financial information). The composite approach has been adopted in the literature to assess whether overall management of the assets involved in a bank merger or acquisition has improved (Berger et al., 2005). We also include profitability figures for the median Ugandan bank (other than Stanbic or UCB) to put the Stanbic-UCB results in context. In part, UCB’s ROA was relatively high prior to privatization due to government-financed loan write-offs. For example, and as noted above, prior to the first privatization attempt, in 1995 the government established a Non-Performing Asset Recovery Trust (NPART), to which it transferred the bad-debt book of UCB (Kasekende, 2004).<sup>17</sup> Prior to the privatization of UCB, Stanbic had higher and less volatile profitability than UCB. Low profitability and high volatility for UCB from 1996 through 1998 is due to the failed first privatization attempt. In 2002, Stanbic’s profitability declined as management strove to combine the two banks into a single operation. Profits then returned to Stanbic’s historic levels in 2003 and 2004. The figures for the composite bank are better suited to gauging the overall benefits of the transaction, and those too show improvement over time. Early in the period, UCB was substantially larger than Stanbic and thus its performance drove the composite figure down, though UCB’s profitability was not especially low in comparison with the rest of the banking sector. Over time, the composite figure improved and, as noted, after the acquisition Stanbic’s profitability reached it highest levels.

> 17 After three years of operations NPART had recovered only $15 million of the $65 million in assets with which it was entrusted.

8
where _yit_ is the household business outcome of interest recorded for month _t_ . In the HBTC regressions, _yit_ represents commune _i_ ’s total suspension (temporary) and closure (permanent) filings. _Post_ is a dummy variable indicating the time period after January 2020, the month the first COVID-19 case was detected in Vietnam. In the LFS regressions, _yit_ represents individual _i_ ’s workload (weekly work hours) or monthly income. We use monthly income residuals as the main dependent variable, which is computed as the difference between the reported absolute income and the predicted income level after accounting for pre-COVID linear trend and individual observables, including age, gender, education level, working industry, and occupational group. The temporal trend and the linear predictive model are presented in Figure A.2 and Table A.3.<sup>19</sup> For LFS regressions, the model further controls for a covariate vector _Xi_ which represents a set of individual characteristics including age, gender, education level, occupation level (ISIC-08 major groups), and work industry. _ci_ and _mt_ are the commune fixed effects and month fixed effects, respectively. In Eq.1, coefficient _α_ 1 captures the average changes in household business outcomes for the period between February and September 2020 relative to before.

We also study how the pandemic affected household businesses over time by estimating a month-by-month event study model. Specifically, where all elements remain the same as in Eq.1, except for the newly-introduced summation term which represents month-specific changes in household business outcomes. This equation allows us to capture COVID-19 effects for each month post-COVID-19 in 2020 (February ( _t_ = 1) to September ( _t_ = 8)).

We also control for differences in the intensity of face-to-face interactions at the commune-level, i.e., the F2F index. The F2F index is interacted with time fixed-effects under a difference-in-differences (DiD) framework to estimate the differential effect of COVID-19 across communes. The DiD model to estimate post-lockdown aggregate impact is as follows: Likewise, the corresponding model to estimate impact observed in each month is

> 19All of our results are robust to using the raw reported income from LFS.

8
of the long‐term evolution of commodity prices supports the thesis that price va‐ riability overwhelms price trends.

# **2. The Nature and Causes of the Recent Commodity Boom**

The recent commodity boom emerged in the mid‐2000s after nearly three dec‐ ades of low and declining commodity prices (Figure 1). The long‐term decline in real prices had been especially marked in food and agriculture. Between 1975‐76 and 2000‐01, world food prices declined by 53 percent in real US‐dollar terms. Such price declines raised concerns, especially with regard to the welfare of poor agricultural producers. In fact, one of the Doha Round’s chief motives (and also one of its perceived main obstacles) was the reduction of agricultural support and trade barriers in high‐income countries—a set of reforms that was expected to induce increases in commodity prices and hence improve the welfare of low‐ income commodity producers (Aksoy and Beghin 2005). Starting in the mid‐ 2000s, however, most commodity prices reversed their downward course, even‐ tually leading to an unprecedented commodity price boom.

Between 2003 and 2008, nominal prices of energy and metals increased by 230 percent, those of food and precious metals doubled, and those of fertilizers increased fourfold. The boom reached its zenith in July 2008, when crude oil prices averaged US$ 133/barrel, up 94 percent from a year earlier. Rice prices doubled within just five months of 2008, from US$ 375/ton in January to $757/ton in June.

The recent boom shares two similarities with the two earlier major com‐ modity booms of the post‐WWII period, during the Korean War and the early 1970s energy crisis (see Radetzki (2006) for a discussion of the three booms). Each of the three booms took place against a backdrop of high and sustained economic growth as well as an expansionary macroeconomic environment, and each was followed by a severe slowdown of economic activity. And all three triggered dis‐ cussions on coordinated policy actions to address food and energy security con‐ cerns.

Yet the recent boom also shows some important differences from the pre‐ vious ones. By most accounts, it was the longest‐lasting and the broadest in the numbers of commodities involved. It was the only one that simultaneously in‐ volved all three main commodity groups—energy, metals, and agriculture—with its peak showing food and agriculture prices increasing less than all other com‐ modity prices (World Bank 2009). It was not associated with high inflation, un‐ like the boom of the 1970s (although the increase in food prices had some nota‐ ble, albeit short‐lived, impact on inflation). Finally, it unfolded simultaneously with the development of two other booms—in real estate and in equity mar‐

**_Page 3_**
manufacturing experience. The rationales are intuitive: plants are likely to have less technical (as well as market knowledge) in a completely new industry. Columns (1)-(3) of Table 7 show the results from estimating our main specifications considering separately product innovations in a 6-digit industry where the plant has already manufactured other products and innovations in an entirely new 6-digit industry for the plant. Both types of innovation are associated with higher survival odds but only innovation in an old 6-digit industry has a statistically significant effect. However, the t-tests suggest that the difference in marginal effects is insignificant. Moreover, in unreported results we consider a potentially larger risk, distinguishing the effects of innovations in a new versus an old 3- digit industry. The findings are qualitatively maintained: more cautious types of innovation in an old 3-digit industry increase plant survival odds significantly while that is not the case for more risky ventures of manufacturing products in new 3-digit industries. But the difference in marginal effects is again insignificant.

The categorization of product innovations into known versus new industries is somewhat crude in that it does not take into account the intensity with which the plant did or did not concentrate in activities close to the new product. Moreover, that categorization does not take into account the relative closeness between the plants‟ established past industries and those of the new product(s). Thus, we introduce a measure that accounts for both these shortcomings: we establish for each new product its proximity to the plant‟s past production activities by computing product proximity indices following Hidalgo et al. (2007) and its application by Boschma et al. (2012). The proximity indices are based on how often countries have comparative advantage in two products simultaneously. The idea is that if countries with a comparative advantage in product A also have comparative advantage in product B with high probability, this implies that products A and B demand the same production capabilities and hence are close to one another. The indices, described in detail in the Appendix, are obtained for each new 7-digit ISIC product relative to the weighted average of the past products of the plant, accounting for the relative weights of the different past products in total plant revenues. We estimate our main specifications allowing differential effects on survival for new products that are closer versus more distant from the plants‟ past expertise. The results shown in columns (4)-(6) of Table 7 suggest that new products that are closer to the plants‟ expertise and thus pose lower technical risks do not impact plant survival any differently than new products that are more distant from the plants‟ expertise. A shortcoming of this approach is that the product proximity indices do not cover all Chilean innovators due to difficulties in establishing concordances between the 7-digit ISIC level and the HS classification of the trade data.

18
|Unified<br>pension<br>system|Eligible and under 55 years of age in 2016.<br>Defined Benefit and Defined Contribution +<br>welfare lump sum of 700 NIS. We only<br>allocate the Defined Benefit and welfare<br>lump-sum, as according to the 2016 Public<br>Expenditure Review (PER). The Defined<br>Contribution proportion is not yet<br>effective.|DB<br>DC|16%<br>6%|7%<br>3%|9%<br>3%|
|---|---|---|---|---|---|
|Gaza civil<br>servant<br>pension<br>scheme|Over 55 years of age in 2016, resident in<br>Gaza & working in the public sector.|22.|5%|10%|12.5%|
|West Bank<br>civil servant<br>pension<br>scheme|Over 55 years of age in 2016, resident in<br>the West Bank & working in the public<br>sector.|2|%|2%|0%|
|Israeli scheme|Allocated to employees only who claim<br>that their employers make pension<br>contributions.|17.|5%|5.5%|12%|

Source: World Bank Public Expenditure Review, 2016.

# **2.3 Tax revenues**

**All taxes are collected by the central government’s Ministry of Finance (MoF).** Local government units do not collect any type of tax, except for “Education Tax or Fees”, and a building tax for operators<sup>21</sup> (90 percent of the tax is distributed to municipalities, and 10 percent is remitted to the national accounts) (Abdelkarim & Jaber, 2018). Two public departments within the MoF are responsible for tax collection (except for property tax, which is collected by the General Administration of Property Tax): the General Administration of Customs and VAT and the General Directorate of Income Tax (GDIT). Collection of tax at source from salaries and wages is a smooth process with relatively few administrative complications; employers deduct the tax directly from employees’ incomes and transfer these to the MoF. Private taxpayers follow the same procedures. Israel collects clearance revenues<sup>22</sup> and customs duties and then transfers them to the PA after deducting amounts owed by the Palestinians for Israeli electricity and water utilities.

## **2.3.1 Direct taxes**

## **Personal income tax (PIT)**

**The applicable tax law (Income Tax Law number 8 of 2011 and its amendments) states that “all realized incomes in Palestine from any source for any person are taxable unless there is a clear tax exemption in the Income Tax Law”.** Palestinian residents and nonresidents are taxed only on income sourced in the Palestinian Territories. West Bank residents pay PIT to the PA, unless they work in Israel, in which case

> 21 A tax building operators pay used to finance infrastructure expenditure on public schools.

> 22 Clearance revenues include import taxes, taxes on Palestinian workers in Israeli-administered territories, and taxes on economic activity in Area C. Source: World Bank (March 2023).

13
self-employment, informal employment, and employment outside the formal sector. All informal employment measures are expressed as a share of total employment.

17. **For output informality estimates in Indonesia, the paper prefers the MIMIC-based estimates.** In the case of output informality in Indonesia, the model-based MIMIC estimates are used mainly in cross-country comparisons, while the SAKERNAS estimates are used in the withincountry comparison (see below for details). The MIMIC model, detailed in Schneider et al. (2010), is estimated in the paper (see Annex 4). According to Feige (2016), one of the limitations of Schneider et al. (2010) is that the base year value used for benchmarking is taken from an unspecified currency demand model. To overcome this limitation, the paper uses SAKERNAS estimates on output informality in 2000 to re-benchmark MIMIC estimates on output informality in Indonesia. It is also worth noting that MIMIC estimates are preferred here as they seem to capture the variation of informality better than DGE estimates in the case of Indonesia, while they are highly correlated with the DGE estimates in cross-country comparisons.

18. **SAKERNAS surveys are used to construct output and employment informality in Indonesia for national, regional, and sectoral level analyses.** These estimates are mainly used for within-country analyses. In addition to national-level estimates, output informality and employment informality are estimated for 34 provinces, 3 major sectors, and 9 subsectors over the period 2000-2022. The estimations use data on informal employment status and monthly income from Sakernas. The following equations are used to compute the share of output informality at the national (excl. i), regional (region =i), or sectoral (sector=i) level in year t:

1. Informal output (% total GDP, simple)it =

2. Informal output (% total GDP, weighted)it = j=nit ∑j=0 informal employment status dummyjit∗monthly incomejit∗weightjit j=nit . ∑j=0 employment status dummyjit∗monthly incomejit∗weightjit The share of informal employment at the regional (region =i) or sectoral (sector =i) level in year t is computed as follows: 3. Informal employment (% total employment, simple)it = 4. Informal employment (% total employment, weighted)it = 7
The Spillovers of Employment Guarantee Programs on Child Labor and Education Tianshu Li and Sheetal Sekhri<sup>_∗_</sup>

# **1 Introduction**

Both developed and developing countries use employment guarantee programs as a safety net mechanism to reduce poverty and economic vulnerability.<sup>1</sup> This paper uses the temporal and spatial variation in the roll-out of the Indian government’s 2005 National Rural Employment Guarantee Act (NREGA, now named MGNREGA) to evaluate the impact of the policy on children’s educational and employment outcomes. Several studies have shown that MGNREGA increased the demand for labor and increased rural wages (Imbert and Papp, 2015; Zimmerman, 2012). In light of this, the scheme can have profound effects on childrens education and employment as it influences the opportunity cost of schooling. Potentially, the value of childrens time both in the labor force and at home can increase.

> _<u>∗</u>_ Sheetal Sekhri (corresponding author) is an associate professor at the University of Virginia, Charlottesville; her email is ssekhri@virginia.edu. Tianshu Li is an assistant professor at the Institute of Urban Development of Nanjing Audit University; his email address is tl4bz@virginia.edu. The authors thank Leora Friedberg, Kartini Shastry, and Heidi Schramm for insightful comments. A supplemental appendix is available with this article at the World Bank Economic Review website.

> 1The earliest experiments with this policy lever date back to the 1817 Poor Employment Act and the 1834 Poor Law Amendment Act in Great Britain (Blaug, 1963, 1964), and the New Deal program of the 1930s in the United States (Kesselman, 1978; Bernstein, 1968). More recently Chile in 1987, India in 1978 and 2001, Pakistan in 1992, Bangladesh in 1983, Philippines in 1990, Botswana in 1960, and Kenya in 1992 have implemented variants of employment guarantee schemes. See Mukherjee and Sinha (2013) for details.
21 Finally, by bringing out the granularity in PHL and storage behavior on the ground and enabling the calculation of nationally-representative estimates from the bottom up, the findings underscore the usefulness of nationally-representative household surveys such as those collected under the LSMS-ISA initiative in studying PHL. The data and insights generated from such surveys can furthermore be used to inform PHL information systems such as APHLIS, directly, to update their annual estimates and, indirectly, to help them fine-tune their underlying algorithms.
**Table 2:  Political parties and redistribution**

<u>(dependent variable:</u> _sharegain:_ <u>going from</u> _Market_ or _Market P_ income to _<u>gross</u>_ income)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Bottom<br>quintile|Top<br>quintile|Bottom<br>quintile|Top quintile|
||Base spec<br>_MarketP_i|ification:<br>ncome|Base specifi<br>income|cation:_Market_|
|Market share of|-0.73|-0.11|-1.13|-0.27|
|respective quintile|(0.000)|(0.004)|(0.000)|(0.000)|
|Continuous years of|-0.0067|-0.064|0.10|-0.11|
|competitive elections|(0.83)|(0.029)|(0.059)|(0.091)|
|**Largest govt. party is**|**0.34**|**-0.42**|**0.74**|**-0.93**|
|**left wing**|**(0.062)**|**(0.023)**|**(0.015)**|**(0.058)**|
|**Age of largest govt.**|**-0.006**|**0.009**|**-0.007**|**0.010**|
|**party if left-wing**|**(0.018)**|**(0.006)**|**(0.11)**|**(0.03)**|
|Log_ppp_-adjusted per|-1.20|1.19|-4.29|3.74|
|capita income, 2005 US<br>dollars|(0.080)|(0.16)|(0.00028)|(0.0040)|
|Log of total population|1.07<br>(0.62)|-2.68<br>(0.22)|-1.34<br>(0.54)|1.28<br>(0.60)|
|Percent of population|-0.083|-0.007|-0.11|0.079|
|that is rural|(0.20)|(0.91)|(0.048)|(0.15)|
|Percent of population|-0.18|0.29|0.27|-0.48|
|<br>that is 65 and older|(0.26)|(0.080)|(0.14)|(0.013)|
|Observations|258|258|263|266|
|R-squared|0.334|0.215|0.562|0.531|
|Number of countries|47|47|49|49|

Note: All specifications are ordinary least squares, controlling for country fixed effects. Clustered, robust _p_ -values in parentheses. _MarketP_ incomes are market incomes including pensions. _Sharegain_ is the change in income share of the respective quintile after comparing the _MarketP_ ( _market_ ) income share of the quintile with its disposable or gross income share. The number of observations in columns 3 and 4 differ from the number in columns 1 and 2 because Colombia (2007 and 2010), Greece (2004), Israel (2007), Malaysia (1989), and South Africa (2008 and 2010) do not have market income data that includes pensions.

# **5. Robustness:  Including the middle class among beneficiaries of redistribution**

In unequal societies, the median income voter is poorer than the voter with average income. Redistributive appeals that meet with the approval of the median voter should therefore increase the income shares of the middle class. Consistent with this, politicians in mature democracies or in leftwing parties devote considerable efforts to mobilizing the support of the median, middle income 15
- **Petri, William A., Jr, Mark Miller, Henry J. Binder, Myron M. Levine, Rebecca Dillingham, and Richard L. Guerrant.** 2008. “Enteric infections, diarrhea, and their impact on function and development.” _Journal of Clinical Investigation_ , 118(4): 1266– 1290.

- **Proos, L. A., Y. Hofvander, K. Wennqvist, and T. Tuvemo.** 1993. “A longitudinal study on anthropometric and clinical development of Indian children adopted in Sweden.” _Upsala J Med Sci_ , 97: 93–106.

- **Ravallion, Martin.** 2012. “Fighting Poverty One Experiment at a Time: A Review of Abhijit Banerjee and Esther Duflo’s _Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty_ .” _Journal of Economic Literature_ , 50(1): 103–114.

- **Shah, Manisha and Bryce Millett Steinberg.** 2013. “Drought of Opportunities: Contemporaneous and Long Term Impacts of Rainfall Shocks on Human Capital.” Working Paper 19140, NBER.

- **Solon, Gary, Steven J. Haider, and Jeffrey Wooldridge.** 2013. “What are we weighting for?” working paper 18859, NBER.

- **Spears, Dean.** 2012a. “Effects of Rural Sanitation on Infant Mortality and Human Capital: Evidence from a Local Governance Incentive in India.” working paper, Princeton University Research Program in Development Studies.

- 2012b. “Height and Cognitive Achievement among Indian Children.” _Economics_

- _and Human Biology_ , 10: 210–219.

2013. “The nutritional value of toilets: How much international variation in child height can sanitation explain?” working paper, Princeton University Research Program in Development Studies.

- **Strauss, John and Duncan Thomas.** 1998. “Health, Nutrition, and Economic Development.” _Journal of Economic Literature_ , 36(2): 766–817.

- **Vogl, Tom S..** 2012. “Height, Skills, and Labor Market Outcomes in Mexico.” working paper, Princeton University.

33
a year even over the later stretch of our time horizon. We have This value, together with the lower branch of (27) for _e_<sup>2</sup> 100<sup>= 0</sup><sup>_._065, completes the second</sup> condition. The resulting values of the parameters are _a_ 1 = 1 _._ 522 and _a_ 2 = 0 _._ 560.

# **4 Demographic Projections for 1990 2050**

In order to estimate the effects of the epidemic and to evaluate public policies designed to combat it, we need the corresponding demographic projections. These include the natural counterfactual case in which the epidemic never breaks out and variations in – which it does so with or without policy interventions. In undertaking this rather speculative task, we shall draw heavily on the projections made by the U.S. Bureau of the Census (USCB) for the period 1985-2050. It should also be noted that the formal economic analysis of the period 1990-2050 requires demographic forecasts for the period 2000-2070.

In what follows, the counterfactual ‘without the epidemic’ and the actual case ‘with the epidemic’ will be denoted by _D_ = 0 and _D_ = 1, respectively. Both projections are based on a whole variety of assumptions, which are known to its authors at the USCB, but are not reported in the data file made available to us.<sup>3</sup> This omission is not especially problematic when analysing _D_ = 0 and _D_ = 1 as benchmarks, since the formal description is clear enough. It does, however, confront us with serious difficulties when attempting to construct and analyse alternative policies, for they may be partly or fully incorporated in the USCB’s variant of _D_ = 1 itself. In any event, the relationship between the level of spending on combating the disease and mortality that

> 3This in no way reflects badly on the authors, of course, for such background material hardly belongs in tabular files of this kind.

27