rafmacalaba/data-use-mentions-extended huggingface dataset:
wage offers (Schmieder et al. 2016) and in France it had no discernible impact on subsequent match quality or wages (Le Barbanchon 2016).<sup>4</sup>

# **2.2. ALMP and labor market outcomes**

The theoretical underpinnings of ALMP are multifaceted, as they encompass a wide range of programs and their effects cut through several economic and social areas. The main objective of ALMP is to increase labor supply and demand and improve labor market matching; however, they also have effects on productivity, output, poverty, inequality, and social cohesion, among others. PES programs are the most commonly used ALMP globally, which include the provision of vacancy database and labor market information, training programs, job search assistance, job placement, internship programs, wage subsidies, public works, and national training funds (Andersen et al. 2015). In countries that have UB schemes, ALMP are also used as a condition to receive UB to prevent moral hazard. Therefore, both the design and implementation of ALMP play a critical role in their success, as well as the institutional, economic, and socioeconomic environments of their respective countries.

Channels through which ALMP affect economic and social outcomes are covered widely in the literature. They help unemployed individuals find work by identifying their individual needs and goals through counseling; increasing their skill sets through training and on the job learning; facilitating job matching, and increasing competition. By enabling job seekers to develop human and social capital, they can also increase social inclusion and the productivity, output, and competitiveness of the labor force (Boone and Van Ours 2004; Calmfors 1995). However, ALMP can also have a negative impact on labor markets and the overall welfare of the economy if the jobs created by one program replace other jobs (displacement effect) or if they lead to the substitution of jobs in other sectors by changing the cost of wages through subsidies. Moreover, they may result in deadweight loss when the jobs they
an economy-wide model developed at the World Bank for MDG and development strategy analysis. Results from MAMS scenarios are passed on to a microsimulation model that relies on household survey data to assess effects on poverty and inequality.

More specifically, the initial set of simulations, which covers the period 2004-2015, addresses the extent of the scaling-up of government services that would have been required to achieve international MDG targets for education, health, and water-sanitation, 4
*Redistribution**

Along with agricultural productivity growth, relatively high redistribution is the main lever to reduce poverty in Vietnam in our model. In the model, it is represented by a flat tax on consumption, which is redistributed to the entire population (except children) as a basic income (i.e. the same amount for each individual). We also model redistribution to the elderly as a tax on income from workers only, redistributed as a cash transfer to people older than 60 years. We apply these transfers to households’ consumption after pre‐existing tax and transfers. They can be seen, therefore, as additional transfers in addition to current ones.

According to the most recent data in the ASPIRE database, 6.3% of Vietnam GDP is used for social transfers (cash transfers only) and people from the first quintile get about 10 times less transfers per capita (in absolute terms) than people from the last quintile. The way we model cash transfers is more favorable to the poor since each person receives the same transfer in absolute terms.

Our results suggest that, to be in the optimistic scenarios in terms of poverty reduction and shared prosperity, Vietnam needs to use 8% of GDP for universal social transfers, assuming that it is financed by a flat tax and people from all quintiles get the same amount. To deal with an aging population while reducing poverty, Vietnam will also need to use at least 5% of workers’ income to finance pensions.

# **Agriculture productivity growth**

If high redistribution is too costly, productivity growth for unskilled agricultural workers is another important driver of poverty reduction, even in scenarios in which the share of workers in agriculture is 7
relies on the “levelized cost” method<sup>8</sup> . The levelized cost (LCOE) of a power plant is calculated as follows: where OC is the overnight construction cost (or investment without accounting for interest payments during construction); OMC is the series of annualized operation and maintenance (O&M) costs; FC is the series of annualized fuel costs; CRF is the capital recovery factor; CF is the capacity factor; r is the discount rate and T is the economic life of the plant.

In this section, we discuss the economics of grid connected PV and CSP under various scenarios. One of the main challenges to the economic analysis of power generation technologies is the variation in cost data across technology type, size of plant, country and time. Since fuel costs are highly volatile and capital costs of solar technologies are changing every year, an economic analysis carried out in one year might be outdated the next year. Nevertheless, the analysis presented here could help illustrate the cost competitiveness of solar energy technologies with other technologies at present.

We have taken data from various sources including Lazard (2009), NEA/IEA (2005, 2010), EIA (2007, 2009) and CPUC (2009). The data were available for different years, so we adjusted them using the GDP deflator and expressed them in 2008 prices for our analysis. Moreover, the existing calculations of LCOE for a technology vary across studies as they use different economic lives, capacity factors and discount rates. Some studies account for financial costs (e.g., taxes and subsidies) (Lazard, 2009; CPUC, 2009), while others include only economic costs (NEA/IEA, 2005, 2010). Therefore, we have taken the maximum and minimum values of overnight construction costs for each technology considered here from the existing studies to reflect the variations in overnight construction costs, along with the corresponding O&M and fuel costs, and applied a uniform 10%
13 non‐poor? As explained on page 6, this paper uses five measures to calculate access. Access statistics by location, quintile, and poverty status using the most expansive definition (definition 5) are shown in Table 1; the statistics using the first definition (grid connection) are reported in Table A.3 in the annex. The results are consistent with the general observations made elsewhere that access rates in rural Africa are significantly lower than in urban areas, the poor are far less likely to have access to electricity than the rich, and in some countries access of the rural poor to electricity is essentially non‐existent. Overall, the poor in nine out of 22 countries had an access rate of less than 5 percent.

Table 1: Percentage of people with access to electricity according to definition 5

||||All people<br>|||Peop<br>|le classified<br>|poor<br>|
|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Q1|Q5|Urban|Rural|Total|
|Angola|75|14|47|8|85|40|6|16|
|Botswana|63|23|46|15|79|42|10|25|
|Burkina Faso|47|3|13|2|38|14|2|3|
|Côte d'Ivoire|88|31|57|41|38|14|2|3|
|Ethiopia|96|12|23|7|45|86|6|10|
|Ghana|89|47|68|37|91|74|32|41|
|Madagascar|38|6|12|1|44|12|3|4|
|Malawi|38|4|
labor. As expected, intra-group inequality is higher for skilled labor and this group is projected to increase its share in total employment from 36.8% in 2015 to 42.3% by 2030 (Figure 12b). The sectoral reallocation of labor (step (b)) helps offset the regressive effects of step (a) by allowing labor to move away from the low-wage and shrinking agricultural sector to the expanding non-agricultural sectors of the economy. Step (c) modifies skilled wage premia by sector in line with general equilibrium conditions. This step typically generates strong regressive effects in the baseline as the overall structure of the economy shifts away from agriculture towards skill-intensive manufacturing and services as a result of a rapid capital expansion. This shift in the economic structure determines a relative increase in the demand for both skilled labor and capital. Lastly, (d) the effect of consumption prices is slightly regressive considering that under baseline conditions the inflation of non-food items is slower than that of food prices, resulting from general-equilibrium conditions. The combined effect of these mechanisms is regressive under baseline conditions, making the ratio

> 18 Global extreme poverty has been measured using an absolute poverty line, currently at PPP (2011) $1.90/day. While this absolute poverty line is adequate for the majority of low-income countries, more adequate definitions are needed to measure the evolution of poverty in middle- and high-income countries. As a matter of fact, as countries reach higher levels of per capita income they either increase the minimum threshold level for poverty or adopt relative poverty lines (Ravallion and Chen (2011)). The World Bank has released a set of additional poverty lines at PPP$3.20 a day for lower-middle income and at PPP$5.50 a day for upper-middle income countries. In the forward-looking context of this paper and considering that Indonesia would reach upper-middle income status under business as usual conditions, we monitor the extent of poverty using these two alternative poverty lines.

23
br>N Sales Tax N Withholding Share Sales Tax Share Withholding<br>30000 .49<br>75000 .23<br>.22 .48<br>70000 25000<br>.85 .6<br>.8<br>.75 .55<br>.7<br>.65<br>.5<br>.6<br>Share of Card Sales .55 Share of Card Sales<br>.5 .45<br>.45<br>.4 .4<br><!-- End of picture text --> Notes: The gure displays the rst stage of the withholding rate reform and analyzes the reform's eect on credit/credit card use. Panel A shows the average realized withholding rate among all rms subject to withholding. Panel B shows the average realized and predicted withholding rate among rms for whom we can predict the withholding rate based on previous semester's tax returns and the withholding rate schedule in Table II. Panel C shows the number of sales tax declarations and credit/debit card withholding declarations presented to the tax authorities, correcting for revisions and duplicates. Panel D shows the share of sales tax declarations matched with at least one withholding declaration and vice-versa. Panel E shows the share of card sales in total sales among rms with a credit/debit card machine, and panel E shows the average over the rm-specic shares of card sales among rms with a credit/debit card machine. The black solid line in all panels marks 08/2011, when the increase in the withholding rate for the sales tax entered into eect. Panel E and F show a linear t that allows for a dierent trend and constant after the reform. The text displays the pre-reform slope of the linear t, and the change in slope and constant after the reform, along with standard errors in parantheses.

44
Figure 10: First-time asset buyers <!-- Start of picture text --> Afghanistan Burkina Faso Cameroon Iraq<br>1<br>.5<br>0<br>Kyrgyz Republic Malawi Mali Mongolia<br>1<br>.5<br>0<br>Nepal Nicaragua Niger Nigeria<br>1<br>.5<br>0<br>Pakistan Sierra Leone Tanzania Uganda<br>1<br>.5<br>0<br>4 6 8 10 4 6 8 10 4 6 8 10 4 6 8 10<br>Annual expenditure per person (log scale)<br>Motorcycle or Car Car Household density<br>Fraction of households with a vehicle<br><!-- End of picture text --> Note: Authors’ calculations using the LSMS surveys.

**Fact 8: Within countries, households spend relatively more on cars as they get richer.** Figure 11 uses data from the Word Bank Global Consumption Database to plot the average share of household expenditure on motor cars by expenditure quintile. In most countries except high-income countries, the share is negligible and only increases for the richest quintile, which reflects the concentration of private car ownership among the few richest households. The amount spent on cars depends whether households own a car but also on the number and quality of cars. Richer households tend to acquire multiple and more expensive cars.

19
Using a very large database of OECD imports at the SITC4 level since 1963, we find that, up to the turn of the century, OECD markets have been diversifying their sources of supplies (geographically) at the product level. This is reflected in decreasing concentration indices and a rising number of export sources. However, the trend in concentration has reversed itself in recent years. We show that this trend reversal is entirely explained by the rising share of Chinese products in OECD imports, as concentration indices keep on decreasing monotonically when China is excluded. We also find that the pattern of import diversification at the product level is broadly consistent with a simple model where buyers screen suppliers for quality and toss them out when they under-perform. The model predicts that diversification happens by ―bouts‖, or temporary episodes, during which OECD buyers search for high-quality suppliers. Each diversification episode is followed by a phase of reconcentration on the best performers, until those fail (which happens stochastically), triggering new search phases. The model is a very simple, finite-horizon version of a classic two-arm bandit problem. It is close in spirit to Jaud (2011) who also uses a multi-arm bandit setup to explore the effect of tightening standards on purchase volumes.

We test the model‘s basic prediction by looking at the evolution of unit values during re-concentration episodes and at how the volatility of concentration indices varies across products types, taking the variation in unit values as a proxy for quality heterogeneity. We find, as predicted by the model, that re-concentration, when it happens, is associated with a _rise_ in unit values. That is, when buyers re-concentrate, they do so on higher-priced (and hence presumably higher-quality) suppliers rather than on the most price-competitive. We also find that concentration indices are more volatile, over time, for products whose quality (as proxied by unit values) is more heterogeneous across suppliers.

The paper is organized as follows. Section 2 analyses the overall trend in OECD import concentration. Section 3 and 4 set
# **Section A. Background on NAIVS**

# _<u>Section A1. Subsidized input package</u>_

The input package consisted of three vouchers:

- 1) Nitrogenous fertilizer—one bag of urea.

- 2) Phosphorous fertilizer. Option 1: one bag of DAP; Option 2: two bags of Minjingu Rock Phosphate (MRP) with nitrogen supplement (also called Minjingu mazao), depending on the farmer’s choice.<sup>1</sup>

- 3) Seed (10 kilograms of a hybrid or open-pollinated maize variety or a rice variety)<sup>2</sup> sufficient for half of a hectare of maize (Table A.1).

**Table OA1: Maize input packages proposed for NAIVS (for 1 ha)**

|**Options**|**N source**|**P source**|**Seed**|
|---|---|---|---|
|Option 1|1 bag urea|1 bag DAP|10 kg OPV or hybrid seed|
|Option 2|1 bagurea|2 bags MRP+10N|10 kgOPV or hybrid seed|

## **Table OA2: Input prices in the Arusha villages in 2009/10 (TZS)**

||Median value of<br>subsidy voucher|Median top-up<br>paymentrequired|Median total<br>input cost|
|---|---|---|---|
|DAP (50 kg)|12,000|16,500|25,000|
|Urea (50 kg)|23,000|17,000|42,000|
|Hybrid orOPVmaize seed (10kg)|17,000|17,000|34,000|

<mark>Source: VVC member baseline survey</mark> <mark>* As a point of
_Aggregate HOIs._ In addition to the sixteen HOIs for the same number of opportunities, this paper presents three aggregate HOIs, two for children between 0 and 4 years and one for children between 9 and 15 years. The first aggregate HOI for children in the 0-4 range, is built from all seven individual opportunities included in the early childhood development, and the nutrition and hunger groups, which are computed with the DHS surveys. The second aggregate HOI for children between 0 and 4 years, is built from six opportunities in the basic housing services group which are reported in the HIECS surveys. The aggregate HOI for children between 9 and 15 years, is based on five opportunities in the basic housing services group and one education apportunity (school attendance). We assume that a child achieves the aggregate opportunity if he has access _to at least four of them_ .<sup>1617</sup> The labels for these four aggregate opportunities are respectively IOHa0-4Nutrition & ECD, IOHa0-4-Housing Services y IOHa9-15-Housing Services.

# **_2.3  Relevant Circumstances:  Household Characteristics and Location_**

The choice of the circumstances vector is limited by the availability of information from different opportunity-measuring data sets – DHS and HIECS household surveys in this case. The circumstances vector includes variables specific to the child, the demographic composition of the household, income/wealth of the household, and variables for location. The following circumstances are included in the exercise:

- Gender of child

- Number of children under age 5 in the household

- Number of children between ages 6 and 17 in the household

- Number of people over age 70 or disabled in the household

- Presence of father and mother in the household

- Education of father and mother (education of the head and his spouse in the HIECS survey)

- Household income per capita (modified to wealth per capita quintile for the DHS survey)

- Location of residence (rural/urban)

- Region of residence (Upper Egypt, Lower Egypt, Metropolitan Egypt, or Frontier Governorates).

#
2006‐10 Total population (UN 2011)<br>Working‐age population/total population (UN 2011)<br>Total population (UN 2017) Total population (UN 2013)<br>Working‐age population/total population (UN 2017) Working‐age population/total population (UN 2013)<br>Note: Dashed lines indicate projections for years beyond UN report dates.<br><!-- End of picture text -->

> 7 This is based on the medium fertility variant – “...considered the most likely future scenario and that is widely used by the literature” (Barro and Lee 2015). This variant has “normal” fertility, mortality and migration rates.

> 8 Percentage of total population aged 15‐64.

> 9 Labor force participation rates for 2013‐2014 are projections. ILO‐modeled estimates and projections are based on estimates and projections for GDP‐related variables and population structure. The 2013 estimates and projections draw on the IMF WEO April 2013 and United Nations (2013). However, the IMF stopped publishing projections for Syria effective 2012, and ILO uses the regional median growth to extrapolate GDP growth for Syria.

6
value) corresponds to the right to use that property until death.<sup>6</sup> This table changes periodically, according to some significant revisions of the two ingredients of the formula. For our analysis, for the sake of simplicity, we will resort to the values contained in these tables over time, which approximate the actuarial formula.

Taking account of use rights that derive from a contract of usufruct in the survey measurement of household wealth implies two different tasks: (1) If the interviewed household has the use rights (bought or received for free, it does not matter), the capital value of such rights should be considered together with other real assets. (2) If the household interviewed instead is granting the use rights to someone else, the value of such a right must be subtracted from the full value of the property, i.e., the valuation of the property must take into account the missing use rights.<sup>7</sup> It is worth noting that in Italy usufruct is not the only form of right on someone’s else property, although it is probably the most frequent. Similar to usufruct is the “right to inhabit”, which typically arises by law when one spouse passes away and leaves behind a house of residence that was jointly owned or owned solely by the deceased spouse. In such a case the surviving spouse keeps the right to live in the house of residence lifetime, even if other heirs acquire a share of ownership. So as the usufructuary, the survivor spouse is responsible for the maintenance of the property and must pay associated expenses like taxes and utilities, while cannot make significant alterations to the property without permission. Contrary to usufruct, however, the right to inhabit is personal, and cannot be transferred or assigned to another individual.<sup>89</sup> It is important to underline that the condition of a usufructuary is substantially different from that of a household that lives in a property offered
across socio-demographic groups, except for pensioners, the elderly and the rural population which show a smaller negative impact of formalization, mostly because they are exempt from taxation (pensioners) or are less likely to be formal (rural population).

**Figure 8: Reform-1: Formalization of 50 percent of informal employees, no changes in taxation. Percentage change in consumable income w.r.t. no-reform scenario by decile, age group, household and location types.**

<!-- Start of picture text --> 1<br>0<br>-1<br>-2<br>-3<br>0<br>-1<br>-2<br>-3<br>Scenario A Scenario B Scenario C<br>Sour: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.<br><!-- End of picture text --> Table 6 summarizes the changes in the distributional and budgetary outcomes for reform 2 scenarios (i.e., the ‘tax maneuver 22/22’). First, if the informality rate remains unchanged (nobody formalizes), the tax maneuver would have a negative effect on the budget, resulting in a budget deficit increase equal to 0.9 percent of the GDP under scenarios A and C, and to 0.6 percent of the GDP under scenario B. As far as the population is concerned, the proposed tax changes are beneficial if we assume that the tax burden is fully shifted to workers (scenario B) and negative if the burden of labor taxes is partially or not borne by employees (scenarios C and A). In the latter case households lose due to increases in VAT, while under scenario B this loss is being offset by the reduced SIC rate. Second, Table 6 shows that formalization could indeed compensate for the fiscal losses due to the tax maneuver. However, under all scenarios, the budget starts to gain when