rafmacalaba/data-use-mentions-extended huggingface dataset:
|||(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_</sup><br>_Annual Import_|_Gains from_<br>_Variety_|
|---|---|---|---|
|_Year_|_Bias_|100<br>_(% of GDP)_|_(% of GDP)_|
|2018|1.024|-2.34<br>43%|-1.01|
|2019|1.000|-0.04<br>39%|-0.02|
|2020|0.995|0.52<br>39%|0.20|
|2021|1.002|-0.16<br>48%|-0.08|
|**Average per-annum**|**0.970**|**3.30**<br>**46%**|**1.49**|
|**Total (1995-2021)**||**89.08**|**40.25**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

Table 5: _Import Price Bias and the Gains from Variety in Mongolia_

||||_Annual_|_Gains from_|
|---|---|---|---|---|
||||_Import_|_Variety_|
|_Year_|_Bias_|(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_ 100</sup>|_(% of GDP)_|_(% of GDP)_|
||_(1)_|_(2)_|_(3)
) are denominated in minimum wages, and eligibility for social programs are tied to it.

**_Careful thought should be given to structure minimum wage institutions for it to be an effective policy tool_** .  Its objectives should be kept narrow and the expectations of its ability to resolve social inequity kept modest.  Changes in minimum wages should be informed by the evolution of labor productivity. A simple structure is easier to enforce and attempting to address all segments of the workforce generally fails. Both facilitate enforcement. The impact on the fiscal position is important and, as inflation falls, its enhanced role as a key nominal rigidity that exacerbates other labor market distortions rises.

# **Further work**

- **<u>Identification of the</u> “right” minimum wage** <u>.  The minimum wage regional study</u>

- documented one country where the minimum wage reduced poverty by benefiting the poorest (Mexico), and two countries with higher minimum wages where the impact was neutral or even detrimental to the poor.  Thus, the question remains, what is the “correct” level of the minimum wage such that it is high enough to maximize benefits for the poor, without tipping the balance such that it benefits other groups to the detriment of the poor.  More country studies, with varying levels of the minimum would allow for a more complete picture.

- **<u>Who benefits from minimum wage increases.</u>** Contrary to the US and Western Europe,

- where the minimum is mostly collected by youth, in LAC primary workers are also minimum wage earners and to date there is poor understanding of the overall profile of the minimum wage earner and hence the types of complementary social safety nets that are necessary.

> 32 See Fiess, Fugazza and Maloney (2003).

16
good set the stage for Haiti's evolution as an independent but deeply divided society." White and Smucker describe social capital as "truncated", rich at the local level but weak at regional and national levels. Peasant social groupings are local, stable, autonomous, self-governed and based on reciprocity. These social groups feel insecure, fear and actively avoid the state, and establish 'furtive' agricultural units on the margins of society. In the presence of an oppressive state and the absence of state provided services, these informal social relations become substitutes and serve as the primary social safety nets for the poor. The Haitian state, despite recent changes, is marked by political intolerance, patronage, extraction of wealth and lack of protection for the majority of its citizens.

Mauricio Rubio (1997) shows how in Colombia the existence of a large and growing illegal and underground economy run by powerful criminal organizations has resulted in parallel institutional environments that reward and favor opportunistic and criminal behaviors. Rubio shows how high levels of social capital within 'criminal' organizations are directed to extra-legal activities, rent seeking and high returns exclusively for those involved in such activities. The organizations that run in parallel with government institutions actually provide higher returns to those who participate than a regular career would offer. He estimates that between 1980 and 1993, the average remuneration for minor criminal activities, excluding drug trafficking and kidnapping, tripled in real terms, while returns for formal work remained stagnant. Currently, the annual income of a petty criminal in Colombia is around $20,000 per year, about 10 times the per capita GDP. Two surveys, National Survey of Quality of Life, and Quality of Life in Bogota provide some sobering statistics. In addition to the staggering statistics on homicides in Colombia, 54 percent of the barrios reported problems with conflict, drugs, gangs or prostitution (Knaul and Patrinos, 1998).

Based on a study of persistent inter-communal violence in India in particular cities, Varshney (I 998) argues that there is
employment rate is based on all individuals aged 10 or above. Shares of employment by economic sector are based on individuals aged 10 or above who are employed and/or who indicated their type of occupation.

resolution of 0.05° (approximately 5km _×_ 5km grid spacing) (Funk et al., 2014). We use the Google Earth Engine API to download daily temperature and precipitation data, and aggregate raw daily values by calculating geo-spatial averages at the district-level. We produce moving averages for precipitation and temperature for a time bin of 90 days, which we match with survey data based on the exact day of interview. A 90-day, or three-month window is a useful period for capturing short-term weather conditions, as this period is long enough to smooth out daily fluctuations and provide a representative snapshot of seasonal weather patterns, but short enough to reflect the specific weather conditions that individuals and households experience and report in surveys. Although the reference period for income is the last month before the interview, potentially negative effects from adverse weather conditions may not be immediate, so while we do cover the income reference period, we allow for a lead time period of another 9
25 Figure 2: Economics of ethanol from sugarcane and comparison of feedstock prices for biodiesel <!-- Start of picture text -->
1.8 1.6<br>1.6 Northwest Europe premium gasoline 1.4<br>1.4 Gasoline equivalent cost of ethanol<br>1.2<br>1.2<br>1.0<br>1.0<br>0.8<br>0.8<br>0.6<br>0.6<br>0.4<br>0.4<br>0.2 0.2 Diesel, Europe Rapeseed oil<br>Soybean oil Palm oil<br>0.0 0.0<br>US$ per liter  US$ per liter<br>Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13<br><!-- End of picture text -->

_Sources_ : Author’s calculations based on data from World Bank (2013), various issues of USDA (2013), and industry sources.

_Note_ : Opportunity costs of ethanol are calculated based on the following parameters used to compute the equivalencies between sugar and ethanol in Brazil: 1.0495 kg of sucrose equivalent to 1 kg of sugar, and 1.8169 kg sucrose equivalent to 1 liter of anhydrous ethanol. Sugar cane is assumed to yield 83 percent sugar and 17 percent molasses. Prices of molasses are assumed to be equal to 25 percent of sugar prices on a weight basis, and the sucrose content of molasses is 55 percent that of sugar. A fuel economy penalty of 20 percent is assumed for ethanol. Sugar prices are raw, FOB, and stowed at greater Caribbean ports. Soybean oil prices are in Decatur, Illinois; rapeseed oil prices are Dutch, FOB, ex-mill; and palm oil prices are for palm oil from Malaysia delivered in Northwest Europe.

As a result of weak
In contrast, highly persistent or permanent income shocks have a substantial effect on the present value of future earnings, and therefore lead to significant changes in consumption even if workers can borrow or have own savings (Constantinides and Duffie (1996) and Krebs (2003a and 2004)). Thus, from a welfare point of view, persistent income shocks matter the most, and we therefore focus on the relationship between trade policy and the persistent component of income risk.<sup>7</sup> More specifically, after obtaining the estimates of the persistent component of income risk for each year and industry, we use these estimates in conjunction with tariff data (as a proxy for trade policy) to study empirically the effect of trade policy on income risk.

In addition to the empirical analysis of the relationship between trade policy and income risk, this paper also provides a quantitative evaluation of the welfare consequences of any changes in income risk that are brought about by changes in trade policy. If insurance markets and other institutional arrangements for sharing individual income risk are missing (incomplete markets), then changes in income risk will alter consumption volatility and therefore workers’ welfare. To find out how income risk is linked to consumption volatility and welfare, we use a dynamic general equilibrium model with incomplete markets in which the consumption/saving choice of workers in the presence of idiosyncratic income risk is explicitly modeled. As is well known, general versions of such models are difficult to solve, and most work in the literature has therefore been computationally intensive (Aiyagari (1994), Huggett (1993), Krusell and Smith (1998), Rios-Rull (1996)). In contrast to this literature, we rely upon an extended version of the incomplete-markets model recently developed and analyzed by Constantinides and Duffie (1996) and Krebs (2004) that is highly tractable,

> 7To see the importance of this distinction more clearly, consider the example of a worker who loses his job due to plant closure or any other “exogenous”
households: 58% of primary men or 11 percentage points less than UP women would be classified as depressed (not shown).

These results confirm that UP levels of consumption, human capital, asset ownership and psychological well-being are significantly lower than other households in their community and among the lowest in the world, indicating that the UP experience multidimensional poverty in both a relative and an absolute sense.

# **Labor Markets for UP and non-UP Households**

Afghanistan has one of the world’s lowest employment-to-population ratios at 41, and 21% of the working population are considered underemployed (working less time than they are willing to). Women’s labor force participation is low at 27%, and women’s unemployment extremely high at 41%. Therefore, the context is one with limited labor opportunities, particularly for women.

Using baseline data on labor participation and labor activities directly from the primary woman in UP and non-UP households and primary man in UP households we find that engagement in income-generating activities is similar to the national average in our study villages:<sup>9</sup> 31% of UP primary women in the control group (UP women) and 25% of non-UP primary women engage in income-generating activities (Panel B in Table 1). These activities include self-employment such as livestock rearing, work in own agricultural and nonagricultural businesses, as well as paid jobs in agriculture, maid services, formal employment, and other activities. These figures hide high levels of underemployment, with women working few full-time days in a given month, and also significant differences in activities across socioeconomic groups. Figure 2 describes the main labor activities in these villages, by showing the share of hours UP women and men devote to them. This figure reveals that UP women devote a higher proportion of their time to “other paid jobs” than non-UP women and substantially less of their working time to own livestock rearing, and other household businesses (23%) than non-UP primary women (58%).
13 during the 1980s. In 1981 the question did not appear in the core questionnaire and in 1985 less than 5% of the sample responded to the question. In the last two or three years of the 1980s the response rate to the race question grew, and it became almost universal following a successful information campaign implemented in the run-up to the 1991 Census<sup>22</sup> . Hence race is only used here for the analysis of 1993 and 2004. Following the standard practice in studies of Brazil, mixed race heads of households are grouped together with black and indigenous heads.

� _Household type._ Five types of households are identified: (i) “single adult” households comprised of only one adult; (ii) “couple, no kids” households comprised of only adults, i.e. all aged over 14 or over; (iii) “couples with kids” households with more than 1 adult plus children; (iv) “single parent” households with a single adult plus children; and (v) elderly households whose head is aged 65 or over, with or without children. This is a simplification of the categories used by Tanner (1987) for Northeast Brazil.

- _Region._ There are five official, standard geographical regions in Brazil: North, Northeast,

- Southeast, South and Centre-West.

- _Urban/Rural location of household._ Urban and rural areas are those so defined by IBGE

- and used in the PNAD.

The point of the static decompositions is to separate total inequality in the distribution into a component of inequality _between_ the above groups in each partition ( _IB_ ) - the explained component - and the remaining _within_ -group inequality ( _IW_ ) - the unexplained component. Unfortunately, many widely used inequality measures are not decomposable, in the sense that overall inequality can not be related consistently to the constituent parts of the distribution. In particular, we are interested in measures where _IB_ + _IW_ = _I_ . This is not generally
<!-- Start of picture text --> Main Destination for Wood Exports (largest export value)<br>non-CU CIS fs}<br>Eu2é<br>Rest of Europe, incl. Turkey<br>MENA .-<br>China a<br>RestNo dataof Asia Pegaa od, a ot we. .<br><!-- End of picture text --> <!-- Start of picture text --> % ir<br>Cal 5 1<br><!-- End of picture text -->
;<br>44 %<br>10 Other EU;<br>7 %<br>0 Czech Republic;<br>5 %<br>Italy; 9 %<br>Year Spain ; 10 %<br>2004 2010<br>Total Installed Capacity |GW|<br>2000 2002 2006 2008<br><!-- End of picture text -->

(a) **Trend of global installed capacity**

**(b) Country share in the global installation in 2010** Source: REN21, 2011

> 4 This, however, represents only about 0.8% of the total global installed power generation capacity of about 4,600 GW in 2008.

7
6

# (8) _E = Aff_

where E is equivalent consumption, A is actual consumption, H is household size, and _e_ an equivalence scale elasticity [24]. Under the assumption that there no economies of scale, _e_ is set equal to 1, and equivalent consumption is simply per capita consumption. Under the assumption that two (or three, or four, of five,...) can live as cheaply as one, _e_ is set equal to 0, and equivalent consumption is simply aggregate household consumption. Although it is not uncommon to find to find _e_ set equal to one (the per capita adjustment), a more plausible position, at least in countries where a sizeable proportion of consumption is on non-food items, is that there are some economies of scale, but that the elasticity e is greater than zero. In their survey of equivalence scales in OECD countries, Buhmann et al. [24] found that most equivalence scales could be approximated quite closely by eqn (8) and that, on average, the implied value of the elasticity _e_ was around 0.4. Hentschel and Lanjouw (op. cit.), in their work on Ecuador, experiment with three values of _e:_ 0.4, 0.6, and 1.0. In what follows, we set _e_ equal to 0.5, which seems a reasonable intermediate position.

## MEASURING MALNUTRITION

Growth assessment is the single measurement that "best defines the health and nutritional status of children as disturbances in health and nutrition, regardless of their etiology, invariably affect child growth" [25]. Among various growth-monitoring indices, there are three commonlyused anthropometric measures that offer a comprehensive profile of malnutrition: stunting, underweight and wasting.

The term "stunting" is used to describe a condition in which children fail to gain sufficient height, given their age. Stunting is therefore an extremely low "height-for-age" (H/A) score. Stunting is often associated with long-term factors
<sup>5</sup> While the effective exchange rate is common to all firms within a country at a point in time, the interaction with a firms’ initial export or destination-market share allows the instrument to vary over time and across firms (so it can explain variation in changes in export shares over time and across firms).<sup>6</sup> We test the model using firm-level survey data and customs data for Georgia (described below), which provides a sufficiently large and rich data set.

# **3. Variable definitions and data sources**

We utilize firm-level data to estimate the effects of within-firm changes in sales structure on firmlevel employment and average wages. The data sets include firm-level employment (total and

> 4 The first year a firm appears in the sample is when it first exported, such that the initial share of exports is not zero for any firm.

> 5 We alternatively control for firm-specific effects by estimating the model in first-differences, which wipes out the influence of time-invariant firm-specific characteristics. Industry and year dummy variables are included to control for industry- and year-specific effects. Because the specification in levels has firm-level fixed effects, which control for time-invariant firm-specific factors, first differencing the model is not necessary to remove the effect of such factors. However, estimating the specification in changes arguably allows for a stronger instrument, given that changes in the effective exchange rate as opposed to the level are a better exogenous correlate to export competitiveness.

> 6 Changes in firms’ sales may also be endogenous to the specification, for example if firm size is correlated with unobservable firm characteristics that change over time. Notwithstanding, because we are less interested in estimating this coefficient, and because not including it potentially biases the coefficient we are interested in estimating, we maintain the specification.

4
-0.0344*|0.8635***|
||(0.0227)|(0.0101)|(0.0162)|(0.1591)|(0.0961)|(0.0943)|(0.0180)|(0.1237)|
|Additional control variables|-0.1112**|0.0521***|0.0296|0.5192*|0.3505**|0.3591***|-0.0217|1.2441***|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2852***|0.0129|-0.1131***|-0.7239***|0.8918***|0.8090***|0.4349***|-0.9731***|
||(0.0391)|(0.0123)|(0.0228)|(0.2336)|(0.0900)|(0.0866)|(0.0628)|(0.2742)|
|Observations|128,446|128,446|128,446|128,446|86,980|86,980|50,645|50,645|
|R-squared|0.232|0.072|0.215|0.193|0.100|0.095|0.284|0.336|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. The additional control variables include education levels of individuals (dummy variables), education levels (dummy variables), household size, the proportion of children in households, the proportion of
toilet, or to upgrade or repair an existing toilet. It was not linked to any specific toilet, and the MFI did not provide any advice or guidance on the construction of the toilet. Clients were free to construct the toilet of their own choice.

# **3 Study Design, Data and Empirical Strategy**

## **3.1 Study design**

The original evaluation exploited a planned expansion of the sanitation loans by the MFI to new areas. For evaluation purpose, the new loan product was initially introduced to clients living in 40 Gram Panchayats (GPs), randomly selected out of 81 GPs in which the MFI had operations at that time. Apart from the introduction of the new loan product, business continued as usual, with all clients, in both the GPs where the sanitation loan product was offered (treatment) and in GPs where it was not offered (control), continuing to have access to other loan products. These included income-generating loans, emergency loans, education and festival loans. The randomisation of GPs into treatment and control was stratified by MFI branch and village size. Figure 1 displays the geographical distribution of the study communities, loans, thereby cross-subsidizing these special purpose loans. This was done on the understanding that a happier and healthier client would ultimately be better placed to pay back other loans.

> 8This estimate is obtained by dividing the number of clients recorded in the partner MFI administrative records in November 2014 (assuming one MFI client per household - as per the MFI’s guidelines) by the number of households listed in the village during the 2011 Government of India population census.

> 9The MFI reduced the interest rate on the loan from an initial 22% to 20% and finally 18% while the experiment was ongoing.

> 10From October 2015 (pre-October 2015), the relevant criteria for clients are: (i) annual household income of at most Rs. 100,000 (Rs. 50,000) for rural customers; (ii) total
Gender Dimensions of COVID-19 Economic Impact in Chad: Insights from a CGE Model and Household Phone Survey<sup>1</sup> Kayenat Kabir,<sup>2</sup> Hasan Dudu,<sup>3</sup> and Fulbert Tchana Tchana<sup>4</sup> JEL codes: C68, J16, O55, Keywords: Chad, gender dimensions, economic Impact, Covid-19, CGE, phone survey

> 1 We are grateful to Aboudrahyme Savadogo, Alison Decker, Claudia Noumedem Temgoua, Muthoni Ngatia, and Theo Thomas for their helpful comments and data assistance in earlier versions of the paper. 2 The World Bank; kkabir@worldbank.org

> 3 The World Bank; hdudu@worldbank.org

> 4 The World Bank; ftchanatchana@worldbank.org
**Table 6. Shorrocks Mobility Index and Short-Term and Long-Term Inequality (balanced sample for each period)**

|||**Gini Ind**<br>|**ex**<br>||**Variance of Lo**<br>|**g Income**<br>|**No. of**|**No. of**|
|---|---|---|---|---|---|---|---|---|
|**Period**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|**observations**|<br>**individuals**|
|1994-1998|0.18|0.44|0.36|0.41|0.76|0.45|19 120|4 780|
|1998-2004|0.22|0.40|0.31|0.45|0.63|0.34|25 452|4 242|
|2004-2009|0.18|0.35|0.29|0.38|0.47|0.29|22 956|3 826|
|2009-2015|0.15|0.31|0.26|0.30|0.35|0.24|27 174|3 882|
|1994-2004|0.27|0.39|0.29|0.51|0.60|0.29|24 111|2 679|
|2004-2015