rafmacalaba/data-use-mentions-extended huggingface dataset:
especially by humanitarian agencies responding to food crises (Schnitzer, 2019). While different variations of this method also exist, it usually categorizes households into wealth groups based on specified criteria that are agreed by the community and guided by a previous, nationwide qualitative assessment of household livelihoods and food security – the ‘HEA baseline.’ Three of the CBT schemes that we assess rely on such type of CBT.

While targeting is not a recent topic in the economics literature,<sup>17</sup> empirical studies based on household survey data have emerged in recent years with the increasing popularity of social safety-net programs. A few studies have compared PMT and CBT efficiency directly and found that PMT is better at selecting households with low per-capita consumption in Indonesia, Cameroon, and Niger (Alatas et al., 2012; Stoeffler et al., 2016; Premand & Schnitzer, 2020). However, in Indonesia, CBT can generate greater community satisfaction, while the opposite result was found in Niger. Two review studies have measured the performance of PMT, CBT and other targeting methods worldwide (Coady et al., 2004; Devereux et al., 2017). Both found important variations within methods and suggest that this result is explained by important variations in implementation. These studies understate the role of widely diverse levels of coverage rates in explaining targeting performance.<sup>18</sup> While the evidence on PMT and CBT methods in the Sahel region was scarce, several studies were conducted on the subject in the past years as safety nets interventions became increasingly important. We reviewed the existing evidence based on the reports from these studies (review available upon request). Most of these studies cannot be directly compared, given variations in performance measurement, but the overall findings are largely consistent. The following conclusions emerged: (i) PMT selects households with lower per-capita consumption than CBT, but no method makes a large difference; (ii) targeting transparency and appropriation is low regardless of the method employed;
industrial data for 1973 and 1983.13 In some cases, this data was provided for an adjacent year but not the year requested. Table 2 describes the data in more detail. 31 countries provided data on inventories for the 1973 survey and 43 countries provided data on inventories for the 1983 survey, yielding a database of inventory data for 52 countries for one or two years. These data were sufficient to calculate the following end-of-year inventory levels: Raw Materials Inventory level (EOY) = Raw Materials Stock (EOY) Raw Materials Consumed Final & Process Inventory level (EOY) = Total Stock (EOY) -Raw Materials Stock (EOY) Sales Beginning-of-year inventories were also reported, permitting the calculation of another set of inventory levels.

Implicit in these calculations is the assumption that inventory levels at a particular point in time are representative of average inventory levels. Since the data are for the entire industry, inventory cycles of individual firms are not important. We do not have to worry that one firm places its orders early in the month as long as another firm orders late in the month. Nevertheless, if inventory cycles are correlated between firms, then the estimate of inventory levels would be inaccurate. For example, if firms consistently run out of inventory after Christmas, then using end-of-year inventory levels would underestimate average inventory levels. Empirically, inventories, at least in developed countries, are cyclical and measuring inventory at any particular point in time may underestimate or overestimate the average inventory level of that country; a country that appears to have high inventory levels may simply be at the top of the cycle. Many developing countries have high rates of inflation leading to additional biases in the inventory level measurements. For example, under a constant annual

> 13 Unfortunately, this program was discontinued after 1983.

12
0**|**9066.5**|**9100.8**|
|**Population projections**|1,896,833|1,902,707|1,909,321|1,916,573|

Source: Matsoso (2015) The dominant manufacturing sector in terms of employment and output remains textiles and clothing, employing 33,686 people in the fourth quarter of 2015. However, this represents a decline of 25.7% against the previous year (Bureau of Statistics 2016).<sup>5</sup> The sector has considerably lower average wages than other manufacturing sectors such as leather and footwear, food and beverages and other manufacturing sectors.

> 5 A recent compilation of data on the textiles and apparel sector, based primarily on surveying 57 individual firms, found that the sector provides 41,706 jobs and supports many more individuals indirectly.

6
Overall, based on 596 observations or data points, the returns to higher education are greater than the returns to primary education in 83 percent of cases. The returns to higher education are greater than the returns to secondary education in 97 percent of cases. And, in terms of latest year data for 104 economies, the returns to higher education are greater than the returns to primary education in 73 percent of cases, and the returns to higher education are greater than the returns to secondary education in 95 percent of cases. However, the returns to secondary schooling are greater than the returns to primary schooling only 33 percent of the time. While in some countries the pattern of increase is not monotonic with the level of education, a generally _mixed_ pattern of returns is observed across most countries (Table 1).

Table 1: Education-Earnings Relationship

||% of countries|
|---|---|
|Higher > Primary|73.3|
|Higher > Secondary|95.2|
|Secondary > Primary|32.7|

|Computed from Montenegro and Patrinos (2014)|
|---|

The very sharp declines, and low overall rate, for secondary education are a bit misleading. There is an _option value_ to completing secondary education – and primary as well. The _option value_ is the value of obtaining further education and the rewards accompanying it (Weisbrod 1962). It is distinct from the conventionally accepted educational benefit of standard expected wage gain. In the case of secondary education, there is option value arising from the fact that completion of secondary education provides a benefit other than wage returns: the option to invest in post-secondary levels of education, which are not available to those who do not complete secondary education. Estimates from Bangladesh suggest significant returns to the option value (Shafiq 2007). Thus, we must keep that in mind. Focus should be on the returns to higher education.

# **Causes of the changed pattern**

The returns to schooling have
wards, or because land planning and zoning at the city level have different implications for the tolerance of municipal authorities to informal settlements across the city.

In particular, we run the following regression where _c_ is a constant term, _F_ is a dummy variable for formal sector, _Z_ are variables describing provision of services by local authorities, and _wi_ are ward dummies. Results are reported in Table 5. In the first column, we do not include ward dummies. We find that inclusion of dummies significantly improves the explanatory power in terms of _R−squares._ Another interesting feature of this result is that rates of return are 19 percent and 22 percent higher in the informal sector with exclusion and inclusion of dummies for wards respectively. This is similar to what we have found in regression (10). As expected, we find that provision of these services have no explanatory power when we run a separate regression for the formal sector. In the informal sector, point estimates suggests that provision of community tap for water supply raises the rate of return, implying a higher probability of destruction. Therefore, informal settlements which rely on community tap for water are more vulnerable in the sense that they face a higher risk of destruction. An interpretation for this result could be that if the government were committed in recognizing a particular informal settlement, households living in that settlement would rely more on individual connections, or some form of piped water supply.

# **4.5 Interpreting Results from Previous Literature**

The remaining of the empirical part of the paper tries to make the link between our approach and empirical findings of Jimenez (1984), Friedman, Jimenez and Mayo (1988). Taking a demand-side approach in which renters and owners are not distinguished, they run hedonic (log) price and (log) rent regressions for urban areas of the Philippines to assess the premium given to dwelling values by property rights. Jimenez (1984) looking at data from Davao, 1979, found a 18 percent differential for the
*Occup**|**ation co**|**ntrols not included**||||Germany 2<sup>f</sup><br>|76|
|Estonia/private|82|Czech R./private|77|||Germany 3<sup>g</sup><br>|77|
|Estonia/public|76|Czech R./public|86|||Netherlands<sup>d</sup>|83|
|Lithuania/private|84|Slovakia./private|82|||||
|Lithuania/public|85|Slovakia/public|93|||||
|Latvia:<br>||||||||
|2000, private<sup>e</sup>|87|||||||
|2000, public<sup>e</sup>|83|||||||
|2002, private<sup>e</sup><br>|77|||||||
|2002, public <sup>e</sup>|74|||||||

_Notes_ :<sup><u>#</u></sup> Other things, including sector of economic activity, being equal. All ratios are significantly different from 1 at 0.01 significance level. The estimates for Latvia presented here for 2000 - 2002 are based on interval data. Those for 2002 are consistent with the results derived from NORBALT (1999) dataset with self-reported wages. _Sources:_<sup>a</sup> OECD (2003a) and own calculations.<sup>b</sup> Hungary (1992) - Pailhé (2000); Bulgaria (1995), Poland (1996), Yugoslavia (1996) - Newell and Reilly (2001); Czech R. and Slovakia (1998) – Juraida
on the unknown joint distribution c yield better well-being comparisons than those of H<sup>ˆ</sup> M (or those of H<sup>ˆ</sup> OD).

The most discussed question in this area asks which particular imputation method yields most reliable results given the particular data constraints (Dang, 2021; Dang et al., 2023). Another question, which perhaps deserves more prominence given our purpose, is whether the use of an imputation method with limited reliability is better than no imputation at all. In the case of multidimensional well-being, an imputation method that is only semi-reliable could improve well-being comparisons by allowing to draw information from both a monetary and a non-monetary survey.

It would be interesting to study the reliability of well-being comparisons yielded by indicators constructed on a “comprehensive” database obtained by imputing the monetary dimension into a non-monetary survey (DHS or MICS). Studying this reliability requires at least two contexts for which the joint distribution between monetary and non-monetary outcomes is observed. The more dissimilar these two contexts, the more conservative the assessment. The imputation model is trained on one context and then tested on the second context. Let uˆ and P<sup>ˆ</sup> denote the well-being indicators we would like to

> 51For instance, one could use the PX identification method described in Section 3.2.

28
based in the Orbis dataset for Turkey. _Notes:_ The regressions are estimated by GLS. TFP is total factor productivity, computed using the Wooldridge-Levinsohn-Petrin methodology (WLP). FO is transformed as (FO _/_ 100) + 1. FO is the share of foreign-owned equity. ∆<sup>_k_</sup> indicates the change between year _t_ and year _t_ − _k_ where _k_ = 1 _,...,_ 4. Standard errors clustered at the firm level are in parenthesis. *** , **, *, denote significance at 1%, 5%, and 10% levels.

Table 4 undertakes the same analysis but this time focusing on affiliates that are fully owned as opposed to the foreign ownership of more than 10% threshold we used in the previous table. As can be seen, results are very similar, though the economic impact is a little bigger.<sup>6</sup> The positive productivity effects are still realized after four years. This effect is a typical finding in the literature of effects of FDI on firm productivity. As has been found for Latvia, Lithuania, and Spain by others, and also for other Eastern European countries by Fons-Rosen et al. (2014), in general it takes 3-5 years to realize potential benefits of FDI. This is as expected if FDI is bringing technology and knowhow. Hence, Turkey is not different from other emerging markets in this regard. The reason why the

> 6 Focusing on companies owned more than 50% (majority owned) delivers a very similar result.

8
border regression design to address selection of local policies. To incorporate these identification elements, we use an IV method paired with a city-border design.

To address price endogeneity due to unobserved product attributes, we use two sets of IVs. The first (set of) IV is central subsidy by leveraging the discrete nature of the central subsidy that varies based on driving range cutoffs. In addition, the amount of subsidy changes from year to year as discussed above. The central subsidy offers a valid instrument as it is related to consumer price but unlikely to be correlated with unobserved product attributes for two reasons. First, we use time fixed effects to control for time-varying national common shocks that could be correlated with the year-to-year variation in subsidy. Second, the driving range of the vehicle is included in the regression so the identifying variation in subsidy is due to the discrete nature of the subsidy around the driving range cutoffs. The identifying assumption is that unobserved product attributes are unlikely to change discretely around the driving range cutoffs.

The second set of IVs for consumer price is constructed based on battery capacity (kWh). In particular, we interact battery capacity with supplier dummies. We distinguish battery suppliers as CATL, BYD, and the rest. CATL is by far the largest EV battery supplier in China with a market share of 41.3% and 50.6% in 2018 and 2019 while BYD is the second largest battery supplier with a market share of 20% and 17.3% in 2018 and 2019.<sup>30</sup> Battery is a major cost component for EVs and could account for about 20-60% of vehicle price. The battery cost has decreased dramatically over time with the (volume-weighted) cost reduced from $1,100/kWh in 2010 to less than $160 in 2019 due to technology improvement and the production scale.<sup>31</sup> By the virtue of their production scale and technology capability, CATL and BYD batteries may have cost advantages. The
# **B.2 Malawi**

The LSMS-ISA data from Malawi includes two separate surveys: (1) Integrated Household Survey, from which we include the first wave and (2) Integrated Household Panel Survey which includes three waves (NSO, 2012; NSO, 2015; NSO, 2017). The two surveys are different in their representation of various households within the country. In this analysis, we rely only on the Integrated Household Panel Survey.

The Integrated Household Panel Survey begins with Wave 1 in 2010 and includes 3,247 households from 204 enumeration areas that were visited as part of the Third Integrated Household Survey 2010/11 and that were designated as “panel” for follow-up, starting again in 2013. The sample was designed to be representative at the national-, urban/rural-, and regional-level at baseline. Wave 2 from 2013 aimed to track all panel households from Wave 1, including all individuals that changed locations between the waves. The Wave 2 household sample size was 4,000, including new households that were formed by split off individuals that were tracked. Finally, Wave 3 from 2016 aimed to track all households and split off individuals that were ever associated with a random half of 204 original enumeration areas that had been visited in 2010. The Wave 3 household sample was 2,500 households, including again new households that were formed by split off individuals that were tracked from previous rounds.

# **B.3 Niger**

The LSMS-ISA data from Niger includes two rounds. In Wave 1. approximately 4,000 households in 270 Zones de D ́enombrement (NIS, 2014). The sample is nationally representative, as well as representative of Niamey, other urban, and rural areas. Households visited in Wave 1 were revisited in Wave 2, including households and individuals who moved after the 2011 survey (NIS, 2016). When the entire household moved within Niger, the household was found and re-interviewed in the second wave. When individuals from the
35 <!-- Start of picture text --> Figure 8. Financial Systems: 2008-2010 vs 2000-2007 (Financial Markets)<br> (cont’d)<br><!-- End of picture text --> <!-- Start of picture text --> Efficiency<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Average 2000-2007<br>Stability<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Average 2000-2007<br>Average 2008-2010<br>Average 2008-2010<br><!-- End of picture text --> Source: authors, based on the Global Financial Development Database.
no natural disasters occurred in the neighboring states of Karnataka and Tamil Nadu, we can causally estimate the impacts of the Kerala floods on economic activity and household income and expenditure using a difference-in-difference estimation strategy.

We find that monthly nighttime light intensity, a proxy for aggregate economic activity, declined for three months during the disaster but increased subsequently, suggesting an economic boom likely due to reconstruction efforts. Automated teller machine (ATM) transactions, which proxy consumer demand, declined for six months and credit disbursal increased, with households borrowing more for housing and less for consumption. Income and expenditure declined strongly during the floods, but while expenditure remained lower in the medium-term, wage income recovered quickly. In September, right after the disaster, wages in affected districts started exceeding wages in unaffected districts. We provide indirect evidence that the overshooting can be attributed to reconstruction efforts. First, data on India’s largest public work program, the Mahatma Gandhi National Rural Employment Guarantee Act 2005 (MGNREGA), suggest that rural labor markets tightened. Second, poorer households benefited more from increasing wages. Third, wages increased most where government relief was strongest, providing corroborative evidence on the link between reconstruction and increased wages.

The next section provides background on the Kerala floods and section 3 discusses related literature. Section 4 describes the data and summary statistics. Section 5 presents the analysis of aggregate economic activity and credit. Section 6 analyzes household expenditure and income, and section 7 analyzes the impact on household balance sheets. Finally, section 8 concludes.

# **2. The Kerala Floods of 2018**

Every year in June, the southwest monsoon enters India and Kerala is usually the first state to be hit. From June 1 to August 19, 2018, Kerala received 2,335 millimeters (mm) of rain, which was about 46 percent above normal. Higher than normal rainfall in June and July led to the first onset of floods toward the end of July. Rainfall further intensified in August, 3
employment as the narrow one in the following text, as opposed to the basic one described earlier.

Therefore there are three estimation procedures used to identify the workers in LFS matching of the shadow workers from the CASE 2007 database:

- Estimations where basic definitions were used and the contract variable was included into the model specification

- Estimations where basic definitions were used but without the contract variable in the model specification

- Estimations with narrow definitions of shadow employees and workers used and with the contract variable used in model specification.

Simple Probit and Logit models have been estimated for variables representing various definitions of shadow employment on the left hand side and all variables created on the right hand side of the model in the basic case. The models have been estimated using the CASE_2007 dataset. In the estimations the employees, the self-employed and the employers have been excluded from the sample. The mining sector has been excluded from all estimations as there were no shadow employees working in this sector and hence it predicted failure (i.e. legal employment) in 100% of cases.

The estimated coefficients have then been used to predict the probabilities of working in shadow employment in the LFS 2003-2008. As a result 12 variables with various shadow employment proxies (probabilities) have been obtained for each employed in the LFS samples:

- 4 for the basic case (from logit model for employees only, from probit model for employees only, from logit model for employees and self-employed and from probit model for employees and self employed),

- 4 for the respective models without ―contract‖ variable in specifications and

- 4 for respective models where narrow definitions of shadow employment where applied (i.e. excluding those with verbal contracts).

# **3. Results on the General Trend in Shadow Employment in 2003-2008**

The resulting general trends estimates for the share of shadow employment in total employment are very similar for both logit and probit models (see Table 1 – all rows). The results (in terms of trends) do not significantly
Puebla.

# **2.4 Poverty**

The integration of income and consumption data from household surveys with census data has enabled the creation of municipal poverty maps in Mexico<sup>9</sup> . We rely on such information for 2000 and 2005, using income poverty levels in three officially-defined (until 2011) alternative

> Fourth Assessment Report.

> 8Information about the basic characteristics of the emissions scenarios used can be found at the SRES Emissions scenarios. http://sdwebx.worldbank.org/climateportal/index.cfm.

> 9Briefly, poverty mapping involves, first, discovering relationships between household characteristics and the welfare level of households as revealed by the analysis of a detailed living standards measurement survey; and second, applying a model of these relationships to data on the same household characteristics contained in a national census to determine the welfare level of all households in the census. The resulting estimates of household welfare and poverty derived from the census are spatially disaggregated to a much higher degree than is possible using survey information (Elbers et al., 2004; Bedi, Coudouel and Simler, 2007).

7