rafmacalaba/data-use-mentions-extended huggingface dataset:
semi‐ parametric model like that in figure 5, we do use alternative cut offs of 75 and 125 mS/m. Results are shown in Tables 6 and 7 for the Mekong River Basin and India, respectively. In both cases, we see that impacts are found at all thresholds. In both cases, results are monotonic and scale with the threshold that is used.

# 6. Simulating Global Impacts of Water Salinity on Agricultural Production

The results of this paper demonstrate just how sensitive global agricultural production is to salinity in water. Using this information, we perform a simulation to quantify the magnitude of agricultural losses that saline water causes. The GEMStat database, though having wide global coverage, does not cover all land where there is agricultural production. Notably, big gaps in the data are present in China and Sub‐ Saharan Africa, two major agricultural areas where high salinity might be a problem (Figure 3).

To fill gaps in the GEMS data, we train a machine learning model to predict continuous values of EC globally at a 0.5‐degree scale between 1992 and 2013. More specifically, we construct a data set covering the drivers of salinity identified in the scientific literature and use a Random Forest algorithm which finds the best fit between observed and predicted values over other commonly used algorithms (linear regressions, support vector machines, etc.). To estimate the model, we proceed as follows. First, we combine EC data from India, the Mekong Basin and GEMStat to have the broadest geographical coverage as possible. Second, we randomly split our data set between a training sample (80% of the observations) and a testing sample (20% of the observations). Third, we train the model on the testing sample and assess the predictive power on the testing sample. Fourth, we choose the best model as the one that maximizes the correlation coefficient between observed and predicted values in the testing sample. Finally, we use the best model to predict EC for every year between
“pending” owners who have not yet exercised their privatization option. However, the new housing legislation recently adopted (the Housing Code) has stipulated a time limitation on these decisions. They would also curb the current practice of tenants in newly built municipal rental apartments privatizing their new units shortly after they get allocated to them.

- The survey data appear to capture well the proportions of the rental sector in Serbia. Official sources suggest a rental sector share among households of 7 percent, of which 5 percent are private tenants. The highest “free or other” share in the sample also concurs with official estimates.<sup>25</sup> It reflects the effect of numerous war refugees, who frequently live with families or in in-kind rental relationships, and a lack of cash in large segments of the population.

> 24  This could explain a part of the large number of unaccounted-for non-owner-occupied housing units that emerged from the 2002 Census. Of the 1.5 million units, only 212,000 are public rental units, leaving a gap of almost 1.3 million dwellings. These units should be vacant (possibly up to 700,000 of them), perform functions as second homes, or be part of the private rental sector.

> 25  See Zivanovic (2003).

18
In the process of putting this cross-country data together, special care was given to comparing the questionnaires used across the surveys and properly mapping variables to ensure compatibility. Only the questions that were worded the same way or with minor changes were put together. For example, informal businesses in Zimbabwe were asked, "In the last completed month, did this business or activity experience harassment by government officials or police?" In subsequent surveys, the reference period was changed from "in the last completed month" to "over the last three months." In such a scenario, responses from Zimbabwean businesses cannot be included among those from other countries, as the reference period of the question significantly changed for a meaningful comparison. If the response categories differed across the surveys, efforts were made to accurately map the categories into common groups. If this was not possible, the questions were treated as different and were not mapped together. While there is no common questionnaire available in the standard format, the data users can rely on the variable labels to get a general idea of the question and consult the documentation file (which includes the questionnaire) of the corresponding country-level survey for the exact wording of each question.

Along with the database that maps and combines raw cross-country survey data, we created a database with indicators. The indicators database simply transforms the raw database into a format that can be analyzed without further edits. In particular, responses such as "don't know" or "refusal" are marked in the database with special codes -9 and -8, respectively. The indicators database removes the special codes, turning them into missing. Furthermore, while the raw database uses codes 1 and 2 respectively to denote Yes and No responses, these are turned into 100 and 0 in the indicators database for easier analysis. For questions with multiple options, separate indicators are created to enable direct analysis. Most of these transformations and indicator definitions are self-explanatory.

The two databases – the raw data with the country-level questions mapped into
-70-

Exact-matching is difficult to realise, because strictly identical products are only rarely available in two or more countries at the same date. In consequence, lower degrees of product comparability have to be accepted for international comparisons than for inter-temporal comparisons within a single country. This is true not only for the present product-based study, but also for expenditure-based studies such as the ICP project carried under the direction of the United Nations.

The following paragraphs describe the problems faced in this study with regard to product matching. We discuss the alternative procedures, and show their different outcomes for the comparison of the motor vehicle industries in the three countries. This industry presents particular difficulties for product matching because of the large number of items produced and the wide range of quality differences within product groups.

# Maximalist Approach

The industrial censuses we used give value and quantity information for 100 automobile products for Brazil, 393 items in Mexico and 101 in the USA. In our first round of comparison we tried to match as many products as possible from the Brazilian and Mexican census reports with those listed in the US census.

At our first attempt we found 36 products from the Brazilian census that appeared to match 62 products as reported by the United States, and 45 products for Mexico matching 59 products for the United States. These product matches are given in tables 5.2 and 5.3 which are shown at the end of the chapter.

At this stage, products were considered "matched" provided that the product descriptions were the same or very similar, and provided also that price and quantity figures were available for both countries. This approach requires the matched products to have a more or less "homogeneous" character as well (to this issue reference will be made later on). This matching procedure is described as "maximalist" because the aim was to obtain the maximum number of matches without regard to the plausibility of the PPPs we derived from them.

It can be seen from tables 5.2 and 5.3 that some
# _Market structure_

Some authors have noted that there has been less innovation in product design following the introduction of WBCIS, despite the large amount of data available. This may be caused by an increase in competition having reduced the private benefits from designing improved indices. States could limit this by tendering for WBCIS contracts in two phases: first a firm could be hired on a multiple-year contract to design weather products across the state; and second, insurers could compete on price or other specified dimensions to offer the stated products. Alternatively, states could tender for multi-year contracts with an insurance provider to design and offer products.

At present there is no uniform procedure adopted by state governments on the selection of insurance providers. It would be prudent to consider establishing and following a sound and transparent policy in insurance provider selection. Decisions about weather indexed insurance products are complex and there is a strong rationale for creating a technical support unit to support central and state governments in making informed decisions about insurance product design, and in choosing between insurance providers. Incidentally, formation of such a unit was one of the key suggestions of a recent evaluation study conducted on WBCIS on behalf of the Government of India (Government of India 2011).

# **4. Ratemaking for Weather Based Crop Insurance**

As for any insurance product, the price of a weather indexed insurance product should reflect the expected cost to the insurer. Moreover, while it is important for estimates of the expected cost to be statistically unbiased, neither systematically too high or too low, it is also important the estimates are statistically efficient, so that the price for any one product is not significantly ‘wrong’. It is commonly assumed that indexed weather insurance products are not subject to adverse selection against the insurer, as the insurer typically has access to long time series data that is at least as good as any information possessed by potential purchasers. However, this relies on the insurer making the best use of available data. If, as seems common across India, ratemaking
<!-- Start of picture text --> 8.c. Market sophistication 8.d.Business sophistication<br>70 60<br>60 50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>Observed Market Sophistication index value Observed Business Sophistication index value<br>Median value Median value<br>Predicted value Predicted value<br><!-- End of picture text --> Source: Own elaboration based on GII dataset and WDI dataset Note: Predicted value dots show the benchmark predicted by a (linear) regression with (the log of) GDP per capita adjusted for purchasing power parity (in 2011) as the explanatory variable; the dependent variable is the enabling innovation factor of interest. The regression used all countries available in the GII and WDI dataset. The median value (for each enabling factor) is calculated within the 127 countries covered by the GII dataset

# **_3. Demand side of innovation: The role of firm characteristics and use of innovation inputs_**

The analysis of the demand side of innovation draws from the World Bank Enterprise Survey (ES) implemented in 2015-2016 for a selected set of developing East Asian countries, as follows: Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Thailand, and Vietnam. The survey collects substantial balance sheet and other information regarding the investment climate and includes an innovation section (section H) containing questions covering innovation outputs and inputs to the innovation process. Table 3 lists the year the survey was conducted for each country as well as the total number of firms surveyed. The sample for each economy is stratified by industry, firm size, and geographic region. Stratification by industry follows a different level of detail depending on the size of the economy. Stratification by size follows three levels: 5 to 19 (
revenue), employment and fixed capital by state which match the officially published numbers.

Since officially published state-level ASI summary statistics do not include data on contract workers, we use unit level ASI data from 2001-2018 to estimate state level contract employment.<sup>28</sup> We drop the urban state of Delhi, union territories and small states with a population less than 10 million in 2001 (Jammu and Kashmir, Himachal Pradesh and north-eastern states expect for Assam) from the control donor pool of the synthetic control analysis. Hence, besides Rajasthan, the panel includes fourteen donor pool states : Andhra Pradesh, Bihar, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Tamil Nadu, Uttar Pradesh, West Bengal, Assam, Gujarat

> 27The state-industry data on factories from 1980-1997 were used by Aghion et al. (2008). We are grateful to the authors and the American Economic Review for making these data publicly available.

> 28We verify that the total state output and employment estimates constructed by us from unit-level ASI data match the officially published numbers.

14
|(2.303)|(6.416)|(26.849)|
|Control Upstream|‐0.010*|‐0.020**|‐0.025***|‐0.007|0.025***|
||(‐1.743)|(‐2.097)|(‐2.947)|(‐1.079)|(3.261)|
|Control intra‐industry|‐0.004|0.021***|0.058***|0.034***|‐0.009|
||(‐0.860)|(3.415)|(8.225)|(8.982)|(‐1.404)|
|Region‐Year FE|yes|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|yes|
|Observations|139,615|137,051|137,051|140,594|140,594|
|R‐squared|0.171|0.171|0.173|0.171|0.178|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same microregion and county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. We always include a similarly calculated upstream, downstream and same industry measure of the different control variables mentioned in the column headings. Standard errors are clustered at the county‐ industry (2‐digit NACE)‐
Let _g0_ be the original poverty gap, or in terms of the discussion above, _g0=G_ ( _0_ ) _/z_ . For _k=1,...n_ , put The _gk_ are the poverty gaps of the approximated expenditure distribution for successively lower poverty lines _z-kz/n_ . Let _ak_ be the per capita transfer needed to bring down the poverty line to _z-kz/n_ : We can now solve for per capita transfers as a function of the intended poverty gap _g_ < _g0_ : 2. The per capita transfers resulting in poverty gap _g_ are This scheme can be implemented using standard spreadsheet software.

# _Simulating the impact of “naïve” geographic targeting_

Simulating the impact of our “naïve” transfer scheme on the basis of the “dump-file” described above is far more straightforward. We take our poverty map as the basic statement on the distribution of poverty in the country. On the basis of the poverty map we identify the localities that will receive priority in the targeting scheme (we consider initially regions, then provinces, then communities, etc.). We calculate the amount _a_ that will be targeted to all persons in the priority regions (budget _S_ divided by the total number of poor people in the country, Np). We simulate the targeting scheme in turn for each replication _r_ by allocating _a_ to all persons in our priority regions (irrespective of whether, in replication _r_ , those regions are particularly poor or not) until the budget is exhausted. We re-calculate the post-transfer 20
with one year of tenure, to 21 for workers with 10 years of tenure. In Indonesia, a worker with 10 years of service receives an average severance payment equal to 22 _months_ of wages.<sup>98</sup> Strict EPL may be one of the reasons for not hiring mature workers.<sup>99</sup>

**“Ageism” and social norms, including those reinforcing age stereotypes, also make the hiring of older workers more difficult** . The assumptions that mature workers are frail, stubborn, less capable of learning, and technologically challenged can result in age-stereotyped hiring practices and workforce management practices that reduce the productivity of mature workers—for example, if mature workers feel insecure about their jobs and thus invest less in skills.<sup>100</sup> There is significant evidence of biased perceptions and management practices with respect to older workers within firms. A survey by Generation 21 across Brazil, India, Italy, Singapore, Spain, the United Kingdom, and the United States showed that 38 percent of managers considered mature workers unwilling to take up new technology, 27 percent considered them unable to learn new skills, and 21 percent answered that mature workers did not work well with other generations. Yet, when hiring workers, the same managers valued experience above all.<sup>101</sup> In countries lacking age discrimination legislation, practices such as defining upper age limits for job vacancies – and to some extent mandatory retirement ages

> 92 Rocha 2017, Dohmen 2003.

> 93 World Bank 2016.

> 94 At the same time, seniority wages may have other underlying rationales, as firms may purposely set wages below productivity levels for younger workers and above productivity levels for older workers, to foster firm fidelity (Zwick 2009).

> 95 https://iuslaboris.com/insights/comparing-social-security-contributions-rates-across-latin-america/

> 96 OECD 2019b.

> 97 Auer and Fortenu 2000.

98 Estimates based on Employing Workers Database, https://www
negative impact of the latter. The puzzle, however, is that the sign of the interaction runs counter to that suggested by theory.

Finally, but importantly, we find no spillover effects from the _KF_ - _METR_ in either the parent or non-host countries—an issue that, so far as we are aware, no previous study has addressed. And this is so on both the intensive and, perhaps more surprisingly, the extensive margin of FAI. Rankings of countries by _KF_ - _METR_ s have little real bearing on cross-border decisions on tangible foreign investments.

# **5.5 Comparing Tax Effects in FAI and FDI Data**

Before applying these results to the analysis of the global minimum tax, it remains to explain why the semi-elasticities emerging from the analysis here are noticeably higher than those in much of the previous literature. The natural suspicion is of course that this difference somehow arises from the use of data on FAI rather than, as in much of literature, on FDI. There are indeed hints from some previous work (albeit mainly on U.S. data) that effects are stronger when looking specifically at investment in plants, closer to the concept of real investment underlying FAI data, rather than at FDI.<sup>53</sup> As a first step towards establishing and explaining whether one should expect any systematic difference in apparent tax responsiveness emerging using FAI and FDI data, Table 11 reports results taking each in turn as dependent variables in an estimating equation of the form of equation (8). The sample period, dictated by the availability of bilateral FDI data, is 2003-2016, and so somewhat different from that of the baseline results of Table 2.<sup>54</sup> Columns (1) and (3) include the composite tax differential, columns (2) and (4) its distinct components.

For FAI, the semi-elasticity on the differential term in column (1) is much as in the baseline, and so too (though somewhat
. A recent study of the impact of education on growth in India over the 19661996 period, for example, suggests that it is primary education that ha a strong causal impact on growth, compared to other education levels (Self and Grabowski 2004). Researchers have also employed vector auto-regression (VAR) techniques to explore the time-series properties of public investment and growth, accounting for potential dynamic interrelationships without imposing a causal structure. A series of studies by Pereira (2000, 2001, 2002) demonstrate the dynamic relationship between public investment in infrastructure and private output (private sector GDP, employment,

> 33 Devarajan, Swaroop, and Zou (1995) using data on 29 countries over the 1970-1990 period suggest that public capital spending has a negative but insignificant effect on growth while current spending has a positive and significant impact on growth. They also find that transport and communication have a negative and significant impact on growth while health and education have a negative but insignificant impact on growth. In contrast, a study of 39 low-income countries over the 1990-2001 period finds that higher public expenditure on capital raises economic growth (Gupta, Clements, Baldacci, and Mulas-Granados 2003). A recent study finds that investment in education is the only expenditure outlay significantly associated with growth in a sample of 30 developing countries (Bose, Haque, and Osborn 2003).

> 34 Mosley and Hudson (2001) estimate the correlation between expenditure components and poverty using cross-country data. Based on this exercise, they calculate a “pro-poor expenditure” (PPE) index or the ratio to GDP of spending components associated with poverty. In addition, results from recent cross-country regressions have been used to quantify the poverty elasticity of growth.

34
without adjusting for missing districts), 24 percent of the existing humanitarian database would be eligible for referral to the MoLSA’s CT program with a high confidence level (90 percent or more). The referral rate increase with the lower referral confidence, about 54 percent of the humanitarian caseload would be eligible for the referral under at a low confidence level (50 percent or higher). To allow flexibility depending on the availability of budget, the analysis is also carried out by expanding the coverage of the national program and changing the referral confidence rate. In general, the number of referral cases increase with higher coverage and lower referral confidence. Given a large proportion of the humanitarian database that could potentially be referred for MoLSA CT program, the results suggest a sequencing of smaller numbers with a high degree of likelihood to qualify could be referred first, with larger numbers being referred later when budgets expand.

The remainder of the paper is structured as follows. Objectives of the joint activities are outlined in section 2. Section 3 presents the analytical methodology, including the proposed pseudo-PMT (PPMT), its performance criteria, and theoretical results. Cross-eligibility results from applying the PPMT on the legacy database and potential eligible households’ profile are presented in section 4. Concluding remarks and discussions of next steps are outlined in section 5.

# 2. Objectives and Activities

The main objective of this initial desk analysis is to understand the extent to which beneficiaries of humanitarian cash assistance would be eligible for MoLSA social protection programs under the MoLSA targeting approach. This means both understanding:

1. The percentage of the existing humanitarian database of current and past beneficiaries identified under the current vulnerability assessment approach who are likely to be eligible;

2. The percentage of new beneficiaries who would be identified under the new vulnerability assessment approach who would be likely to be eligible.

In addition, it is desirable to understand who would be eligible and who would not. This requires understanding where eligible (and ineligible) households live, as well as their demographic characteristics.