rafmacalaba/datause-encoder-data huggingface dataset:
42% of these wells were recorded to contain As concentrations >10 μg/L (WHO drinking water standard). Our analysis applies interpolated As concentrations (Figure 2a) for developing groundwater risk maps at the national scale in Bangladesh. Arsenic concentrations at geographic points ( _n_ =3,207) were interpolated using the Inverse Distance Weighting (IDW) algorithm and resampled at 2.5-km × 2.5-km grid resolution using the ‘raster’ package in the ‘R’ programming language (R Core Team, 2017).

# **2.2 Groundwater salinity: Electrical conductivity**

Elevated groundwater salinity is common at shallow depth (<150 mbgl) in coastal aquifers of southern Bangladesh and is generally defined by total dissolved solids (TDS), or Electrical Conductivity (EC) or chemical constituent such as chloride (Cl) (Zahid et al., 2013). There is no national-scale, frequent (e.g. monthly) monitoring of groundwater salinity in Bangladesh. Over the years, a few studies (see Supplementary Texts for further details) have generated some contour maps of groundwater EC at the national scale but there is little detail on the data sets used for mapping. Here, we have generated a groundwater EC map at the national scale using data primarily from two sources. First, we collated groundwater EC data from 461 newlyinstalled monitoring boreholes that were installed recently under a regional-scale hydrogeological study conducted in 19 coastal districts by the Bangladesh Water Development Board (BWDB) (BWDB, 2013). Second, we have digitized and georeferenced the contoured map of groundwater EC (Rahman and Ravenscroft, 2003) and extracted point data of EC at 102 locations throughout the country, predominantly in the northern part where there is limited groundwater EC measurement as groundwater is generally fresh. Finally, we interpolated the point data ( _n_ =563) at the national-scale using the IDW algorithm in ArcGIS environment. The interpolation error was 50 μS/cm compared to the data range of 27 to 43,950 μS/cm with a mean of 5,251 μS/cm. We then rasterized, using the ‘R’ programming language, the interpolated EC data at a grid resolution of 2.5-km × 2.5-km (Figure 2b).

# **2.3 Depth to dry-season groundwater levels**

We use mean depth to dry-season (December–April) groundwater levels as a hydrological indicator for representing groundwater storage depletion in Bangladesh. This is an important measure of sustainability of shallow groundwater abstraction in Bangladesh using low-lift pumps, for example, the No. 6 hand-operated pump, which is widely used for drinking and domestic purposes (see Supplementary Texts for further details). Mean depths to dry-season groundwater levels were estimated from weekly monitoring records from 236 boreholes from 5
27 efficiency of the credit programs has not been high even if they have been effective in reaching the targeted population.

The third condition linking credit and output in the targeted sector was discussed with regard to agriculture and exports. The paper by Calomiris, Hubbard, and Stock reported a significant link in agriculture. Exports, however, show no such linkage.

The final condition concerns the rate of growth in the targeted industry relative to the rest of the economy. Evidence from small business studies suggests that small businesses supported by the SBA have not in fact grown faster than the rest of the economy.

Overall, none of the studies address all four conditions which must exist for directed credit to be effective toward growth. The scant evidence that does exist suggests that for most credit programs at least one of the four conditions will not be satisfied. The conclusion, therefore, is that the directed credit programs in the U.S. have not been particularly effective in stimulating growth. However, it should be noted that the goal of many of the programs in the U.S. was not growth, but equity. To the extent that marginal borrowers have received funds, some progress toward equity has been achieved.26 Furthermore, in most cases, the credit programs have been effective at increasing the credit allocated to the targeted group. Thus, the directed credit programs have been effective in changing the allocation of credit and perhaps in achieving their objective, which was generally removing market imperfections and enhancing equity, but not 'However, note that the funds generally had to come from somewhere. Either other nontargeted borrowers were squeezed out of the market for loans or an increase in interest rates was able to attract more funds. Further analysis is necessary to determine whether the marginal nontargeted borrowers losing access to funds are poorer than the marginal targeted borrowers who gain access to funds. Furthermore, those who hold government bonds or other interestbearing assets also gain at the margin from an increase in interest rates when the government borrows to raise funds which would tend to adversely affect the distribution of income from an equity standpoint.
_where, AperformancelApolicy consistency = P foa , and apolicy consistency_ _performanceo = initial performance level._

# **Stage 2 - Examine institutional rates of return**

The illustrative equation below shows the percentage change in performance<sup>through</sup> making policies consistent. In that sense, it is the rate of return on reform<sup>effort to make</sup> policies consistent. Therefore, it is called the institutional rate<sup>of return of policy</sup> consistency (or policy consistency IRR): _IRpolicycDnsistency_ A _performance /A policy consistency performanceo_ In general, a comparatively high-value IRR for any reform effort means<sup>that the</sup> intervention is more likely to have a bigger impact on performance than another<sup>with</sup> low-value. A comparatively low value would indicate that the effort<sup>is less likely to have</sup> impact and should not be taken up if considerable results are desired. IRRs<sup>can be</sup> calculated for each of the eleven reform interventions listed above in the Bolivian examnple.

A detailed econometric model specification, the estimation method, regression<sup>results,</sup> and how the estimated coefficients were used in deriving IRRs for the Bolivian<sup>public</sup> s.ector (based on this formula which was described above) are all presented<sup>in Appendix</sup> 6. Since all the input variables are capturing institutional environment,<sup>it is theoretically</sup> possible that the input variables are correlated. For example, rule credibility<sup>could be</sup> associated with resource predictability. However, two factors ensure that multicollinearity is not a serious problem. First, as the model is offered as<sup>a heuristic</sup> device for attracting policy-makers attention to particular points of entry to<sup>reform, this is</sup> not a particular drawback provided that any correlation is positive. If policy<sup>credibility</sup> were to deteriorate as resource predictability improved, the resulting combined<sup>effect</sup> would be hard to interpret. However, in all the survey results analyzed to<sup>date the</sup> correlation where it exists has been positive. Second, multicollinearity<sup>is a sample</sup> phenomenon as well as a theoretical one. A good rule of thumb is to find variables that are theoretically relevant (for meaningful interpretation) and that are also statistically<sup>non</sup> multi-collinear (for meaningful inference). As the correlation matrix in Appendix<sup>6</sup> shows, the correlation coefficients between the explanatory variables<sup>in the Bolivia data</sup> are low.<sup>54</sup> The term "Institutional Rate of Return" has been coined deliberately to echo<sup>the more</sup> common Internal Rate of Return. Its purpose is to draw attention to the heterogeneous 54 In addition to presenting this matrix, Appendix 6 further explains how survey responses were converted to a binary mode thus lowering correlation among the explanatory variables.

29
12 <!-- Start of picture text --> C)~~~~~~~~~~~~~~~~~~~O<br>1.4<br>r4 GD<br>Time<br><!-- End of picture text --> <!-- Start of picture text --> u<br>1-4<br>Time<br>I I I I I ... I.7. ,<br><!-- End of picture text --> Figure 1: Investment and GDP I -Al * GDP Figure 2: Investment and GDP I - A2 * A GDP <!-- Start of picture text --> w ~~~~~GDP?1<br>f**4 '~~~~~~~~~~~~~~~~~~~~~~~~~~-4I<br>Mi<br><!-- End of picture text --> Time <!-- Start of picture text --> ~~~~~~~~~~Time<br><!-- End of picture text --> Figure 3: Intermediate Imports Figure 4: Capital Imports I - A2* A GD?
Generally, the studies suggest that differences in household access to assets, such as capital and land, partly determined the degree to which families were able to take advantage of economic opportunities. For example, the studies from India and Vietnam found that farmers with access to land benefited directly from new shrimp export markets. Moreover, those with better access to capital were able to take advantage of more profitable production technologies. In contrast, benefits for unskilled landless workers were less direct and came from employment in soil preparation for ponds, sludge removal, caring for shrimp, processing companies and transportation. Nevertheless, the Vietnam study noted that more than half the households in a survey of the region said this kind of aquaculture-related income was increasingly important to them. In Madagascar, a select group of households with landholdings near the Export Processing Zone were the primary beneficiaries of farm contracts linked to exports.

Importantly, the studies suggest that access to natural resources mattered as well. For example, in Pingbian, rainfall and soil conditions determined the extent to which households could convert to more profitable crops. In South Africa, the authors note that access to land and water resources largely determines livelihood strategies and income among the poor in Incomati. The case studies also suggest that, to a degree, poor households were able to supplement incomes limited by access to private assets by drawing on natural common-pool assets. For example, in India, a large number of poor landless households generated income by harvesting wild prawn larvae. In Pingbian, some households earned a portion of their livelihood from harvesting herbal plants from surrounding forests. In Vietnam, households with limited access to capital relied more heavily on tidal flows and natural food sources to raise shrimp for sale into export markets.

The study on Chile, which covered a much longer time period, illustrates how the links between trade and income can also evolve with time. The Chile study found that growth was at first driven by export-oriented primary production, but over time it became increasingly driven by resource-based manufacturing, processing and upstream service and input supply, with increased value added. This is consistent with a study of agricultural growth in Chile, which found that the biggest poverty impacts came not from farming itself, but from the development of these related upstream and downstream activities (Valdés and Foster, 2003).

22
But aerial spraying, forced eradication, and alternative development programs are not the only measures taken by producer countries to fight illegal drug production and trafficking. Other policies include, but are not limited to: curtailing the flow of raw materials used in the cultivation of coca and the processing of cocaine, discovering and destroying the small local workshops and laboratories where coca base is processed, destroying the landing strips used by trafficking organizations to ship drugs, interdicting drug shipments, dismantling the drug cartels and the networks and (many times fake) firms that are created for the money laundering of the proceeds obtained from the (huge) profits derived from illegal drug trafficking.

Available measures of the efforts undertaken in these other fronts on the war on drugs show an increasing number of operations against the different links in the cocaine production and trafficking chain. For instance, coca base and cocaine seizures have increased in the past few years in all the producer countries. Figures 14 (a) and (b) show coca base and cocaine seizures in Bolivia and Colombia respectively. Other measures such as the number of destroyed illegal laboratories used for processing coca paste and cocaine also show increasing efforts to combat illegal drug production (see Figures 15 (a) and (b) for Bolivia and Colombia respectively<sup>35</sup> ).<sup>36</sup> Cocaine seizures have increased rapidly in the three producer countries as well as in the US in the last few years (see Figure 16).

# **Side effects of anti-drug policies**

> 35 For Peru, the numbers of illegal laboratories destroyed are: 964 in 2003 and 861 in 2004. 36 See the UNODC (2005b - Coca Cultivation Surveys for each of the producer countries) for other measures of recent success in the war against cocaine production and trafficking. For the case of Colombia, the Dirección Nacional de Estupefacientes – DNE publishes every year a summary of results in the war against illegal drug production in Colombia (see DNE, 2004).

31
right. However, the calculation does serve to confirm that the $US values of low national poverty lines have fallen with the 2005 ICP.

There are a number of possible explanations for why the mean 2005 line corresponding to the 1993 line is so much lower than that implied by simply updating for inflation in the US. We cannot rule out the possibility that the inflation rates are substantially miss-measured, but it seems more likely that the problem lies with the 1993 PPPs. Weaker standards used in defining comparable goods in the 1993 ICP price surveys (relative to 2005 ICP) could easily produce the kind of upward revisions to the 1993 PPPs implied by the above findings.

The implied revisions are substantial for poor countries. To see this, let _PPPit_ * denote the true PPP exchange rate derived from the ICP round for date _t_ . Under the purchasing power parity principle, the PPP rate for a given country changes over time according to differences in that country’s rate of inflation and that for the numeraire country, the US, i.e., where _Dit_ * is the true deflator to convert that country-specific poverty line to the PPP reference date, _t_ . While equation (7) holds for the true values of all variables, our measurements are based instead on the observed values, _PPPit_ and _Dit_ . To focus on the implications for the errors in the historical PPP data for developing countries we assume that the 2005 PPP and the deflators are accurate. Under these assumptions, the revision to the PPP for 1993 that is implied by the observed data can be readily derived as follows: The sample mean of this variable is 1.568 (with a standard error of 0.068; n=72). Thus a sizable underestimation of the 1993 PPP is implied by the new PPP data and the data on rates of inflation. Furthermore, the extent of this underestimation tends to be greater for poorer countries. Figure 6 plots the implied values of _PPPi_ 93* / _PPPi_ 93 against log PCE per person at 2005 PPP. We see a marked negative gradient; the correlation coefficient is -0.472, which is significant at the 1% level (t=-4.70). Amongst the poorest countries in terms of PCE the data suggest a marked upward revision is required to the 1993 PPP. In other words, the 1993 ICP round underestimated the price level in these countries, relative to the US. This is consistent with 18
the bottom left panel contains all the firms that have paid workers and are greater than three years old, which means that they should be registered with the federal treasury, should have their (paid) workers registered with IMSS, and should have been enumerated in the Census. However, of this group only 72.9 percent are registered with the federal treasury, 63.8 percent pay taxes, 31.8 percent participated in the Census but not IMSS, while 7.2 percent participate in IMSS but not the Census.

Clearly, participation is a question of degree and spans many dimensions. This suggests that previous research that lumped together all small firms as representing the informal sector _(e.g._ Rauch, 1991) obscured important differences among them. In the interest of avoiding some of the conceptual confusion that surrounds the topic, it may therefore be preferable that future analysis employ the term "informal" to exclusively refer to the issues of participation discussed here. '[his would leave considerations of firm size, wages/productivity, labor market segmentation, _etc._ to be addressed under labels that correspond more precisely to the phenomena being studied. s More generally, our evidence indicates that models of regulatory and tax compliance may need to consider possible complementarities between different institutions when modeling participation and enforcement for individual institutions.

# _2. Distribution of Formality Across Firm Characteristics_

Points 2a-2c above argue that participation decreases with 0 and increases with the probalbility of long run success. Although we cannot observe either, the framework shows that they are monotonically related, respectively, to firm revenue/size and to firm age (conditional on size). This leads to the following empirical specification: Pr(Participation) = _PoConstant + PIRevenue + f 2Age +_ e (4) where revenue is total firm revenue, age is the number of years the firm has been in business (or

**18** This is consistent with many characterizations of the informal sector (Thomas, 1992; Portes, 1994). Strictly speaking, in our framework firms choose between different institutional arrangements (Lin and Nugent, 1995). Informality encompasses a set of institutional arrangements including enforcement of contracts through social networks and self-insurance against employee health problems. Formality encompasses a different, complementary set of institutional arrangements including compliance with government reporting requirements. See also Peattie (1987) who critiques usage of the term "informal sector."

13
C14e_temp1 What is the main reason that you do not have a permanent contract or permanent verbal agreement in this MAIN job? **[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT ALL THAT APPLY.]**

1. This employer did not offer me a job with a permanent contract

2. I did not want to have a job with a permanent contract 3. I am unable to do continuous work 4. I was not able to find any job that offered a permanent contract or verbal agreement 5. Other (specify) 888. Don’t know 999. Refused C15a_sick1 How many days of sick leave does your employer provide PER YEAR in this MAIN job?

**[If “As needed” then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C15b_casual1 How many days of PAID earned or casual leave does your employer provide PER YEAR in this MAIN job?

**[If “As needed” then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C15c_holiday1 How many days of holiday does your employer provide EACH WEEK in this MAIN job?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16a_maternityactual1 Have you taken maternity leave from this MAIN job?

1. Yes 2. No à **Go to C17** 888. Don’t know à **Go to C17** 999. Refused à **Go to C17 [ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16b_maternityactuallength1 How many weeks of maternity leave (paid or unpaid) did you take?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999] [ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16c_maternityactualpaid1 How many weeks of those weeks were paid?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]** à **Go to C18**

6
per capita in the most arid countries, the relationship between rainfall and GDP is flat. This initial decrease is in fact driven by a specific set of countries – rich oil and gas exporting countries from the gulf. The downward relationship between GDP per capita and temperature is preserved with country level data, with the notable exception again of the Gulf countries (Figure 1-D). In the following section we provide more robust evidence of these patterns.

## **2.B Empirical Strateg** y

We start the analysis at the grid level using data from Kummu, Taka and Guillaume (2018). For each 0.5-degree grid cell and year between 1991 and 2014, we calculate the annual growth rate of the cell’s GDP per capita adjusted for inflation ( _g_ ). We use a panel regression to determine if _g_ is impacted by rainfall and temperature. We estimate the following model: g�,� �β�Rainfall�,� �β� Rainfall�,�� �β�Temperature�,� �β�Temperature�,�� �γ� � φ� �μ��� �μ���� �ε�,� �1� Where cells are indexed by _i_ and year by _t_ . We allow a quadratic relationship between the two weather variables, total annual rainfall in liters and average annual temperature in Celsius degrees, and GDP per capita. Time invariant factors that affect cells’ growth rates are controlled by grid cell fixed effects (γ�). Global events are captured by year fixed effects (φ�). Unobserved country changes are accounted for by quadratic country � specific time trends μ��� �μ��� . Deviations in rainfall and temperature are likely to be randomly distributed and unexpected and therefore orthogonal to any possible confounders. The rich set of fixed effects in this specification isolates these localized and unexpected fluctuations in rainfall and temperature, facilitating causal inference. Standard errors are clustered at the Administrative 1 level (one level below country, i.e., a state in the United States) in order to account for spatial and serial correlation. Following Dell et al. _op_ cit among others, we weight observations based on grid cell population, to ensure that results are representative of the global economy. This implies that quasi empty cells in Siberia and say densely populated San Francisco receive different weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).

Grid-cells data are then aggregated at the Administrative 1 level and at the country level to investigate how aggregation of weather data affects the results. Our identification strategy relies on the assumption that annual variations in precipitation and temperature are exogeneous with respect to cells’ economic activity.

# **3- Results**

## **3.A Causal Results**

Our main results are presented in Table 1. Column 1 displays the results of the baseline model (equation 1). Columns 2 and 3 aggregate data at the administrative 1 and country levels. Our results suggest a consistent increasing and concave relationship between rainfall and GDP growth -- a result that departs from findings in 5