rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 3: The Non-Routine Wage Premium in the US<br>(A) Routine vs. Non-Routine Wages (B) Non-Routine Wage Premium<br>Non−Routine NR Premium (PS)<br>Routine 2 Std. Err.<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Year Year<br>6.4 40<br>6.2 30<br>6 20<br>Wage Premium (%)<br>5.8<br>10<br>Log Mean Real Annual Earnings<br>5.6<br>0<br><!-- End of picture text --> _Notes:_ Panel A plots the unconditional mean of log real annual earnings in each occupation group. Panel B graphs the coefficients from annual regressions of individual level log real earnings on a non-routine dummy and a host of demographic control variables, including flexible functional forms in industry, age, and education. Occupation and individual specific earnings are based on the the annual march supplements in the CPS (MARCH) provided by IPUMS (Ruggles et al., 2010). We deflate eranings data with the chain type implicit price deflator for personal consumption expenditures. Non-routine workers are those employed in “management, business, and financial operations occupations”, “professional and related occupations”, and “service occupations”. Routine workers are those in “sales and related occupations”, “office and administrative support occupations”, “production occupations”, “transportation and material moving occupations”, “construction and extraction occupations”, and “installation, maintenance, and repair occupations” (Acemoglu and Autor, 2011).

# 2.1. Price-Quantity Decomposition

The declining routine labor income share relative to the non-routine labor income share could be driven either by a change in relative wages, a change in relative labor inputs, or both. We find that both an increasing non-routine wage premium and an increase in non-routine labor inputs contribute to this trend.

A large body of literature has documented increasing wage _polarization_ over the past three decades—a relative increase in wages for high- and low-paying jobs relative to middle-income jobs.<sup>12</sup> Closely related, we provide evidence of an increasing non-routine wage premium, providing further support for this view.

As a baseline reference, we start with estimating simple averages of log real earnings for each type of labor. Panel A of Figure 3 illustrates these estimates and gives a first indication of a steadily increasing wedge between non-routine and routine pay. However, to ensure that this wedge is not

> 12See Acemoglu and Autor (2011) for a comprehensive summary of this literature.

9
covariance matrix is necessary. The GSTSLS-HET estimator combines instrumental variables (IV) and generalized method of moments (GMM) estimation. For the computation, I use the library _sphet_ (Piras, 2010) of R statistical software (R Core Team, 2014).

For the spatial hedonic model above to be estimated, a spatial weight matrix _W_ needs to be constructed. Based on the assumption that spatial interaction is stronger between houses in closer distances, I create a matrix of binary inverse distance spatial weights that assign weight to every pair of households inversely proportional to the distance between them (Bivand et al., 2008). The maximum distance between a pair of households is 16.4 kilometers (mean= 4 _._ 5, median= 3 _._ 9) (Figure 3). I choose households living within a one-kilometer radius of its neighboring household based on the additional assumption that the value of a house does not affect other houses beyond the cut-off distance. I also test alternative spatial weights matrices in order to examine whether this construction of spatial weights matrix affects the estimation of tenure security premium. While a spatial weights matrix is commonly row standardized so that the elements of each row sum to unity, such standardization of an inverse distance weights matrix results in a misspecification problem. Following Elhorst (2001) and Kelejian and Prucha (2010), I instead normalize the weights matrix by dividing all of the elements by the largest characteristic root in the matrix.

(Insert Figure 3 here)

# **Heterogeneity in the Marginal Willingness to Pay**

Household characteristics in the data allow me to examine the heterogeneity in the estimated premium of slum declaration. Of particular interest is how the marginal willingness to pay changes depending on household characteristics that contribute to their _de facto_ tenure security. Rosen (1974) proposes a two-step approach to recover demand functions, though the problem of its identification is well known (Taylor, 2008). I rely on Bajari and Benkard (2005) and Bajari and Kahn (2005), who developed a strategy to identify structural parameters in a single market setting.

Let us consider the following linear utility function for each household _i_ in housing _j_ : where _γk_ is the utility function parameter for the _k_ th housing attribute. In case of a dichotomous attribute such as slum declaration, utility-maximizing households choose to consume the _k_ th amenity if 15
employment and food are bound to starve for an extended period. This leads to serious malnutrition and death in extreme circumstances.

But seasonality in agriculture is a basic fact of agrarian life. Households must be able to adopt effective ways to smooth consumption via income smoothing and other means so as to avoid seasonal hunger. Government policies must come to aid households in need of smoothing consumption. However, when none of these means is effective, a failure to smooth consumption results in _monga_ or starvation. The intensity of _monga_ varies by households and local socio-economic conditions and the extent of flood or drought preceding the _monga_ period. _Monga_ is a reflection of not only seasonality of agriculture but also a failure of public policies and programs that are expected to mitigate seasonality in income and consumption.

This paper discusses the extent of seasonality in income, consumption, and food deprivation using the nationally representative household income and expenditure surveys (HIES) of 2000 and 2005, carried out by the Bangladesh Bureau of Statistics (BBS). As the data are drawn over two periods, this paper will examine the changes in the severity of _monga_ in greater Rangpur vis-à-vis other regions of Bangladesh over time. The paper makes an attempt to identify the causes (both seasonal and non-seasonal dimensions) of _monga_ and determine possible short- and long-term remedies of seasonal poverty. An important aspect of the policy exercise is to identify the role of targeted programs such as micro-credit and the vulnerable group feeding (VGF) program in mitigating _monga_ .<sup>9</sup>

> 9 VGF program, administered by the government, provides food to a select number of households in a community that are affected by disasters or during a period when acquiring food is difficult for beneficiary households. Priority is given to households with low income, lacking agricultural land or other productive assets, day laborers or those headed by women. In a normal year, a distressed household receives 2 to 3 months of food rations, with no work or labor participation required.

5
vector _θ_ , which consists of all parameters of the model for skill group _s_ , i.e. _α_ ’s, _φ_ ’s, and _β_ . Since there are eight moving cost parameters, _n_ location utility parameters and _β_ , our _θ_ vector has _n_ + 8 + 1 elements.

Solving the equilibrium values involves finding a fixed point. We first consider the matrix _V_ 0<sup>_s_</sup> consisting of all _V_ 0<sup>_s,i,j_</sup> s and define the function _V_ 0<sup>_s_=</sup><sup>_F_(</sup><sup>_V_</sup> 0<sup>_s_;</sup><sup>_θ_),usingequations(5),(6),(8)</sup> and (9). Finding the fixed point of function _F_ , given the parameter vector _θ_ , leads us to the equilibrium values of _V_ 0<sup>_s,i,j_</sup> . We should note that _V_ 0<sup>_s_is an</sup><sup>_n×n_dimension matrix which means</sup> we have a large system of _n_<sup>2</sup> equations with _n_<sup>2</sup> unknowns.<sup>10</sup> After solving for _V_ 0<sup>_s,i,j_</sup> s, we can insert them in (9) to calculate the moving probabilities, _p_<sup>_s,i,j,k_</sup> 0 . This procedure allows us to write these moving probabilities as a function of the parameter vector, denoted as _p_<sup>_s,i,j,k_</sup> ( _θ_ ). Then it becomes possible to calculate the log-likelihood contribution of an agent from origin _i_ , living in _j_ , and moving to _k_ as log _p_<sup>_s,i,j,k_</sup> ( _θ_ ) for any given _θ_ .

# **3.1 Likelihood Function:**

In the American Community Survey (ACS) data, as discussed in the data section above, we have detailed mobility and birthplace information for people who migrated to the United States. Transit migrants are defined as the people born in _i_ and living in _j_ before moving to the US. We denote their number as _M_<sup>_s,i,j,U_</sup> .<sup>11</sup> Next, using the stocks of migrants born in _i_ and living _j_ in 2000 from the global bilateral migration database and DIOC (see Artuc et al, 2015, and Arslan et al, 2014, for details) and _M_<sup>_s,i,j,U_</sup> matrix, we calculate the approximate number of people who are staying in their current country or moving to destinations other than the US. We denote this matrix as _M_<sup>_s,i,j,S_</sup> . In other words, we divide the migrants born in _i_ living in _j_ ( _i_ = _j_ ) into two groups: those who move to the US, given by _M_<sup>_s,i,j,U_</sup> , and those who moved elsewhere or stayed in _j_ , given by _M_<sup>_s,i,j,S_</sup> .

Using these data matrices, vectors and implied migration probabilities, the log-likelihood con-

> 10Fortunately, the equations can be solved by iteration since _F_ is a contraction mapping.

> 11Note that the United States is the “only” destination for which we have transit migration data.

18
# **Trans-sectoral**

## **TRANS-SECTORAL**

|**_Year_**|**_Country_**|**_Measure_**|**_Content_**|
|---|---|---|---|
|1997|**Indonesia**|II|BKPM requires presidential signature for approvals<br>(eliminated in 1999).|
|1997|**Indonesia**|III|Foreign Investment Law 1/1967 requires foreign enterprises<br>to meet their manpower needs with Indonesian citizens.<br>There are limits to expatriate employment to those<br>occupations which cannot be filled by Indonesians. The<br>Personnel Director must be Indonesian, but all others may<br>be foreign.|
|1997|**Indonesia**|IV|A fully foreign-owned company must sell a nominal (5%)<br>stake to a local investor within 15 years.|
|1997|**Indonesia**|IV|Foreigners may not own land but may receive long-term<br>leases for business purposes|
|2003|**Indonesia**|III|BKPM approves expatriate employment in foreign<br>companies based on economic needs test. Personnel<br>Director must be Indonesian.|
|2003|**Indonesia**|IV|A fully foreign-owned company must sell a nominal (5%)<br>stake to a local investor within 15 years.|
|2003|**Indonesia**|IV|Foreigners may not own land but may receive long-term<br>leases (99 years) for business purposes|
|2006|**Indonesia**|III|Foreign Investment Law 1/1967 requires foreign enterprises<br>to meet their manpower needs with Indonesian citizens.<br>There are limits to expatriate employment to those<br>occupations which cannot be filled by Indonesians. The<br>Personnel Director must be Indonesian, but all others may<br>be foreign.|
|2006|**Indonesia**|IV|Foreigners may not own land but may receive long-term<br>leases for business purposes|
|2006|**Indonesia**|IV|A fully foreign-owned company must sell a nominal (5%)<br>stake to a local investor within 15 years. Removed in the<br>2007 Investment Law.|
|2010|**Indonesia**|IV|Foreigners may not own land but may receive long-term<br>leases (99 years) for business purposes|
|2010|**Indonesia**|III|BKPM approves expatriate employment in foreign<br>companies based on economic needs test. Personnel<br>Director must be Indonesian.|
|2011|**Indonesia**|IV|Foreigners may not own land but may receive long-term<br>leases (99 years) for business purposes|
|2011|**Indonesia**|III|Manpower Act 13/2003: BKPM approves expatriate<br>employment in foreign companies based on econ. needs<br>test. Personnel Director must be Indonesian.|
|2012|**Indonesia**|IV|Foreigners may not own land but may receive long-term<br>leases (99 years) for business purposes|
|2012|**Indonesia**|III|Manpower Act 13/2003: BKPM approves expatriate<br>employment in foreign companies based on econ. needs<br>test. Personnel Director must be Indonesian.|

# **Source: OECD**

41
trajectory. Four future climate scenarios in 2100 are then projected using this emissions path and four different climate models. A cyclone generator is then used to predict how hurricanes in the Atlantic Ocean may change (Emanuel et al. 2008). In both the current climate and the future climate, a total of 5000 hurricanes are generated that strike the United States, for each climate model. This provides an extensive data set concerning the distribution of hurricanes that would strike the country. It is equivalent to observing over two thousand years of data on hurricanes in each climate and for each model. Instead of using an average change for all hurricanes, we use the change in the entire distribution of hurricanes to predict the impact of climate change.

We estimate a hurricane damage function using historic data from storms that have hit the United States from 1960 through 2008. The damages are matched with characteristics of the storm, including minimum barometric pressure, maximum wind speed, and location at landfall (NOAA 2009). Estimates of county income and population density are generated from census data. A regression is then used to predict the damages caused by historic storms.

The damage function is then used to predict the damages that would be caused by each storm in the generated data set. The 40,000 storms generate a rich data set that describes the expected value and distribution of hurricane damages. It also describes how the risks vary along the coast of the United States given each climate scenario.

The analysis then compares the damages from hurricanes under the current climate versus the future climate. We perform two analyses. The first analysis examines the growth in hurricane damages caused by higher income and population along the coast. This analysis assumes no climate change. The second analysis examines the additional damages caused by climate change given the future baseline. The change in damages is predicted from the change in the entire set of future storms versus current storms.

The next section of the paper describes the methodology in more detail. The empirical findings of the paper are then reviewed in Section III. The paper concludes with a review of the major findings and some policy observations.

# II. Theoretical Methodology

3
<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Girls Boys Girls Boys<br>Girl‐targeted intervention: Incentives to General education intervention: Parent‐<br>Learn , Kenya (Kremer, Miguel and teacher meetings and student outcomes,<br>Thornton 2009) Bangladesh (Islam, 2016)<br>Increased learning<br>(standard deviations)<br><!-- End of picture text -->

# **Figure 4: Review method**

<!-- Start of picture text --> Impact Evaluations of Educational Interventions from a recent database<br>(Evans and Popova 2016 and 3ie 2017 and miscellany)<br>[330 studies]<br><!-- End of picture text --> <!-- Start of picture text --> Without gender‐differentiated impacts<br>[205 studies]<br><!-- End of picture text --> <!-- Start of picture text --> Authors provide new estimates (43 studies)<br>OR we run the regression using shared data<br>(9 studies)<br>[52 studies]<br><!-- End of picture text --> <!-- Start of picture text --> General<br>interventions<br>with gender‐<br>differentiated<br>impacts<br>[105 studies]<br><!-- End of picture text --> <!-- Start of picture text --> Girl‐targeted<br>interventions<br> [20 studies]<br><!-- End of picture text --> <!-- Start of picture text --> Extract point estimates and calculate effect sizes for 270 interventions from 177 studies<br><!-- End of picture text --> 22
# —@=—batch1 —@batch3 <= matched comparison

<!-- Start of picture text --> 2TN<br>orN<br>2008/2009 «2008/2010 2020/2011 9 2011/2012: 2012/2013<br>[ay3m} [5y3m} [6y8m) [7¥8m) (8y3m)<br><!-- End of picture text --> <!-- Start of picture text --> ; “a<br>2ASN<br>oO =z i<br>2008/2008 20/2010 2010/2011 01/2012 2012/2013<br>[1yBm) [2yBm) [3yBm) [4yam} (Sym)<br><!-- End of picture text -->

# —@=—batch1 —@batch3 <= matched comparison

<!-- Start of picture text --> '<br>oSh =<br>7008/2009 «3008/2010 2010/2011 O11 /2012 9012/2013<br>(4y3m} (5y3m} (6y3m} (7y3m} (By3m}<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>= t<br>oO eg—) —<br>2008/2009 2009/2020 «2010/2011 «=-2011/2012 2012/2013<br>(1y¥3m} (2y3m} (3y3m)} (442m) (52m)<br><!-- End of picture text -->
**Table 5: Average tariff by consumer category (constant 2014 U.S. dollars per kWh billed)**

|**Country**|**Residential**|**Commercial**|**Industrial**|**Government**|**Export**|**Total**|
|---|---|---|---|---|---|---|
|<br>**Group 1: residential pric**|**ed lower than**|**commercial**|||<br>|**average **|
|**Guinea**|0.06|0.24|n/a|n/a|n/a|0.11|
|**Botswana**|0.07|0.09|0.06|0.13|n/a|0.08|
|**Malawi**|0.10|0.17|0.12|n/a|0.07|0.09|
|**São Tomé and Príncipe**|0.12|0.30|0.17|0.39|n/a|0.23|
|**Niger**|0.16|0.17|n/a|0.39|n/a|0.17|
|**Kenya**|0.18|0.23|0.14|0.14|0.22|0.15|
|**Mauritius**|0.19|0.25|0.12|0.26|n/a|0.19|
|**Group 2: residential pric**|**ed higher than**|**commercial**|||||
|**Lesotho**|0.08|0.06|0.05|n/a|n/a|0.06|
|**Burundi**|0.11|0.09|0.09|0.09|n/a|0.09|
|**Uganda**|0.19|0.19|0.15|0.19|n/a|0.17|
|**Mali**|0.21|0.17|n/a|n/a|n/a|0.21|
|**Mauritania**|0.22|0.22|n/a|0.25|n/a|0.22|
|**Gabon**|0.21|0.16|n/a|n/a|n/a|0.21|
|**Senegal**|0.25|0.21|0.14|n/a|n/a|0.23|
|**Cape Verde**|0.49|0.43|n/a|0.41|n/a|0.42|
|**Liberia**|0.51|0.50|n/a|0.40|n/a|0.50|

_Sources_ : Utility financial statements and annual reports. _Note:_ Where data are categorized by the utility according to LV, MV, and HV customers (Gabon, Guinea, Mali, Mauritania, Senegal), they are recorded in this table under residential, commercial, and industrial customers, respectively. For the purposes of grouping analysis, mining customers are included under industrial, and street lighting and diplomatic categories are included under government. Relatively rare customer categories such as nonprofits are excluded. For Cape Verde, sales to commercial, industry, and agriculture customers are recorded under commercial. n.a.= not applicable.

34
|**Market**<br>**structures**|||||
|---|---|---|---|---|
|Private credit<br>(proxy FD)|+|All relevant studies|+|Outreville (1990a,<br>1990b, 1992), Millo<br>and Carmenici, Feyen<br>et al|
|Bond Market<br>development|+|Feyen et al|||
|Legal<br>enforcement/<br>property rights|+|Web et al, Beck and<br>Web, Feyen et al,<br>Avram et al, Chui and<br>Kwok, Ward and<br>Zurvruegger|+|All relevant studies|
|Car fleet|||+|Feyen et al,|
|Political risk|-|Ward and<br>Zurbruegger, Web et<br>al, Beck and Webb|-|Park and Lemaire|

Note: A full listing of explanatory variables and papers appears in Outreville (2013).

Most of these explanatory variables have expected causality. The positive effect of inflation on P&C premium levels may reflect a tendency for property investment to increase as inflation rates rise and a desire to anticipate nominal value increases..

Swiss Re. (2011) point out that emerging insurance life markets are growing at 10% p.a. faster than in industrial economies and emerging non-life markets are growing 7% p.a. faster. However most of the emerging market growth is presently confined to Asia and Latin America (specifically China, India and Brazil – projected to be 3 of the top 10 economies by 2021). The Swiss Re. paper develops an on the ground, experience based model of insurance growth in emerging markets based on four drivers:

- A sound economic environment

- Improving insurance regulation and supervision

- Product innovation, and

- Effective use of alternative distribution channels

Major economic trends noted include the transition from rural to urban societies and the development of vehicle fleets, and resultant demands for personal loans and mortgages. On the other hand economies suffering from poor governance and high inflation are seeing reduced demand for life insurance.

Regarding insurance regulation Swiss Re. notes that some markets are being opened up to competition, price controls are being removed and bank-insurer (including private insurer/ public bank) links are being permitted. In addition some countries (India, Brazil, Philippines, Bangladesh, CIMA members, South Africa, Pakistan) are now developing 35
Table 8 Percentage change in real GDP in response to an additional year of education per person of working age in 1985

|||**Africa**|**East**<br>**Asia**|**Mena**|**Latin**<br>**America**|**South**<br>**Asia**|
|---|---|---|---|---|---|---|
|Primary|(1)|<br>-0.31|4.71|-0.52|2.67|0.08|
|Secondary|**(2)**|<br>3.55|5.17|5.13|3.82|-7.67|
|Total|(3)|<br>-0.26|3.32|1.51|2.34|0.77|
|Primary<br>Secondary|(4)|-1.32<br>5.79|3.01<br>3.28|-1.12<br>5.65|1.29<br>2.52|1.64<br>-9.09|

(1) Calculated from Table 5, column 1.

(2) Calculated from Table 5, column 2.

(3) Calculated from Table 5, column 3.

(4) Calculated from Table 5, column 4.

20
|**Ln value added per worker**|1.188***|
|---|---|
||(0.044)|
|**Has unpaid workers (no omit.)**||
|Yes|0.516***|
||(0.083)|
|**Has wage workers (no omit.)**||
|Yes|1.460***|
||(0.119)|
|**Percentage of workers female**|0.999|
||(0.001)|
|**N**|51323|
|**Pseudo R-squared**|.133|

Source: Authors’ calculations based on EcC 2012/13 Notes: *p<0.05; **p<0.01; ***p<0.001.

We now turn to the results of Model 2b and Model 3 estimated on the two waves of the ELMPS data (Table 4). The results on the effect of the firm characteristics do not change appreciably once we add the owner characteristics in Model 3, so for these characteristics we discuss both models for both waves together and then move to the results on owner characteristics in Model 3.

First, we note that firm size plays the same steadily increasing and highly significant role we observed in Model 1, but, as in the case of Model 2a, adding additional regressors attenuates the effect of size. The effects of industry become more mixed and lose significance as more regressors are added to the model. We conclude from this that industry is not a reliable predictor of formality once other characteristics have been taken into account. The effect of region on formality also becomes somewhat mixed when additional regressors are added. Firms in the Alexandria and Suez Canal region are more likely to be formal only in 2012 but not in 2018. Thus, region is also not a reliable predictor of formality. Firm age is more likely to be significant in 2012 than 2018 and effects are attenuated as more regressors are added.

As we found with the EcC data, firms with collective ownership are more likely to be formal relative to sole proprietorships across years and across the two models, but the effect is larger in 2018 than in 2012. Having more capital increases the odds of being formal, but the effect appears to kick in only when the capital exceeds EGP 1000. As in the case of labor productivity in Model 2a above, higher log net revenue per worker is associated with a higher odds of being formal.

27