rafmacalaba/datause-encoder-data huggingface dataset:
future profitability; and (ii) the cost of switching. But that does not allow us to separate out the future relative profitability from the responsiveness. Having, then, a panel of such ‘pull’ estimates and the costs, we can put them together in Stage 2 with wages to see how much the ‘pull’ is affected by changes in wage differentials. This allows us to separate out the ‘responsiveness’ factor<sup>_<u>β</u>_</sup> _ν_<sup>andcompletetheestimation.Essentially,ifgrossflowsdonot</sup> respond very much to future wage differentials, a low value of<sup>_<u>β</u>_</sup> _ν_<sup>willbeindicated,otherwise</sup> a high value.

A qualification that should be noted is that in principle equation (5) can be used to estimate _β_ as well as _ν_ , but in practice it turns out to be difficult to do so. The reason is that significant changes in _β_ induce only small changes in equilibrium aggregates. As a result, we impose a value of _β_ that seems reasonable based on the literature, and examine how robust results are to changes in its value.

# **2 Data and Regression Analysis**

## **2.1 Sector and Occupation Categories.**

We use the March Current Population Surveys (CPS) from 1975 to 2000 of the US Census to estimate the model. Dimensionality issues force us to aggregate sectors and occupations, with the result that we consider 4 sectors and 5 occupations. The sectors are: 1. Agriculture and Construction; 2. Manufacturing; 3. Non-traded Service; and 4. Traded Service.<sup>3</sup> Similarly, we need to aggregate occupations. We follow the Census categories to do this, with one small exception. The occupations we use are:

1. _White collar_ (the Census category of “Managerial and Professional Specialty Occupations,” occupation codes 3-199). Managers and accountants are examples.

> 3“Agriculture and Construction” is composed of Census industries 1-3, respectively “Agriculture,” “Mining” and “Construction.” “Manufacturing” is composed of Census industries 4-5, respectively durables and non-durables manufacturing. “Non-tradable services” is composed of Census industries 8, 10, 14, and 16, respectively “Utilities,” “Retail Trade,” “Entertainment and Recreational Services,” and “Public Administration.” “Tradable services” is composed of Census industries 6, 7, 9, 11, 12, 13, and 15, respectively “Transportation, Communication, and Other Public Utilities,” “Communications,” “Wholesale Trade,” “Finance, Insurance, and Real Estate,” “Business and Repair Services,” and “Personal and Related Services,” and “Professional Services.”

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that it is not profitable to do so given current technology and the institutional environment (legal, regulations and other requirements) they face in a particular market. Question is whether these mismatches between demand and supply need to and can be remedied. While there is much (relevant) analysis on what affects financial sector development and the role of the institutional environment there in (e.g., World Bank, 2001), evidence on what is affecting households’ and firms’ access to financial services across countries is very limited to date. What exists though gives some insights on what the most binding constraints are.

Across countries, it appears that access to micro-finance for poor or the near-poor is less in countries with higher GDP per capita, in countries with better “institutional” quality and a larger market size (Honohan, 2004a and 2004b). This suggests that there is some element of the overall general development, including a greater usage of advanced technology, allowing more developed countries to offer financial services profitably to lower segments. Of course, the lower segment in these more developed countries represents a higher income level, so it does not mean that the same technology can also reach the lower segments in developing countries. The same analysis shows that the quality of the main banking system discourages the spread of micro-finance institutions. Specifically, countries with higher spreads and higher profitability in their main banking system have fewer micro-finance institutions. This suggests that more competition in banking system can foster greater access to financial services from micro-finance institutions.

It also appears that access to savings can be a function of the distribution networks, including that of postal, saving banks and other more specialized financial institutions. In Brazil, for example, the size and scope of the some branch networks, as well as the split between public/private banks and domestic/foreign banks plays a role in the degree of access (Kumar et al. 2004; and World Bank 2005). In other markets, more specialized financial institutions such as savings banks and other proximity banks that have, besides profitability, an objective of providing financial services, have had some impacts on broadening access (Peachy and Roe, 2004). These findings suggest that what is driving 15
<!-- Start of picture text --> Figure 1b: Weighted da ta<br>4<br>4<br>0<br>2<br>-4<br>0<br>-2 -8<br>-4<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Growth Trend Cycle<br><!-- End of picture text --> percent of the region’s GDP, grew in per capita terms by an average of only 0.12 percent a year; and Nigeria, the region’s second-largest economy, (13.50 percent of GDP) grew by 0.28 percent.

Both the un-weighted and weighted series show a positive trend beginning in the mid-1990s. In the period 1995–2005, un-weighted average GDP growth per capita was 1.81 percent, more than twice the long-term average. In order to test for statistically meaningful breaks in mid-1990s, we ran recursive residual estimations and other stability tests. Figure 2 shows the recursive estimation for the growth series. There is statistical evidence that growth accelerated around 1995. Both the Chow breakpoint and forecast tests support the conclusion that a structural break in the income growth series occurred in mid-1990s.<sup>7</sup>

**Figure 2 – Stability test – Recursive residual estimation of growth rates**

<!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05<br>Recursive Residuals ± 2 S.E.<br><!-- End of picture text -->

> 7 We also find evidence of a structural break in the per capita income series at about the same time.

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for the same households over three different coffee seasons coinciding with Ethiopia’s rainfall seasons: i) First survey: July-August 2015, during the _Meher_ or main rainy season. This is the period of final coffee fruit development and the sowing of other crops (Moat et al 2017). This period is also known as the lean season due to the relatively low agricultural activity and its negative impact on agricultural income. This is also the time of year during which children are out of school.

ii) Second Survey: during April-May 2016, the _Belg_ season or short rainy season. This is the period that corresponds to coffee planting, seeding, weeding, and early development of the coffee fruit. Land preparation for other crops takes place as well.

iii) Third Survey: during December 2016-January 2017, harvest season or dry season, the busiest agricultural season for coffee-growing households (Dercon and Krishnan 2000). Red cherry coffee crops are harvested and coffee processing and selling take place.

Importantly, the levels of attrition over time are very low. Out of 1,198 households surveyed in 2015, only one household was not surveyed in early 2016 and 10 households were not surveyed in late 2016. Regarding children aged 6 – 14 years, we surveyed 1,890 children in the first survey. We observe some children dropping out of the sample in the second and third surveys due to reaching the age of 15 during that year (100 children) and other children not present in the first survey but returning to the household in later surveys (46 children).<sup>9</sup>

# **2.4 Sample Characteristics**

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We assume Cobb-Douglas production functions for each sector _i_ : where _qt_<sup>_i_is the value of output,</sup><sup>_Li,k_</sup> _t_ is the effective human capital in sector _i_ and occupation _k_ , which is the human-capital-adjusted sum of number of college graduates and non-college graduates: _L_<sup>_i,k_</sup> _t_ = _ψ_<sup>_ik_</sup> _L_<sup>_i,k,c_</sup> _t_ + _L_<sup>_i,k,n_</sup> _t_ .<sup>5</sup> We do not require the _α_<sup>_ik_</sup> weights in (10) to sum to unity for any sector, in order to allow for sector-specific capital, which is absorbed by the _A_<sup>_i_</sup> factor, together with the price of sector- _i_ output.

The consumers have identical Cobb-Douglas preferences with shares _θ_<sup>_i_</sup> . This matters because all wages are real wages, meaning the marginal value product of the particular kind of labor in the particular sector-occupation cell, divided by the consumer price index (CPI) derived from the common utility function. For example, real wages in a non-importcompeting sector will tend to rise with liberalization because it tends to lower the prices of other sectors’ output and therefore the CPI.

We set the values _A_<sup>_i_</sup> and _α_<sup>_ik_</sup> to minimize a loss function; specifically, for any set of parameter values, we can compute the predicted wage for each sector and that sector’s predicted share of GDP using (10) and its derivatives together with empirical employment levels for each sector. The loss function is then the sum across sectors and across years of the square of each sector’s predicted wage minus mean wage in the data, plus the square of the sector’s predicted minus its actual share of GDP. (The sector GDP figures are from the BEA, but the wages are from our sample.) In addition, we assume that all workers have identical Cobb-Douglas preferences, using consumption shares from the BLS consumer price index calculations for the consumption weights. The values of _ψ_<sup>_ik_</sup> are given by the average skill premium within each cell. The calibration results are presented in Table 8. Note that,

> 5We examined a more flexible CES within-occupation aggregation of the two types of labor, but our estimates of the elasticity of substitution between the two types of labor within occupations produced very high values, 60 and above. Perfect substitutability within each industry-occupation cell seems like a good approximation. Please note that this is different from the elasticity of substitution between high- and lowskilled workers examined by other authors in models without occupations, such as Ottaviano and Peri (2012, Table 5), for example. Their estimates of the elasticity of substitution between high- and low-skilled labor take a value around 2, but since generally high- and low-skilled workers have different mixes of occupations, this should be thought of as picking up substitutability across occupations as well as across educational classes.

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# Figure 11

|Papers testing|sa|vings be|<br>havior after financial education inte|**%**<br><br>rvention|
|---|---|---|---|---|
|Authors|Date|Country|Intensity<br>Channel<br>**ES (95% CI)**|**Weight**|
|Cole Sampson Zia|2011|Indonesia|2<br>1<br>**0.01 (-0.07, 0.09)**|**11.54**|
|Drexler Fischer Schoar|2011|Dominican Rep|15<br>1<br>**0.14 (-0.02, 0.30)**|**3.73**|
|Bruhn Ibarra McKenzie|2012|Mexico|4<br>7<br>**0.03 (-0.01, 0.06)**|**24.33**|
|Doi McKenzie Zia|2012|Indonesia|13<br>1<br>**0.10 (0.00, 0.20)**|**8.29**|
|Berg & Zia|2013|South Africa|26<br>3<br>**-0.03 (-0.09, 0.03)**|**15.98**|
|Bruhn Leão et al|2013|Brazil|36<br>1<br>**0.05 (0.04, 0.06)**|**36.13**|
|Overall (I-squared = 54.|5%, p =|0.051)|**0.03 (0.00, 0.06)**|**100.00**|
|NOTE: Weights are from|random|effects analysis|||
||||**0**<br>-.297<br>.297||

The results are presented with the statistical output from the meta-analysis as well as with data on important characteristics of the interventions. The weight assigned to each of the six studies is provided in the far-right column and is based on the reported strength of the statistical result in the various studies. The size of the shaded squares for each study reflects their weight in the meta-analysis. The horizontal line through the shaded square is the confidence interval for the reported results. When this line intersects the vertical line which represents the null hypothesis (no effect from financial education) then the null cannot be rejected. In this particular meta-analysis, only two of the six papers reject the null hypothesis and indicate a positive impact on savings from financial education while four cannot reject the null hypothesis. Taken together, however, the meta-analysis finds that these papers do provide evidence of impact for financial education at the 95% confidence interval. The confidence interval for the pooled data across the six papers is represented by the diamond at the bottom of the graph and is on the right hand side of the null hypothesis, rejecting a finding that financial education has no impact on savings behavior. We lack the data on control group means to determine the true effect size for this finding. The meta-analysis in Figure 11 was based only on data from RCT studies to increase the quality of the data and thus to strengthen the cross-study analysis.

The I-squared statistic, which is also presented in the forest plots, describes the percentage of variation across studies that is due to heterogeneity rather than chance. Thus, the I-squared is useful for determining the consistency of results between studies in a meta-analysis. A lower I-squared would indicate more consistency underlying the results of the sample studies while a higher I-squared would 20
observations, impacting their macroeconomic effectiveness (Havenga and Pienaar, 2012b; Bergquist et al., 2016).

There are numerous national and international freight transportation models discussed in the literature, for example the Italian national model, the REGARD model in Norway, the Walloon Region freight model in Belgium, the SCENES European model system for passengers and freight, the Dutch TEM‐II and SMILE models, as well as the Swedish SAMGODS model (De Jong et al., 2004; Müller et al., 2012; Ivanova, 2014). The lack of a standardized modeling approach is attributable to limited methodological and calibration details being made available publicly, the challenges involved in the estimation of freight demand given that reliable data on freight volumes are scarce and fragmented, and data availability is different for each geography modeled, as well as the variety of objectives driving freight demand modeling which give rise to models that are context‐specific, hampering the transferability of models (Mommens et al., 2017).

To improve the continuity and application of its country‐level FDM, the chosen approach for India’s model is a demand‐side model due to the comprehensive nature and resulting application possibilities of demand‐side modeling. The spatial and sectoral disaggregation is enabled through utilization of hybrid input data i.e. actual and modeled data, to improve accuracy. The model outline is discussed below.

# **Methodology for developing the India Freight Demand Model (FDM)**

The underlying driver of the India FDM is sufficient disaggregation of the national transportable economy on a spatial and sectoral level, followed by flow generation via gravity modeling, to enable detailed analysis on all the core components of the national freight system.

## **<u>Data collection</u>**

The depth of data available in India is immense yet dispersed, therefore giving rise to the typical onerous data cleaning and data integration challenges demanded by heterogeneous data sources. The following data was received from various sources and agencies in India:

1. Economic data from the Prowess Database that specified intermediate demand, mining and agricultural production, and beneficiation of products at specific locations throughout India over multiple years.

2. Handbook of horticultural statistics from the Ministry of Agricultural Statistics, and mining statistics from the Ministry of Mines.

3. Import and export data per commodity for all ports in India. This included import and export data per port for major commodities. For some ports import and export data ‐ from ports such as the Kolkata Dock System (KDS) – were more detailed on a commodity level, described origin‐ destination states and specified whether freight was containerized or not. Having access to hinterland ODs is not common, and this provided valuable input into modeling of some corridors.

4. Freight rail movements on commodity and OD level 5. Production volumes for agricultural and mining commodities per state or district 6. Industry reports for some key commodities 19
# Tables

|Table 1.|Paae<br>Time Elapsed Between Board Approval and<br>Final Disbursement ......................................................<br>47|
|---|---|
|2.|Trend of Key Financial Indicators<br>....................................<br>54|
|3.|Distribution of the Rate of Return on Assets ............................<br>61|
|4.|Regional Breakdown of Operating Ratios for<br>Completed Projects ......<br>.............................. 63|
|5.|Self-financing Ratios for Completed Projects, 1966-83 ........... 65|
|6|Debt Service Ratios for Completed Projects ............................<br>66|
|7.|Days Receivable for Completed Projects .................................<br>67|
|8.|Conformity with Loan Covenants for<br>Completed Projects, 1966-82 .................................................<br>73|
||Fl- ures|
|Figure1.|Implementation of Projects ...................................<br>41|
|2.|Percentage of Cost Overruns ......................................<br>44|
|3.|Trend of Percentage Cost Overruns .........<br>..........................<br>45|
|4.|Trend of SalesForecasts<br>......................................................**..**. 55|
|5.|Trend of Rate of ReturnonAssets......................................<br>56|
|6.|Trend of Operating Ratlo ............................................................<br>57|
|7.|Trend of Self-Financing Ratio..............................................<br>58|
|8.|Trend of Debt Service Coverage Ratlo ......................................<br>59|
|9.|Trend of Days Recelvable ..........................................................<br>60|
||||(0.00646)||||(0.00652)||||(0.00658)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Remittances & Others|||0.00383||||0.00563||||0.00245||
||||(0.01204)||||(0.01252)||||(0.01158)||
|Property income and investments|||-0.01115||||-0.01024||||-0.01178||
||||(0.00937)||||(0.00930)||||(0.00956)||
|Transfers and other benefits|||-0.00709||||-0.00892||||-0.00548||
||||(0.00868)||||(0.00873)||||(0.00880)||
|Own > median size of land (2.5 acres)|||0.00541||||0.00652||||0.00369||
||||(0.00663)||||(0.00674)||||(0.00654)||
|Observations|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|
|Root MSE|0.434|0.434|0.447|0.435|0.436|0.436|0.449|0.437|0.434|0.434|0.446|0.433|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,<br>education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

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government procurement practices conducted by the World Economic Forum and reported annually in the _Global Competitiveness Report._ Table 4 presents the average responses (scored on a scale from one to seven) of business people to five questions about public procurement policies in 46 developing countries in 2001.<sup>13</sup> (The five questions are stated at the top of table 4.) For each of the five questions posed, business people perceive that 10 developing countries fall below the respective 46-country sample average; suggesting that these 10 countries are seen to have poor procurement practices across-the-board. The perception that state officials “favor well connected firms and individuals” is strong in Bolivia, the Dominican Republic, Ecuador, Guatemala, Honduras, and Zimbabwe (all of which score less than two on a scale of one to seven.) Furthermore, “irregular extra payments or bribes connected with public contracts/investment projects” appear to be more prevalent in Bangladesh, Bolivia, Nigeria, Paraguay, the Ukraine, and Zimbabwe (all of which score less than three) than elsewhere.

While only suggestive, the survey responses reveal there is considerable variation in the perceived even-handedness of procurement practices across countries.<sup>14</sup> This in turn may have implications for domestic and foreign firms’ investment decisions. Unfortunately, no information can be distilled from such surveys regarding the extent to which differences in underlying procurement laws and their implementation affect these aggregate perceptions, or how responsive the latter are to reforms. To the best of our knowledge, no one has attempted to answer this empirical question. The paucity of quantitative, cross-country analyses of national procurement practices is striking.

> 13 For comparison purposes, the mean values of responses in the OECD nations are also reported.

> 14 These survey responses have been criticised as subjective, as potentially biased by different cultural norms, and by the fact that few business people are familiar with the procurement policies in many jurisdictions—so raising the question as to how the survey’s compilers aggregate across the many respondents’ answers when producing summary statistics for a sample of dozens of countries. Yet the reported “average” survey responses are being used more and more often in public discussions and now form part of the empirical (if not factual!) record on national procurement regimes.

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