rafmacalaba/datause-encoder-data huggingface dataset:
Σ Coefficients of Log Size =0; Σ Coefficients of Tobin’s Q=0; and Σ Coefficients of each of the time dummies =0.

In Table 6, we report the combined effects of Operating cash flow and the interaction terms each year. Panel A reports the results from the SUREG system for the sample of firms with positive operating cash flows while panel B reports the results from the SUREG estimation for the sample of firms with negative operating cash flows. In unreported logit regressions, we find that larger firms are more likely to have positive operating cash flows than negative operating cash flows.

Panel A shows that in the years before the crisis (2005-2007), a $1 increase in cash flow is associated with an increase in cash balances by 53.8 cents, increase in capital expenditures by 17.2 cents, increase in other investments (including acquisitions) by 31.7 cents, increase of 0.4 cents in dividends (insignificant) and a 5.3cents increase in share repurchases. The $1 increase in cash flow is also associated with an insignificant increases in asset sales, an increase in long-term debt by 5 cents, decrease in short-term debt by 3.8 cents, increase in equity issuance by 8.1 cents and a decrease in other financing by 0.9 cents. Thus, during the pre-crisis period we see that when there is a positive one dollar shock in cash flow, the use variables increase by $1.08 (=0.538+0.172+0.317+0.004+0.053) and other sources variables also increase by $0.084 (=0.050-0.038+0.081-0.009) thus summing the response across the system (Uses-Sources) to one dollar.

When we look at the different uses of cash during the crisis and recovery periods, we find that firms facing a positive cash flow shock continue to add to their cash balances and make investments, although the capital expenditures are lower than pre-crisis levels. A $1 increase in cash flow results in an increase in cash savings of 58.1 cents in 2008, 70.9 cents in 2009 and 56.2 cents in 2010. Increase in capital expenditures during crisis and recovery periods are lower than pre-crisis levels where as increase in other investments is about the same as pre-crisis levels. A $1 increase in cash flow is associated with an increase in capital expenditures of 14.4 cents in 2008, 7.2 cents in 2009 and 10.2 cents in 2010 and an increase in other investments by 29.5 cents 27
# Do Labor Market Distortions Cause Overvaluation and Rigidity of the Real Exchange Rate?

by Ram6n L6pez and Luis Riveros

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|The Conceptual<br>Model|4|
||2.1<br>Wage Determination<br>in<br>the<br>Formal<br>Sector<br>2.2<br>Wage Determination<br>in<br>the<br>Informal<br>Sector<br>2.3<br>Determination<br>of<br>the<br>Price<br>of<br>Non-Tradables|5<br>9<br>11|
|3.|Comparative<br>Statics<br>fthe<br>Model|12|
|4.|Empirical<br>Estimation|15|
|5.|Conclusion|22|
|Tables||24|
|Refer|ences|28|
|Data|Appendix|29|

Comments on an earlier draft by Bela Balassa, Sonia Bhalotra, Ed Buffie, Arvind Panagariya, Claudia Sepulveda, and James Tybout, as well as the able research assistance of Larry Bouton and Isidro Scloaga, are deeply acknowledged. This research was financed by the Research Committee of the World Bank (RPO 679-30).
**Annex 8. ICP/COICOP Detailed Description of Transport Products or Services**

|**Transport product/service**|**Description of transport product / service**|
|---|---|
|Motor cars|Motor cars, passenger vans, station wagons, estate cars and the like<br>with either two-wheel drive or four-wheel drive; excludes invalid<br>carriages;camper vans; golf carts.|
|Motorcycles|Motorcycles of all types, scooters, and powered bicycles; includes<br>sidecars; snowmobiles; excludes invalid carriages;golf carts.|
|Bicycles|Bicycles and tricycles of all types; includes rickshaws; excludes toy<br>bicycles and tricycles.|
|Animal drawn vehicles|Animal drawn vehicles includes animals required to draw the vehicles<br>and related equipment (yokes, collars, harnesses, bridles, reins, etc.);<br>excludes horses and ponies, horse or pony-drawn vehicles and related<br>equipmentpurchased for recreationalpurposes.|
|Fuels and lubricants for<br>personal transport<br>equipment|Petrol and other fuels such as diesel, liquid petroleum gas, alcohol and<br>two-stroke mixtures; lubricants, brake and transmission fluids,<br>coolants and additives; includes fuel for recreational vehicles covered<br>under; excludes charges for oil changes andgreasing.|
|Maintenance and repair of<br>personal transport<br>equipment|Services purchased for the maintenance and repair of personal<br>transport equipment (fitting of parts and accessories, wheel balancing,<br>technical inspection, breakdown services, oil changes, greasing and<br>washing; includes total value of the service (both the cost of labor and<br>the cost of materials); excludes separate purchases of spare parts,<br>accessories or lubricants made by households with the intention of<br>undertaking the maintenance or repair themselves; roadworthiness<br>tests.|
|Other services in respect of<br>personal transport<br>equipment|Hire of garages or parking spaces not providing parking in connection<br>with the dwelling; toll facilities (bridges, tunnels, shuttle ferries,<br>motorways) and parking meters; driving lessons, driving tests and<br>driving licenses; roadworthiness tests; hire of personal transport<br>equipment without drivers; excludes hire of a car with driver; service<br>charges for insurance in respect ofpersonal transport equipment.|
|Passenger transport by<br>railway|Transport of individuals and groups of persons and luggage by train,<br>tram and underground; includes transport of private vehicles; excludes<br>funicular transport.|
|Passenger transport by<br>road|Transport of individuals and groups of persons and luggage by bus,<br>coach,taxi and hired car with driver.|
|Passenger transport by air|Transport of individuals and groups of persons and luggage by airplane<br>and helicopter.|
|Passenger transport by sea<br>and inland waterway|Transport of individuals and groups of persons and luggage by ship,<br>boat, ferry, hovercraft, and hydrofoil; includes transport of private<br>vehicles.|

55
Teach ECE scores). Transforming scores in this way deviates from standardized AIM-ECD and Teach ECE guidance, but doing so does not alter any statistical approaches (World Bank, 2021, 2022).

# **_Multiple Regression Analysis_**

In addition to the simple descriptive statistics and disaggregation by covariates that help answer research questions 1-3, we build a model to investigate the joint relationships between classroom quality and child outcomes. To accomplish this, we use the overall and domain-specific AIM-ECD scores discussed in **Calculation of scores** (above) to analyze the associations between AIM-ECD by various child and classroom factors. As described above in _Quality Benchmarks, Exemplar Items, and Standardized Scores_ , we standardize both the AIM-ECD and Teach ECE scores prior to fitting these models. Data management and statistical analysis was conducted using R 4.2.1 with the lme4 and tidyverse packages and in Stata 17.0 (Bates et al., 2015; R Core Team, 2021; StataCorp, 2019; Wickham et al., 2019).

Due to the sampling and data collection design, children are nested within classrooms as well as enumerators. This leads to a complex error structure that should not be ignored in analysis. Children studying in the same classroom may have non-independent errors given within-site homogeneity and exposure to the same teacher’s lessons. Similarly, enumerators may be systematically more or less lenient, leading to correlations within enumerators.

To account for this data structure, we use a mixed effects (random effects) model with children’s AIM-ECD scores nested in classrooms and also add a random effect to account for differences in enumerators with the covariance of enumerator and class intercepts constrained to zero (i.e., assuming that enumerator leniency across classrooms was not correlated with overall classroom averages). For each AIM-ECD domain or overall score, we fit the following model to predict the overall or domain standardized Z-score for child in classroom assessed by enumerator . Random effects at the enumerator and classroom allow us to examine associations both within and between classrooms and to partially control for differences by ii jj kk jj enumerator leniency. kk vv uu A A i 0C C 1E E E ii CC,jj CCE EE,kk<sup>+ ββ2A</sup> 0C C 00 01TAADD = ββT + ββ02 03<sup>GGMMGGMMee1eeii+ ββ3SSeexx</sup> 04<sup>iiMMuuM+ εεiijjkk</sup> ββ CC,jj = ββ + ββ05 AAT TTjj + ββ06 MMuuM iiAAM jj<sup>+ ββ</sup> 07 jj<sup>+ ββ</sup> 08<sup>iiAAM</sup> × GGMMGGMMee1 jj jj jj jj jj + ββ TTeeGGTThTTMM + ββ 1ETTeeGGTThTTxxEEE E + ββ TTeeGGTThSSeexx + ββ TTMMGGC ssGGssAA + uu where CCE EE,kk<sup>= ββ20 + vvkk</sup> ββ jj uu ~NN(0, θθ) �= 0 kk vv ~NN(0, Ψ) jj kk Given the above model, we are primarily interested in the coefficients given by TTCCvv�uu , vv 01—i.e., the estimate of the association of a given aspect of quality and child development conditional on the age and sex of a child and structural aspects of the classroom. ββ 14
company contacts located outside of India. The first row documents that 39% of ethnic Indians choose India for their initial outsourcing contract. This rate compares to 32% for non-ethnic Indians, and the 7% difference between these shares is statistically significant at the 1% level. The next two rows show a strong contrast when looking at second contracts. Differences across ethnicities no longer link to differences in propensities to choose India; the more critical factor is whether the initial contract outsourced by the company contact went to India. Subsequent contracts have similar properties to the second contract. Thus, with all the caveats that need to be applied to sample averages, these simple descriptives suggest that ethnicity could play an important role in initial contract placements, with path dependency then taking on a larger role.

What drives this strong persistence in geographic choices? A very likely candidate is whether or not the company contact has a good experience on the first contract. Good experiences can create inertia where other options are not considered or adequately tested. Table 3 examines this possibility with linear probability models of the location choice of second contracts. The first column considers the second contracts formed by company contacts that chose India on their initial contracts. The estimating equation takes the form _Outcomei_ =η _tjc_ + δ⋅ _FirstContractSuccessfuli_ + β⋅ _CompanyContactEthnicIndiani_ + χ⋅ _FirstContractSuccessfuli_ ⋅ _CompanyContactEthnicIndiani_ + ε _i_ , where contracts are indexed by _i._ In the first column, the dependent variable is an indicator variable that takes the value of one if the company contact chooses India again. The primary independent variables are an indicator variable for the first project being a success (“good” performance rating or higher on the public feedback score or a successful evaluation in the private post-employment survey), the probability that the company contact is of ethnic Indian origin<sup>13</sup> , and their interaction. To control for many potential confounding factors, regressions include fixed effects for the (year _t_ ) x (job category _j_ ) x (country _c_ ) of each company contract. Thus, the analysis compares, for example, ethnic Indians and non-ethnic Indians outsourcing

> 13 This probability is assigned from the name matching algorithm. Indian names are linked to 5.3% of company contacts. Indian names are fairly distinct, so that in 90% of these cases the ethnic assignment is unique to the Indian ethnicity. Where the Indian assignment overlaps with another ethnic group due to a shared name, the regressor takes a proportionate value between zero and one. Table 2 excluded fractional values for convenience. By comparison, about 0.2% of contracts to Indian have a common surname, indicating the broader foundation of these ethnic connections than that likely due to family-based connections or similar.

12
Tabic 2: Effect of Intcrvcntions on Agricultural Priccs Nominal Ralcs of Protcction at OffIcial and Frcc-Tradc

||E|xchnngc<br>Rates.|||
|---|---|---|---|---|
|Crop|<br>1960-87|<br>1984-87|1991-92|1992-93|
|Basmati|-38 (-60)|_-59_(-69)|-49 (-54)|a7 (2)|
|Cotton|-19 (-46)|-14 (-36)|-48 (-54)|-18 (-29)|
|Inmi|-29 (-51)|-13 (-35)|-22 (-30)|30 (14)|
|Sugarcanc|24 (39)|10 (-18)|70 (53)|56 (371|
|Vlhcat|-10 (42)|-30 (-48)|-31 (-39)|-35(-43)|

Sourccs: Hamid, Nabi, and Nasim (rablc 5.2) for 1960-87 and 1984-87, Longmirc and Debord (Table 9) for 199192, and Shabbir for 1992-93. Before 1992-93, wc show protcction at both thc official cxchangc ratc and thc frcctrade adjusted cxchangc rate (in parcnthcscs). Thc 1991-92 cstimates arc trcnd valucs and locations are Gujranwala (whcat and basmati), Multan (cotton), Faisalabad (sugarcanc), and Larkana (ir).

# Taking Account of Input Prices

The Government recognized that depressing the price of agricultural output could have adverse incentive effects, so an extensive system of input subsidies was put in place. As the green revolution progressed, the key inputs to modern agriculture in Pakistan (irrigation water and fertilizer) became heavily subsidized. Electricity and pesticides also were subsidized. In the early 1 9 80s, the pesticide subsidy was removed, and the fertilizer subsidy reduced. There is still a subsidy on diesel, electric tubewells, and the purchase of seed. A variety of subsidized credit schemes existed and, to the extent that agriculture uses imported inputs, it benefited from exchange rate overvaluation.

It is convenient to distinguish inputs by whether they are traded or non-traded. Measures of nominal protection of outputs above can be adjusted to take account of the fact that price and trade policy will make tradable inputs cheaper, or more expensive, than their free-trade prices. The effective rate of protection (ERP) takes this into account.<sup>3</sup> Tradable inputs would include fertilizer, pesticides, and tractors (Box 3). Since Pakistan has now eliminated subsidies on most tradable inputs, rates of effective protection do not differ that much from rates of nominal protection (Table 3). The subsidy effect of traded inputs is minuscule.

3 It shows how the value added by Pakistani farmers at current farm prices (value of tradable outputs minus value of tradable inputs) compares with value added at social (parity) prices. The tradable inputs considered included fertilizer, agro-chemicals, fuel, machinery, seed, and concentrate feed. In 1991-92, both nitrogen and phosphate fertilizer were around 30 percent cheaper at domestic prices rather than world prices. Agro-chemicals were about 10 percent more expensive, as was fuel.

5
**21 |** P a g e

intensity of Slovak automotive network was 76 percent in 2002 and that for Czech Republic amounted to 53 percent in 2002 down from 97 and 89, respectively, in 1995 (Kaminski and Ng 2005).

The data tabulated below do not capture fully BiH firms’ involvement in global automotive networks for one reason. There are several tiers in their supply chains. According to Hill (1989, p. 466), the average Japanese automaker’s production system constitutes of 170 first‐tier, 4,700 second‐tier and 31,600 third‐tier subcontractors. Historically, BiH firms specialized in intermediate goods supplying, for instance, up to 30 percent of components used in VW’s assembly line in Sarajevo as well grade steel and other metals together with engineering components used in cars are produced in Serbia (MIGA 2006). Their products would fall into second‐ or third‐tier of subcontracting: without a detailed examination of their end‐use, it would be impossible to identify them in Standard International Trade Classification. Table 13 summarizes calculations presented in Annex Table 2 including only those products whose exports exceeded US$ 1 million in any single year over 2002‐07.

**Table 13: Developments in trade in automotive networks of production and distribution in 2002‐06 (in million of US dollars and percent)**

|||<br>|Export<br>|Value($|million<br>|s)<br>|LS Growth (%)<br>|
|---|---|---|---|---|---|---|---|
|Product|2002|2003|2004|2005|2006|2007|2002‐07|
|Auto network parts exports|17|70|127|192|211|205|46.1|
|Of which:||||||||
|Parts nes int‐c engines (SITC 7139)|5|59|103|155|164|157|58.9|
|Motor vehicleparts/access(SITC 784)|6|8|17|32|41|37|41.7|
|Auto network final products exports|2|3|13|7|8|18|36.1|
|Total Auto network exports|20|73|140|199|220|223|45.2|
|Share of auto network in total exports (%)|1.7|5.8|7.0|7.6|6.7|5.8||
|Auto network import intensity (%)|52|51|59|74|65|78||

Sources: Based on partners' data from UN COMTRADE Statistics.

Thus, the combination of joint ventures, as the mode of entry, and BiH’s historical specialization, disrupted by the war, in machine building and automotive products explains a “jump” in BiH’s network trade share, but both the mode of entry of foreign firms and narrow specialization base raises some concerns as to sustainability of these exports. Joint venture is a preferred mode of entry for firms when there is a need for a local partner because of ‘murky’ business climate and a firm’s competitive advantage does not derive from intangible factors. The latter largely exclude high‐technology firms with low capital cost of entry. Put differently, marketing driven firms (e.g., Coca Cola) tend to opt for a joint venture as a mode of entry whereas high‐technology firms establish commercial presence only insofar as they fully retain property rights, i.e., they enter through establishing subsidiaries.
and 2000 resulted in losses that ranged from 10 – 16% of GDP as a result of the associated floods and droughts (World Bank, 2004).

The direct effects of climate events on a society or a region are straightforward to quantify. There is also evidence of indirect effects on societies due to repeated exposure to climate variability. The most relevant to this analysis is evidence that repeated exposure to climate extremes leads to risk aversion and a counterproductive reduction in investment, leading to a reinforcing negative wealth cycle or “poverty trap” (Dercon, 1996; Barrett and Swallow, 2006). This evidence is largely derived from information gathered at the household and village level. For example, Rosenzweig and Binswanger (1993) studied household wealth, investment choices and rainfall for 6 villages in India and found that risk aversion due to rainfall variability negatively influenced choices, leading to less profitable investments. Farmers are less likely to make investments in fertilizers or high value crops if there’s a significant probability that the investment will be lost due to insufficient rainfall or a flood. As a result, there are major opportunity costs imposed by climate variability that impedes their ability to accumulate savings that might see them through the next shock. They remain trapped in a low level subsistence equilibrium. Dercon (2001) identified rainfall shocks (droughts) as the primary reason that households fell into poverty in a study of 6 Ethiopian villages. Income growth was reduced by up to 20% relative to growth without the rainfall variability. The level of wealth was found to play an important role in the climate effect. In a study of six villages in Burkina Faso, the average farmer experienced food shortfalls in one out of five years due to income variability associated with rainfall, while the poorest farmers (bottom quartile in land holdings) experienced shortfalls in four out of five years, and the top quartile had shortfalls in only one of ten years (Carter, 1997). It was estimated that poor farmers forego about 18% of their income to buffer against climate risks, while the better off farmers forego only 0.4% of income buffer risk (Zimmerman and Carter, 2003).

Do the direct and indirect effects of climate variability aggregate to a detectable signal that influences economic growth at the scale of national economies? Few studies address this question directly. Studies 5
to move away from beef cattle causing large damages. Interestingly, small farmers can substitute sheep and goats for beef cattle and would therefore not be vulnerable to warming.

The theoretical choice model is developed in the next section. Section 3 discusses how data were collected from over 2000 farmers in seven countries across Latin America. Section 4 discusses the estimation procedure and the empirical results. Three climate change scenarios from Atmospheric Oceanic General Circulation Models (AOGCM’s) are then examined in Section 5. The paper concludes with a summary of results and policy implications.

# **2. Theory**

In this paper, farmers are assumed to maximize their profits. Farmers choose the desired species to yield the highest net profit. Hence, the probability that a species is chosen depends on the profitability of that animal. We assume that farmer _j’_ s profit in choosing livestock _i (i=1, 2,...,IJ)_ is where _K_ is a vector of exogenous characteristics of the farm and _S_ is a vector of characteristics of the farmer _._ For example, _K_ could include climate, soils and access variables and S could include the age of the farmer and family size. The profit function is composed of two components: the observable component _V_ and an error term, ε. The error term is unknown to the researcher, but may be known to the farmer. The farmer will choose the livestock that gives him the highest profit. Defining _Z_ = ( _K_ , _S_ ) , the farmer will choose 3
this directive also seems consistent with the fact that Malaysia increased its intervention rate less than other crisis countries did (Figure 2). With the benefit of hindsight, it is quite likely that this directive played -- _possibly by causing banks to over-react in the wake of the crisis --_ a role, both in curtailing the availability of credit and in increasing lending rates relative to other market rates.

<!-- Start of picture text --> Figure 1. Excess Domestic Credit Growth Figure 2. Evolution of Overnight Rates<br>0 25 - __ _ _ _ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ 00<br>02_.10Q fMlaysia ~ Korea<br>Thailand - .. . Indonesia<br>.a..Philippines<br>80-<br>0.20_<br>80-<br>0 15-<br>40_<br>0.10-<br>20-<br>1990 1991 oos 1992 1993 1994 1995 1996 1997 o e1 r 9d019d049d079 109 9 0499079 ffi R 17lw 109d01 eLI0g :049 0!llsl :079:10<br><!-- End of picture text --> In line with the authorities' efforts to invigorate the economy by engineering an increase in domestic demand, interest rates have declined noticeably (Figure 5). Despite the marked decline, the annual growth rate of loans has remained anemic and, indeed, turned negative in real terms in September (Figure 3). Although this observation appears, at first blush, to imply that the slowdown in lending largely emanates from the demand side, several comments are in order.

First, the base lending rate is more reflective of administered changes and more representative of the lending rate charged to prime-customers. The average lending rate incorporates the lending rate that is charged to borrowers perceived to be more risky. Accordingly, an increasing spread between the latter and the former implies that banks are still 10