rafmacalaba/datause-encoder-data huggingface dataset:
An increase in openness raises the level of foreign R&D ( _NRD_ ). In the case of R&D-intensive industries in LAC, foreign R&D (ln _NRD_ * _DR_ * _DLAC_ ) interacts positively with education, with an effect on TFP of .224. This implies that the positive impact of education on TFP in LAC’s R&D-intensive industries increases with the degree of openness, and so does the impact on TFP growth in the virtuous growth cycle. Thus, the quantitative importance of the virtuous growth cycle described above rises with openness. For instance, if openness (say) doubles uniformly across all R&D-intensive industries in all LAC countries, the interaction effect of education on TFP doubles as well.

So far, we have found that in the case of LAC countries, education and foreign technological knowledge (as measured by R&D stocks) are mutually reinforcing in their effect on TFP in R&D-intensive industries. Second, from the construction of foreign R&D, the results for LAC imply that education and openness are also mutually reinforcing in their impact on TFP in R&D-intensive industries.

> Third, these results imply that _education can have a permanent effect on TFP growth_ in R&D-intensive industries in LAC, even in the absence of a virtuous growth cycle. So far, we have abstracted from the growth of R&D stocks in the OECD. As R&D stocks in OECD countries increase over time, foreign R&D in all industries, including in R&D-intensive industries in LAC ( _NRD*DR*DLAC_ ), rises as well. Assume that foreign R&D rises at a continuous rate of 5% per year because of the increase in OECD R&D stocks. This 10% increase in _NRD*DR*DLAC_ has an impact on the growth of TFP equal to .05 of .224*E or .0112*E. Thus, the growth rate of TFP in R&D-intensive industries in LAC rises with education, with an increase in education of one year in a country with an average of 6 years of education (with a 16.7 percentage increase in education, raising

10
- 22 -

# Terms, Risks, and Incentives in Informal Money-Keeping

Interest payments or service charges are unheard of in village money-keeping, and the depositor often expects to receive back the same notes or coins lent. It is generally agreed that the money-keeper should not use the money deposited, but it is not so clear who holds the legal right to the money during the period of the deposit. A Basse court record describes a civil case in which a shepherd entrusted 80 dalasis to a shopkeeper for safe keeping, and the shop's cash was later stolen by thieves. The shepherd sought the full 80 dalasis. The court ruled that the shopkeeper owed the shepherd 40 dalasis, half the amount deposited. Villagers interviewed disagreed among themselves about the correctness of the decision.

If moneykeeping can entail some financial risk to the moneykeeper, why does he or she agree to perform the service? The obvious and ostensible reasons include helping the saver, cementing a social bond, and demonstrating trustworthiness to the community by eventual word of mouth. Another possible reason is that the cash, though normally expected not to be used, none he less gives the money-keeper a reserve available for family emergencies: if need arose to taxi a snakebitten child to the hospital or to pay a son's court bail at short notice, cash would be on hand. Since the money belongs to someone else, this emergency reserve is easier to defend from the daily requests of spouses, other kin, or neighbors than money of one's own.

A bigger question is why the depositor chooses the money-keeper system, rather than one that might pay interest, as with a bank. Islamic prohibitions against interest, discussed more fully elsewhere (Shipton 1990a), are only part of an answer, since local loans with interest are countenanced in practice. When
taking world prices as given. The formula for this calculation is where ∆ _Wi_ is the change in welfare in country i associated with the improvements in customs, _tradei_ is the value of trade (imports or exports) in 2012 COMTRADE data, _daysi_ is the reduction in the number of days that occurs when country i’s policies are moved to the frontier and _elasticity_ is the response of imports or exports to a given trade cost reduction. The number 0.006 is the lower bound of the tariff equivalent of a day in transport estimated by Hummels and Schaur (2013). The first term in the brackets reflects the welfare costs of iceberg melt on existing trade, the second term captures the welfare gain from increased trade following reform.<sup>41</sup> For the cases of imports and exports, respectively, Figure 4 provides graphical representations of the calculations described in equation 6.

Assuming a value of 5 for the elasticity (on both import and export sides), we calculate a gain of US $101 billion on the import side. This is our conservative estimate of the gains from full implementation of the Bali agreement.<sup>42</sup> To put this figure in perspective it implies annual average gains to citizens of WTO member countries of approximately US$16.01 per person.

Our estimate of gains from reduced time in exports are possibly more tenuous than gains on the import side, because countries are more specialized on the export side. In countries with poorly performing customs the existing export bundle is not likely to be especially sensitive to customs performance (causality could go in either direction). Exports of bulk commodities, for example, may not be overly affected by improvements in customs. This means that that on the export side our estimates may overstate the true (partial equilibrium) gains from full implementation of the TFA.<sup>43</sup> All of this noted we can use equation 6 to estimate gains from policy-related reductions in the time to clear export customs. We calculate global gains of US $109 billion on the export side,

> 41The size of the Harberger triangle is small relative to the size of the rectangle that captures implicit ‘iceberg’ melt on existing flows. This means that the triangle has a only a small effect on the total, so assumptions about the size of trade elasticity are relatively unimportant for the calculations. The key parameter assumption that affects scale of the estimates is the estimated tariff equivalent of a day in transit. Results scale almost 1 to 1 with changes in this parameter.

> 42We are constraining ourselves to a particular channel: reductions in time at customs. This seem the most plausible, and the most easy to value, as time spent in customs is time that the goods are in transit. Hummels and Schaur (2013) are valuing time while the goods are in transit.

> 43TFA implementation may allow the country to export additional time-sensitive goods. Unfortunately, we are not able to evaluate this margin of adjustment in our framework.

33
- Hazell, Peter, Carlos Pomerada, and Alberto Valdes (eds.). 1986. _Crop Insurance for Agricultural Development: Issues and Experience._ Baltimore: Johns Hopkins University Press.

- Heath, John, and Hans P. Binswanger. 1996. "Natural Resource Degradation Effects of Poverty and Population Growth are Largely Policy-induced: The Case of Colombia." _Evironment and Development Economics (1):_ 65-83.

- Helleiner, Gerald K. 1966. _Peasant Agriculture, Government, and Economic Growth in Nigeria._ Homewood, III: R. D. Irwin.

- Hoff, Karla, and Andrew B. Lyon. 1994. "Non-Leaky Buckets: Optimal Redistributive Taxation and Agency Costs." National Bureau of Economic Research Working Paper 4652, Cambridge, Mass.

- Hong, Yoo Soo. 1987. "Power Structure and Rent Competition." _International Economic Joumal_ 1 (4): 45-60.

- Horowitz, Andrew W. 1993. "Time Paths of Land Reform: A Theoretical Model of Reform Dynamics." _American Economic Review_ 83 (4): 1003-10.

- Jodha, N.S. 1992 "Common Property Resources: A Missing Dimension of Development Strategies." World Bank Discussion Papers 169, World Bank, Washington, D.C.

- Johnston, Bruce F. 1960. "The Nature of Agriculture's Contribution to Economic Development." _Food Research Studies 1_ (November): 335-56.

- Just, Richard E. and John A. Miranowski. 1989. "United States Land Prices: Trends and Determinants." In Allen Maunder and Alberto Valdes (eds.), _Agricultural and Governments in an Interdependent World._ Proceedings of the Twentieth International Conference of Agricultural Economists, Buenos Aires, Argentina, August 24-31, 1988. Dartmouth: Aldershot.

- Kevane, Michael. 1996. "Agrarian Structure and Agricultural Practice: Typology and Application to Western Sudan." _American Journal of Agricultural Economics 78_ (1): 236-45.

- Kriger, N. J. 1992. _Zimbabwe's Guerrilla War, Peasant Voices._ Cambridge, U.K.: Cambridge University Press.

- Krueger, Anne. 1992. "Political Economy of Agricultural Pricing Policy." In _A Synthesis of the Political Economy in Developing Countries_ 5. Baltimore: Johns Hopkins University Press.

- Krueger, Anne, Maurice Schiff, and Alberto Valdes. 1991. _Political Economy of Agricultural Pricing Policy._ Baltimore: Johns Hopkins University Press.

- Lal, Deepak, 1988. _Cultural Stability and Economic Stagnation: India, c. 1500 BC-AD._ Oxford (Oxfordshire): Clarendon Press.

- Lal, Deepak. 1993. _The Repressed Economy: Causes, Consequences, Reform._ Brookfield, Vermont: E. Elgar Publishing Company.

71
**Figure 7: TBM for married couple with 2 earners (principal fixed approximately at MW) - 1 child**

<!-- Start of picture text --> 50000<br>45000<br>40000<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>Percent of Average Wage – Total HH Labor Income<br>T_TAX_SS NETpl FBpl HBpl SApl<br>GROSSpl Net+500 500+<br>Annual Income in PLN<br>46.9 49 51.1 53.2 55.3 57.4 59.5 61.6 63.7 65.8 67.9 70 72.1 74.2 76.3 78.4 80.5 82.6 84.7 86.8 88.9 91 93.1 95.2 97.3 99.4 101.5 103.6 105.7 107.8 109.9 112 114.1 116.2<br><!-- End of picture text -->

**Figure 8: Change in income accounting for childcare costs for married couple with 1 child**

<!-- Start of picture text --> 160 Total income<br>+10.3?%<br>140<br>120<br>100<br>80<br>60<br>40<br>Transfers<br>20 income<br>‐50.5?%<br>0<br>Earnings Composition before Composition at MW Composition of Earnings Lost<br>Married 2 Earner - Principal fixed at MW Spouse moving into Labor<br>ITpl FBpl HBpl SCpl SApl 500+ Net Labor Income Child Care<br>% if Minimum Wage<br><!-- End of picture text --> _Source:_ Authors’ calculations.

_Note:_ Earnings’ composition before the single parent is working (left) and after taking a job at MW (center). Net labor income represents total labor income minus childcare costs, income tax, and social contributions.

15
# Model Results

## Economics of solar

The SREP (2015) study projected costs of large‐scale solar parks at $1,551/kW (excluding cost of land). Given the low cost of domestic gas and relatively low resource quality of solar in Bangladesh (that yields average capacity factor around 16%), it is not economic at those costs. Smaller scale solar technologies have been shown to be even more expensive (Chattopadhyay et al, 2016). In light of the significant drop in cost that has taken place over the last few years and the potential for further decline, we have considered a capital cost of $1,000‐1,200 per kW (including land and connection costs) for projects in 2020 further declining at a rate of 5%‐10% pa through to 2025. We have also considered pessimistic scenarios in which (a) LNG prices increase at a lower pace than we have considered in the base case to reach $8.7/MMBTU instead of $10.5/MMBTU by 2025; and (b) spinning reserve requirement of 5% is imposed commensurate with international standard, but spinning reserve availability remains restricted to an absolute maximum of 800 MW (reached by 2025) that would effectively limit the amount of variable renewable energy resources that can economically be added. In **Table 1** , below we discuss the summary outcomes for the following alternative sets of conditions:

1. **$1,200 (5%):** Solar cost of $1,200/kW declining at 5% pa;

2. **$1,000 (10%):** Solar cost of $1,000/kW declining at 10% pa; 3. **$1,200 (5%) LOWLNG:** Sensitivity 1 with lower LNG price of $8.7/MMBTU in 2025; and 4. **$1,200 (5%) LOWSPIN:** Sensitivity 1 with lower spinning reserve availability of 800 MW.

**Table 1 Comparison of solar sensitivities**

|Scenario|NPV of System<br>Cost($m)*|||Solar ent|ry (MW)|||
|---|---|---|---|---|---|---|---|
|||2020|2021|2022|2023|2024|2025|
|$1,200(5%)**|47,061|0|0|2400|2400|2400|2900|
|$1,000(10%)|46,587|1200|2650|4200|4200|4200|4200|
|$1,200(5%)LOWLNG|45,728|0|0|1000|1000|1000|1000|
|$1,200(5%)LOWSPIN|49,061|0|0|1000|1000|1800|2710|

* Discounted system cost at 6% over 2017‐2025 including annualized cost of capital, all operating expenses and penalties for unserved energy and reserve.

** Scenario name represents base capex $1200/kW and rate of decline 5% in parenthesis.

The salient aspects of these results include:

1. Large‐scale grid‐connected solar in Bangladesh is not economic until 2022 in three out of four of these scenarios. The only circumstance when solar entry occurs in 2020 assumes solar costs starting at $1000/kW and falling rapidly at 10% pa. The results provide a context for understanding the current realities of no large‐scale grid‐connected solar project yet getting off the ground in Bangladesh, despite concessional financing and substantial amounts of interest from donor agencies. The results demonstrate the value of using a systems approach to understand the economics of large‐scale solar relative to other options.

2. Economic entry of solar by 2025 varies from 1 GW to full potential of 4.2 GW, with the latter achieved using relatively aggressive cost assumptions such that all‐in cost of solar parks drops below $500/kW in 2025.

14
Figure B.13: Labor’s Income Share: MORG vs. MARCH <!-- Start of picture text --> Routine (CPS-MARCH, PS)<br>Non-Routine (CPS-MARCH, PS)<br>Routine (CPS-MORG, PS)<br>Non-Routine (CPS-MORG, PS)<br>1970 1980 1990 2000 2010<br>Year<br>40<br>35<br>30<br>Income Share (% of GDP) 25<br>20<br><!-- End of picture text --> _Notes:_ Occupation specific income shares are based on CPS earnings data from the annual march supplement (1968 and after) and the monthly outgoing rotation groups (MORG, starting in 1979) extracts and rescaled to match the aggregate income share in the Non-Farm Business Sector (BLS). The underlying earnings data for both series are top-code adjusted using Piketty and Saez’s (2003) updated estimates of the income distribution (PS). The MORG series shows annual averages of monthly data that was seasonally adjusted using the U.S. Cenusus X11 method. The graphs are constructed analogously to panel A in Figure 1. For details see Section 2.

thick lines are the shares reported in panel A of Figure 1 and are based on the march supplements in the CPS (MARCH). The thin lines use usual weekly earnings from the outgoing rotation groups (MORG).

# Appendix C. Labor Expenditure Embodied in Net Exports

Panels A and B of Figure C.14 illustrate the numerator of equation (6) for goods and services separately. Panel A makes clear that the US is predominantly trading sectors that are very capital intensive and therefore the labor expenditure embodied in goods trade is very small (compared to GDP). Panel Breveals that the US is a net exporter of labor embodied in services, predominantly in goods that are very non-routine labor intensive. Panel C aggregates goods and services, and panel D plots _sNX ,l,t_ for both routine and non-routine labor.

39
<!-- Start of picture text --> 70<br>65<br>60<br>#55<br>2<br>= 50<br>a 45 -<br>£e ao40<br>35 +<br>30<br>25 rsrs as |<br>0 5000 10000 15000 20000 25000 30000 35000 40000 45000<br>GDP per capita (2005 PPP)<br><!-- End of picture text -->
conditions typical of poor countries, make it harder to convert their notional SP demands into public spending outcomes—in short, that poorer countries face higher costs in effectively implementing this type of public spending. Prices of competing human development services (health and education) may also play a role, to the extent that these services are relatively expensive in richer countries, encouraging substitution toward SP transfers within public budgets.

Testing the luxury good hypothesis can be readily recognized as a classic issue in causal inference. The idea of omitted variable bias (OVB) provides a natural framework for structuring our effort. We formulate a series of alternative hypotheses to the luxury good hypothesis. These suggest specific country characteristics that might be expected to influence the SPEC while also being correlated with national income. (We strive to avoid a “kitchen sink” approach to controls, but instead to carefully formulate hypotheses based on the literature or our reasoning.) We interpret these characteristics as reflecting differences in the implicit prices faced in providing SP; for example, that the implicit price of SP is higher when government effectiveness or technology access in general is lower. We do not claim that our list of potential confounders is exhaustive, but (as we will see) it is enough to be confident about the answer to the main question posed by this paper.

The specific hypotheses we consider relate to relative prices for human development services, political economy (governmental accountability, the capacity for public service provision more broadly, and redistributive policy making), the distributional impacts of economic growth, access to appropriate technologies, population ageing, and the selection processes for observed public spending data. We argue that some of the hypotheses we consider can already be dismissed from what we know, based on the literature. Among the rest, each hypothesis points to one or more potential confounders in estimating the SPEC, which we use in testing for OVB. We also point to some likely theoretical ambiguities in how the confounders can influence SP spending; for example, having a more capable government can be expected to reduce the unit cost of attaining a given level of social protection (thus reducing SP spending), but also increase the desired level of protection (increasing spending).

The paper uses two new data sets on public spending on SP. The first spans 142 countries over 1995-2020, combined with data on identified covariates. The second data set focuses on the 4
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reconfirming the strength of the relationship between the cotton oil price and the edible oil price index.

**Table 3**

**Co‐movement among Commodity Prices**

||**_μ_**|**_1_**|**_2_**|**_100*_****_3_**|**_Adj‐R_**<sup>**_2_**</sup>|**_ADF_**|
|---|---|---|---|---|---|---|
|**I. COTTON OIL‐OIL INDEX**|||||||
|**Cotton Oil – Oil Index**|1.68<sup>***</sup><br>(5.06)|0.88***<br>(10.36)|0.11<br>(1.05)|‐0.01<br>(0.11)|0.90|‐5.25***|
|**Soy Oil – Oil Index**|1.30***<br>(5.60)|1.14***<br>(19.21)|‐0.17**<br>(2.24)|0.40<br>(1.61)|0.95|‐7.54***|
|**Palm oil – Oil Index**|0.99***<br>(3.82)|1.17***<br>(17.85)|‐0.12<br>(1.51)|‐0.11<br>(0.40)|0.94|‐4.83***|
|**II. OILS**|||||||
|**Cotton Oil – Palm Oil**|1.24***<br>(3.03)|0.66***<br>(8.64)|0.27**<br>(2.37)|‐0.03<br>(0.07)|0.88|‐4.67***|
|**Cotton oil – Soy Oil**|0.64**<br>(2.11)|0.78**<br>(14.04)|0.23**<br>(2.97)|‐0.35<br>(1.23)|0.93|‐5.40***|
|**Soy Oil – Palm Oil**|0.59**<br>(2.12)|0.90***<br>(17.35)|0.01<br>(0.07)|0.46*<br>(1.64)|0.94|‐4.30***|
|**III. GRAINS – PALM OIL**|||||||
|**Maize – Palm Oil**|0.03<br>(0.08)|0.54***<br>(6.93)|0.37***<br>(3.23)|0.04<br>(0.10)|0.87|‐5.58***|
|**Wheat – Palm Oil**|0.04<br>(0.12)|0.55***<br>(8.32)|0.29***<br>(2.99)|‐0.04<br>(0.41)|0.87|‐4.87***|
|**Rice – Palm Oil**|0.37<br>(0.60)|0.66***<br>(5.67)|0.31*<br>(1.83)|‐0.63<br>(1.01)|0.69|‐5.28***|
|**IV. GRAINS**|||||||
|**Maize‐Wheat**|0.31<br>(1.54)|0.86***<br>(14.91)|0.04<br>(0.50)|‐0.26<br>(1.10)|0.95|‐4.82***|
|**Rice ‐ Maize**|0.53<br>(1.06)|1.08***<br>(7.69)|0.04<br>(0.23)|‐0.52<br>(0.97)|0.77|‐5.80***|
|**Rice ‐ Wheat**|0.62|1.07***|‐0.20|‐0.74|0.80|‐4.25***|
||(1.34)|(8.52)|(0.14)|(1.45)|||

**Source:** Author’s estimates based on World Bank price data.

**Notes** _: μ_ , _β1_ , _β2_ , and _β3_ are the estimates of the constant term and the coefficients of the price, in‐ flation, and time trend, respectively as specified in model [3]. For other notes see Table 1.

**_Page 13_**
# **Table 10 Sectoral aggregation of the FTAP model**

|_Sectors in the FTAP model_|_Corresponding GTAP sectors_|
|---|---|
|Live animals, products|Cattle, sheep goats, horses, animal products nec, meat, meat|
||products nec|
|Dairy products|Raw milk, dairy products|
|Coffee, etc, sugar, cut flowers|Sugar cane, crops nec, sugar|
|Fruit and veg|Vegetables|
|Cereals|Paddy rice, wheat, cereal grains, processed rice|
|Oil seeds and fats|Oil seeds, vegetable oils|
|Beverages and tobacco|Beverages and tobacco|
|Other agricultural products|Plant-based fibres, wool, silk-worm cocoons, forestry, food|
||products nec|
|Fish and products|Fishing|
|Mineral products|Coal, oil, gas, minerals nec.|
|Metals, products|Ferrous metals, metals nec, metal products|
|Chemical etc|Petroleum, coal products, chemical, rubber, plastic<br>products, mineral products nec|
|Leather products|Leather products|
|Wood products, pulp, paper|Wood products, paper products, publishing|
|Textiles and apparel|Textiles, wearing apparel|
|Transport equipment|Motor vehicles and parts, transport equipment nec|
|Other machinery and equipment|Machinery and equipment nec|
|Electrical machinery|Electronic equipment|
|Other manufacturing|Manufactures nec|
|Electricity|Electricity|
|Gas and water|Gas manufacture, distribution, water|
|Construction|Construction|
|Trade|Trade|
|Other transport|Sea transport, transport nec|
|Air transport|Air transport|
|Communications|Communication|
|Other financial services|Financial services nec|
|Insurance|Insurance|
|Other business services|Business services nec|
|Other services|Recreation and other services, public admin, defence,|
||health, education, ownershipof dwellings|

_Source_ : Based on GTAP version 6 database.

40
**Figure 3: Average Daily Wage Bill (USD) in the Treatment and Control Group over Time (Administrative Data)**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br>Treatment group average Control group average<br>Daily wage bill (USD)<br>Program implementation<br><!-- End of picture text --> Source: Administrative data on from the Mexican Social Security Institute (IMSS). Includes only the 253 firms in our sample that were found in IMSS records (89 treatment group firms and 164 control group firms).

Table 1: Topics that Firms Worked on with Their Consultant Based on Eight Qualitative Case Studies of Treated Firms

|Topic|# of firms that covered this topic|
|---|---|
|Define mission and vision statements|6|
|Accounting and record keeping (training and/or new software)|5|
|Clarify organizational structure, clearly assign responsibilities|5|
|Sales strategy and advertising (marketing)|4|
|Strategically select location and number of sales points|2|
|Quality control|2|
|Access to credit or alternative financing solutions|2|
|Human resources management and hiring practices|2|
|Mediate family problems in family firms|1|
|Pricing strategy|1|
|Reduce costs (negotiate with suppliers, find alternative suppliers)|1|
|Figure out which products are most profitable and focus on these|1|
|Team work and communications training for employees|1|
|Leadershiptrainingfor firm owners|1|

22