rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Regression of height-for-age z-scores in pooled OLS, RE, FE and IV models**

||I<br>OLS with<br>income|II<br>OLS with<br>one-child|III<br>Full<br>OLS|III<br>RE|IV<br>FE|V<br>IV OLS|VI<br>IV FE|
|---|---|---|---|---|---|---|---|
|Sex (Male=1)|-0.021<br>(0.039)|-0.031<br>(0.038)|-0.029<br>(0.036)|-0.012<br>(0.026)|-0.002<br>(0.033)|-0.02<br>(0.036)|0.001<br>(0.026)|
|Age (years)|-0.085<br>(0.021)***|-0.055<br>(0.021)***|-0.059<br>(0.021)***|-0.068<br>(0.019)***|-0.075<br>(0.021)***|-0.083<br>(0.021)***|-0.081<br>(0.020)***|
|Age squared|0.006<br>(0.001)***|0.004<br>(0.001)***|0.004<br>(0.001)***|0.005<br>(0.001)***|0.005<br>(0.001)***|0.005<br>(0.001)***|0.005<br>(0.001)***|
|Maternal education (“no formal education” is o|mitted):|||||||
|1-3 yrs primary|0.005|-0.021|-0.012|0.017|0.041|-0.05|0.044|
||(0.102)|(0.1)|(0.095)|(0.062)|(0.088)|(0.098)|(0.063)|
|4-6 yrs primary|0.059<br>(0.089)|0.038<br>(0.087)|0.025<br>(0.082)|0.024<br>(0.047)|0.023<br>(0.076)|-0.033<br>(0.088)|0.021<br>(0.048)|
|1-3 yrs lower middle|0.298<br>(0.081)***|0.245<br>(0.078)***|0.201<br>(0.074)***|0.152<br>(0.044)***|0.118<br>(0.066)*|0.081<br>(0.083)|0.105<br>(0.047)**|
|1-3 yrs upper or more|0.523<br>(0.098)***|0.416<br>(0.091)***|0.336<br>(0.085)***|0.251<br>(0.053)***|0.173<br>(0.081)**|0.188<br>(0.093)**|0.159<br>(0.057)***|
|Mother’s height (cm)|0.063<br>(0.005)***|<br>0.06<br>(0.005)***|<br>0.056<br>(0.005)***|<br>0.05<br>(0.003)***|<br>0.047<br>(0.004)***|<br>0.054<br>(0.004)***|0.046<br>(0.003)***|
|Log real per adult income (yuan)|0.1<br>(0.026)***|0.084<br>(0.024)***|0.064<br>(0.024)***|0.045<br>(0.019)**|0.021<br>(0.022)|0.462<br>(0.087)***|0.138<br>(0.087)|
|Year dummies:||||||||
|1993|0.142<br>(0.026)***|0.143<br>(0.025)***|0.12<br>(0.033)***|0.127<br>(0.033)***|0.148<br>(0.027)***|0.137<br>(0.037)***|0.149<br>(0.034)***|
|1997|0.279<br>(0.046)***|0.256<br>(0.044)***|0.271<br>(0.051)***|0.288<br>(0.039)***|0.314<br>(0.046)***|0.281<br>(0.051)***|0.31<br>(0.040)***|
|2000|0.317<br>(0.063)***|0.24<br>(0.060)***|0.27<br>(0.073)***|0.296<br>(0.050)***|0.34<br>(0.063)***|0.377<br>(0.070)***|0.357<br>(0.054)***|
|Index child is only-child||<br>0.426<br>(0.051)***|<br>0.325<br>(0.047)***|<br>0.208<br>(0.035)***|0.104<br>(0.046)**|0.311<br>(0.049)***|0.119<br>(0.039)***|
|Non-self<br>cluster<br>means<br>of<br>healthcare<br>variables:||||||||
|Cost of treatingcold(realyuan)|||0.015|0.014|0.007|0.005|0.003|

11
|**Table 10: Average Pensions by Instrument (in UFs), 1990-2007, Period Averages**|
|---|

|Period|PWs|Annuities|TWs|Average|
|---|---|---|---|---|
|1990-04|3.8|7.5|25.0|5.7|
|1995-09|4.8|7.5|23.4|6.6|
|2000-04|5.0|7.9|26.7|7.0|
|2005-07|5.4|9.5|26.7|8.1|

Source: SP

**<u>Table 11: Types of Annuities Issued in March 1999, 2002, 2003, 2004 and 2005</u>**

||**March**<br>**1999**|**March**<br>**2002**|**March**<br>**2003**|**March**<br>**2004**|**March**<br>**2005**|
|---|---|---|---|---|---|
|Number of annuities issued|937|1,517|1,193|1,490|1,391|
|Number of joint annuities|670|1,069|823|973|763|
|Number of deferred annuities|199|331|307|409|419|
|(% of total)|(21.2%)|(21.8%)|(25.7%)|(27.5%)|(30.1%)|
|Number deferred for 12 months|164|275|238|322|315|
|Number of guaranteed annuities|708|1,191|948|1,153|1,093|
|(% of total)|(85.6%)|(78.5%)|(79.5%)|(77.4%)|(78.6%)|
|Numberguaranteedfor 10 and15 years|666|1,088|846|1,016|912|

Source: SVS There is a very strong association between annuitization and early retirement. As shown in Figure 7, approximately two-thirds of normal age retirees use PWs and only one-third purchase annuities. By contrast, 90 percent of early retirees buy annuities and only 10 percent use PWs. Examining the distribution from the point of view of the retirement product (the bottom of Figure 7), 60 percent of all annuitants are early retirees and only 15 percent are normal age retirees (the remainder are disabled retirees and survivors). If disability and survivors are excluded, the share of early retirees in the number of annuities increases to 80 percent.

16
respectively). But African countries would also experience large increases in income (0.8 percent) driven by higher exports.

**Table 3** . Impact of the “multilateral liberalization” scenario compared to the “managed trade” scenario (percent)

||Income||Total expor|ts|Total impor|ts|
|---|---|---|---|---|---|---|
||2020|2021|2020|2021|2020|2021|
|USA|-0.24|-0.50|-1.17|-2.05|-0.81|-1.43|
|CHN|0.84|0.46|27.62|27.61|29.70|29.77|
|EAP excl. China|0.08|0.25|0.09|0.26|0.22|0.56|
|SAR|0.03|0.09|0.21|0.37|0.32|0.48|
|Mexico|-0.02|-0.07|-0.38|-0.45|-0.34|-0.40|
|LAC|0.82|1.07|2.83|3.28|2.28|2.80|
|AFR|0.55|0.77|1.33|1.60|1.17|1.49|
|ECA|0.33|0.35|0.37|0.38|0.67|0.68|
|MENA (Egypt, Arab Rep.)|0.08|0.16|0.08|0.11|0.18|0.22|
|HICs|1.14|1.24|2.64|3.01|3.00|3.19|
|ROW|0.68|0.86|0.99|1.24|1.14|1.39|
|Global|0.19|0.37|3.59|3.75|3.69|3.84|
|World excl. USA and CHN|**0.53**|**0.63**|**0.73**|**0.90**|**0.97**|**1.12**|

Source: Linkage model simulations.

# **5. Making the China-U.S. trade agreement work for development**

Having identified the valuable opportunity offered by the agreement, it is useful also to highlight four risks—and how they might be averted.

First, even as the United States and China engage in managed coupling, they face internal pressures to decouple. Growing strategic rivalry is creating a strong impulse in both the United States and China to reduce mutual dependence and increase self-sufficiency. Yet, a deal that forces China to buy more from the United States encourages increased intertwining. This outcome may reflect the ascendancy within the United States of those who seek a new equilibrium of greater mutual openness, over those who seek to reduce U.S. dependence; and of those within China who seek a more liberal economy over those who wish to maintain greater state control. But the unresolved tensions could make it hard to implement the deal through mutual liberalization—particularly if the United States persists with export restrictions and China maintains general trade barriers.

Second, even though the deal affirms in its preamble the benefits of market-based “harmonious development and expansion of world trade,” it could turn into an exercise in state-driven, bilateral mercantilism. The preamble emphasizes market-based outcomes, international norms and catalyzing broader international cooperation. But the deal specifies quantitative goals for the expansion of U.S. exports to China, and therefore risks diminishing the role of the market in China. That is because meeting 15
**INThooucTnoN** LXXXIX

Washington, D.C.

- Coale, Ansley J., and Paul Demeny, with Barbara Vaughan. 1983. _Regional Model Life Tables and Stable Populations._ 2nd ed. New York: Academic Press.

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- Eurostat. 1989. _Denographic Statistics._ Luxembourg: Statistical Office of the European Communities.

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- Jamison, Ellen, Peter D. Johnson, and Richard A. Engels. 1987. World Population Profile: 1987. Washington, D.C.: Bureau of the Census, U.S. Department of Commerce.

- Monnicr, Alain. 1988. "La conjoncture demographique: L'Europe et les pays developpes d'outre- _mer," Population_ 43(4-S):893-904.

- Shryock, Henry S., Jacob S. Siegel and Associates. 1975. The Methods and Materials of Demography. Washington, D.C.: Bureau of the Census, U.S. Department of

- Commerce.

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- United Nations. Department of International Economic and Social Affairs. 1990. _World Population Prospects 1990._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Demographic Yearbook._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Population and Vital Statistics Report._ New York: United Nations.

- U.S. Bureau of the Census. 1983. _World Population 1983--Recent Demographic Estimates for the Countries and Regions of the World._ Washington, D.C.

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- World Bank. 1990. _World Development Report 1990._ New York: Oxford University Press. Zachariah, K. C., and My T. Vu. 1988. _World Population Projections: 1987-88 Edition._ Baltimore and London: The Johns Hopkins University Press.
function for the sample selection model can be split into two parts: (i) a Probit for the probability of being selected; and, (ii) an OLS regression for the expected value of _y_ in the selected sub-sample.<sup>19</sup> We believe that exporting firms differ in systematic ways from non-exporting firms. Thus, the observation of different export intensities cannot be reduced to a unique linear function. This suggests that the Heckman selection model is an improvement in modeling the export intensity variable. In order to confirm this, the Appendix reports results for our basic model using the Tobit selection model (for export intensity). As will be further discussed, the Heckman selection model estimates show the presence of a selection bias and that the significance of independent variables (and not the outcome equation) suggests that export intensity of Ecuadorian firms cannot be explained through a unique linear equation.

# **4.2. Data**

This analysis employs data at the firm-level collected by the ICS-Ecuador, a survey performed in 2003 by the World Bank and a private company. In the survey, data were collected using direct interviews with a questionnaire. Only few questions, usually related to accounting, finance, and labor refer to the two previous years (i.e., 2000 and 2001). The survey covered a stratified sample of 441 firms in ten manufacturing sectors, which were grouped in six categories, and five distinct provinces.

In our sample of 441 Ecuadorian firms, the first procedure we adopted with respect to firm size was to look for outliers. We measured firm size in terms of total employment in 2001, which accounts for the number of permanent and temporary workers hired in that year.<sup>20</sup> The box plot elaborated for this purpose can be found in the Appendix (Figure A1). In order to get more normally distributed data, nine outliers were omitted, whose size was above 500 employees in 2001. MICIP (2002), in an analytical work done with 803 small and medium Ecuadorian enterprises, found that 67.2 percent, 22.6 percent, and 10.4 percent of them had between 1 and 20 employees, between 21 and 50 employees, and more than 50 employees, respectively.<sup>21</sup> Following this employment ranges, we divided our sample into three size categories (small, medium, and large), trying to keep an even distribution among them, which were, then, used as a proxy for 19 The likelihood function for the sample selection model is: 20 Employment was lagged one period because the causality between this variable and the dependent variables would, otherwise, not be obvious.

21 The sample of 803 firms estimated the country’s proportions of firms in each size category at the 95 percent confidence level.

15
_Annex 1: Program take-up, anthropometric outcome construction, and the robustness of results to data trimming_ In terms of program saturation, the impact evaluation survey and program Management Information System (MIS) database yield slightly different estimates. 1,418 households surveyed were eligible to become _Pantawid_ beneficiaries in 2008, yet only those in treated villages were offered the program in 2009 by design. Among the 704 beneficiary eligible households sampled in the _Pantawid_ villages, 85 percent (581) reported being beneficiaries of the program, while 1 percent (7) in the control villages also reported being beneficiaries. According to the program Management Information System (MIS) database, however, the control villages did not have any beneficiary households, and 91 percent (647) of the 704 sampled beneficiary eligible households in the _Pantawid_ villages were considered participating beneficiaries of the program.<sup>34</sup> Five percent of the households not eligible for the program reported being _Pantawid_ beneficiaries, even though none of these households were program beneficiaries according to the program MIS database.

The survey data include complete height-for-age data on 172 out of 186 nonbeneficiary children 6 to 36 months of age in treated areas and 151 of 163 nonbeneficiary children of the same age range in control areas. Weight data were collected for 177 6-to-36-month-old nonbeneficiary children in treated areas and 156 nonbeneficiary children in control areas. Anthropometric z-scores were calculated based on the WHO (2006) growth standard. Scores of more than 5 standard deviations above or below the reference mean were dropped from the sample (WHO, 2006). This trimming resulted in 14 of the 172 treated children and 6 of the 151

> 34 The lower percentage of households in _Pantawid_ villages that reported being program beneficiaries in the survey may be explained in part by the fact that program participation is voluntary. Some households that identified as potential beneficiaries may have waived their right to the program. Another possibility is that through the community validation process of NHTS-PR, these households may have been taken off the list of eligible households. It is also possible that a potential beneficiary household was unaware of the community assembly where attendance is required for potential beneficiaries to sign up for the program and confirm their basic household information collected for the PMT. Although very small in number, it is more difficult to explain why nonbeneficiary households according to the program MIS reported themselves to be _Pantawid_ beneficiaries in the survey. There is no official way for a household that was not identified as poor by the NTHS-PR to be registered as a _Pantawid_ beneficiary. It is possible that the respondent was thinking of some other program that they received rather than _Pantawid_ .

63
# **Figure 6. Deposit and lending rates**

# **A. Euro area**

# **B. Sweden**

<!-- Start of picture text --> Percent Percent<br>5 Loans-NFC Mortgages-HH Loans-NFC Loans-HH<br>Deposits-NFC Deposits-HH 6 Deposits-NFC Deposits-HH<br>4<br>3 4<br>2<br>2<br>1<br>0 0<br>C. Switzerland D. Denmark<br>Percent Percent Loans-NFC Loans-HH<br>4 Investment Loans Mortgages 4<br>Time deposits Savings Deposits-NFC Deposits-HH<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>E. Japan F. Hungary<br>Percent Percent<br>1.2 Loans Deposits 12 Loans-NFC Loans-HH<br>Deposits-NFC Deposits-HH<br>10<br>0.8 8<br>6<br>0.4 4<br>2<br>0.0 0<br>Jan-14 Jul-14 Jan-15 Jul-15 Jan-16 Jan-14 Jul-14 Jan-15 Jul-15 Jan-16<br>Jan-14 Jul-14 Jan-15 Jul-15 Jan-16 Jan-14 Jul-14 Jan-15 Jul-15 Jan-16<br>Jan-14 Jul-14 Jan-15 Jul-15 Jan-16 Jan-14 Jul-14 Jan-15 Jul-15 Jan-16<br><!-- End of picture text --> Sources: European Central Bank, Swiss National Bank, Danmarks Nationalbank, Bank of Japan, World Bank. Notes: Last observation is March 2016 and rates are annualized unless otherwise reported. The vertical lines refer to the date at which corresponding central bank started implementing NIRP.

A. B. F. Loan and mortgage rates are for lending with maturity over 5 years. Deposits are with agreed maturity up to two years. “HH” stands for “Households and non-profit institutions serving households”. “NFC” stands for “Non-financial corporations”. Rates for domestic outstanding amount in local currency.

C. Investment loans are with fixed interest rate. Mortgages are with fixed interest rates and 10 years of maturity. Time deposits are at least CHF100,000 and maturity in 12 months. Reported rates are on domestic new business amount in local currency. All series represent median values across banks. Last observation is February 2016 for Investment Loans and March 2016 for the rest.

D. “HH” stands for “Households and non-profit institutions serving households”. “NFC” stands for “Nonfinancial corporations”. Rates for domestic outstanding amount in local currency.

E. Deposits represent average interest rates on new time deposits. Loans represent average contracted interest rate on new loans and discounts with domestically licensed banks. The last observation of loans is February 2016. Rates on domestic new business in local currency.

18
pact of EPA. The positive association between these variables (GDP per capita, exports, and EPAs’ budget) does not say anything about the causal relationship between EPAs and export performance. Understanding the causal effects of EPA programs on export performance, a topic we discuss in section 3, is a key question for policy makers and a major challenge for empirical studies.

Figure 1: GDP per capita, exports, and population size are positively associated with EPAs’ budget and EPAs’ size <!-- Start of picture text --> 4 6 8 10 12 -5 0 5<br>Log of GDP per capita Log of EPAs' budget per capita<br>4 6 8 10 12 12 14 16 18 20 22<br>Log of GDP per capita Log of Population<br>Source: World Bank’s EPAs survey 2010 and WDI (2016).<br>12<br>5<br>10<br>8<br>0<br>6<br>Log of Exports per capita<br>Log of EPAs' budget per capita<br>4<br>-5<br>20<br>8<br>18<br>6<br>16<br>4 Log of EPAs' budget<br>Log of number of employees 14<br>2 12<br><!-- End of picture text --> With respect to the sources of EPAs’ budget, more than 75 percent is publicly funded, in most regions. The only exception is Latin America and the Caribbean, where between 50 and 75 percent of the budget is publicly funded. Moreover, a median EPA from the OECD has a larger share of budget that is privately funded or based on fees charged for services they provide (between 10 and 25 percent, while the other regions have less than 10 percent of their budgets coming from these sources).

For the majority of EPAs, a large amount of resources (between 25 and 50 percent) is allocated to marketing activities. This amount is larger for Asian countries, and smaller in OECD and SSA countries. The second activity to which EPAs allocate most of the resources is export support (between 10 and 25 percent on average). Other activities represent less than 10 percent of their 8
# _Analysis of Price Stickiness_

An important concept in measuring deviations from the Law of One price is the rigidity or “stickiness” of prices over time. This is a concept examined in depth by Crucini _et al._ (2010). Price stickiness measures the probability that prices will change by more than 1 percent in a given period. Using Indian retail and wholesale data, the measure is calculated by first generating the change in commodity prices between the current and previous month at the state, district, commodity, variety, and period level.

A dummy is then created equaling 1 if the price change is either greater or lower than 0. The dummy equals 0 if the price change is 0. In other words, if prices in a month either increased or decreased the dummy will show a 1 but if prices remained the same then the dummy equals 0.

This dummy is then summed at the state, district, commodity, variety, and period level to get a count of all month in which a change in prices occurred. This is then divided by the total number of months.

Where, s represents the state, d the district, c the commodity, v the variety, and p the period.

Figure 22 displays information on the median, minimum, maximum, first quartile and third quartile for price stickiness by commodity **.** These box‐and‐whisker plots and are a good way of seeing the dispersion in the data. The plots in Figure 22 show these values for price stickiness at the commodity level for all states, district, varieties and periods under inspection. This allows for easier comparison of the distribution of price stickiness across commodities.

Price stickiness varies by commodity at both the retail and wholesale level. The median price stickiness varies by commodity. Onions have a median price stickiness around 25 percent, which may be interpreted as a 25 percent probability that onion prices will change by more than 1 percent in a given month. Within onions, price stickiness varies less than in other products, ranging from 5 to 50 percent. In contrast, groundnut oil, and all oil in general, has a median price stickiness of 40 percent and ranges from 10 to 95 percent. In other words, the range of price stickiness suggests that there is between a

> appear to raise the risk of collusive outcomes among CAs, wholesalers, and retailers. For example: the physical set‐up of APMC markets where onion trade is carried out near other traders, APMC rules on price transparency, the presence of trade associations, and low seller power on the part of the individual farmer. The prevalence of vertical integration of CAs as well as dominant players ‐ can also raise the risk of abuse of dominant positions: for example, through foreclosure of non‐ integrated wholesalers by integrated CA‐wholesalers.

31
Policy Research Working Paper 10762

# **Abstract**

This study documents the impacts of climate change on firm-level productivity by matching a globally comparable and standardized survey of nonagricultural firms covering 154 countries with climate data. The findings show that the overall effects of rising temperatures on productivity are negative but nonlinear and uneven across climate zones. Firms in hotter zones experience steeper losses with increases in temperature. A 1 degree Celsius increase from the typical wet-bulb temperature levels in the hottest climate zone (25.7 degrees Celsius and above) results in a productivity decline of about 20.8 percent compared to firms in the coldest climate zone. The effects vary not only based on the temperature zones within which firms are located, but also on other factors such as firm size, industry classification, income group, and region. Large firms, firms in manufacturing, and those in low-income countries and hotter climate zones tend to experience the biggest productivity losses. The uneven impacts, with firms in already hotter regions and low-income countries experiencing steeper losses in productivity, suggest that climate change is reinforcing global income inequality. If the trends in global warming are not reversed over the coming decades, there is a heightened risk of widening inequality across countries. The implications are especially dire for the poorest countries in the hottest regions.

This paper is a product of the World Bank Office of the Chief Economist, Africa Region and the International Monetary Fund. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at wkassa1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team