rafmacalaba/datause-encoder-data huggingface dataset:
agriculture whereas Liu et al. found it was beneficial. We believe that this difference may lie in choice of data sets. We believe that the farm data set in this study is far more reliable than the county data set used by Liu et al. However, not all of the results of the two studies were different. Both studies found that increased rainfall was beneficial. Both studies found that climate effects are nonlinear and effects differ by season. Hence, although the temperature results are different, many of the results of the two studies are similar.

What about comparisons between our economic analysis and crop studies? Although both analyses predict that global warming will be harmful to China’s agriculture, the economic analysis suggests that the impact will be smaller. What explains the difference between the economic results and the crop study results? We believe that the crop study models lead to more pessimistic results because they do not consider adaptation. They do not include the possibility of crop switching, changes in irrigation, or other changes that farmers might undertake. These adaptations are implicitly captured in the Ricardian method.

The marginal effect of higher temperature for China is only mildly harmful for two important reasons. First, a very large fraction of farms in China are irrigated. Second, the rainfed land in China is largely in temperate or cool regions. Small amounts of warming are consequently not as harmful. Of course, some regions of China may suffer large damages. The dry Western region is vulnerable to global warming scenarios. However, the agricultural sector as a whole in China is only mildly vulnerable.

An important message in the research is that irrigation is critical to China’s agriculture system. Part of China’s ability to cope with future climate change depends on its capacity to use water for irrigation; nearly 60 percent of cultivated land in China is irrigated. Our analysis assumes that water will continue to be available. Data was not available to measure the amount of water each farmer was using. It was therefore not possible to measure the importance of available water. This could be a critical problem for China if climate warming makes water increasingly scarce. The negative results of this study could become much larger if warming forces many irrigated farms to become rainfed farms. Clearly there is a strong need in China for further analysis of the effects of climate change on water.

Can China continue feeding itself if climate changes? Based on our empirical results, the answer is yes, the likely gains realized by some farmers will nearly offset the losses that will 16
Table 3: Deep trade agreements and Domestic Value‐added exports – intermediates vs final, PPML

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||DV|A of final expo|rts|DVA of|intermediate e|xports|DVA of int|ermediates re|‐exported|
|No. of provisions|0.00220|||0.00480***|||0.00484***|||
||(0.00158)|||(0.00139)|||(0.00158)|||
|Core provisions||0.00424|||0.00940***|||0.0106***||
|||(0.00312)|||(0.00300)|||(0.00331)||
|PCA depth (1st)|||0.0181|||0.0345***|||0.0376***|
||||(0.0122)|||(0.0113)|||(0.0126)|
|BIT|0.117**|0.116**|0.116**|0.121**|0.117**|0.119**|0.115**|0.111**|0.113**|
||(0.0551)|(0.0552)|(0.0551)|(0.0472)|(0.0472)|(0.0473)|(0.0528)|(0.0526)|(0.0527)|
|PTA (in force or inactive)|0.118***|0.0990*|0.106**|‐0.107**|‐0.144***|‐0.118**|‐0.0754|‐0.124**|‐0.0937|
||(0.0442)|(0.0539)|(0.0485)|(0.0492)|(0.0553)|(0.0519)|(0.0578)|(0.0628)|(0.0597)|
|Observations|26,520|26,520|26,520|26,520|26,520|26,520|26,520|26,520|26,520|
|R‐squared|0.996|0.996|0.996|0.991|0.991|0.991|0.989|0.989|0.989|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Clustered standard errors at the country pair level in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

23
- 16 -

**interest** rate adjustment in Egypt. The fact is that the two markets are highly segmented, offering very **limited scope** for asset substitution.

Real Interest Rate Differentialst It is, however, the consideration of real rather than nominal interest rate differentials between local and foreign markots which are most relevant for assessing the extent to which interest rates can be adjusted in Egypt. Measured in real terms, the differentials are presumably much higher due to both the higher rate of inflation in Egypt than in major foreign ma2kets and due to the nature of exchange rate policy in Egypt, which is svbject to a considerable degree of influence and intervention by the Central Bank.

To incorporate the influence of inflation, it is necessary to analyze the portfolio behavior of the household sector in real terms. The starting point is the accumulation of the household sector financial asset holdings in real terms. This is described by the following equation:

## wheres

a dot above a variable indicates its rate of change over time, and m - real (local) money balance; d - real time deposits held in local currency;

- f - real deposits held in foreign currency (expressed in domestic currency)

s - real household savings;

- x^ - foreign rate of inflation, measured by WPI in the U.S.

- r* =foreign nominal interest rate measured by London Inter Bank Borrowing Rate (LIBOR);

- x - domestic rate of inflation measured by WPI in Egypt;

# and

r - domestic nominal interest, measured by rates on time deposits with maturity of one to two years.
# **WHY ARE THE ELDERLY MORE AVERSE TO IMMIGRATION WHEN THEY ARE MORE LIKELY TO**

# **BENEFIT? EVIDENCE ACROSS COUNTRIES**

Simone Schotte<sup>*</sup> and Hernan Winkler<sup>†</sup> JEL no. D70, J14, F22

> * University of Göttingen and GIGA German Institute of Global and Area Studies

† The World Bank, Corresponding Author: hwinkler@worldbank.org.

The views reported in this paper are the authors’ own and do not necessary reflect those of the Organizations they are affiliated with or their member countries. We would like to thank the participants of the Aging conference organized by the Munich Center for the Economics of Aging (MEA) and the World Bank and those of the 2015 European Public Choice Society meeting for valuable comments.Two anonymous referees also provided many useful comments.
1. Poisson regression for the model without interaction ( )

2. Poisson regression for the model with interaction ( ) 3. OLS for the model without interaction ( ) 4. OLS for the model with interaction ( ) The graphs in Annex 2 plot for Poisson and OLS the predicted values for both standards and modified gravity.

# **Table 1 results**

|20710 observations|1|2|3|4|
|---|---|---|---|---|
|R2/ pseudo R2|nd|nd|0.749|0.820|
|Log distance coeff ( β<br>−<br>)|-0.837|-0.802|-1.739|0.837|
|Wald Chi2/ F|68035|55339|175|266|
|,, z statistics|-34.0|-35.9|-77.4|24.2|
|,, standardized “beta”<br>coefficient|-1.008|-0.965|-2.093|1.008|
|Interaction coeff ( γ<br>−)||0.114||0.298|
|,, z statistics||16.1||89.4|
|,, standardized “beta”<br>coefficient||0.013||0.034|

The regression results suggests the following conclusions

1. As expected the Poisson regression has the most consistent result with trade spatial interaction data (Silva & Tenreyro 2006)(Arvis & Shepherd 2013)

2. The improvement in fit provided by the model (R2 or pseudo-R2) is relatively small in both models (also visually apparent in the shape of distributions in Annex 2).

3. In Poisson, the coefficient :

- has a small standardized (“beta”) value meaning that the effect of the non-linear covariance term is small as compared with the classical log-linear impact of distance

- is negative and significant, which means a relatively small effect of stronger suppression of small flows.

- the predicted values for the trade flows are relatively closed in each models

- the distance coefficients are close in both Poisson model

4. The OLS regressions has observed by various authors are less robust than the Poisson ones 14
vulnerable group in net buyer households has declined between 1993 and 1998 even though rice prices have increased substantially.

**<u>Table 4. Net Buying Behavior of Households</u>**

|||**1993**<br>|||**1998**<br>||
|---|---|---|---|---|---|---|
||Rural|Urban|National|Rural|Urban|National|
|Intensity of net buying (all hholds)|-9.1%|14.4%|-4.7%|-10.2%|9.0%|-6.1%|
|Intensityof net buying (buyer hholds)|18.0%|16.2%|17.5%|15.5%|12.6%|14.6%|

Source: Author’s own calculations using VNLSS 93 and 98.

## **- -** **<u>Table 5. Intensity of Net Buyers (only net buyer households)</u>**

||**Ru**<br>|**ral**<br>|**Urb**<br>|**an**<br>|**A**<br>|**ll**<br>|
|---|---|---|---|---|---|---|
||1993|1998|1993|1998|1993|1998|
|Marginal (0-10%)|32%|38%|35%|44%|33%|40%|
|Medium (10%-30%)|50%|51%|53%|52%|51%|51%|
|Large (>30%)|17%|11%|12%|4%|16%|9%|

Source: Author’s own calculations using VNLSS 93 and 98. The numbers represent the distribution of netbuyers across the three intensity categories.

# **South-North**

There are distinct regional differences between the Northern and Southern part of Vietnam. Since most of the rice farmers live in rural areas and hence the net sellers are mostly in the rural areas, (in urban areas 95% of the households are net buyers), we will focus our attention on rural households.

Table 6 presents a summary of sources of income for rural households, by their net selling position, and also by the general region (North and South). The difference in income between South and North is still valid for the sub groups of net sellers and buyers. The households in the South are on average richer than their Northern counterparts. On average Northern households has a larger share of agricultural income than that of the South (for both years). However, a closer look into the detailed sources of income reveals that the agricultural income in North is mainly due to own consumption and livestock income. Hence, we can deduce that on average rural northern households are generally subsistence households with respect to crops; while in the South they are mostly rice farmers and cash crop producers (higher share among net-buyers).

14
- 13 -

**<u>Table 3. Estimation results with infrastructure availability variables</u>**

||<br>OLS|||<br>NBREG|||<br>SPTOBIT|||
|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|MAI|4.714|(2.423)|*|2.278|(2.185)||7.780|(2.523)|***|
|PAI|-0.319|(0.214)||0.667|(1.208)||1.903|(1.811)||
|FAI|0.325|(0.388)||2.947|(3.546)||0.969|(2.492)||
|N|0.120|(0.035)|***|0.214|(0.066)|***|0.264|(0.068)|***|
|ALLN|0.003|(0.000)|***|0.001|(0.001)||0.002|(0.001)|***|
|POP|6.328|(3.209)|**|6.516|(4.985)||13.609|(8.063)|*|
|EDU|0.027|(0.014)|*|0.194|(0.094)|**|0.376|(0.142)|***|
|WAGE|-0.087|(0.069)||-0.910|(0.522)|*|-0.605|(0.604)||
|MOBL|0.038|(0.135)||0.569|(1.002)||1.113|(1.337)||
|PC|-3.355|(2.277)||2.671|(6.130)||4.911|(9.395)||
|WATR|-0.065|(0.076)||0.115|(0.544)||-0.103|(0.675)||
|POWR|0.013|(0.285)||-0.253|(1.265)||-2.176|(1.836)||
|LATR|-0.196|(0.107)|*|1.002|(0.501)|**|1.319|(0.709)|*|
|constant|-0.038|(0.102)||-3.614|(0.566)|***|-6.425|(2.274)|***|
|Obs|1,549|||1,549|||1,549|||
|F statistic|19.33|||||||||
|Wald chi2||||133.89|||2507.27|||
|R-squared|0.775||||||0.620|||
|Pseudo||||||||||
|R2||||0.159||||||
|Spatial para|meters:|||||||||
|λ|||||||-0.086|(0.544)||
|ρ|||||||-0.039|(0.440)||

Note: The dependent variable is ΔN. Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

To see the relative importance of each factor, the elasticity of the number of agrobusinesses is computed by evaluating the SPTobit result at the sample means ( **Table 4** ). In terms of magnitude of the expected impacts, education is the most important factor to attract more agrobusinesses. The market accessibility is also among the most important determinants, followed by localization economies ( _N_ ) and urbanization economies ( _ALLN_ ). Both are significant. Numerically, however, agrobusinesses are more likely to flock together with other agrobusinesses rather than different industries. A policy implication is that the currently existing agrobusiness clusters would likely continue to grow further. It would be critical to develop and maintain transport infrastructure to connect them to rural areas.
**Table 8. Political Stability and the Breakdown of Expenditures for Fuel Exporters**

|Dependent variable:|Consumption<br>expenditure/GDP (%)|Capital<br>expenditure/GDP (%)|Wage<br>expenditure/GDP (%)|
|---|---|---|---|
|Constant|-13.57***<br>(-7.62)|8.39***<br>(9.86)|-1.09*<br>(-1.76)|
|Dummy fuel > 25%<br>exports|2.45***<br>(9.05)|-5.85***<br>(-3.44)|7.31***<br>(6.93)|
|Dummy ores and<br>metals > 10%<br>exports|-1.10**<br>(-2.20)|0.765***<br>(2.89)|-0.209<br>(-1.03)|
|Hard peg dummy|-4.16***<br>(-8.39)|0.466<br>(1.40)|-0.930***<br>(-6.39)|
|Political stability|0.547***|-0.0593***|0.125***|
|(0 to 100)|(19.69)|(-4.82)|(13.16)|
|Political stability *|-0.367***|1.42***|-0.117***|
|fuel exporters<br>dummy|(-7.98)|(4.59)|(-6.48)|
|Time trend|0.289***|0.0189|0.0334*|
|(=0 in 2000)|(6.04)|(0.82)|(1.65)|
|n|1,286|1,232|1,128|
|R-squared|0.305|0.098|0.121|
|RMSE|7.59|3.87|3.02|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

16
Legal **Department** The World Bank g W ' .! S, 5,. **i:W** . **ve** ?ILi. **'LEGISLATIVE** s **RSiVsH WRs** ts.:: ... -{Eg.. -..... . ~ ~ ~ ~ ~ ......... ... ~ . . 4 * **ave successf.ly -t aUed direct fo** .. . **i'.lltO** the~. sector ~ ........... ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ **r."-**

**USED TOila FOTER POnoraoLEM**

# **Wifllm T. Onorato**

**Identifies and explains the** essetild **elements of legisatve frmwrswhich have successfully attracted irect foreign invesment into the sector and which** have, consequen, fostered development of petrolem **resources** in host States.
institutional quality the source country is above of below the average of competitor countries.

Our specific measure of institutional quality in the host country is the simple average of the six governance indicators identified by Kaufmann et al. (2005) discussed in the previous section. Although, taking the simple average can hide certain indicatorspecific effects on foreign bank entry, we believe this bias is limited as correlation between the six indicators is very high, ranging from 0.60 to 0.96. However, in a robustness test, we test whether our results are different when using the individual indicators. The original indicators of Kaufmann et al. (2005) vary between -2.5 and 2.5, which makes it harder to interpret the results. To avoid this, before constructing the competitive advantage variable, we linearly transform the original indicators so their value is never below zero. Data for 2000 are used to minimize bias in the estimation due to endogeneity, e.g., foreign entry raising the institutional quality of the host country.

A positive sign of the competitive advantage variable would suggest that if the institutions of the source country are better than that of its competitors, good institutions in the host country have a positive impact on foreign bank entry. If, on the other hand, the institutions in the source country are weak relative to that of the competitors, banks from this country will be more inclined to locate in countries with weak institutions. In other words, for our competitive advantage hypothesis to hold the variable should be significant and have a positive sign.

# _Empirical framework_

In order to explain FDI in the banking sector of developing countries, we use a gravity model. The gravity model has been used extensively to explain trade patterns. Recently the gravity model has been applied more frequently in studies on explaining FDI (see, among others, Galindo et al, 2003, Di Giovanni, 2005, and Van Horen, 2006). The model in its most simple format explains bilateral flows (i.e., trade or investment) between two countries on the basis of the product of the GDPs of both countries, and the distance between them. The model is typically extended by including dummies that indicate whether the two countries share a common border, common language, past colonial links, possibly time-zone and other geographical information etc.

9
estimation methods, on the other hand, “borrow strength” across areas through a regression framework that is quite flexible and can incorporate different types of data, including census data, remote sensing data, and administrative data, to name but a few. In traditional unit-level models like Elbers, Lanjouw, and Lanjouw (2003), the key predictors were household-level values derived from a census. However, this requires that there exists a (recent) census with variables also available in a survey with monetary poverty measures. This is a restrictive requirement, as censuses like this are few and far between in many developing countries. In this paper, we instead use AGEB or municipality aggregate variables when we implement the household-level models. Implementing a household or unit-level model with only aggregate variables is sometimes referred to as a “unit-context” model.

In all three methods discussed here, poverty or welfare is regressed on a selection of predictive features and random effects, which we discuss in more detail in the methods section and the appendices. Thus, each in-sample target area has two separate estimates: the direct survey estimate and the synthetic prediction estimate created through the regression. The key to small area estimation is the combination of these two separate estimates, based on relative variances. Intuitively, when the sample estimates are more precise for an area relative to synthetic predictions, there is “less strength” that can be borrowed from the prediction and the direct sample estimates are given more weight in the estimate. When the sample estimates are less precise relative to the synthetic predictions, on the other hand, areas can “borrow more strength” through the regression (Jiang and Rao, 2020). However, note that not all municipalities in Mexico appear in our sample. As such, this means that some municipalities do not have a direct estimate. For these out-of-sample areas, the sample contains no information and therefore gets zero weight in the estimate, which is a purely synthetic prediction from the regression.

The three methods also differ in sometimes important ways. Sub-area and area-level models can be estimated more quickly and easily than unit-context household models. This comes at a price, however. Area-level models require estimates of the sample variance for each area, which can be unstable when derived from a survey, since the survey is not designed to provide adequate sample size to generate reliable estimates at the area level (Bell, 2008). This instability can be alleviated by variance smoothing, but that adds an additional layer of complexity to the estimation procedure. The sub-area models considered in this paper, meanwhile, require a non-trivial reweighting exercise. This entails both correcting for heteroscedasticity when the number of sample households 9
|**Computer-electronic**|Legal-accounting-consultancy|
|---|---|
|**Electrical-equipment**|Architectural-engineering|
|**Machinery-Equipment-nec**|Scientific-research-development|
|**Motor-vehicles-trailers**|Advertising|
|**Other-transport-equipment**|Other-professional-Veterinary|
|**Furniture-Other-manufacturing**|Rental-leasing|
|**Repair-installation**|Employment-activities|
|**Transmission and Distrib.**|Travel-agency-Tourism|
|**CoalBL**|Other-serv|
|**GasBL**|Public-administration|
|**WindBL**|Education|
|**HydroBL**|Health|
|**OilBL**|Residential-care|
|**OtherBL**|Creative-arts-cultural|
|**GasP**|Sports|
|**HydroP**|Membership-organisations|
|**OilP**|Repair-of-computers-and-personal-goods|
|**SolarP**|Other-personal-serv|
|**Gdt**|Households|
|**Water**||

# **_Taxes_**

Information on major taxes in Italy is obtained by reconciling tax return data with information from Istat (the Italian National Institute of Statistics) national accounts. Among commodity taxes in the model, the following are explicitly considered:

- Value Added Tax (VAT), whose aggregate amount is published by the Istat, is allocated by commodity according to the VAT Microsimulation Model developed by the Department of Finance (DF). The VAT DF Microsimulation Model estimates VAT by commodity distinguishing between final consumption of households, intermediate goods and capital goods.

- Excises, published by Istat with the details of the goods they are imposed on, have been allocated by commodity accordingly and associated to both intermediate and final consumption using the use table from Istat.

- Tariffs on Imports are computed by applying estimated import tariffs to imports.

- Other Net Tax on Products (other indirect taxes less subsidies on products) are calculated as a residual by subtracting VAT, excises and tariffs on imports by commodity from the net indirect taxes on products published by Istat in the Supply table for 2017.

5
# **e. Summary**

Table 6 summarizes our benefit-cost ratio results and, for comparison, table 7 reports the quantile values of benefit-cost ratios from the complete set of benefit-cost ratios prepared under the Ghana Priorities project. In table C.5, we also locate our estimates among the intervention-level benefit-cost ratios estimated under the Ghana Priorities project.

One of our estimates of benefit-cost ratios for remedial teaching methods is in the top quartile of the full set of benefit-cost ratios reported by the Ghana Priorities project and the rest are in the third quartile. The benefit-cost ratios for GSFP and additional years of schooling are mostly in the second quartile. However, almost all interventions examined by the Ghana Priorities project with higher benefit-cost ratios than those we estimate for remedial teaching methods are for health and nutrition interventions.<sup>28</sup> Calculating benefits for these interventions relies on estimates of the value of a statistical life, which are quite uncertain but tend to produce spectacular benefit-cost ratios around the world, not just in Ghana. Excluding these, our benefitcost ratios for remedial education are among the best in the remaining sample, while those for GSFP and additional years of schooling remain in the second quartile.

# **Table** **<u>6: Summary of Benefit-Cost Ratios</u>**

|Intervention|BCR|
|---|---|
|1 Provide teachers of grades 4–5 with training in remedial instruction|8.3|
|2 Support weaker students in grades 1–3 with remedial lessons, in class|8.0|
|3 Support weaker students in grades 1–3 with teaching assistants, after school|7.1|
|4 Support weaker students in grades 1–3 with teaching assistants, in class|5.2|
|5 Provide teachers of grades 4–5 and their supervisors with training in remedial instruction|4.9|
|6 Support weaker students in grades 1–3 with teaching assistants, split class|3.3|
|7 Provide Ghana School Feeding Programme|3.3|
|8 One additional year of basic schooling|2.4|
|9 One additional year of senior postsecondary schooling|1.9|
|10 One additionalyearofsecondary schooling|1.8|

_Note_ : BCR = benefit-cost ratio. BCR for rows 1, 2, 4, and 5 is the average of BCRs for the full test of English and the test of foundational English calculated via test scores only and via equivalent years of additional schooling. BCR for rows 3 and 6 is the average of BCRs for the combined English and math test and the English-only test calculated via test scores only and via equivalent years of additional schooling. BCR for row 7 is the average of BCRs calculated via test scores only and via equivalent years of additional schooling. BCR for rows 8 to 10 are the average of BCRs for each year of schooling at each level; primary and junior secondary are years 1 to 9; senior secondary is years 10 to 12; postsecondary is over 12 years.

> 28 The rest are usually administrative reforms that require little in the way of budget.

35