rafmacalaba/datause-encoder-data huggingface dataset:
Table 12: Trade, Legal Origins <u>and Control of Corruption, Robustness</u>

|Dep. Var.: log of average tra|de flow|||||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Different Legal Origin|-0.08|-0.04|-0.12|-0.06|-0.09|-0.02|-0.13|
||(-1.70)|(-0.88)|(-2.49)|(-1.33)|(-1.90)|(-0.52)|(-2.80)|
|Control of Corruption|0.27|0.27|0.23|0.13|0.17|0.09|0.05|
||(25.11)|(24.73)|(20.24)|(10.88)|(14.12)|(7.16)|(4.25)|
|log distance|-1.08|-1.08|-1.09|-1.09|-1.02|-1.10|-0.97|
||(-37.03)|(-37.10)|(-36.56)|(-37.56)|(-34.36)|(-37.20)|(-33.97)|
|log product of GDP|<br>1.13|<br>1.12|<br>1.10|<br>1.02|<br>1.14|<br>0.99|<br>0.98|
||(108.66)|(106.79)|(102.99)|(91.43)|(111.02)|(87.10)|(91.33)|
|log product of land area|-0.21|-0.21|-0.21|-0.12|-0.23|-0.10|-0.11|
||(-23.48)|(-22.90)|(-22.78)|(-11.77)|(-23.27)|(-9.90)|(-11.37)|
|no. of landlocked|-0.35|-0.35|-0.36|-0.29|-0.40|-0.31|-0.34|
||(-7.84)|(-7.90)|(-8.19)|(-6.56)|(-9.29)|(-7.32)|(-8.30)|
|no. of islands|-0.03|-0.02|-0.01|-0.11|-0.17|-0.11|-0.07|
||(-0.55)|(-0.42)|(-0.18)|(-2.40)|(-3.46)|(-2.57)|(-1.71)|
|common border|0.91|0.90|0.98|0.93|1.01|0.98|0.70|
||(6.33)|(6.17)|(6.91)|(6.59)|(7.08)|(6.98)|(5.00)|
|common language|0.62|0.56|0.62|0.65|0.69|0.50|0.49|
||(11.05)|(9.62)|(11.16)|(11.90)|(11.76)|(9.14)|(9.65)|
|currency union|2.15|2.16|2.12|2.54|2.24|2.22|1.43|
||(8.59)|(8.36)|(8.44)|(10.04)|(9.17)|(8.57)|(4.83)|
|common colonizer||0.06||||0.50|0.40|
|||(0.64)||||(5.04)|(3.30)|
|colony-colonizer||<br>0.92||||<br>0.91|<br>0.86|
|||(6.51)||||(6.54)|(6.88)|
|both in GATT/WTO|||-0.17|||0.05|-0.02|
||||(-1.51)|||(0.47)|(-0.10)|
|only one in GATT/WTO|||-0.11|||-0.01|-0.05|
||||(-0.97)|||(-0.13)|(-0.31)|
|generalized system of<br>f P|||<br>0.61|||<br>0.54|<br>0.29|
|preerences (GS)|||(15.27)|||(13.94)|(7.65)|
|regional trade agreement|||0.47|||0.34|1.73|
||||(3.23)|||(2.37)|(10.08)|
|log of product of phone||||0.24||<br>0.24|<br>0.21|
|lines per 1,000 people||||(19.02)||(19.03)|(16.94)|
|log of product of road|||||013|||
|<br>length per 1,000 people|||||.<br>(860)|||
|average weighted tariff|||||.||-0.01|
||||||||(-9.73)|
|Observations|77594|77594|77594|74576|46074|74576|<br>32466|
|R^2|0.67|0.67|0.67|0.68|0.71|0.69|0.75|
|% increase in trade due to<br>1s.d. institution|70%|69%|56%|29%|40%|18%|10%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant, year and country dummies are not reported. The sum of control of corruption ranges from 0 to 12 with mean 7.012 and standard deviation 1.972. The percent increase in trade due to 1s.d. institution (control of corruption) is calculated as follows: exp(coef*sd)-1.

47
**Figure 6. Infrastructure Investment (average of 2000-05, % of GDP)**

<!-- Start of picture text --> (a) Total Investment<br>VNM<br>JOR<br>BOL<br>TRK<br>CHL<br>IND COL<br>EGY<br>PHL THA<br>MNG PAK<br>KHM<br>IDN<br>SA<br>BRA<br>PER ARG<br>MEX<br>7 8 9 10<br>Log per capita GDP at year 2000<br>(b) Public Investment<br>VNM<br>JOR<br>ALB HUN<br>BOL EGY UKR COL SVK<br>MNGKHM INDARM BLR TRK LTU SVN<br>PAK IDN THA<br>MAR CHL<br>PHL LVASA<br>GEO DZA BRA<br>PER<br>MEX ARG<br>7 8 9 10<br>Log per capita GDP at year 2000<br>(c) Private Investment<br>CHL<br>BOL<br>TRK<br>IND PHLJOR<br>THA<br>COL<br>PAK<br>VNM EGY PER BRA SA ARG<br>MEX<br>MNGKHM IDN<br>7 8 9 10<br>Log per capita GDP at year 2000<br>10<br>8<br>6<br>4<br>2<br>Total Infrastructure Investment (% of GDP)<br>8<br>6<br>4<br>2<br>Public Infrastructure Investment (% of GDP)<br>0<br>4<br>3<br>2<br>1<br>Private Infrastructure Investment (% of GDP)<br>0<br><!-- End of picture text --> Data source: Calderón, Odawara, and Servén (2008).

37
The franchise and grant treatment coefficients reported in panel C, columns (3) and (6) of tables 3 and 4 are estimated jointly from a regression specification where the omitted category is the group randomized to receive none of the treatments. The IV coefficients reported in column (7) of both tables are estimated from two separate where we each treatment to the control regressions compare sample independently group. All regressions control for baseline household size, education level, and indicators for having given birth, having received any vocational training, or having any paid work experience prior to the baseline survey.

# **C.4 Sri Lanka Microenterprise Survey (SLMS)**

The experiment was designed to study the effect of incremental cash investment on firms’ outcomes. Small cash or in-kind grants were provided to a sample of microenterprises with less than 100,000 Sri Lankan rupees (LKR, about $1,000) in capital other _∼_ than land and buildings. The four types of grants awarded are: (i) 10,000 LKR ( $100) of equipment or inventories, (ii) 20,000 LKR in equipment/inventories, (iii) 10,000 LKR in cash, or (iv) 20,000 LKR in cash. Control firms were granted 2,500 LKR after the fifth wave, since following waves represented an extension of the survey relative to what initially agreed before baseline.

We focus on the sample of male-owned businesses as DeMel et al. (2008) and DeMel et al. (2012) find no short- or long-term effects of the intervention on female-owned enterprises.

The survey took place in three districts of Sri Lanka (Kalutara, Galle, and Matara), and 25 Grama Niladhari (GN) divisions within these districts. Of the 618 firms initially surveyed, 210 are directly affected by the December 26, 2004, Indian Ocean tsunami. These are excluded from the since the affect firms analysis recovery process might returns to The effects 10 after the of the are capital. years implementation program of similar magnitude if we consider the full sample of surveyed firms instead of the sub-sample that excludes firms directly affected by the tsunami.

The baseline is carried out in 2005. waves are con- survey April Eight following ducted at quarterly intervals, from July 2005 to April 2007, and semi-annually – in October 2007 and April 2008. Further long-term follow-up surveys were carried out in June and December 2010, in September 2015, and in March 2016. Overall, the survey included 15 waves of data collection. In the analysis in section 4 we consider outcome variables 5 years after the intervention, as measured in the June 2010 and December 2010 survey rounds and by observation and proxy reports for firms not interviewed, and 10 years after the intervention, as measured in the March 2016 survey round.

61
**<u>Figure 3.3: Primary School Enrollment Rates, 1988-99</u>**

<!-- Start of picture text --> 110<br>100<br>90<br>80 Brazil<br>Northeast<br>Paraíba<br>70<br>60<br>50<br>1980 1991 1994 1998 1999<br>Source : Data from MEC/INEP/SEEC.<br><!-- End of picture text -->

|**Table 3.4:**<br>**Total an**|**Years of**<br>**d Indige**<br>**19**<br>**19**<br>|**Effectiv**<br>**nt Poor**<br>**81–99**<br>**81**<br>|**e Educa**<br>**Populati**<br>**19**<br>|**tion,**<br>**on,**<br>**99**<br>|
|---|---|---|---|---|
||**Total**|**Indig**|**Total**|**Indig**|
|**Brazil**|4.46|2.19|6.41|3.78|
|**Maranhão**|2.57|1.72|4.28|2.88|
|**Piauí**|2.43|1.56|4.14|2.78|
|**Ceara**|2.84|1.61|4.75|3.15|
|**Rio Grande**<br>**do Norte**|3.48|1.95|5.57|3.69|
|**Paraíba**|3.37|2.03|5.46|3.06|
|**Pernambuco**|3.47|2.05|5.36|3.46|
|**Alagoas**|2.61|1.6|4.67|2.83|
|**Sergipe**|3.02|1.64|5.42|3.46|
|**Bahia**|3.15|1.86|4.69|3.09|
|Indig: Below indig<br>_Note:_Effective edu<br>_Source:_Calculation|ence poverty<br>cation evalua<br>s based on P|line.<br>ted as years o<br>NADs 1981 a|f completed<br>nd 1999.|education.|

**_School attendance._** School attendance of poor students in Paraíba is also increasing. In Paraíba, poor students’ (from the bottom 20 percent of the income distribution) school attendance increased from 60 percent in 1981 to more than 90 percent in 1998. As in Brazil as a whole, in Paraíba children from rich households have, on average, a higher school attendance than children from poor households. The enrollment was 99 percent for children from the 20 percent richest households. Furthermore, children from richer households are also less likely to repeat a school year and they complete more years of schooling than children from poor households.

20
## _Flow Fast_ **_HOa_** _Chinese Indwry Gro*"? 3_

price indexes for industrial products, output value calculated at "constantu prices becomes the chief indicator of industrial growth and structure. Intertemporal comparisons within a time period spanned by a single set of fixed prices pose no difficulty. When the period of analysis crosses from one to another fixed price base, as in como';ting growth between 1975 and _1985,_ ratios of output totals in the bridging years 1957, 1971 o ,981 are used to link figures across time periods served by different sets of fixed prices. If a calculation crosses more than one such gap, e.g. when comparing the levels of industrial output in 1952 and 1987, a series of chain-linked calculations is used (see Field, JEC 1986, p. 509] and the resulting time series is described as being based on "comparable* (kmi) rather than "constant' (bbian) prices.

# **III CHN S INDusiAL** PEmFQFMANCE-SUMMARz OF RECENr OmaAL DATA

Official statistics of China's industrial performance during the past decade indicate a rapid expansion of output coupled with considerable structural change. This is the picture that emerges from the summary figures presented in rables 1-4.

Part A of Table 1 reproduces data on the growth of overall industrial output in constant and in current prices for a comprehensive industrial aggregate (designated 'Industry+') that includes enterprises managed at and below the village level. Output totals for village-level industry are shown separately; subtraction of these figures from the global total generates an output series (designated as "Industry") restricted to enterprises ope.ated at and above the township (xiang) level. Indexes of output growth from a 1978 base derived from these figures appear in Part B of Table 1; annual output changes for each category are derived in Part C.

These data indicate a continuation of the rapid and sustained growth that has characLerized Chinese industry throughout the history of the People's Republic of China. Using the d-aa at constant prices (labelled GVIO), the ate of output growth clusters around the 10 percent annual level observed over the long term in China. If village industry is excluded, the average annual growth rate of OVIO for 1978-87 is 10.4 percent. Even though village industry reported annual growth averaging 24.1 percent in real terms during 1978-R7, the village component is so small that adding its inclusion in the total produces only a marginal increase in average growth to 11.3 percent for 1978-87. This is important because, as will be seen below, there can be little dovbt that the figures shown in Table 1 considerably exaggerate the growth of real output in the village segment of industry.

The figures reported in Table 1 indicate a distinct acceleration of industrial growth during the mid-1980s, with reported real output growth approa.hing the 15 percent mark in 1983/84

1. One official of China's **Sate Statitca** Bumau **oomniud tht Shim** chnp **enhanc Uth** vemcity of **repored** daa.
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37
28 Figure 2: Nominal rate of assistance, trade and welfare reduction indexes, OECD countries, 1960 to 2007 (percent)

# (a) EU countries

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07<br>NRA WRI TRI<br><!-- End of picture text -->

- (b) All OECD sample countries

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07<br>NRA WRI TRI<br><!-- End of picture text --> Sources: Authors’ calculations based on data in Anderson and Valenzuela (2008).
WPS4135

# **Climate Change, Irrigation, and Israeli Agriculture: Will Warming Be Harmful?**

Aliza Fleischer<sup>1</sup> , Ivgenia Lichtman<sup>2</sup> and Robert Mendelsohn<sup>3</sup>

## **Abstract**

This paper utilizes a Ricardian model to test the relationship between annual net revenues and climate across Israeli farms. The study finds that it is important to include the amount of irrigation water available to each farm in order to measure the response of farms to climate. With irrigation water omitted, the model predicts climate change is strictly beneficial. However, with water included, the model predicts that only modest climate changes are beneficial while drastic climate change in the long run will be harmful. Using the AOGCM Scenarios we show that farm net revenue is expected to increase by 16% in 2020 while in 2100 farm net revenue is expected to drop by 60% to 390% varying between the different scenarios. Although Israel has a relatively warm climate, a mild increase in temperature is beneficial due to the ability to supply international markets with farm products early in the season. Our findings lead to the conclusion that securing water rights to the farmers and international trade agreements can be important policy measures helping farmers adapt to climate change.

**Acknowledgement:** We would like to thank Dubi Wolfson from the Israel Ministry of Agriculture for sharing his knowledge with us. The work leading to this paper was funded in part by the German Federal Ministry of Education and Research (BMBF) in collaboration with the Israeli Ministry of Science and Technology (MOST) in the framework of the project GLOWA Jordan River and by the Research Committee of the World Bank under the study 'Climate Change and Rural Development' that was tasked managed in the Agriculture and Rural Development Department of the World Bank by Ariel Dinar.

## World Bank Policy Research Working Paper 4135, February 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ. worldbank.org._ 1 1Corresponding author: Department of Agricultural Economics and Management, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel. Tel. +972-8-9489144; Fax: +972-8-9466267; email: <u>fleische@agri.huji.ac.il.</u> 2 Department of Agricultural Economics and Management, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel.

3 School of Forestry and Environmental Studies, Yale University, 230 Prospect Street, New Haven, CT 06511, USA. Tel. +1-203-432-5128; email: <u>robert.mendelsohn@yale.edu</u> 1
6. The average years of secondary schooling in the total population (15 years of age and over) for 1960, 65, 70, 75, 80, 85 and 90 come from the Barro-Lee data set.

7. Data on real investment shares of GDP come from the World Bank except for Jordan and Nepal whose investment data come from Summers-Heston Penn World Table 5.6.

8. Data on black market premium for time periods 1960-1984 are from Wood, A., _"Global Trends in Real Exchange Rates, 1960 to 1984,"_ World Bank, 1988. 1985's data are from World Currency Yearbook (1987-89). Data for 1990-1993 are from International Currency Analysis (1990-1993). Some missing observations are approximated by the data from the Barro-Lee data set.

9. Data on terms of trade are from the World Bank for 1965-1992. Data from Barro-Lee for the period 1960-64 are also used.

10. Data on population growth are from the World Bank.

31
outward FDI flows due to hedging, and (iv) market clearing/exchange rate effect—more inflows appreciate the exchange rate which encourage outward FDI. In the end they conclude that these do not fully explain the high correlation between inflows and outflows of FDI or the strong correlation between FDI inflows and the US policy rate. They instead find the correlation between inflows and outflows to be negatively correlated to the corporate tax rate and positively to capital controls.

# 3. **Data**

We use quarterly data from the IMF’s Balance of Payments Statistics between 1990 Q1 and 2015 Q4 for 34 emerging countries.<sup>2</sup> The data are patchy for the earlier years; coverage improves over time, yielding an unbalanced panel. The capital flow data are in US dollars. We scale them by annual trend GDP for the purposes of analysis.

We analyze inflows and outflows separately. Data are available separately for FDI- and nonFDI flows. The latter are further decomposed into portfolio flows (and into portfolio equity and portfolio debt), versus what are labelled “other” flows. The “other” category includes flows through the banking sector (loans, deposits and banking capital), loans raised by the private sector, trade credits, official government flows, and other smaller residual components. We exclude flows to the general government and monetary authorities, retaining only private flows. The largest share of (private) other flows are made up of flows through the banking sector. Hence some researchers simply refer to them as “bank flows.”<sup>3</sup> We represent this taxonomy in a tree diagram in the Appendix.

# **4. Magnitude, Persistence, and Volatility of Capital Flows**

Figures 1-4 and Table 1 show that the average FDI and non-FDI inflows are roughly equal in magnitude. Median average annual flows are 2.6 percent and 2.4 percent of GDP annually.<sup>4</sup> Within non-FDI flows, other (bank) flows are the largest, followed by portfolio debt. Portfolio equity flows remain relatively small, averaging 0.2 percent of GDP over the entire period and just 0.16 percent a year in the last five years. Outflows are smaller than inflows on average (these being emerging markets). Figure 2, as we read it, suggests that this figure confirms that the relative magnitude of other flows has declined and that portfolio debt has increased since the Global Financial Crisis.

We measure volatility by the standard deviation and coefficient of variation. By these measures, non-FDI flows are relatively volatile. Portfolio debt flows and banking flows are among the most volatile. Non-FDI flows are more volatile than FDI flows and less persistent.

> 2 The same set of countries is included in Eichengreen and Gupta (2016).

> 3 See Bluedron et al (2013) e.g.

> 4 These are unweighted averages for the 34 sample countries.

5