rafmacalaba/datause-encoder-data huggingface dataset:
International concerns regarding the increasing scarcity of fresh water and food production sustainability have been growing (Molden 2007). As a rapidly growing economy with expanding demand for water and food, China exemplifies this global picture in many aspects. Although the situation looks more severe in northern China, southern China also suffers from increasing water scarcity and faces a rapid increase in water demand for urban and industrial use (Loeve et al. 2004). Since the region’s paddy production has been considered the largest water consumer, how to save irrigation water has been the major policy issue.

The major effort to promote water conservation in the agricultural sector began in the 1990s with considerable regional variation in intervention tactics (Lohmar et al. 2007). In 2002, twenty provinces implemented a reform on taxes and fees called _fei gai shui_ . Before the reform, a water fee was included in the land tax, which is equivalent to area-based pricing (i.e., zero marginal cost for irrigation water). Hence, there was no economic incentive to save water. The reform called for separate water fees and increased independent management for each reservoir. In reaction to this change, many reservoirs began volumetric water pricing and expected this pricing to have a significant impact on farmers’ water saving behaviors.

However, volumetric pricing’s effectiveness is primarily an empirical question for two reasons. First, some people argue that the demand for irrigation water is inelastic, and farmers thus do not change their behaviors significantly, even under high water prices (Yang et al. 2003). Second, in a surface irrigation system, which is the major mode for paddy production, effective collective action among water users is needed to save water. This is because a feasible pricing method measures the volume at a canal’s intake, and the total fee is charged to the water user group (WUG) rather than to individual farmers. The total fee is then divided among the WUG members by cultivated area. Therefore, the group has an incentive to save water, while individual farmers _within_ a group may overuse water unless they are closely supervised. Institutional change must play an important role in preventing this free-rider problem within a WUG. Under this setting, a key research issue is exploring how the volumetric pricing contributes to the institutional change and water savings. However, only a few attempts have been made to explore this issue (Vos and Vincent 2011; Dono et al. 2010; Huang et al. 2010).

The purpose of this study is to fill this research gap by providing micro-level evidence of institutional change and water saving behaviors under volumetric pricing in rice farmers’ water user groups (WUGs), using data collected in 2008 from the Zhanghe Irrigation System in Hubei, China. Several distinctive features of the study site make this dataset suitable for our 2
for cash holdings rather than agency conflicts.<sup>25</sup> Kahle and Stulz (2010) focus on the 2008 US financial crisis and find that cash holdings went up substantially after September 2008 which is consistent with the observation that given the increase in risk and cost of financing, firms strengthened their balance sheets by hoarding cash.

Re-examining these hypotheses taking into account cross-country differences, one would predict that cash holdings should be larger in countries with weak investor protection (agency motive) and in countries with poorly developed capital markets (precautionary motive). However, Dittmar, Mahrt-Smith, and Servaes (2003) and Kalcheva and Lins (2007) find no evidence that firms hold more cash when capital markets are poorly developed but do find evidence that firms hold more cash when shareholder protection is weak. Lins, Servaes, and Tufano (2010) reconcile these findings with the theory in their survey of CFOs in public and private firms in 28 countries. They find that non-operational cash holdings (i.e. excess cash and marketable securities above that which is used in normal course of business) are only a small fraction of book assets and are used as a general insurance policy against future cash shortfalls. Lines of credit, on the other hand, are the dominant form of corporate liquidity and are strongly related to a firm‟s need for external financing to fund future investment opportunities. So while they find no evidence that non-operational cash is higher when credit markets are less developed, they do find that lines of credit, which are the more dominant source of liquidity, are larger when credit markets are less developed, thus consistent with the precautionary motive.

There is an emerging literature looking at corporate liquidity management choices during times of financial crises that has somewhat contradictory findings to the above. Campello, Giambona, Graham, and Harvey (2009) survey CFOs from 31 countries regarding liquidity choices before and after the 2007 credit crisis and find that firms view cash and lines of credit as liquidity substitutes – firms with high internal liquidity find credit lines less valuable and the <mark>positive sensitivity of credit line use to cash flows is unique among firms with low cash holdings. While Ayyagari, Demirguc-Kunt, and Maksimovic (2011) do not compare cash lines with lines of credit, they find that during sudden systemic stop (3S) episodes in emerging market</mark>

> 25 The pecking order theory of capital structure however would predict that cash is built up when profits exceed investment needs and dividends are a way of depleting cash reserves. Though De Angelo, De Angelo, and Skinner (2000) note that disgorging cash via a special dividend payment is rare and building up cash through a dividend cut has negative market reactions.

42
15 To ascertain the cumulative impact of financial deepening on the real exchange rate, we developed the following model and then used annual data for the period 1970-87:

- RER = f(FIR, NFC, GEXP, BMP, TOT) fl>l; f2 ,f3,f4 ,f5 <0

Where,

- RER = the real exchange rate, defined as the relative price of traded goods to nontraded goods,

- FIR = the financial/intermediation ratio (M3/GDP),

- NFC = net foreign capital as a ratio of GDP,

- GEXP = government expenditures measured as a proportion of GDP,

- BMP = the black market premium on the official exchange rate measured as (BER-ER)/ER where BER and ER represent the black market and the official exchange rates, defined as rupees per US dollar, and

- TOT = terms of trade (1980 = 1.0).

Because of the difficulties involved in directly measuring the prices of traded and nontraded goods, the real exchange rate is usually proxied by the nominal exchange rate, adjusted for change in the weighted average of the wholesale price indexes of trading partners relative to the domestic consumer price index (e.g., Edwards. 1988: Favaro and Spiller. 1989: Cottani and Garcia, 1989). This proxy measure is based on the (rather restrictive] "law of one price" assumption (Warr, 1986). In this study we measured the RER using implicit deflators derived directly from the national income accounts for tradable and nontradable production.

The degree of financial intermediation (the key regressor in the model) was represented by the M3/GDP ratio. The black market premium was included to capture the impact of government intervention on the real exchange rate. The expected effect of an overvalued exchange rate, of course, is that it tends to suppress the prices of tradables. Thus, we would expect a negative sign for the coefficient of this variable. The use of the other three variables is now standard procedure in determining the real exchange rate.

The model was estimated using the ordinary least squares method. As in the case of estimating money demand functions (Table 7), a dummy variable technique was employed to detect anv structural break ir the hvpothesized relationships that might have followed the 1977 reforms. Onlv the slope dummy for FIR was found to be imilortant. Thor; was high collinearity between TOT, and NFC and CE.XF. and ex( luqir 'i the former impr':ed the statistical significance of the- *'ettii iTltz +'t 'h'. latter tW' 'VaLial'l-'. The results are reported iTn TaIl'l 1.. The coptfitient ot FIR wac nlt icti'allv signiticant for thi. peri(1970-77. Eut it was highl; -icnitiant !jith thW expe'ted sign ttr tlh poqstreform period (1978-? . -ip'r tillLc '11 hV: Phecic that V.eal, 'L finiancial intermediation (a hIiev.!! t jant nh''Ic ial retorm Tit Lil'ut d t improvement in the competitiveneqq ,t tira alVl 'rod'iUt ion. We us.sper t thtla the failure of our model to Jete(t a icoitic ant relationshij between REF
14 Table2 Estimated Gini coefficients and poverty headcounts in transition economies <!-- Start of picture text --> (annual)<br>Proms | ot wf | | a |<br>fours | sf om | ate | te<br>frommoin | ssf Ca | Cte |<br>es es 2 ss<br>ff<br>lamin<br>TCTs<br>[sone =| ef| Soof<br>Ee eS<br>a 0 =<br>fewooie | ws | oe | |<br>Es co 7 8 es<br>funn a ww<br>FE 8 9<br>[Mowe CP lw Cl | sls<br>[Keaton | metews<br>[tonmenican | | tw | to | |<br>[vicki | oT ls lle<br>jcomperors | | E<br>a es 8 sd<br>fruteyFw<br>Es eT Td<br>a ee ee ee ee eee<br>Notes:The poverty headcountas 9 percent of total population. Basedon the poverty line=$120 per month per capita at 1990 international prices. Regional means<br>for Ginis and growth rutes are unweighted avernges; regional poverty rates are population-weighted averages. Macro incomes used to "blow" up HBS incomes whenever HBS data<br>underreport income relative to macro data. Source: Countries’ bouschold budget surveys. For details see Milanovic (1995, forthcoming).<br><!-- End of picture text -->
At the other extreme, studies based on aggregated data have correlated crosscountry patterns of productivity growth or productivity levels with various proxies for countries' exposure to foreign knowledge and/or their ability to absorb it. These proxies include capital goods imports (e.g., de Long and Summers, 1991; Keller, 2000), trade with countries possessing large R&D stocks (Coe and Helpman, 1995; Keller, 1998; Keller 2000), foreign direct investment inflows (Blomstrom, Lipsey and Zej an, 1994), and domestic patent stocks (Eaton and Kortum, 1996).' Unlike case studies and descriptive surveys, cross-country regressions document broad patterns of association, and in that sense they provide a basis for generalization. However, most are subject to a variety of econometric criticisms, including aggregation bias, omitted variable bias, measurement error bias, and simultaneity bias.

Finally, plant or firm-level econometric studies correlate proxies for firms' exposure to foreign knowledge with their productivity levels or growth rates. To cite a few examples, Aitken and Harrison (1999), Haddad and Harrison (1993), and Djankov and Hoekman (2000) study foreign direct investment; and Chen and Tang (1987), Aw and Hwang (1995), Clerides _et al_ (1998), Kraay (1999), and Bigsten _et al_ (1999) study exports.<sup>2</sup> By sacrificing the nuance and detail provided by case studies, these micro econometric studies gain the ability to treat large numbers of producers and make statistical inferences. They also do better than the macro studies in terms of identifying the specific correlates of productivity and avoiding aggregation bias.

- i Unlike the other studies mentioned here, Eaton and Kortum (1996) do not attempt to empirically isolate a conduit for knowledge transfer

- 2 A more extensive literature review may be found in Tybout (2000)

3
|Annual Mean Temperature (∞C * 10)|[0.228]<br>0.413|[0.134]<br>0.000|
|---|---|---|
||[0.261]|[0.931]|
|HH Distance in (KMs) to Nearest Road|0.006|-0.001|
||[0.987]|[0.784]|
|HH Distance in (KMs) to Nearest Population Center with +20,000|-0.085|-0.003|
||[0.689]|[0.160]|
|Price of fertilizer in the EA|-0.013|-0.000|
||[0.403]|[0.604]|
|Price of maize grains in the EA|0.083**<br>[0.024]|0.001**<br>[0.029]|
|Time dummy (year 2010=1)|-2.343|-0.037|
||[0.567]|[0.623]|
|District dummies|Included|Included|
|Time average of explanatory variables|||
|Constant|-100.016<br>[0.354]|9.223***<br>[0.000]|
|Observations|5,321|5,321|
|Number of households|2,764|2,764|

Source: Generated by authors using LSMS data Note: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination.

The dependent variable in column 1 and 2 are levels of HHPCE. In column 3 and 4, the dependent variable is Log (HHPCE). The symbols ***, **, *, and + indicate that the corresponding regression coefficients are statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. P-values based on clustered robust standard errors between brackets.

51
# FIGURE 19. TRENDS r ILiFANT MORTALITY COMPONENTS AMONG THE URsAN POOR IN URBAN COTE D'IVOIRE, 1970-86

<!-- Start of picture text --> ALL-PNN<br>PA<br>o~~~~~~~~~~~L "]<br>a<br>ol .\ . /<br>5575 1975 to s<br><!-- End of picture text --> Year (1)

> NNMR NEONATAL - PMHR POSTNEONATAL NOTE: (1) IMR6 - YEAR MOVING AVERAGE NOTE: (2) POOR - POOREST 60 PERCENT
# **Table 17: World Trade Liberalization: Macro and Trade Performance (Including Trade-Productivity Dynamic Linkages)**

|||Percenta|ge change from|base||
|---|---|---|---|---|---|
||Real GDP|Real<br>Absorption|Real<br>exchange<br>rate|Terms of<br>trade|Export/<br>output<br>share|
|United States|0.24|0.27|2.87|0.16|0.24|
|Japan|1.74|1.58|8.29|-1.55|0.59|
|China|2.92|2.76|2.79|-0.42|0.79|
|ASEAN 4|5.82|5.17|2.86|-1.27|1.96|
|Asian NIEs|3.31|3.21|8.68|-0.17|1.15|
|European Union|0.21|0.23|2.01|0.10|0.17|

|||Billion US$||Percent|age change fr|om base|
|---|---|---|---|---|---|---|
||Trade<br>expansion|Trade<br>creation|Trade<br>diversion|Trade<br>expansion|Trade<br>creation|Trade<br>diversion|
|United States|21.11|40.17|-19.07|3.66|12.69|-7.34|
|Japan|35.62|42.46|-6.84|8.75|13.89|-6.74|
|China|11.49|15.39|-3.90|8.11|13.93|-12.46|
|ASEAN 4|18.56|8.08|10.49|13.51|7.02|46.93|
|Asian NIEs|17.94|17.95|-0.01|7.01|9.03|-0.02|
|European Union|17.70|21.14|-3.43|2.42|9.92|-0.66|
|_Total,FTAmembers_|122.43|145.19|-22.76||||

Notes: _Real GDP_ (C + I + G + E-M) provides a production-based measure of economic activity.

_Real Absorption_ (C + I _+_ G) provides a welfare measure based on economywide real final demand by households, government, and investment. Changes in this measure equal the _equivalent variation_ for the economy, with changes in govermnent consumption and investment valued according to private consumer's preferences. _Export/output share_ indicates the change in the aggregate export/output ratio for the economy. _Trade expansion_ is the increase in total exports for each region.

_Trade creation_ is the increase in exports to _members of the FTA;_ the trade creation figure for the European Union indicates the change in EU exports to PTA members.

_Trade diversion_ is the increase in exports to _non-FTA members,_ comprised of EU and rest of world.

# **5. Conclusions**

The simulations reported in this paper provide an assessment of the implications of an Asian FTA from several different perspectives. First, considering the impact of tariff and NTB elimination in a static, neoclassical experiment with no other changes, we find that the formation of an FTA is generally beneficial for its members, although the benefits range from quite small (for the US and China) to moderate (for the Asian NIEs). Trade creation exceeds trade diversion by a factor of four. While the _Lewis, Robinson, and Wang 50_
13 should? How do you do that? How many of them are not doing as well as they should?

Case management. Ask the worker to pick out the growth charts for all the children with severe malnutrition. Note whether these children are being monitored regularly. Spend some time asking about each child; you must be sure whether the worker knows each child and its family well. These are the children most likely to die.

1. Why do you think that this child has moved down into grade three? Has it had any illnesses? Do you know how many brothers and sisters the child has? Are they malnourished too? Is this the first or last child in the family, or what?

2. How far away does this child's family live? How many incomeearners are in the family? Are they regularly employed, and what do they do? Are there any particular problems in the family?

3. Does the mother bring the child regularly for weighing? Does she have a copy of the child's growth chart? What nutrition advice have you given her? Does she follow it?

4. Does the child come for supplementary feeding? If not, why not? (Does the mother take the child to work with her?) If yes, who brings the child? Does the child seem to like the food? Can the child finish the food? If not, what do you do about that?

5. Has the local health worker seen this child? What did she have to say about her? (In the case of a persistently severely malnourished child). Has the child been taken to see a doctor? Do you know what his diagnosis was? Do you know if it was the local health center doctor. or a specialist child doctor?

6. If you have time, ask to be taken to the house of one of the severely malnourished children. Talk to the mother about the causes of her child's problem, and what she is doing about it.

SupRlementary feeding. How many mothers are receiving supplementary feeding (SF) in your area? How many children? Can we go through your records and see the nutrition status of these children? (Check to see whether there are malnourished children who are not being fed; or whether significant numbers of children who are not malnourished are being fed.) 2. Have you had any breaks in the supply of your SF in the past year? Does it arrive in good condition? Where do you store it? Does it last without going bad?

3. What is your system for deciding which mothers or children will get SF? Are they eligible immediately they fall into malnutrition/fail to gain weight, or after some weeks of observation? What advice do you give to mothers whose children need SF?
Columns 5 and 6 show the results for the strategic sectors, i.e. the 11 sectors where Advanced Technology Products are found, and for other sectors separately. The larger coefficient in column (5) than in column (6) indicates that tariffs had a stronger effect on imports in strategic sectors compared non-strategic ones.

We next investigate whether US imports became more diversified across partners as they shifted away from China and whether overall imports grew more slowly in tariffed products, to look for evidence of potential reshoring, using the following specifications: In equations (3) and (4), the dependent variable ΔY is either the total import growth in a product or the change in diversification, as measured by the Herfindahl-Hirschman Index. In equation (4), we also focus on the group of tariffed products and examine whether import growth was slower or diversification greater in products China retreated from most swiftly. In this case, the independent variable is a dummy for the decrease in US market share of China between 2017 and 2022 being greater than the median of products subject to tariffs.

Table 2 shows the results for overall import growth. There is some evidence that aggregate import growth was slower in the tariffed products (column 1), but the coefficient is not statistically significant when the sample is split (columns 2 and 3) and economic effect on all trade is small. Total US import growth was 39 percent between 2017 and 2022 for the set of products in the regression (that had codes that existed in both periods). Overall import growth of the subset of tariffed goods was 38 percent as compared with 43 percent for non-tariffed goods, a 5 percentage point difference.<sup>3</sup> The -0.055 coefficient on all trade suggests a 2.4 (.055 * 43) percentage point difference in growth rates at the product level due to the tariff. This decline is small considering the 40 percent decline in US imports from China in the tariffed goods (Table 1). If the tariffs had led to large-scale reshoring, we would expect substantially lower aggregate growth in tariffed products, given China’s average share of US imports of 7 percent in the tariffed goods.

> 3 Import growth rates are calculated using products that exist in both the HS 2017 and 2022 classifications.

8
# **1 Introduction**

The number of preferential trade agreements (PTAs) has been increasing steadily since the 1980s, as has the number of signatories and coverage of non-trade policy areas in such agreements (Dür _et al._ , 2014; Lechner, 2018; Mattoo _et al._ , 2020). Non-trade provisions (NTPs) are a prominent feature of the more recent PTAs concluded by the EU and the US, as well as by many other OECD member countries. They pertain to such matters as environmental protection, labor standards and human rights and have become central to political support for PTA negotiations (Van den Putte, 2015; Lechner, 2016). The increasing prevalence of NTPs in PTAs in recent decades is accompanied by extensive heterogeneity in national non-trade outcome indicators. As discussed further in Section 2. The direction of change in some non-trade outcome indicators associated with NTPs has often been negative.

Given the prominence of non-trade issues in recent debates on globalization in general and trade agreements in particular, whether the inclusion of NTPs in PTAs supports the realisation of underlying non-trade policy objectives, i.e. they actually work to promote climate and socioeconomic goals, is a fundamental policy issue. This is the primary – research question analyzed in this paper does inclusion of NTPs in PTAs improve associated non-trade outcomes? We address this question by exploiting new data on trade agreement coverage (Mattoo _et al._ , 2020) and the synthetic difference-in-difference (SDID) estimator recently proposed by Arkhangelsky _et al._ (2021) to address endogeneity concerns. We extend the literature by allowing for the potential effectiveness of NTPs to depend on the nature of the commitments made. Specifically, we distinguish between NTPs depending on whether they take the form of binding, legally enforceable commitments or non-binding (non-enforceable) soft law provisions that only provide a framework for policy dialogue on the matters covered.<sup>1</sup> In both cases, NTPs provide a potential focal point for technical and financial assistance to support cooperation to improve associated

> 1The literature on the effectiveness of binding vs. non-binding provisions in treaties (and the associated institutional conflict resolution mechanisms) goes well beyond PTAs. For example, McLaughlin Mitchell and Hensel (2007) find that peaceful resolution of disputes between states is more likely if pertinent treaties establish binding conflict resolution mechanisms serviced by an international organization.

1
<!-- Start of picture text --> 900 wennwenn nena n wenn nnn nnn nnn<br>850<br>=<br>750<br>0 5 1 ws 2<br>Discarded observations (%)<br><!-- End of picture text --> <!-- Start of picture text --> Mean<br>Discarded obs (%) Bottom Top<br>@ 897.88 897.88<br>1 900.64 795.75<br>2 904.37 764.14<br><!-- End of picture text --> <!-- Start of picture text --> a6844 ee“=L<br>z mo<br>8 ae<br>; 82 Tres<br>z 8 eeeae<br>FE mS<br>7.8 Stee<br>7.6 ~<br>o 5 1 15 2<br>Discarded observations (%)<br>Povey tna: 300 ——— Top outliers 9 ------- Bottom outliers<br><!-- End of picture text --> <!-- Start of picture text --> H<br>Discarded obs (%) Bottom Top<br>(7) 8.41 8.41<br>1 8.09 8.51<br>2 7.63 8.62<br><!-- End of picture text --> <!-- Start of picture text --> 40 aaa ee<br>z 38<br>5<br>: 36<br>34<br>32<br>0 5 1 15 2<br>Discarded observations (%)<br><!-- End of picture text --> <!-- Start of picture text --> Gini<br>Discarded obs (%) Bottom Top<br>t) 40.63 40.63<br>1 40.49 33.84<br>2 40.31 31.98<br><!-- End of picture text --> <!-- Start of picture text --> 818 _—_—oN eeeFfFT<br>= ~s<br>z ee<br>§ 17 ms,<br>Fi3 1.6 eeese<br>a<br>15 Ween,<br>14 ss<br>o 5 1 15 2<br>Discarded observations (%)<br>Povey ine: 30 ——— Top outliers 9 ------- Bottom outliers<br><!-- End of picture text --> <!-- Start of picture text --> PG<br>Discarded obs (%) Bottom Top<br>1) 1.86 1.86<br>1 1.65 1.88<br>2 1.42 1.90<br><!-- End of picture text -->