rafmacalaba/datause-encoder-data huggingface dataset:
and required the United States to renew China’s NTR status annually. When China joined the World Trade Organization (WTO) in 2001, the United States eliminated this renewal process, reducing uncertainty about U.S. tariffs on Chinese goods going forward.<sup>2</sup> The renewal process applied to all products, although the effect of non-renewal was heterogeneous across industries, owing to differences in the gap between NNTR and NTR tariffs. In total, these reforms propelled China from the United States’ smallest trading partner to its largest (see Figure 1).

The key challenge in assessing the sources of China’s trade integration is disentangling the effects of past policy and uncertainty about future policy. The low initial trade volumes, the scale of the reforms, and the uncertainty about the persistence of the reforms likely created a long transition that may have lasted the entire sample period. These transition dynamics make determining the impact of expected future trade policy difficult, including during the well-studied period leading up to China’s accession to the WTO. The effects of future trade policy can only be measured by viewing trade growth through the lens of a model in which firms make intertemporal decisions about whether to start or stop exporting. The observed path of tariffs alone provides very little information about agents’ expectations about trade policy during this transition.

We find the transition from the initial liberalizations was long lived and that uncertainty about their duration played an important role in shaping China’s export growth. However, trade-policy uncertainty (TPU) was primarily important in the years following the 1980 liberalization and was only of minor importance in the years surrounding WTO accession. We estimate that the probability of a reversal of the 1980 liberalization was an order of magnitude larger in the 1980s than in the 1990s. Our results suggest much of the trade growth that occurred following WTO accession was a delayed response to the previous liberalizations.

We study the path of China-U.S. trade integration using U.S. import data from 1974 to 2008. We show that initial conditions, measured by initial trade flows or trade policies, exert a long-lasting effect on trade flows. One way of summarizing the effect of these initial conditions is to estimate a short- and long-run trade elasticity and speed of adjustment using

> 2The imposition of tariffs on Chinese imports to the United States in January 2018—and those that followed—suggests the uncertainty was not eliminated. Exactly how much uncertainty about U.S. policy decreased after China joined the WTO is a primary focus of this paper.

2
obtained a higher return had they not changed fund and 79% would have been better off if they had remained on the default path.<sup>18</sup> Similar concerns have been identified in Cost Rica after the recent introduction of multiple portfolios in the system. There is evidence that advertisement tools based on 12 months returns used by pension providers (OPP) have been effective in influencing member investment choices. Consequently, the regulator is reviewing the provisions on annual returns that must be included in statements and on which OPPs can advertise. Instead of 12-month returns, returns over 36, 60 and 120 months will be published.<sup>19</sup> The same pattern can be found in other countries, with switching following short-term investment performance. For example, Costa Rica, Mexico and Estonia, switching among providers is more common during years of poor performance. In the case of Chile, both type of transfers, across portfolios and across providers, are larger when pension funds display low returns.

**Table 1. Correlation between calendar-year returns and transfers**

||**Across Fund**<br>**Providers**|**Across Portfolios**|
|---|---|---|
|LAC|||
|Chile|-0.46|-0.41|
|Colombia|0.15|0.04|
|Costa Rica|-0.23||
|Mexico|-0.41|0.71|
|Peru|0.18|-0.10|
|EEC|||
|Estonia|-0.28|-0.14|
|Romania|-0.03||

Mexico has an interesting approach to performance measurement based upon age. Performance measures are communicated to affiliates in their statement of accounts (every 4 months) and are published on the CONSAR webpage at the end of every month. The net return index (IRN), net of administrative fees charged by the AFORE, of 7 years, 5 years, 3 years and 1 year for basic funds is shown. The IRN is a 6-month moving average of end-to-end net returns (the latter computed over the point in time horizon). Individuals younger than 45 are shown 72 month returns, older participants a shorter duration.

# 3. Fund Outflows and Short-term Assets

For LAC countries and Estonia, short-term investments are positively correlated to transfers across pension fund providers. While the evidence is consistent with fund managers using short-term assets to manage redemption risks, there are at least two potential limitations to this interpretation. First, the share invested in short-term assets varies mechanically in response to differences in returns across asset types. For highfrequency data (e.g. day, week, and month), changes in portfolio weights might be more indicative of differences in asset returns than of managerial strategies. To mitigate this concern, Table 2 reports the

> 18 Superintendencia analysis. Calderon, Dominquez and Schwartz (2008) produced similar findings for the Mexico system up to 2008.

> 19 In Poland returns up to 40 years can be quoted. Romania is a more standard 2 years.

15
Or~~~~~~~~~~~~~~~~~~~~~' Abken Peter, "Over-the-Counter Financial Derivatives: Risky Business?", _Federal Reserve Bank of Atlanta Economic Review_ (March/April 1994).

_Basle Committee on Banking Supervision,_ "An Internal Model-Based Approach to Market Risk Capital Requirements" (April 1995).

_Basle Committee on Banking Supervision,_ "Amendment to the Capital Accord to Incorporate Market Risks" (January 1996).

Berger Allen, Herring Richard and Szego Giorgio, "The role of capital in financial institutions", _Journal of Banking and Finance_ 19 (1995).

Bliss Robert, "Risk-Based Bank Capital: Issues and Solutions", _Federal Reserve Bank of Atlanta Economic Review_ (September/October 1995).

Buser Stephen, Chen Andrew, and Kane Edward, "Federal Deposit Insurance, Regulatory Policy, and Optimal Bank Capital", _The Journal of Finance,_ Vol. 35, No. 1 (March 1981).

Dale Richard, "Regulating Investment Business in the Single Market", _Bank of England Quarterly Bulletin_ (November 1994).

Dale Richard, "Derivatives: the New Regulatory Challenge ", _Butterworths Journal of International Banking and Financial Law_ (January 1995).

_Derivatives Strategy_ magazine (29 May 1995).

Dewatripont Mathias and Tirole Jean, _The Prudential Regulation of Banks_ (1994).

Diamond D. and Dybvig P., "Bank runs, deposit insurance and liquidity", _Journal of Political Economy_ (June 1983).

Dimson Elroy and Marsh Paul, "Capital Requirements for Securities Firms", _The Journal of Finance,_ Vol. 50, No. 3 (July 1995).

_Economist_ magazine (various issues).

_Federal Register,_ "Risk-Based Capital Standards: Market Risk", Vol. 60, No. 142 (25 July 1995).

_Financial Times_ newspaper (various issues).
# **Table A.3.2. Conditional correlations of educational outcomes with aspired and expected education with levels as categorical variables**

||**Standardized**|**average grades**|**Number of sch**<br>**in the pa**|**ool days missed**<br>**st month**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Aspired education***|||||
|<br>Middle school (complete or incomplete)|**-0.066****||**0.373*****||
||(0.03)||(0.09)||
|Vocational high school|0.022<br>||**-0.102****<br>||
||(0.02)||(0.05)||
|Vocational Tertiary|**0.081*****<br>||**-0.184****<br>||
||(0.03)||(0.08)||
|Bachelor|**0.180*****||**-0.395*****||
||(0.02)||(0.05)||
|Magister|**0.218*****||**-0.359*****||
||(0.02)||(0.05)||
|Doctorate|<br>**0.228*****||<br>**-0.365*****||
||(0.02)||(0.05)||
|**Expected education***|||||
|Middle school (complete or incomplete)||**-0.158*****||**0.506*****|
|||(0.02)||(0.07)|
|Vocational high school||**-0.025****||0.003|
|||(0.01)||(0.03)|
|Vocational Tertiary||**0.052****||**-0.207*****|
|||(0.02)||(0.04)|
|Bachelor||**0.117*****<br>||**-0.192*****<br>|
|||(0.01)||(0.03)|
|Magister||<br>**0.069*****||<br>**-0.102*****|
|||(0.02)||(0.04)|
|Doctorate||-0008||-0057*|
|||.<br>(0.01)||.<br>(0.03)|
|Self-esteem|-0.004|-0.005|-0.050***|-0.047***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Sense of belonging|0.063***|0.066***|-0.101***|-0.104***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Perseverance|0.042***|0.046***|-0.029**|-0.033***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Growth mindset|0.145***|0.150***|-0.090***|-0.095***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Effort beliefs|<br>0.053***|<br>0.056***|<br>0.002|<br>-0.003|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Learning orientation|0.122***|0.129***|-0.095***|-0.102***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Performance-avoidance orientation|-0.058***<br>|-0.059***<br>|0.039***<br>|0.039***<br>|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Age (with decimals)|-0.100***|-0.103***|0.144***|0.146***|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Being a girl (dummy)|<br>0.578***|<br>0.599***|<br>-0.362***|<br>-0.393***|
||(0.01)|(0.01)|(0.02)|(0.02)|
|Living in urban area (vs. rural)|<br>-0.115***|<br>-0.111***|<br>-0.109***|<br>-0.108***|
||(0.01)|(0.01)|(0.03)|(0.03)|
|Living in Java (vs. in Sumatra)|<br>0.040***|<br>0.034***|<br>0.011|<br>0.015|
||(0.01)|(0.01)|(0.03)|(0.03)|
|Constant|0.979***|1.160***|-0.294|-0.553***|
||(0.08)|(0.08)|(0.21)|(0.21)|
|Observations|54,639|54,615|55,039|55,014|
|R-squared|0.24|0.23|0.05|0.04|

_Source_ : Authors’ calculations based on the 2018 survey “Getting to Know 8th Grade Students.” _Note_ : *The omitted category in aspired and expected education is general high school. Conditional correlations are Ordinary Least Squares regressions. Yellow and blue highlights show significant correlations at least at the 95 percent level for the variables of interest and covariates, respectively. Grades are standardized at the school level, with a mean of zero and a standard deviation of one. Measures of socioemotional skills are standardized for the entire sample. Standard errors are clustered at the school level.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 36
**Page** 16

**Livestock Insurance in Mongolia**

With the mortality index insurance there<sup>islittleopportunityforadverse</sup> selection and moral hazard. The administrative<sup>costsshouldbeconsiderablyless</sup> than traditional insurance. Thus the major<sup>loadsthatwill beaddedto theprernium</sup> rates will be almost totally a function of the<sup>commissionpaidsalesagentsandthe</sup> premiums that are needed to build reserves<sup>andpayreinsuresforthecovariaterisk.</sup> More will be said about these important issues<sup>inthenextsection.Another major</sup> advantage of this recommendation is that<sup>qualityhistorical dataexistandgood</sup> systems are in place to develop these data all<sup>across Mongolia. Thereisno needto</sup> start a new system.

# **How might a contract appear to the**<sup>**herder?**</sup>

While neither author is a lawyer, it may be helpful<sup>to showhowsimplethe</sup> language might be for an index insurance contract<sup>witha herder in Saintsagaan.</sup>

# _DRAFT LANGUAGE FOR ILLUSTATION ONLY_

_This insurance is solely based on the official sum statistics_<sup>_onadult_</sup> _livestock losses for cattle and yak in sum Saintsagaan_<sup>_in aimag Dundgobi._</sup> _The insurance will pay you when the mortality rate_<sup>_(theratio of adult_</sup> _losses during the year 2002 divided by the total herd_<sup>_population at the_</sup> _beginning of the year) exceeds a rate of 6.5%._<sup>_Tobeeligible, you must_</sup> _register for this insurance by May 1 of 2001._<sup>_Registration involvesa_</sup> _statement of intent to purchase and a reporting of your animal_<sup>_numbers at_</sup> _that time._

# _Value of Insurance_

_While we believe the average value of cattle and_<sup>_yaktobeaboutTg_</sup> _100,000, you may purchase any value of insurance_<sup>_betweenTg 20,000 and_</sup> _Tg 200,000 per animal reported._

# _Paying Premium_

_You will pay a premium rate of 4% times the value of insurance_<sup>_you chose._</sup> _The premium payment is due on January 1 of 2002. Should_<sup>_no payment be_</sup> _received by that time, we will cancel this insurance_<sup>_policy._</sup>

# _Paying for losses:_

_If the mortality rate for the sum of Saintsagaan_<sup>_inaimag Dundgobi_</sup> _exceeds 6.5%, we will pay you the product of the mortality_<sup>_rate times the_</sup> _value of insurance you have chosen. Please understand_<sup>_that you may have_</sup> _livestock losses when the sum mortality rate does not_<sup>_trigger a payment._</sup>
# **Table C.26: Robustness to training effects**

**Panel A: Effects on women’s promotions and income (Pooled waves)**

||(1)<br>Supervisor<br>(Dummy)|(2)<br>Personal<br>income<br>(Taka)|(3)<br>Household<br>income<br>(Taka)|
|---|---|---|---|
|selected|0_._16**|78_._84|_−_371_._28|
||(0_._07)|(621_._47)|(1556_._63)|
|selected*softonly|0_._16**|1037_._16|1593_._06|
||(0_._07)|(631_._93)|(1362_._50)|
|selected*hardsoft|0_._09|833_._10|_−_229_._93|
||(0_._07)|(638_._16)|(1412_._45)|
|N|363|363|363|
|Nominated mean|0_._09|9883_._31|23758_._90|
|Nominated SD|0_._29|3203_._56|7963_._02|

**Panel B: Effects on women’s decision-making power**

||Decisio|n-making (In|dex)|
|---|---|---|---|
||Pooled waves|Wave 1|Wave 2|
|selected|0_._07|0_._30|_−_0_._08|
||(0_._16)|(0_._29)|(0_._11)|
|selected*softonly|_−_0_._01|0_._09|_−_0_._10|
||(0_._12)|(0_._22)|(0_._11)|
|selected*hardsoft|0_._13|0_._26|_−_0_._01|
||(0_._13)|(0_._24)|(0_._11)|
|N|363|181|182|
|Nominated mean|_−_0_._16|_−_0_._48|0_._22|
|Nominated SD|1_._13|1_._38|0_._58|

**Panel C: Effects on assignable goods expenditures and remittances (Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
||(A|ll outcomes a|s % in househ|old income)||
|selected|3_._32**|0_._54|0_._35|_−_0_._49|3_._29*|
||(1_._43)|(0_._60)|(0_._75)|(0_._75)|(1_._71)|
|selected*softonly|_−_2_._60|0_._75|_−_0_._20|_−_0_._17|0_._80|
||(1_._77)|(0_._62)|(0_._75)|(0_._49)|(1_._92)|
|selected*hardsoft|0_._78|1_._06|0_._10|0_._82|_−_0_._22|
||(2_._68)|(1_._20)|(0_._69)|(0_._77)|(1_._96)|
|N|363|146|363|161|362|
|Nominated mean|5_._32|1_._65|3_._92|2_._69|6_._00|
|Nominated SD|4_._48|2_._41|3_._99|3_._65|8_._63|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample in Panel C, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

90
26 percent (net of shared revenues).<sup>9</sup> Districts are allocated 90 percent of these funds.

The remainder is distributed across provinces.<sup>10</sup>

**Figure 1: Sub-National Revenues in Indonesia (Aggregated by Province)**

<!-- Start of picture text --> 4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>PCOSR PCSDA PCTAX PCDAU PCDAK<br>Jawa BaratJawa TengahBantenJawa TimurNusa Tenggara BaratSumatra UtaraLampungSumatra SelatanYogyakartaKalimantan BaratSulawesi SelatanNusa Tenggara TimurSumatra BaratSulawesi UtaraSulawesi TenggaraBengkuluKalimantan SelatanSulawesi TengahBaliNanggroe Aceh DarussalamBangka BelitungGorontalo JambiMaluku UtaraKalimantan TengahMalukuKalimantan TimurRiauPapua<br>in thousand Rp<br><!-- End of picture text --> Source: Authors’ calculation using MoF 2004 data. The data is aggregated at the Provincial level. DKI Jakarta is omitted from the figure because of it has a different autonomy set up.The 2004 data is based on 2003 definition of provinces (30). The figure does not include special autonomy fund transfer to Papua which accounted for 2% of total DAU pool.

Table 1 summarizes the existing establishments costs, formula-based allocations, and hold-harmless in the overall allocation of the DAU from 2001-2006. Policymakers in Indonesia introduced a formula-based intergovernmental transfer system with the launch of the 2001 Big Bang Decentralization. However, DAU allocations were still driven largely by existing establishment costs. These estimations were based on the sum of previous transfers for deconcentrated structures and staff, which were handed over to local governments, as well as prevailing development transfers to local governments.

Over time, policy makers envisioned increasingly moving to a transparent formula-based allocation (Lewis 2001;World Bank 2003).

> 9 Law 33/2004 over Law 22/1999 stipulates that during the transitional period the DAU pool should be at the minimum 25.5 percent of net national income and should be 26 percent in 2008. For the 2006 budget, however, the parliament agreed to allocate 26 percent of net national income for DAU pool.

> 10 Currently Mininstry of Home Affairs is drafting implementing regulations of Law 32/2004 to regulate and clarify expenditure assignments between provincial and local governments.

9
Democracy <u>(ACLP)</u> 423 .55 .48 0 1 Bureaucratic reform may be easier in countries with greater human capital, so we therefore check for robustness to controls for gross secondary school enrollment. Again, this control can lead to the spurious rejection of the hypothesis, since education is precisely the type of broad-reaching policy that politicians in programmatic parties should be more likely to support. Results, though, are robust to its inclusion in the base specification.

Most discussions of public sector organization and conflicts of interest between executive and legislature emphasize information asymmetries. We use newspaper circulation in countries, taken from _World Development Indicators_ , to account for this effect. Gehlbach and Keefer (2010) argue that leader willingness to allow for collective action (by a ruling party or by a bureaucracy) is attenuated when natural resource rents are high. Knack (2008) shows that natural resource exports weaken government incentives to design efficient tax systems. We therefore control for natural resources as a percent of exports, from WDI.

# **Specification and results**

The dependent variables are dichotomous, so we use a logistic specification to estimate the effect of party organization: IEGi = β0 + β1 ∗(programmatic party) + Xiβ2 + εi. Each observation _i_ is a loan. We estimate only clustered standard errors, assuming that errors are distributed independently across countries, not across loans within countries. X _i_ is a vector of covariates; _εi_ is an error term; and _β0_ , _β1_ , and _β2_ , are (vectors of) parameters to be estimated.

Table 2 reports the results of logistic regressions for the baseline and full models. The baseline control variables include region dummies, country characteristics (GDP per 28
12 combines information on the satisfaction of various basic needs. The index can be produced at a number of different levels of geographical aggregation, ranging from regions and provinces down to municipalities. The smallest geographical units for which such information was available are neighborhoods (or barrios). On average, these are units of some 300-400 households.

In order to decide whether it was desirable to allocate water subsidies on a zonal basis, it was important to establish how successful a geographical criterion was likely to be at reaching the poor households in the target group. Using data from the 1997 national Living Standards Measurement Survey, a model was developed to simulate the effects of a zonal subsidy. The simulation model picked out all households residing in zones that met a particular set of socio-economic criteria and then counted how many of these households were genuinely poor.

Targeting errors can take one of two different forms. Perhaps the most serious of these are the Type I errors which arise when members of the target group are not captured by the eligibility criteria and hence fail to receive the subsidy. A subsidy scheme with a high Type I error clearly fails on its own terms. Also of concern is the possibility that people outside of the target group may fortuitously comply with the eligibility criterion and consequently receive the subsidy. A high level of Type II error thus indicates leakage of funds to unintended beneficiaries, and suggests that the subsidy scheme is comparatively inefficient in reaching its target population.

Table 4.1 below presents the results of one such simulation. In this case, a subsidy was allocated to all households living in a zone where more than 50% of households were below the extreme poverty line, and where telephone coverage was below 30%4. The results indicate that using a zonal subsidy of this kind is a very imperfect method of targeting subsidy funds towards the poor. In the first place, the level of Type I error is extremely high at 94%; which is to say that only 6% of households in extreme poverty (1,000 out of the total 17,200) actually receive the subsidy. The level of Type II error is also substantial, with 31% of subsidies going to households above the extreme poverty line. Changing the eligibility criteria by reducing the telephone coverage limitation or the percentage of households in the zone living below the extreme poverty line did not change the general conclusions. More vulnerable households could be targeted, but more resources would at the same time be filtered to non-deserving households.

4Owing to the constraints of data availability, the zones used for the simulation were not, in fact, neighbourhoods, but sampling units from the national Living Standards Measurement Survey. These sampling units were actually smaller than neighbourhoods, numbering less than 15 households. Consequently, the targeting properties for sampling units might be expected to be better than those that would have been obtained using neighbourhoods.