rafmacalaba/datause-encoder-data huggingface dataset:
whereas tariffs affect both consumption and production. In other words a 10 percent PSE can be much less restrictive than a 10 percent tariff (it would only be as restrictive as the tariff if the domestic demand is in fi nitely inelastic). So even if we had production levels, one would need to transform the PSE into a tariff equivalent (or some sort of Trade Restrictiveness Index in the spirit of Anderson and Neary, 1996). In order to do so, we would need estimates of domestic supply and demand elasticities, which do not exist at this level of disaggregation.

Import and export data comes from United Nations’ Comtrade (also available through WITS). We took the average between 2001 and 2003 to smooth any year speci fi c shock. If data is missing for a particular country, then we use data for 2000 to calculate the average (Bhutan, Nigeria, Nepal and Gabon). If there was no trade data reported to Comtrade during the 2000-2003 period, then we mirror data from trading partners. This was the case for Cote d’Ivoire, Cameroon, Egypt, Lao, Mozambique, Chad and Vietnam.

Elasticities of import demand elasticities, or rather the slope of import demand functions (i.e., the price parameters ann) are borrowed from Kee, Nicita and Olarreaga (2004).

# 5 Empirical Results

We fi rst discuss the estimates of AVEs of NTBs and we then turn into the calculation of trade restrictiveness indices.

## 5.1 AVEs of NTBs

We run 4545 non-linear regressions (for each 6 digit HS category where at least one country imposes either a core NTBs or domestic support) to estimate the impact that the two different types of Non-Tariff-Barriers (NTBs) have on imports. The average adjusted R<sup>2</sup> is 0.56; with a median at 0.58, a maximum at 0.88, and a minimum at -0.05. The Kernel density estimate of the R<sup>2</sup> estimates is given in Figure 1. There is less than 1 percent of the regression that had an R<sup>2</sup> below 0.10, suggesting that the fi t of (8) was relatively good across the different tariff lines.

Each of these regressions provided us with 10 coefficients that measure the impact of NTBs (core and agricultural domestic support) on imports. These are the coefficients in front of the two NTB variables, interacted with a constant and four factor endowment variables that allow us to 16
<!-- Start of picture text --> A Round 2 v. Round 1 Diff-in-Diff Coeff.<br>Seldom experience delay in budget eo —e—<br>a Percentage of settlements unplanned —e— —o—<br>g Master plan up to date oe —e—_<br>R Master plan last approved previous 2 years :—o —eo—<br>ep Disbursements from central government timely : oe —e—<br>= Budget executed as expected : oe —eo—<br>q Seldom experience delay in guidelines : oe —eo—<br>a Has formal plan of capacity building _——@— ——_o—<br>Showed formal plan —o——_ ——_o———_-<br>Auditor position filled —o—_ —o—<br>> Show copy 1st quarter audit —o— ——_oe—_-<br>B Show copy 2nd quarter audit —o—_ —_o—_<br>g Show copy 3rd quarter audit —o— —_o——_<br>= Show copy 4th quarter audit —_e— —_e——<br>Agrees office of internal audit is independent :-e —o—<br>Agrees office of internal audit is effective > oe —eo—<br>a=] Number of grievances handled (year) oe —e—<br>2 Grievance handled through formal system eo oe<br>© Have handled grievances personally oe eo<br>o) Exists official system to handle grievances oe oe<br>Pay property tax land eo eo<br>Challenge to raise local revenues is political -e —eo—<br>g Pay property tax on mixed use building —e— —o—_<br>Be Percentage of tax invoice collected —eo—_ —_eo——_<br>4 Some incentive for tax collection [oe oe<br>ica Pay property tax on residential building —-@- -o—<br>Sees opportunities to raise local revenues :—e— —eo—<br>Pay property tax on commercial building : eo —eo—:<br>2 Number engineers adequate —eo— —o—<br>& Number meetings discussing infrastructure oe —eo—<br>4 Payment to suppliers on carried out on time : oe —e—<br>rsrs ns as<br>-2 -1 0 1 2 -2 -1 0 1 2<br><!-- End of picture text -->
Mellor, J.W. 1976. _The New Economics of Growth: A Strategy for India and the Developing World_ . Ithaca, N.Y.: Cornell University Press Monitor Group 2011. _Promise and Progress:Market-Based Solutions to Poverty in Africa. (Michael Kubzansky, Ansulie Cooper, and Victoria Barbary)._ Report, May, Available at: <u>www.monitor.com</u>

------- "PovcalNet: the on-line tool for poverty measurement developed by the Development Research Group of the World Bank" and provide the web link to PovcalNet. http://iresearch.worldbank.org/PovcalNet/povcalNet.html

Timmer, C. Peter . 1988. ―The Agricultural Transformation.‖ In H. Chenery and T.N. Srinivasan,eds., _Handbook of Development Economics_ . Vol. 1. Amsterdam: NorthHolland, pp.275-331.

Timmer, C. Peter, and Selvin Akkus. 2008. ―The Structural Transformation as a Pathway out of Poverty: Analytics, Empirics and Politics.‘‖ Working Paper No. 150, Center for Global Development, Washington, DC.

Van de Walle, Dominique. 2003 ―Are Returns to Investment Lower for the Poor? Human and Physical Capital Interactions in Rural Vietnam‖ _Review of Development Economics_ , 7 (4): 636-653 World Bank. 1990. _World Development Report: Poverty_ . New York _:_ Oxford University Press

---------2000. _Can Africa Claim the 21_<sup>_st_</sup> _century?_ New York _:_ Oxford University Press

--------. 2001. _World Development Report 2000-2001: Attacking Poverty_ .  New York _:_ Oxford University Press.

33
**Table B.6: Correlations between CLASS Domains**

||**Emotional Support**|**Classroom**<br>**Organization**|
|---|---|---|
|**Classroom Organization**|0.71||
|**Instructional Support**|0.78|0.46|

A look at the correlation between CLASS domains reveals an interesting disparity: while Classroom Organization is highly correlated with Emotional Support, it is much less correlated with Instructional Support (table B.6). Even if a teacher is effective at keeping the class organized, that does not necessarily imply that students are being engaged successfully in an instructional manner.

# _Cronbach’s alpha_

Cronbach’s alpha is a measure of the internal consistency of data. In this case, the alpha is calculated from the component dimensions of each domain, measuring the intercorrelation of the separate dimensions. A higher value indicates higher intercorrelation between the dimensions, and therefore a higher validity of the overall domain.

**Table B.7: Cronbach’s** **_alpha_**

||**Guangdong**|**S-MTP**|**MET**|**UTQ-A **|
|---|---|---|---|---|
|**Emotional Support**|0.74|0.87|0.87|0.90|
|**Classroom Organization**|0.75|0.88|0.90|0.86|
|**Instructional Support**|0.85|0.88|0.92|0.91|
|**Overall**|0.86||||

Note: Cronbach’s alpha ranges from 0 to 1. Under 0.5 is generally considered unacceptable, above 0.9 is excellent, and anything above 0.7 can be considered good.

The scores for Guangdong were slightly lower than those achieved by other CLASS studies, although they are still high overall (table B.7).

# _Summary_

The reliability tests of raters and the tool show results in line with comparable assessments, and should be viewed as a positive endorsement of the validity of the tool within the Guangdong context. There are certainly weaknesses that should be remedied with further training and evaluations, but as a starting point, the initial observations are quite promising, especially given the small sample size.

38
main destinations (data reported in Boruchowicz et al. (2021) up to June 2019). According to recent estimates, there are a total of 5.6 million Venezuelan immigrants worldwide, and the number of Venezuelans living in Peru has increased from 6,615 in 2016 to more than 840,000 by June 2019 (see Figure 1), and has gone up to one million by 2021 (R4V, 2021). This wave of immigration has increased Peru’s population by around 2 percent.

Travelling from Venezuela to Peru entails a journey of over 4,500 kilometers, and before 2017 Peruvian authorities required all immigrants to be in possession of a passport (without any visa requirements). However, obtaining a passport in Venezuela at the time was difficult, as processing times were extremely long and the overall process was relatively expensive. In light of this situation, in 2017, the Peruvian government implemented a temporary residence permit ( _permiso temporal de permanencia_ , henceforth PTP). This permit allowed immigrants to legally work and study in the country, pay taxes and open a bank account. According to the national statistical institute (INEI), 97% of Venezuelan immigrants were able to get a PTP by 2019, implying that the large majority of Venezuelan immigrants having arrived up to that point were legally in Peru and able to work both in the formal and informal sector.

Unlike in other episodes of crisis migration, such as that of Syrian refugees during the recent civil war or Central American immigrants in the US, Venezuelan immigrants in Peru are relatively similar to Peruvians in terms of language and religion. Nevertheless, they were, on average, significantly more skilled than Peruvians: as of December 2018, 47.8% of Peruvians had less than secondary education which was true of only 17.2% of Venezuelan immigrants (see Table 1). However, there is anecdotal evidence that Peruvians view Venezuelans as contributing little to the economy and that their presence in the country has increased criminal activities (Janetsky, 2019). Furthermore, there is some evidence that this has started to lead to political backlash (Winter, 2020).

Before the pandemic, the Peruvian economy was averaging around a 5% annual growth rate, which made it an attractive destination for migrants. Moreover, the labor market is highly informal (and therefore flexible): In 2018, only 21% of Peruvians held a formal job. Boruchowicz et al. (2021) show that the Venezuelan exodus had negligible effects on the Peruvian labor market, and argue that this is precisely due to the flexibility associated with the high levels of informality in the labor market.<sup>2</sup> Very little is known about the actual discrimination affecting Venezuelan immigrants in the country. Still, it is plausible that part of the discrimination reported by immigrants is related

> 2There is some contrasting evidence that the increase in Venezuelan immigrants in Peru led to small decreases in employment rates and earnings of low skilled (and specially female) Peruvian workers in the informal sector (Morales and Pierola, 2020; Asencios and Castellares, 2020).

5
vocational education. The share attending vocational school jumped in 2007, as the vocational school expansion policy took effect. In light of the historical trend, it is extremely unlikely that the ministry will meet either the 50:50 target in 2010 or the 70:30 goal five years later.

<!-- Start of picture text --> Figure 2. Vocational School Enrolment 1992 - 2007<br>Number of vocational students (Left axis) Vocational Share (Right axis)<br>1,700,000 38<br>36<br>1,600,000<br>34<br>32<br>1,500,000<br>30<br>1,400,000 28<br>26<br>1,300,000<br>24<br>22<br>1,200,000<br>20<br>1,100,000 18<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Number of vocational students<br>Share of secondary students in vocational<br><!-- End of picture text --> _Note: figures calculated from the National Socioeconomic Survey (_ Susenas _), various years_

# **III. Data**

The primary data source for this study is the Indonesia Family Life Survey (IFLS), a longitudinal household survey that began in 1993. Three full follow-up waves were conducted, in 1997, 2000, and 2007. The first wave represented about 83% of Indonesia’s 1993 population, and covered 13 of the nation’s 27 provinces. This initial wave interviewed roughly 7,200 households. By 2007, the number of households had grown to 13,000 as the survey attempts to re-interview many members of the original sample that form or join new households. Household attrition is quite low, as around 5 percent of household are lost each wave. Overall, 87.6% of households that participated in IFLS1 are interviewed in each of the subsequent three waves (Strauss et al., 2009).

The sample is constructed as follows. We began with respondents who were interviewed at least once between the ages of 18 and 50, as a detailed education history is only available for respondents aged 50 or younger. Next, we limited our sample to individuals who were born between 1940 and 1980. We then dropped individuals who were never 7
Table 5: Correlates of WTP in BDM sample

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||||Elicited|WTP in J$0|00s|||
|Credit|0.03|0.01|-0.17|-0.11|0.11|-0.19|-0.17|-0.15|
||(0.80)|(0.78)|(0.81)|(0.82)|(0.78)|(0.78)|(0.81)|(0.77)|
|Discount Frame|-1.20|-1.23|-1.34|-1.40<sup>_∗_</sup>|-1.18|-1.51<sup>_∗_</sup>|-1.50<sup>_∗_</sup>|-1.40<sup>_∗_</sup>|
||(0.82)|(0.81)|(0.83)|(0.84)|(0.80)|(0.80)|(0.81)|(0.78)|
|Credit and Discount Frame|-0.86<br>|-0.88<br>|-0.93<br>|-1.01<br>|-1.20<br>|-1.10<br>|-1.31<br>|-1.23<br>|
||(0.85)|(0.83)|(0.85)|(0.87)|(0.84)|(0.83)|(0.84)|(0.82)|
|Wealth Index||1.27<sup>_∗∗∗_</sup>|||||0.67<sup>_∗∗_</sup>|0.76<sup>_∗∗_</sup>|
|||(0.28)|||||(0.32)|(0.30)|
|Exp. sales incr. 10%-20%|||1.76<sup>_∗∗_</sup>||||1.34||
||||(0.87)||||(0.86)||
|Exp. sales incr. by more than 20%|||1.61<sup>_∗∗_</sup>||||1.16||
||||(0.80)||||(0.82)||
|Took previous bus. training|||-1.10<sup>_∗_</sup>||||-0.73||
||||(0.65)||||(0.66)||
|Reported WTP (in J$000s)|||0.01<sup>_∗_</sup>||||0.00||
||||(0.01)||||(0.01)||
|Share of bus. practices||||0.77|||-0.40||
|||||(1.16)|||(1.18)||
|Personal initiative index [1-7]||||0.29|||0.38||
|||||(0.36)|||(0.34)||
|Comparison with others on bus. practices||||0.13|||0.05|0.13|
|||||(0.20)|||(0.19)|(0.15)|
|Comparison with others on proactiveness||||0.14<br>(0.18)|||0.08<br>(0.19)||
|Age|||||-0.10<sup>_∗∗∗_</sup>||-0.07<sup>_∗∗∗_</sup>|-0.08<sup>_∗∗∗_</sup>|
||||||(0.03)||(0.03)|(0.03)|
|Female|||||-0.75||-0.33||
||||||(0.58)||(0.61)||
|Education: more than secondary|||||1.04||0.58||
||||||(0.90)||(0.88)||
|Risk taking index [0-10]|||||0.25<sup>_∗_</sup>||0.23<sup>_∗_</sup>|0.23<sup>_∗_</sup>|
||||||(0.13)||(0.14)|(0.13)|
|Has employees||||||1.08<sup>_∗_</sup>|0.73|0.70|
|||||||(0.58)|(0.58)|(0.57)|
|Low Sales||||||-0.28|-0.43||
|||||||(0.74)|(0.78)||
|High Sales||||||2.13<sup>_∗∗∗_</sup>|1.44<sup>_∗_</sup>|1.72<sup>_∗∗∗_</sup>|
|||||||(0.72)|(0.75)|(0.62)|
|Observations|457|457|457|457|457|457|457|457|
|R-squared|0.041|0.088|0.065|0.057|0.099|0.088|0.173|0.145|
|Mean dep. var.|13.80|13.80|13.80|13.80|13.80|13.80|13.80|13.80|

OLS regressions of elicited WTP for the BDM sample on covariates. The specification in Column 8 was selected using the Lasso. All regressions control for week and location of demonstration sessions when the WTP was elicited. For all the baseline covariates, we replace missing values with zeros and include dummies for covariates with missing values. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 34
# **Table 3: Changes in Freight Rate during lockdown and post lockdown periods**

Dependent Variable: ln (Freight Rate)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Lockdown dummy|||0.388***|1.004***|
||||(0.005)|(0.068)|
|Post-lockdown dummy|0.316***|0.209***|0.317***|0.230***|
||(0.003)|(0.044)|(0.003)|(0.046)|
|Lockdown*ln(distance)||||-0.087***<br>(0.010)|
|Post-lockdown*ln(distance)||0.015**||0.012*|
|||(0.006)||(0.006)|
|Intercept|2.957***|2.979***|3.057***|3.015***|
||(0.022)|(0.024)|(0.023)|(0.025)|
|Observations|28,227|28,227|29,690|29,690|
|R-squared|0.813|0.813|0.805|0.805|

Note: All regressions control for ln(distance). Lockdown spans (from March 24, 2020, to June 30, 2020). On July 1, 2020, India entered the unlock phase 2 when inter and intra- state travel restrictions were removed though many of the NPI restrictions were still in effect at the local level. Post lockdown spans July 1, 202,0 to February 24, 2021. Standard errors are clustered at the route level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

**Table 4: Estimation of trade gravity regression: OLS Results**

||Control 1|Control 2|Control 3|
|---|---|---|---|
|**Ln (value of trade)**|(1)|(2)|(3)|
|Ln (Freight rate)|-0.241***|-0.270***|-0.240***|
||(0.060)|(0.064)|(0.060)|
|**Ln (Volume of trade)**||||
|Ln (Freight rate)|-0.792***|-0.807***|-0.790***|
||(0.061)|(0.070)|(0.061)|
|Origin Fixed Effects|Yes|Yes|Yes|
|Destination Fixed Effects|Yes|Yes|Yes|
|No. Absorbed FEs|1080|1003|1080|
|No. ODpairs|23288|13793|23288|

Note: Dependent variables are in bold. Each cell represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price). Control 2: Control 1 plus ln (diesel price/litre along OD route). Control 3: Control 1 plus ln(average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

27
In this paper we have used per capita household income as a measure of household economic welfare.' The income concept used is the personal income which includes [McAuley (1979. pp.9-12)] thr, following:

# Money earnings from employment

- (a) in the state sector (excluding holiday pay)

- (b) in collective fanrs.

# Transfer payments

- (a) pensions

- (b) other social security payments

- (c) stipends.

# Receipts from the rmancial system

- (a) interest from saving deposits

- (b) lottery prizes

# Other receipts

- (a) the value of agricultural production for own consumption

- (b) receipts in kind from collective farms

- (c) other income from rnvate econonic activity.

The consumption of home-grown products is evaluated at state prices. In the case of many products, especially meat and frit, this may understate the true value of the product to the household. The imputed rent of owner-occupied housing is not included. Since rents charged by the state are very low, the distortionary effect of this omission should not be too great.

When the index of household welfare is constructed, the next step is to detrmine the welfare of the individuals in the households. In this paper individual welfare was derived by assing every individual in a household a value equal to the per capita income for that household (Kakwani 1986). If there are severe intrahousehold inequities in the distibuin of food and non-food items, poverty and inequality will both be undeimatd. Ihis problem could not be corrected because of non-availabilty of information concerning the intra-household distribution of resources. But intrahousehold inequality may not be bad in Ukraine. Ukrainians 6A better measure of household welfare will of course be the per equivalent adult income which corrects for the differing needs of adults and children. But this measure could not be employed because the FBS data were available only in grouped form (the groups formed on the basis of per capita household expenditure). We could have remedied this only by assuming that the rankdng of households by per capita income is the same as that by income per equivalent adult. This assumption which is unlikely to hold will result in more serious estimation errors.

8
_Role of NGOs in Health Delivery Afifca Heaith Policy Study_

-13-

the missionaries forced to leave the country. (These, however, were not operating health care services.) Thus while Ideology may be a galvanizing force for NGO personnel, it may also present a point of vulnerability in NGO-government relations. Ideological predilections may also affect the performance of NGOs in the delivery of health care. The refusal of several Catholic missions in Africa to provide contraception in their family health clinics is one such example (Bradley 1989).

38. It has been argued above that the change in the world economic context has increased the visibility and role of NGOs in developing countries. In Nigeria, for example, the government discouraged some NGOs and nationalized others in the health field during the oil boom, but as its fortunes began to decline, it found itself trying to attAct them back. Tihis effect of economic decline has also been noted In Zambia (Baldwin 1988). Yet economic change may have a detrimental effect on NGOs. Since the 1970s, for example, many missions have undergone severe financial difficulties, sometimes necessitating closure, bmcause government grants have often failed to keep up with the escalation of costs and revenue from user charges is not sufficient to maintain their operations. Often, this financial pressure changes the nature of mission services towards an emphasis on rendering medical services rather than participation in promotive or educational activities.5 Whatever the effect, therefore, the underlying point is that NGOs' vulnerability to the political and economic context prevents long-term planning of their activities in a stable environment.

39. Just as the successes of NGOs need to be seen in light of their dependence on the economic context of their activities, or on foreign associates or "parent organizations," so too they must be seen in the context of their relationship to the public sector. The work of the international NGO, Save the Children, for example, often builds on exisdng government fhcilities and reinforces them in turn (USAID 1987). The performance of NGOs may be made possibie or facilitated by complementary activities of the public sector. As the example of Malawi and Ghana will show, governments frequently provide subsidies or personnel which enable missions to carry out their activities effectively and without which many would not survive. The deteriorating economic context is likely to put increasing pressure on governments to assist NGOs whose operations are ailing financially.

# Government Policies Toward NGOs

40. The starting pcint for discussing policy options with regard to the nongovernmental health sector is the fact that NGOs are already providing a high volume of services in most of Sub-Saharan Africa. Table 2 pieces together disparate estimates of proportions of health services provided by NGOs, although because of obvious 5. Mr. Ngatia of the National Council of Churches in Kenya in an interview with the author (June 1989) said that this has been the case for many missions in Kenya.
**Table 17.  Village expenditures by category**

||2004<br>||
|---|---|---|
|_RMBper capita_|Mean|% of total|
|**Expenditures for recurrent costs**|**40.1**|**64**|
|Salaries|_14.4_|21|
|Administration|_6.7_|13|
|Maintenance|_8_|15|
|Social welfare|_6.1_|5|
|Others|_5_|9|
|Repayment ofprincipal|12.9|16|
|**Capital investment expenditure**|**42.5**|**33**|
|Financed bycurrent rev|17.3|21|
|Financed bydebt|25.2|12|
|**Total expenditures excl. debt**|**70.3**|**100**|
|Total expenditures incl. debt|95.5|112|

Source: 2005 village survey.

Nationwide, spending on rural basic infrastructure has increased enormously in recent years. Reflecting the tilt toward the rural areas, the government took the opportunity of the fiscal stimulus program (introduced in 1998) and the new resources created through state bond issues to channel additional funding to rural infrastructural investments. In 1998 alone, the amount jumped by RMB 30 billion, leading to a near tripling of expenditure from the level of 10-15 billion in the mid-1990s (Figure D). Since 2003, annual spending on rural basic capital investment has remained above RMB 50 billion.

**Figure D.  Rural basic capital investment spending (RMB billions)**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Source: CSY 2007.

The increased funding from the central government enabled an enormous expansion of 32