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38
**Table 8a: Manufacturing Only**

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|DeepProvisions|AnyLabor Clause|Main Categories|
|Trade Agreement|0.043|0.110|0.131*|
||(0.067)|(0.069)|(0.069)|
|Tariff Preference Margin|0.027***|0.028***|0.027***|
||(0.011)|(0.009)|(0.009)|
|Any Labor Clause||-0.217***||
|||(0.060)||
|1. Aspirational|||-0.116|
|goals/objectives|||(0.160)|
|2. Substance-related|||-0.233|
|clauses|||(0.315)|
|3. Substance-related labor|||0.069|
|clauses: investment|||(0.101)|
|4. Cooperation over|||0.012|
|labor clauses|||(0.120)|
|5. Institutions overseeing|||-0.166|
|labor commitments|||(0.235)|
|Anti-Dumping|0.045|0.061|0.022|
||(0.341)|(0.341)|(0.324)|
|Competition Policy|0.034|0.029|0.046|
||(0.078)|(0.076)|(0.077)|
|Subsidies|-0.398***|-0.264***|-0.240***|
||(0.088)|(0.092)|(0.090)|
|Technical Barriers A|0.777***|0.673***|0.688***|
||(0.214)|(0.215)|(0.216)|
|Technical Barriers B|-0.773***|-0.670***|-0.637***|
||(0.226)|(0.212)|(0.219)|
|Technical Barriers C|-0.072|-0.015|-0.060|
||(0.131)|(0.116)|(0.128)|
|Technical Barriers D|-0.231|-0.300|-0.222|
||(0.293)|(0.291)|(0.281)|
|Constant|16.217***|16.191***|16.176***|
||(0.034)|(0.035)|(0.036)|
|Observations|1,080,596|1,080,596|1,080,596|

**Notes:** Robust standard errors in parentheses (clustered on country pairs).. *** p<0.01, ** p<0.05, * p<0.1.  The data cover 1995-2018. Estimation in all columns include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).

pg. 34
<!-- Start of picture text --> 40%<br>30%-<br>20%<br>10%<br>0%r<br>-20% 0<br>-30% A<br>-40% -<br>-50%<br>-60%<br>-70%<br>(millions of pesos)<br>Domestically Owned Banks U Foreign-owned Banks<br><!-- End of picture text --> Figure 8: Estimated growth of lending to small businesses by bank size for foreign and domestic banks in Argentina.

Note: Estimated growth rates use coefficients from Table 5 and are calculated using the median values of continuous variables for banks of that type. Calculations are only performed for bank sizes between (approximately) the 5"' and _95"'_ percentiles for banks of that type.

<!-- Start of picture text --> 20%<br>i5%/-e<br>5%<br>-5%<br>-10%<br>-15%<br>(billions of pesos)<br>-+4-Domestic - Foreign<br>Figure 9: Estimated growth of lending to small businesses by bank size for foreign and domestic<br>banks in Chile.<br><!-- End of picture text --> Note: Estimated growth rates use coefficients from Table _5_ and are calculated using the median values of continuous variables for banks of that type. Calculations are only performed for bank sizes between (approximately) the 5th and 95h percentiles for banks of that type.

<!-- Start of picture text --> 34<br><!-- End of picture text -->
election timing, it also reflects the fact that elections were less “regular” before the Organic Law, regulating, village administrations including elections, was passed in 1998.

Migration rates (the share of registered rural residents working as migrants) of the sample of roughly 7000 individuals aged 16 to 50 over the 1995 to 2002 period are reported in Table 3 and Figure 1. On average, 16 percent of the sample had experience working outside of their home county. Consistent with the trends observed in other panel data (e.g., the CHNS), the share of residents working as migrants tripled from 9 percent in 1995 to 27 percent in 2002. Migration is clearly correlated with age, gender and education level, which will be important for the empirical specification developed below. Even as migrant employment increases at all ages over the time‐period under study (Figure 1), within any year the probability of working as a migrant declines after age 25 for both genders. More specifically, at any age, men have a higher migration rate than women, and there is no sign that the gender gap is closing over time. The young are more likely to migrate than the old, and so are the more educated.

# **3.2 Specification and Identification**

One implication highlighted in the model is that, conditional on individual heterogeneity, an increase in the probability of land reallocation in the village during the following period may decrease an individual’s propensity to work as a migrant. Our theoretical framework thus suggests estimating the following model for the migration decision: in which migration of individual � in village � in year � , ���� , is a function of an expected village land reallocation in year _t+1_ , ����� , where expectations of forward‐looking village residents will informed by local knowledge of factors influencing reallocation decisions. Also included are characteristics of the individual ( ���� ), his/her household ( ���� ) and time‐varying village effects ( ��� ) control for local _Land Tenure Insecurity and Migration, Page 15_
- 19-

# Table 1. Growth in Sub-Saharan African Exports, 1990-99 1/

(in US$ billion)

||Va|lue|Rateofgrowth|Sh|are|
|---|---|---|---|---|---|
||1990|1999|1990-99<br>(in percent)|1990<br>(inpe|1999<br>rcent)|
|Totalexportsofwhich|48.6|49.6|0.2|100|100|
|Europe|23.1|18.3|-2.5|47.5|37.0|
|UnitedStates|11.7|11.3|-0.3|24.0|22.9|
|Other OECD|3.5|2.5|-3.7|7.2|_5.0_|
|Africa|0.4|0.4|2.4|0.7|0.9|
|Developing|10.0|17.0|6.1|20.5|34.2|
|Non-oilexportsofwhich||||||
|Europe|14.2|14.5|0.3|55.8|54.3|
|UnitedStates|1.7|2.0|2.1|6.5|7.4|
|OtherOECD|2.8|1.7|-5.4|10.9|6.2|
|Africa|0.3|0.4|4.0|1.2|1.7|
|Developing|6.5|8.1|2.5|25.6|30.4|

# Source: World Bank

Note: OECD denotes the organization for Economic Cooperation and Development.

1/ Excludes countries in the South Africa Customs Union 2/ Annual average.
25

**Figure 12  Distributions of age at retirement for men (left-hand side) and for women (right-hand side)** **<u>derived form 15 Swiss pension funds.</u>**

<!-- Start of picture text --> men, 1990−1994 women, 1990−1994<br>600 350<br>300<br>500<br>250<br>400<br>200<br>300<br>150<br>200<br>100<br>100 50<br>0 55 60 age at retirement 65 70 0 55 60 age at retirement 65 70<br>men, 1995−1999 women, 1995−1999<br>700 450<br>400<br>600<br>350<br>500<br>300<br>400 250<br>200<br>300<br>150<br>200<br>100<br>100<br>50<br>0 55 60 65 70 0 55 60 65 70<br>age at retirement age at retirement<br>men, 2000−2003 women, 2000−2003<br>300<br>600<br>250<br>500<br>200<br>400<br>150<br>300<br>100<br>200<br>50<br>100<br>0 55 60 65 70 0 55 60 65 70<br>age at retirement age at retirement<br>Source: Bütler, Huguenin and Teppa (2005)<br>number of individuals number of individuals<br>number of individuals number of individuals<br>number of individuals number of individuals<br><!-- End of picture text -->
derived from the standard normal cumulative density function , with estimated expectation and variance of h and h respectively. ΦΦ (. ) We apply this method primarily to derive aggregate income thresholds that separate the XX ββ̂ XX θθ<sup>�</sup> We apply this method primarily to derive aggregate income thresholds that separate the following three groups: the poor and vulnerable, the aspiring middle class, and the middle and upper class.<sup>12</sup> Note that the grouping in this scenario differs considerably from the classification in the previous scenario. Let us call the threshold between the first two groups in this scenario the Chaudhuri vulnerability line, and the line dividing the last two groups the middle class line. The Chaudhuri vulnerability line is the average predicted per capita income of the group of households that have a certain probability of being poor in the future, 10 percent in our application. Building on this, the middle class line is the average predicted income of households with a certain probability of being vulnerable, again 10 percent in our application. Households are then classified into the three groups according to these two lines.

# 2.4. Relative mobility

limitation of such absolute mobility analysis is that what happens once households move comfortably beyond such thresholds is out of our analytical scope. Analyzing relative mobility across the five quintiles of the income distribution enables us to take a broader view and to consider movements along the entire income spectrum. Quintile thresholds are determined based on the cross-sectional income distribution in a given year, so they differ from year to year. The synthetic panel is then employed to estimate the probability that a household has made a particular transition from one quintile to another over adjacent survey rounds. We obtain a 5 x 5 transition matrix once estimates are aggregated.

For the point estimates of relative mobility, we apply the method as described above, substituting the quintile thresholds for the poverty lines in the bivariate standard normal cumulative distribution function above. For the bounds, we take a different approach from the one described in section 2.1. Because we want to estimate the probabilities of twenty-five transitions we need higher precision: the standard bounds approach would likely result in uninformative estimates. Hence, we take the smallest and largest correlation values in our sample of income correlations to estimate our relative mobility bounds.

> 12 We use this terminology in line with the umbrella study on inequality in Malaysia to which this paper contributed.

7
The project-related variables also presented reasonable results. Those who had already heard in detail about the project for Puzhehei water quality improvement were willing to pay on average 17 yuan more than those having only vague information. Those who believed that the project would increase their household income, improve their lake view and swimming conditions, or protect the ecological system, and who believed the project would have the anticipated effects, were willing to pay more. However, the negative signs of “project will improve health,” “project will improve tourism” and “project will benefit next generation” were not what we had expected, but the signs are not statistically significant.

Finally the respondents with individual one-on-one interviews were willing to pay significantly less than those with a group setting, and the difference was roughly 10 yuan. This implies either that the individual interviewer effect is negative or that the group interview effect is positive. As anonymity was ensured in the group setting of interviews, the former implication may be relevant. This implication is inconsistent with the findings obtained in the studies conducted in the developed world, but is consistent with several study results obtained in the developing countries.

We found only few determinants in the standard variance estimation function significant. Age correlated negatively with the variance in respondents’ WTP distributions. Income in log terms and education showed positive and significant correlation with WTP variance. The belief that project would improve health generated a smaller standard variance. Individual face-to-face interview survey mode resulted in significantly smaller standard variance. Other variables presented insignificant and generally reasonable associations with standard variance.

The estimation in the second-stage also gave a predicted mean WTP value as well as standard variance value based on simulation. The mean WTP predicted by second-stage is 11.31 yuan, very close to the median of mean WTP estimated from the first-stage. The WTP standard variance is predicted to be 2.29 yuan.

# **6. Conclusion and Discussion**

This paper reports an economic valuation study which aims to estimate the total value of a project to improve the water quality of Lake Puzhehei by one grade level, i.e., from current Grade III to Grade II in the lake and from current Grade IV to Grade III in the surrounding rivers. The lake is located in a rural area of Qiubei county, which is far from big cities, but the water quality has been deteriorating fast in the past 10 years. The potential hypothetical bias associated with this contingent valuation study is expected 15
business owners”. For both samples, 400 participants were randomized to be offered business training, and half of these to additionally receive the cash grant conditional on finishing the training.

We focus on the sample of potential business owners, as for the sample of current business owners the program impacts dissipate in the medium-run (2 years after treatment).

All sampled business owners originally live in greater Colombo or greater Kandy, the two largest urban areas in Sri Lanka. The project area includes 7 of the 25 districts of Sri Lanka (Colombo, Kalutara, Gampaha, Kandy, Matale, Kegalle, and Kurunegala).

The study participants were surveyed at baseline in January 2009. Training took place in April and May 2009, and the cash grant was given in June 2009. There were five rounds of follow-up surveys, conducted in September 2009 (3-4 months after training), January 2010 (7-8 months after), September 2010 (15-16 months after), June 2011 (24-25 months after), and June 2015 (6 years after training).

## <u>Variable details:</u>

We rely on the replication file published by DeMel et al. (2014) on the World Bank Microdata Library for variable construction up to follow-up round four. We then construct variables for the 6-year endline as follows:

- Self-employment: indicator equal to one if the respondent is still running the business (question F.4. ”Is the enterprise owner still running the business?”) or currently owns a business (question 1.1 ”Do you currently own a business?”). We set the indicator to zero if the respondent’s answer to question F.4 is that the business is closed, or if she answers that she does not currently own a business.

- Wage worker: indicator equal to one if the respondent is currently working (question 3.1 ”Are you currently engaged in any economic activity?”) and reports ”working as a wage worker” or being ”casual/daily paid worker” in question 3.2 (”Which of the following best describes your current economic activity?”).

- Migration: indicator equal to one if respondent changes province or district between baseline and follow-up.

# **C.6 Ghana microenterprises**

The intervention randomized a treatment of either cash or in-kind grants of 150 Ghanaian cedis (about $120) among a sample of 793 microenterprises. The experimental design follows closely the one adopted in the Sri Lanka Microenterprise Survey (see online appendix section C.4).

63
<!-- Start of picture text --> 100%<br>90%<br>v 80%<br>uvE 70%<br><=os3 60%<br>£ 50%<br>cGo 40%<br>vu<br>ao30%<br>20%<br>10%<br>0%<br>Qi Q2 Q3 Q4 Q5<br>@Non Subsidy @PartialSubsidy MFull Subsidy<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>2 70%<br>ri5 60%<br>5o 50%<br>£aD 40%<br>i—o<br>§ 30%<br>a<br>20%<br>10%<br>0%<br>Qi Q2 Q3 a4 Q5<br>@Non Subsidy ®Partial Subsidy TM Full Subsidy<br><!-- End of picture text -->
sensitivity of measurement to the differences in length and complexity between the base survey and target survey questionnaire design, provided that the identical questions are utilized across the surveys. It is thus reasonable that changes in the distributions of the predictor variables over time for these four models can capture the change in the poverty rate between the rounds (i.e., satisfying Assumption 2 in our imputation framework discussed in the next section).

On the other hand, Table 1 also shows that the food and non-food consumption aggregates that can be created with the TA 2 and TA 3 data, as explained above, present large and statistically significant differences vis-à-vis the TA 1 counterparts. Relative to TA 1, the TA 2 and TA 3 food consumption modules decrease the reported household food consumption expenditures, respectively, by 22 and 31 percent. The non-food consumption module administered in TA 3 decreases the reported household non-food consumption expenditures by 46 percent compared to TA 1. The results lower our expectations regarding the predictive accuracy of Models 3 and 4 that would be applied to target survey data with reduced food and non-food consumption modules. Hence, as discussed later, we also explore “standardizing” the distributions of the predictors in TA 2 and TA 3 as to match the distributions of the same variables that are obtained in the base survey (Dang _et al._ 2017). In practical terms, making the standard assumption that the variables to be standardized have a normal distribution, standardization implies (1) subtracting each variable from its mean (i.e., demeaning) in the target survey, (2) multiplying the demeaned variable with the ratio of the square root of the variable variances in the base survey and target survey, and (3) adding the base survey mean (see Appendix C for further discussion).<sup>9</sup>

> <mark>variation in questionnaire design provides identical poverty predictions irrespective of the short versus longer questionnaire treatment.</mark> 9 Alternatively, we can employ a rescaling approach since food and non-food expenditures reported in NPS can provide the benchmark against which we can adjust (rescale) the corresponding expenditures under TA 2 and TA 3 such that these expenditures are equal. While rescaling the shares of these expenditures against those for the corresponding groups in NPS improves imputation accuracy for food expenditures in TA 2, it does not improve estimates of food and non-food expenditures in TA 3.

12
**Table 2.8   Creative content sector growth in Singapore, 1986-2001**<sup>**1**</sup>

||Establishments|Employment|Operating Receipts<br>($million)|Value Added<br>($million)|Value added/ Labor<br>($’000)|Value added/Firm<br>($’000)|
|---|---|---|---|---|---|---|
|1986|718|14092|1240.9<sup>2</sup>|526.1|37.3|732.7|
|1990|958|22856|2475.9<sup>4</sup>|1000.4|43.8|1044.2|
|2001|2573|26488|5399.4|1590.2|60.0|618.0|
||||Compound averag<br>|e annual growth rate|(%)||
|1986-2001|8.8|3.9|9.4<sup>6</sup>|7.4|3.4|-1.4|

1 Excludes broadcasting.

2 If operating receipts from Singapore Broadcasting Corporation are included (1985 figure) then sector operating receipts becomes $1,356.7 million 3 If remuneration from Singapore Broadcasting Corporation is included (1985 figure) then sector remuneration becomes $387.9 million 4 If operating receipts from Singapore Broadcasting Corporation is included (FY1990 figure) then sector operating receipts becomes $2,671.9 million 5 If remuneration from Singapore Broadcasting Corporation is included (FY1990 figure) then sector remuneration becomes $681.8 million 6 If operating receipts from Singapore Broadcasting Corporation are included (1985 figure) then sector growth rate becomes 9.7% 7 If operating receipts from Singapore Broadcasting Corporation are included (1985 figure) then sector growth rate becomes 9.4% _Note_ : The creative content sector includes the following industries: IT development; publishing; motion picture production, distribution & allied services; radio & television services, theatrical producers, entertainment. IT development has been included in both the KIBS sector and the creative content sector _Sources_ : Singapore Department of Statistics (DOS). (Various years). _Economic Surveys Series: Business Services and Real Estate_ . Singapore: DOS. Singapore Department of Statistics (DOS). (Various years) _Economic Surveys Series: Community, Social & Personal Services._ Singapore: DOS. Data obtained from DOS 18