rafmacalaba/datause-encoder-data huggingface dataset:
from other studies using Chinese prefectural-level data (Moreno-Monroy, 2008; Bosker _et al._ , 2010; Hering and Poncet, 2010a). Hence, our assumption  = 3. Studies which have estimated a prefectural-level regional wage equation for China using aggregate wage data have obtained estimates of the elasticity of substitution in the range 5 – 8 (Moreno-Monroy, 2008, p 26; Bosker _et al._ , 2010). Therefore, it was felt reasonable to assume  = 5, on the grounds that, _a priori_ , we might expect there to exist more substitutability between rural varieties than between urban varieties. Again, the possible consequence of these assumptions is to introduce error into the measurement of _RMAU_ and _RMA R_ , thereby further justifying our IV approach to estimation.

Related to the above problem is the need to also specify values for the parameters  _0_ ,  _1_ ,  _0_ ,  _1_ ,  _U_ and  _R_ in the transport cost functions [8] and [9]. Again, this is necessary for the construction of both _RMAU_ and _RMA R_ , as well as our measures of _G U_ and _G R_ . Following Fingleton (2005b, 2007), we assume  0   1   0   1  1 so that the urban and rural transport cost functions reduce to _TijU_  1  ( _tij_ )  _U_ and _TijR_  1  ( _tij_ )  _R_ respectively. This still leaves  _U_ and  _R_ as free parameters whose values need specifying. We, therefore, calibrate these parameters, selecting values which satisfy two criteria: ( _a_ ) a good fit between our "after" model solution and actual 2007 data, and ( _b_ ) satisfactory regression diagnostics, especially relating to the validity of our instruments, for our NEG wage equations. The values  _U_  .045 and  _R_  .075 satisfy ( _a_ ) and ( _b_ ), and imply stronger economies of distance for urban goods transportation than for rural produced varieties.<sup>15</sup> Our estimation of equations [10] and [11] also takes account of simultaneity and spatial autocorrelation. Regarding simultaneity, its presence is clear from the system of structural equations [3] – [7]. The feedback involving wages and RMA calls for an IV approach to avoid simultaneity bias as well as solving the measurement error problem discussed above.<sup>16</sup>

> 15 These values seem in line with those estimated or assumed in other empirical NEG studies on China which have used a similar functional form for transport costs.  Bosker _et al._ (2010), for example, estimate  = 0.63 in assuming the functional form _Tij_ = ( _dij_ )<sup></sup> , where _dij_ measures the distance between prefectural city _i_ and the capital of province _j_ in their study and _Tij_ the associated transport cost. It will be recalled that Bosker _et al._ estimate an aggregate wage equation and, therefore, do not distinguish between transport costs in the urban and rural sectors.

> 16 An IV-based approach also helps to deal with the issue of the endogenous placement of transport infrastructure.  To the extent that the NEN has specifically targeted the improved connectivity of lagging (and more rural) Western regions, such endogeneity can be expected to have biased downwards the estimated co-

19
Table 11: Durable goods (survey round 2)

|Dependent variable:|(1)<br>Indicator:<br>HH owns at least 1<br>of the 32 durable goods|(2)<br>Number of<br>durable goods<br>owned|(3)<br>PCA index<br>for<br>durable goods|
|---|---|---|---|
|PWP|-0.034|-0.256|-0.084|
||(0.022)|(0.209)|(0.191)|
|PWP * Top-up|0.009<br>(0.019)|-0.033<br>(0.120)|-0.095<br>(0.101)|
|R-squared|0.04|0.06|0.03|
|P-value for non-zero efect on top-ups|0.24|0.15|0.34|
|Upper bound (total efect)|0.017|0.107|0.190|
|Lower bound (total efect)|-0.067|-0.685|-0.548|
|Observations|2831|2831|2253|
|Mean of dep. var.|0.82|2.89|-0.03|
|in control group||||
|OLS estimates. Standard errors cluster<br>The PCA index for durable goods is on<br>Estimates include district and week-of-<br>_∗p <_0_._10_, ∗∗p <_0_._05_, ∗∗∗p <_0_._001|ed at the EA level.<br>ly defned for HHs that ow<br>interview fxed efects.|n at least one g|ood.|

Table 12: Agricultural inputs (survey round 2)

|Dependent variable:|(1)<br>Indicator: HH<br>used any<br>fertilizer|(2)<br>ln (kg<br>fertilizer, 1st<br>application)|(3)<br>ln (kg<br>fertilizer, 2nd<br>application)|(4)<br>ln (exp.<br>fertilizer, 1st<br>application)|(5)<br>ln (exp.<br>fertilizer, 2nd<br>application)|
|---|---|---|---|---|---|
|PWP|0.010|0.020|0.051|-0.084|0.009|
||(0.032)|(0.148)|(0.165)|(0.263)|(0.283)|
|PWP * Top-up|0.003<br>(0.018)|0.040<br>(0.083)|0.082<br>(0.106)|0.038<br>(0.152)|0.135<br>(0.178)|
|R-squared|0.24|0.24|0.11|0.23|0.11|
|Total efect on top-ups|0.013|0.060|0.133|-0.046|0.145|
|P-value for non-zero efect on top-ups|0.68|0.68|0.40|0.86|0.58|
|Upper bound (total efect)|0.074|0.343|0.438|0.453|0.660|
|Lower bound (total efect)|-0.048|-0.223|-0.173|-0.545|-0.371|
|Observations|2764|2764|2764|2764|2764|
|Mean of dep. var.|0.68|3.07|1.98|5.26|3.41|
|in control group||||||
|OLS estimates. Standard errors cluster<br>Estimates include district and week-of-<br>_∗p <_0_._10_, ∗∗p <_0_._05_, ∗∗∗p <_0_._001|ed at the EA leve<br>interview fxed ef|l.<br>ects.||||

44
FSALs. Similar to other figures for post-19<sup>9</sup> O FSALs, FCF/GDP is heavily influenced by two observations, Peru and Rwanda (9.4% and 9.3%, respectively). " After removing those, the FCF/GDP improvement drops to 0. 19%, which is simnilar to that for pre- 1990 FSALs. It is puzzling that the banking indicators perform relatively well for pre-90 FSALs relative to the other three subsets of interventions while FCF/GDP improvements are relatively comparable (and meager). One possible explanation is that, because FCF data was not available for as many countries as was M2 and DBPC data, the average improvement figures may be less reliable than the others. Alternatively, FCF/GDP may measure something quite distinct from financial sector development as measured by, say, DBPC/GDP. In particular, improvements in indicators that measure relatively liquid assets (DBPC) may precede those for indicators that measure illiquid assets (FCF).

With respect to reducing central bank credit to the private sector, pre- 1990 nonFSALs performed better than other interventions. That result is, to some extent, also driven by one observation (Chile, -8.4%). 16 Even after correction, however, the improvements are somewhat more pronounced for pre-90 non-FSALs than for the other subsets (-0.48%). **'7** However, like the FCF/GDP indicator, the CB/GDP improvement may not measure precisely what was desired. For most of the countries in the sample, central bank credit to the private sector was at or near zero at the time of intervention.

15 No other countries in the sample exceeded 5% improvement on this measure. 16 No other country in the sample had a CB/GDP figure higher than 1.0% at the time of the intervention.

17 Indeed, central bank law was a point of emphasis for non-FSALs prior to 1990 (see Appendix 3).

14
_Social Capital, Household Welfare and Poverty in Indonesia_ 3

# **AcknoNvledgment**

The author would like to thank Ormar Azfar, Gloria Davis, Deon Filmer, Scott Guggenheim, Jonathan Isham, Stephen Knack, Young Lee, Deepa Narayan, Lant Pritchett, Anand Swamy, Michael Woolcock, and Manfred Zeller for helpful comments on an earlier draft of this paper. Thanks are also due to the participants of seminars at Cornell University, the International Fooid Policy Research Institute (IFPRI), and the IRIS Center at the University of Maryland.

The Local Level Institutions (LLI) study was conducted under the leadership of Gloria Davis, Director, Social Developmrent Department. In the initial phase, task managers were Anthony Bebbington (July 1995-June 1996) and Christiaan Grootaert (July 1996-December 1997). The final phase of the study was undertaken as a joint venture between the ESSD and PREM Networks and co-managed by Christiaan Grootaert and Deepa Narayan. The study received financial support from the Government of Norway.

The Bolivia country-study was undertaken by a team from the consulting firm Sinergia. Coordinator was Godofredo Sand6val and the research team consisted of Julio Cordova, Beatriz Ascarrunz, Afredo Balboa, Griselda Gonzales, and Gloria Velasquez.

The Burkina Faso country-study was coordinated by Paula Donnelly-Roark. The field work was undertaken under the auspices of the Commission Nationale pour la Decentralisation.

The Indonesia country-study was coordinated by Scott Guggenheim and the research team consisted of Kamala Chandrakirana, Pieter Evers, Sjari Manaf and Silvia Werner.

The processing and management of the data files was the responsibility of GiTaik Oh and Kalpana Mehra. General research assistance was provided by Susan Assaf. Staff assistants for the study were Gracie Ochieng and Anju Sachdeva.
- 24 -

The CBK would annually issue a net-worth certificate equivalent to the deficit in provisions needed against the unsecured notes issued under the settlement program.

Second, the government would place funds with the CBK to supplement the 'deficit' banks' necessary provisions and allow them to distribute an appropriate level of profits. The CBK could use these funds and favorable treatment through other instruments under its control, such as the discount window and swaps, to support 'surplus' banks profitability as well.

While the CBK has not disclosed the financial position of each bank, five of the six commercial banks were deficit banks. The specialized banks were not significantly involved in giving unsecured personal loans or lines of credits. The investment companies' problems went beyond losses in loan portfolios, so their activities were assessed as part of the restructuring exercise. Only the banks were entitled to financial support; other financial institutions such as the Kuwait Finance House, were only entitled to the net worth certificates.

The irregular debts of the banking system by December 1987 amounted to KD 2.2 billion and involved 1,827 borrowers (about KD 200 million of this was lent at the request of the government to debtors to enable them to pay cash). Of these, 698 borrowers, with KD 405.5 million of debt were found to be solvent, and the remaining were insolvent or illiquid IQ/ . Ninety-two percent of the distressed borrowers had approached the banks for settlement under this program. Of the 663 settlements completed in 1987, the collatoral level averaged 30 percent. Assuming that this ratio will apply to all irregular loans, a minimum specific provisioning level of KD 1.3 billion will be required; that is, 116 percent of declared shareholders' equity. Court action was taken against 159 borrowers constituting 6 percent of irregular debt. The CBK placed KD 550 million in interest-subsidized funds with the banks beside offering them favorable discount rates and other benefits to supplement their provisioning abilities.

A March 1985 study iI/ of the banking systems 'own' funds, including inner reserves and provisions, estimated the total at KD 1.6 billion, with shareholders' equity KD 1 billion. Using these figures as a base, banks' 'own' funds reached an estimated KD 2.1 billion in 1987. The provisions required against irregular debt would amount to approximately KD 1.3 billion. While this would exceed the declared shareholders' equity of KD 1 billion, inner reserves were sufficient to provide the banks an estimated KD 783 million in unimpaired equity. The strong reserve position of the banking system effectively insulated the system's deposit base. The existence of unimpaired equity also explains the government's willingness to protect shareholders' equity and assist in building provisions.

Q/ Various press releases.

i;/ Proposal by the commercial banks to set up an "Asset Management Company" that would buy out nonperforming loans.
- 18 -

Even though classified as a concession, it may not necessarily involve significant investment. In Colombia, for instance, four major port operations were contracted out to private operators under the concession agreements. But little investment was involved in the initial concessions. Private sector involvement focusing on operational efficiency together with deregulation could increase the port performance remarkably; the number of working days per year increased from 280 days in 1993 to 365 days in 1996 (Table 5). The initial problem was a lack of competition among ports under the national ownership, rather than a shortage of port capacity (Kessides, 2004).

Table 5. Operating <u>performance of ports in Colombia</u>

||Before<br>1993|1996|
|---|---|---|
|Average vessel waiting time (days)|10|0|
|Working days per year|280|365|
|Average working hours per day|16|24|
|Tones per vessel per day|||
|Bulk cargo|500|>2,500|
|General cargo|750|1,700|
|Containers pervesselper hour|16|25|

Source: Kessides (2004).

Nonetheless, integrating multiple relevant public tasks into the same ownership is in general an optimal choice, as claimed in Propositions (7) and (8). In the above-mentioned case of Conakry, the water management contract achieved some performance improvements, such as metering and labor productivity. However, other important factors still indicated poor performance, because of the compromised ownership system. Although the majority of a joint venture is owned by a 10-year private lease contractor (51 percent), the government owns 49 percent and is deeply involved in operations. The asset is owned by a state-owned enterprise, which is responsible for large investments in treatment plants, reservoirs and pipes more than 160 mm in diameter. The joint operating company is responsible for only small investments (Ménard and Clarke, 2002). Apparently, the separated ownership seems to have weakened incentives for more aggressive investments in quality improvement and cost cutting. Worse, the sector may remain dominated by the public nature, violating Proposition (4). Unaccounted-for water remained high at 48 percent, compared with the international standards of 10 to 20 percent. Labor productivity jumped immediately
|_, X_)_−E_(_Y |T_ = 0_, X_)<br>(4)<br>(5)<br>Probit<br>IV-BP<br>0.072**<br>0.071*<br>(0.031)<br>(0.038)<br>0.036<br>0.011<br>(0.039)<br>(0.046)<br>0.072**<br>0.074*<br>(0.033)<br>(0.041)<br>0.141***<br>0.173***<br>(0.045)<br>(0.061)<br>0.141**<br>0.177**<br>(0.059)<br>(0.070)<br>0.085<br>0.088<br>(0.056)<br>(0.069)<br>0.045<br>0.032<br>(0.042)<br>(0.052)<br>Yes<br>Yes<br> The outcome variable_Y_ is an indicator<br>d zero otherwise. Columns (1) and (2)<br>lumns (3) and (4) report the difference<br>he difference between households who<br>trumenting program participation with<br>lustered at village level are reported in<br>of the respondent, household size, tribe<br>illage characteristics (size and share of|
|---|
|_E_(_Y |T_ = 1<br>(3)<br>Probit<br>0.049<br>(0.035)<br>0.032<br>(0.047)<br>0.048<br>(0.039)<br>0.082**<br>(0.041)<br>0.097*<br>(0.054)<br>0.059<br>(0.056)<br>0.031<br>(0.045)<br>No<br>selected sub-sample. <br> before the survey an<br>tions in brackets. Co<br>lumn (5) estimates t<br>se who didn’t by ins<br>). Standard errors c<br>and marriage status<br>zone dummies and v|
|se)<br>the<br>ight <br>evia<br>. Co<br> tho<br>l 18<br>age<br>sub-|
|Y = 1(Net U<br>(1)<br>(2)<br>Control<br>Treatment<br>0.380<br>0.429<br>[0.486]<br>[0.495]<br>0.497<br>0.529<br>[0.500]<br>[0.500]<br>0.362<br>0.411<br>[0.481]<br>[0.492]<br>0.270<br>0.352<br>[0.444]<br>[0.478]<br>0.403<br>0.500<br>[0.491]<br>[0.501]<br>0.241<br>0.300<br>[0.429]<br>[0.459]<br>0.441<br>0.471<br>[0.497]<br>[0.500]<br>p_<_0.1. One observation per individual in<br>ual reportedly slept under a bed net the n<br>o control and treatment group, standard d<br>roups using probit regression (model 17)<br>ying campaign in the last 5 months and <br>d using a 2-equation probit model (mode<br>nder and age of the individual, education,<br> access to water, dwelling characteristics,|
|Sub-sample<br>All members<br>Children under 5<br>Youth aged 5 to 20<br>Adult male workers<br>Adult female workers<br>Adult male unemployed<br>Adult female unemployed<br>Controls<br>Note: *** p_<_0.01, ** p_<_0.05, *<br>variable equal to 1 if the individ<br>report sample means restricted t<br>between treatment and control g<br>report to have received the spra<br>the treatment group indicator an<br>parentheses. Controls include ge<br>and religion, information about <br>women living in the village).|

24
The base case growth path is driven by the exogenous variables including population, labor force quality, capital quality growth, total factor productivity and saving rates. Our assumptions for the drivers are based on our reading of the historical record (Cao and Ho (2014) and are described in Section A.4 in the Model Appendix A. This base case is not designed to replicate precisely any particular projection; it is only intended to provide a point of comparison for the policy cases. The base case growth is summarized in Table 1 and Figure 2. Between 2010 and 2030 the population is projected to rise from 1,360 to 1,470 million, while the working age population falls from 938 to 883 million. With the assumed increase in average hours worked per person (including longer work lives) and labor quality, effective labor supply increase by 8% over these 20 years despite the fall in working population. Our model projects that GDP will grow with an average rate of 6.4% per year during 2015-20 and decelerates to 4.6% during 202030.<sup>8</sup> The 6.4% rate for the 2015-2020 period is very close to that assumed in 13<sup>th</sup> Five-year plan (6.5% for the 2016-2020 period). The consumption share of GDP rises from 35% in 2010 to 54% in 2030 due to falling trends in household saving rates. More precisely, growth decelerates due to lower TFP, lower savings rate, and falling working age population.

Primary energy use grows at 3.9% during 2015-20, implying a fall in energy intensity of 3.4% per year, close to the 13<sup>th</sup> FYP target of a 18% reduction in intensity. This is to be compared to the historical record given in Figure 2; the intensity index was quite volatile and averaged a decline of 4.1% per year during the 11<sup>th</sup> Five-year Plan (2006-10). During 2020-30 projected energy use decelerates more, but with the slowing GDP growth, the intensity falls at only 2.1% per year. There is a big change in the composition of energy sources; coal use only grows at 2.2% compared to oil at 4.3% and gas at 7.7% per year during 2015-20.

> 8 GDP projection is endogenous to the model which is calculated based on several variables which are exogenous. They include  population growth, share of working age population to total population, saving rates, dividend payout rates, government taxes and deficits, world prices for traded goods, current account deficits, rate of productivity growth, rate of improvement in capital and labor quality, The values of these variables are provided in Table A3 in Appendix A. Note that GDP projections vary from across the sources. For example, IMF (2014) projections were 6.9% for 2012-2020 and 5.3% for 2020-30. World Bank (2013) projections were 7.0% for 2016-2020 and 5.0% for 2026-30. Projections change across the sources and also overtime from the same source due to different assumptions.

‐8‐
offsetting some of the economic gains from migration in an overall welfare assessment, these results suggest that migration brings positive benefits beyond economic gains. However, there are at least two caveats to the results. First, the migrants studied here have lived in New Zealand as permanent residents for less than three years and it may be that mental health disorders develop over a longer period (Bhugra, 2004). Second, there appear to be low levels of mental health in Tonga, related especially to employment concerns (UNICEF, 2001),<sup>2</sup> while the migrants entered a New Zealand labor market that was unusually strong (the lowest unemployment rate in the OECD). Migrants in other settings may not encounter such large improvements in economic opportunities and consequently migration may have less a positive impact on mental health.

The only other paper that we are aware of that considers selection bias in the context of the mental health consequences of migration is Mark, Fu and Tran (2006). These authors compare three samples of Vietnamese: immigrants in the United States, non-migrants in Vietnam, and returnees in Vietnam who initially made it to a country of first asylum but were then repatriated because they could not prove a bona fide risk of persecution in Vietnam. This research strategy relies on two comparisons: the first is of returnees with non-migrants to estimate _selection effects_ (due to the unobserved characteristics that increase the propensity to migrate). The second comparison is of immigrants with returnees, which they expect will show _migration effects_ net of selection. However, the flaw with this research design is that forced repatriation is likely to have its own negative effect on mental health. Consequently the returnees are unlikely to be a valid control for what the mental health of the immigrants would have been had they never left Vietnam.

The next section summarizes the findings of existing literature on migration and mental health. Section 3 describes the survey and the MHI-5 measure of mental health used in this study. Section 4 provides a description of patterns of mental health within Tonga, while Section 5

> 2 According to a UNICEF study, one-third of school age youth in Tonga reported experiencing severe sadness or depression in the previous six months. Almost one half of the sample felt it was unlikely or impossible that they would find a job upon leaving school (UNICEF, 2001).

3
teacher ratio; they are more likely to have electricity (which is essential for the PSI-PMI program), a library, and a computer lab; and there is a higher ratio of students in program schools taking the WASSCE exam.

To construct a reliable control group, we used extensive administrative data from 2014,<sup>8</sup> in addition to detailed information on the selection process of program schools, to select a list of non-program schools that would have been ex-ante comparable to the program schools, through propensity score matching.

Although there were 135 senior secondary schools in The Gambia in 2014, only 71 of these schools comprise our sampling frame. This is because the other schools are religious schools or follow a nonstandard curriculum. As such, students in those schools do not take the grade 12 WASSCE exam, which is our main outcome variable. This means that the total number of senior secondary schools from which we could select our control schools was 55. Table 2 provides the average differences between the pilot schools and this universe of control schools.

After applying the propensity score to match schools based on the above pretreatment variables (using nearest neighbor matching), 16 control schools were selected. The descriptive statistics on these schools and how they compare with the pilot schools are presented in Table 3. The table shows that our matching produces a comparable control group at the school level. We also used the Kolmogorov-Smirnov test to check the equality of the distribution for each variable before and after matching. Figure 1 shows the effects of matching.

Although there is one set of 16 control schools, there are two different sets of control students as stated before. This is because even in the PSI-PMI pilot schools, some students were not exposed to the program due to budgetary constraints. As a result, there were within-school control students (unexposed students in pilot schools) and between-school control students (students in schools that were not part of the pilot program). The within-school control group

> 8 In spring 2014, all program schools had been selected and started PSI-PMI instruction, but there were no beneficiaries taking the WASSCE until 2015.

13