rafmacalaba/datause-encoder-data huggingface dataset:
provided in Jensen and Tarr (2010, Appendix E). Given the importance of foreign direct investment in services in our model, we mention here that Arnold, Mattoo and Javorcik (2007) show that in the Czech Republic, services sector liberalization led to increased productivity of downstream industries, and the key channel through which reform led to increased productivity was allowing foreign entry. Fernandes and Paunov (2008) found a positive and significant effect of foreign direct investment in services on productivity growth in Chile. Fernandes (2007) finds a positive and significant effect of services liberalization in both finance and infrastructure on the productivity of downstream manufacturing in the fifteen Eastern European countries.

In our model, the parameter that reflects the ability of a region to increase total factor productivity through the transmission of new technologies is the elasticity of varieties with respect to the price. Based on the literature summary in Jensen and Tarr (2010, Appendix E), we assign central values to this elasticity based on the region and the research and development intensity of the sector. The assigned central values for these parameters by sector and region are in table 6B. We conduct extensive sensitivity analysis on this parameter, both piecemeal and systematic.

# **V.  Data of the Model**

# **Social Accounting Matrix**

The key data source for our study is the social accounting matrix taken from Kiringai. Thurlow and Wanjala (2006). This is a social accounting matrix (SAM) for the year 2003. The table is very rich in agricultural detail, with 20 agricultural sectors. Given our focus on services, we found it necessary to disaggregate the single transportation sector into five sectors (based on value of output data of the various transportation sectors published in the _Economic Survey, 2006_ for Kenya by the Central Bureau of Statistics (2007a, p. 198)) and the single financial services sector into insurance, and banking and other financial services, from data in Central Bureau of Statistics (2007a, pp. 95-98. We assumed that the input output structure for all sectors using these services was identical for the disaggregated sectors. A full listing of the sectors and factors of production is provided in table 1. Kiringai et al. (2007) also provide a

> does not. On the other hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

21
_Africa’s Macroeconomic Story_

# **Figure 4: World Commodity Prices**

_Constant $2005 (index 2005=100)_ <!-- Start of picture text --> Real terms<br>Real terms<br>(2005=100)<br>(2005=100)<br>175<br>150<br>150<br>100<br>125<br>50 100<br>75<br>0<br>1985 1990 1995 2000 2005 2010<br>1970 1980 1990 2000 2010<br>Food Raw materials<br>Energy Metals and minerals<br><!-- End of picture text --> _Source_ : World Bank _Global Economic Monitor (GEM) Commodities_ database.

The growth acceleration has been more pronounced in oil exporting countries (Figure 5, left panel). Trend growth in non-oil resource-rich countries (countries highly dependent on mining exports) was very similar to that in non-resource abundant countries, until just a few years ago. The differential widened to 1.5 percentage points in 2012 and is expected to widen further over the next few years.

**Figure 5: Trend Real GDP per Capita Growth – Resource-Rich Countries, 1962-2012**

_Percent_ <!-- Start of picture text --> Population-Weighted<br>4<br>2<br>0<br>-2<br>-4<br>1962 1972 1982 1992 2002 2012<br>Oil Exporters Non-oil Resources Other<br><!-- End of picture text --> <!-- Start of picture text --> Median<br>4<br>2<br>0<br>-2<br>-4<br>1962 1972 1982 1992 2002 2012<br>Oil exporters Non-oil resources Other<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ January 2013.

13
# **Box 2.2:** Developing **a** Regional **Risk** Management Strategy

**Disaster Mitigation Facility for the Caribbean (DMFC).** In 2001, with support from the USAID Office of Foreign Disaster Assistance, the Canbbean Development Bank (CDB) established the Disaster Mitigation Facility for the Canbbean, marking an important step toward the promotion and coordmation of risk management within the region. The CDB assists borrowing member states, across a broad range of activities and sectors, including poverty reduction, infrastructure development and environmental management, placing **it** in a strong position to promote and coordinate activities **in** sectors that have not traditionally addressed directly hazard nsk management. The DMFC will focus.on the incorporation of hazard nsk management into development decision making within the internal operations of the CDB, its borrowing member countries and regional institutions. Activities of the DMFC include support for strengthened building standards and enforcement mechanisms and assistance to member countnes with the development of national-level nsk management policies and plans.

**Comprehensive Disaster Management Strategy (CDM).** Also in 2001, a working group, representing regional and national disaster management organizations, the private sector, regional technical institutions and multi- and bi-lateral donors and lending institutions developed a proposed _Strategy Results Framework for Comprehensive Disaster Management in the Caribbean._ Funding for the development of this strategy was provided by USAID and UNDP. This strategy was undertaken with the objective of integratng comprehensive disaster management into the development process within the region, providmg an important framework for strengthening and coordinating risk management efforts. The proposed CDM strategy emphasizes hazard risk reduction. Within this framework, the institutional capacity and role of the Canbbean Disaster Emergency Response Agency (CDERA) to promote CDM at the regional level will be strengthened. At the national level, consultations are being held to encourage governments to develop national strategies within the CDM framework and to identify champions for hazard nsk management at the ministerial level and within the private sector.

Source: World Bank, 2002(b).

8
household is subject to the experiment questionnaire treatment, the likelihood of reporting positive non-food consumption also increases. The household attributes that are underlining the statistically significant interaction effects are commonly associated with richer households, whose higher likelihood of reporting different answers to same questions in different questionnaires is likely partly "mechanical". Since these households also consume more, they also have more scope for answering differently. On the other hand, the experiment questionnaire treatment effect on the reporting of shocks does not seem to vary by the selected household attributes (column 4).

# 4. QUESTIONNAIRE DESIGN’S IMPACT ON POVERTY MEASURED BY PROXIES

Numerous methods to proxy poverty via proxies already exist. We focus on methods that rely on a consumption regression to deduct proxy weights (i.e., beta coefficients), as exemplified by where yi is log household consumption expenditures per capita (hereafter referred to as consumption), xij the vector of proxy variables, and βj the coefficients (weights) of interest. Examples of such methods include Elbers et al. (2003), Tarozzi (2007), and Mathiassen (2013).

To predict consumption, and in extension thereof poverty and inequality, we utilize the prediction methods developed in Elbers et al. (2003). This prediction method has the advantage of also producing standard errors of poverty and inequality estimates, and implementation is tractable with the PovMap software.<sup>22</sup> To ensure that the results are not model driven and to gauge the sensitivity of poverty and inequality predictions to differences in questionnaire design, we predict consumption with four different models of varying poverty proxy scope. In all cases, we estimate the model in the IHS3 data, using the IHS3 sub-sample interviewed during the months of April-October (i.e., the implementation period for the IHPS), and predict consumption using the IHPS data. To compute predicted poverty rates, we use the official IHS3 poverty line of 37,002 Malawi Kwacha per person per year.

One model that we experiment with is the original WMS prediction model, with updated coefficients from the IHS3 data. In the other three models, variables were selected by _stepwise_ in PovMap, which is a statistical method we relied on to avoid selection by researchers. Although the accuracy of the models is not our main interest in comparing predictions based on observationally equivalent samples that are subject to different survey treatment, the complete set of results from the prediction models is provided in the Appendix Tables A6 through A9. The list of possible poverty proxies included in each of the 4 prediction models are as follows:

> 22 PovMap software and documentation are freely available on http://iresearch.worldbank.org/PovMap.

11
**Table S4:** Program impacts on sexual behavior (beneficiaries: **extensive** margin)

# **<u>Panel A:  Baseline Dropouts</u>**

||=1|if Ever Ha|T Y<br>d Sex|Numbe|r of Sexual<br>(lifetime)|T Y<br>Partners|=1 if Sexu<br>P|ally Active<br>ast 12 Mon|T Y<br>During the<br>ths|
|---|---|---|---|---|---|---|---|---|---|
||During<br>Program|End of<br>Program|wo ears<br>After<br>Program|During<br>Program|End of<br>Program<br>|wo ears<br>After<br>Program<br>|During<br>Program|End of<br>Program|wo ears<br>After<br>Program|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|=1 if Conditional Schoolgirl|-0.036*|-0.034|-0.004|0.004|-0.118|-0.023|-0.123***|-0.094**|-0.046|
||(0.020)|(0.021)|(0.010)|(0.153)|(0.153)|(0.095)|(0.035)|(0.037)|(0.028)|
|Mean in Control Group|0.814|0.918|0.976|1.395|1.734|2.063|0.503|0.674|0.830|
|Sample Size|698|718|744|698|718|744|697|718|744|
|**Panel B: Baseline Schoolgirls**||||||||||
|=1 if Conditional Schoolgirl|-0.009|-0.003|0.005|-0.023|0.005|0.005|-0.009|0.001|-0.030|
||(0.017)|(0.024)|(0.035)|(0.040)|(0.048)|(0.061)|(0.023)|(0.029)|(0.035)|
|=1 if Unconditional Schoolgirl|-0.022|0.003|0.041|-0.044|-0.007|0.108|-0.021|-0.036|0.037|
||(0.021)|(0.030)|(0.036)|(0.049)|(0.036)|(0.066)|(0.030)|(0.032)|(0.044)|
|p-value UCT vs. CCT|0.581|0.864|0.414|0.699|0.815|0.142|0.728|0.327|0.177|
|p-value Treatment|0.551|0.984|0.519|0.627|0.969|0.218|0.768|0.514|0.395|
|Mean in Control Group|0.303|0.455|0.701|0.335|0.559|1.045|0.175|0.308|0.563|
|Sample Size|1,965|2,016|2,048|1,964|2,016|2,047|1,965|2,015|2,048|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. We correct for inconsistencies in 'ever had sex' across rounds. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
respondent with two questions simultaneously: the sensitive or embarrassing question, and a completely innocuous question. For example, question A might be, "Have you ever bribed a government official?" Question B might be, "Is your birthday in December?" The respondent then flips a coin (not revealing the result to the interviewer) and on seeing heads answers question A and on tails answers question B. The surveyor simply records the response. As a result, the respondent can give a completely truthful response knowing that the surveyor has no way of knowing which question she is answering.

Determining the percentage of a sample that has bribed a government official then becomes simple. Assume that 400 of 1200 respondents answered "yes" to the question. We would expect 50 percent of the respondents to answer the birthday question, and 1/12 of those to answer the question affirmatively. That is, 1200(0.5)/12 = 50 of the "yes" responses were to the birthday question. The expected number of people answering "yes" to the bribery question is therefore 400 - 50 = 350. Because 600 (i.e., 1200*0.5) people answered the bribery question we could conclude that 350/600 z 58.3 percent of the sample had bribed a government official. This method complicates hypothesis testing because of the measurement error it introduces, but World Bank surveys intent on measuring particular behavior in populations could potentially use this method to great effect.

# V. **CONCLUSIONS**

This review has attempted to examine existing World Bank firm surveys and the state-ofthe-art in questionnaire design. World Bank surveys have dramatically improved our understanding of firm behavior in developing countries. In addition, most of the data and measurement requirements we spell out in this paper are fulfilled in some surveys, such as the use of panel data, and detailed measurements about various themes. Needless to say, _not_ all Bank surveys should pay heed to the specific themes and hypotheses this paper mentions; we recognize that no survey can address all the concerns raised here given time constraints, _ad hoc_ purposes of specific surveys, and survey costs. We also recognize that our coverage in this survey of surveys is not comprehensive. Ultimately surveyors must decide how they wish to address the question at hand, and design questions accordingly. However, if a firm survey aims 35
for the three members of the FGT family discussed above. In the case of Guinea, we obtained the following results by area of residence.

The results presented in table 3.4 reveal that, poverty in Guinea (as measured by the FGT class of indices) is essentially a rural phenomenon. About 53 percent of the rural population lived in poverty in 1994. The next poorest area consists of the urban centers outside of the capital city, where poverty incidence is estimated at 24 percent. Income poverty is lowest in the capital city of Conakry where incidence stood at 7 percent.

## **_Sen 's_** _Measure of_ **_Poverty_**

The FGT family of poverty indices relies on the coefficient of variation in order to account for inequality among the poor. This dimension of poverty may be implemented by combining truncation with the weighing scheme expressed by (2.4). If **Gp** represents the Gini coefficient of the distribution of the living standard among the poor, according to Sen (1997:173), the following expression is an indicator of welfare among the poor:

## (3.17)

# _w,_ =,u,(l-Gp)

It represents the level of _per capita_ level of living, which, if enjoyed equally by every poor person, would provide the same level of social well-being among the poor as the current distribution.

Table 3.5. Guinea 1994: Inequality by Poverty Status

|Aversion|Sen|GiniforPoor|GiniforNon-PoorOverallGini|
|---|---|---|---|
|1.00|0.13|0.00|0.00<br>0.00|
|1.50|0.16|0.10|0.23<br>0.28|
|2.00|0.18|0.16|0.32<br>0.40|
|2.50|0.19|0.21|0.37<br>0.47|
|3.00|0.20|0.25|0.40<br>0.52|
|3.50|0.21|0.27|0.42<br>0.55|
|4.00|0.21|0.30|0.44<br>0.58|
|4.50|0.22|0.32|0.45<br>0.60|
|5.00|0.22|0.34|0.46<br>0.61|
|5.50|0.23|0.35|0.47<br>0.63|
|6.00|0.23|0.36|0.48<br>0.64|

Source: Computed Sen (1997:173) proposes a poverty index that is analogous to the poverty gap indicator of the FGT family. The only difference between the two is that Sen uses **wp** in lieu of **[tp.** Hence the expression: <!-- Start of picture text --> (3.18) 5 =P Pt12 wp 6<br><!-- End of picture text --> 26
**Table 1: Model overview—Intention to Treat (ITT) youth share as treatment variable—regression on firm profitability as dependent variable**

||Base model (FE)<br>(I)|2007 not a reform<br>year (FE)<br>(II)|Only calendar year<br>reporting (FE)<br>(III)|Reduced period<br>(FE)<br>(IV)|Base model (RE)<br>(V)|2007 not a reform<br>year (RE)<br>(VI)|Only calendar year<br>reporting (RE)<br>(VII)|Reduced period<br>(RE)<br>(VIII)|
|---|---|---|---|---|---|---|---|---|
|**Treatment (ITT)**|-0.00307|0.00619|0.00181|-0.0119|-0.000363|0.00702|0.0115|-0.00813|
||(-0.28)|(0.59)|(0.13)|(-1.11)|(-0.03)|(0.69)|(0.83)|(-0.81)|
|Reform Period|-0.0318***|-0.0328***|-0.0450***|-0.00833|-0.0318***|-0.0326***|-0.0472***|0.0464***|
||(-3.40)|(-3.44)|(-3.02)|(-1.36)|(-3.37)|(-3.41)|(-3.17)|(5.67)|
|Pre-reform youth share|N/A|N/A|N/A|N/A|0.0439***|0.0398***|0.0562***|-0.00894|
||(.)|(.)|(.)|(.)|(5.23)|(5.63)|(5.46)|(-1.49)|
|Pre-reform firm<br>education level|N/A|N/A|N/A|N/A|-0.0570***|-0.0570***|-0.0858***|-0.0511***|
||(.)|(.)|(.)|(.)|(-6.15)|(-6.15)|(-6.39)|(-7.20)|
|Pre-reform firm size|N/A|N/A|N/A|N/A|-0.0000192**|-0.0000192**|-0.0000176**|-0.0000202**|
||(.)|(.)|(.)|(.)|(-2.20)|(-2.20)|(-1.98)|(-2.31)|
|Constant|-0.0183|-0.0184|-0.000932|0.0978|0.198***|0.199***|0.275***|0.181***|
||(-0.23)|(-0.23)|(-0.01)|(0.67)|(9.90)|(9.89)|(9.70)|(11.28)|
|Year Dum.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.0001|0.0001|0.0001|0.0001|0.0007|0.0007|0.0013|0.0009|
|Observations|1 363 022|1 363 022|840 610|1 099 578|1 363 022|1 363 022|840 610|1 099 578|
|Hausman test statistic|0.0000|0.0000|0.0001|0.0000|||||

**t-statistics are shown in parentheses, treatment variable bolded**

- **p <0.10 ** p < 0.05 *** p < 0.01**
Table 2: Migration Network: Dependent Variable - Number of Migrants in 2011 (1,000)

||All VDCs|in Nepal|NLSS|VDCs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Share of migrants 2001|3.758***|3.779***|6.606***|6.714***|
||(0.101)|(0.101)|(0.676)|(0.678)|
|Constant|0.013***|0.001|0.037***|-0.005***|
||(0.001)|(0.001)|(0.006)|(0.002)|
|District Fixed Eect||Yes||Yes|
|Observations|100880|100880|11648|11648|
|_R_<sup>2</sup>|0.145|0.161|0.060|0.097|

Data Source: 2001 and 2011 Nepal Census. Standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 05 , _∗∗ ∗∗∗ p <_ 0 _._ 01 , _p <_ 0 _._ 001 . Standard errors are clustered at VDC level. Estimated model is _Mj,d,_ 11 = _β_ 0 + _β_ 1 _Yj,d,_ 01 , where _Mj,d,_ 11 is number of migrants, in 1,000, to destination country _d_ from a VDC _j_ in 2011. _Yj,d,_ 01 is share of migrants to destination _d_ from a VDC _j_ in 2001. Share of migrants is calculated as _Yi,d,t_ = _P OPMj,dj,t,t_<sup>, where</sup><sup>_Mj,d,t_</sup> is number of migrants from VDC _j_ to destination _d_ in year _t_ . _POPj,t_ is the population of VDC _j_ in year _t_ .

28
DESTA also provides an extensive database of PTAs signed between 1945 and 2015. Building on the list held by the World Trade Organization (WTO) and World Trade Institute (WTI), it combines agreements from a large number of other sources such as web pages of foreign ministries or governmental institutions. Besides its large coverage, the dataset also measures the depth of each agreement as an additive index that combines seven key provisions. For the purpose of this paper, I use the dummy variable (SERV) indicating whether the trade agreement includes a substantive provision on services. Furthermore, to examine the heterogeneous effect of services agreements by contents, I use the more detailed data on different types of provisions found in services trade agreements.<sup>33</sup> In particular, the database codes whether the services agreement contains an MFN clause, national treatment clause, the right of non-establishment, and movement of natural persons, and so on.<sup>34</sup> Using these data sources, I construct the main variables of which the summary statistics are provided in Table 1 over time, and in Table 2 across — income groups. The three indicators of GVC participation gross ex— ports in intermediate goods, backward-, and forward-linkage measures are expressed as shares of total gross bilateral exports, and the two trade agreement variables are dummy variables taking value one if the country pair has an FTA or stronger ( _RTA_ ), or if the country pair has a services agreement ( _SERV_ ). Provisions in the services trade agreement are also expressed as dummy variables equal to one if the services agreement includes a specific provision: _MFN_ , _NT_ , _NonEst_ (right of non-establishment), and _Move_ (movement of natural persons).

Table 1 shows that the average GVC participation rate has been steadily increasing since 1995. At the same time, regional trade agreements in goods and in services also saw a rapid increase. In 2011, 46 percent of the country pairs in the dataset have an RTA covering goods, and 42 percent have an RTA for services as well. The provisions data show that a smaller share of services agreements includes an MFN clause, while provisions on NT, non-establishment, and movement of persons are more frequently found.<sup>35</sup> The comparison between different income pairs also shows interesting trends. The share of intermediate goods in total gross manufacturing trade is highest between developed countries. For the backward and forward GVC-measures, developing countries tend to have higher backward linkages

> 33The World Bank’s Content of Deep Trade Agreements database (Hofmann et al., 2017) also codes RTAs that include specific provisions (including services). However, DESTA’s information on the detailed provisions included in the services agreement makes it more suitable for the analysis in this paper.

> 34See Appendix A.2 for more details.

> 35The provisions are not mutually exclusive and different combinations of these provisions are included in services trade agreements. See Table A.1 for the pattern of complementarity between provisions.

17
Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1801 Regional Integration as Diplomacy|Maurice Schiff<br>L. Alan Winters|August 1997|J. Ngaine<br>37947|
|WPS1802 Are There Synergies Between<br>World Bank Partial Credit<br>Guarantees and Private Lending?|Harry Huizinga|August 1997|P. Sintim-Abcagye<br>38526|
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|WPS1804 Financial Sector Adjustment Lending:<br>A Mid-Course Analysis|Robert J. Cuil|August 1997|P. Sintim-Aboagye<br>37644|
|WPS1805 Regional Economic Integration and<br>Agricultural Trade|Junichi Goto|August 1997|G. Ilogon<br>33732|
|WPS1806 An International Statistical Survey<br>of Government Employment and<br>Wages|Salvatore Schiavo-Campo<br>Giulio de Tommaso<br>Amitabah Mukherjee|August 1997|M. Guevara<br>32959|
|WPS1807The Ghosts of Financing Gap:<br>How the Harrod-Domar Growth<br>Model Still Haunts Development<br>Economics|William Easterly|August 1997|K. Labrie<br>31001|
|WPS1 808 Economic Transition and the<br>Distributions of Income and Wealth|Francisco H. G. Ferreira|August 1997|M. Geller<br>31393|
|WPS1 809 Institutions in Transition: Reliability<br>of Rules and Economic Performance<br>in Former Socialist Countries|Aymo Brunetti<br>Gregory Kisunko<br>Beatrice Weder|August 1997|M. Geller<br>31393|
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|WPS1812 Managing Guarantee programs in<br>Support of infrastructure Investment|Michael Klein|August 1997|S. Vivas<br>82809|
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|WPS1814 Making Education in China Equitable<br>and Efficient|Sha.kh i. Hossain|August 1997|C. Anbiah<br>81275|