rafmacalaba/datause-encoder-data huggingface dataset:
# 3. Methodology

# 3.1. Developing the Proxy Means Test models

PMT predicts consumption using verifiable and measurable proxies (variables) closely correlated with consumption. Using data from the national household surveys, it identifies the best predictor of the welfare variables from a set of independent variables such as location, housing quality, household characteristics, and ownership of durable goods and assigns weights to each proxy. Although the two main targeting models examined in this paper - the MOLSA-PMT-model and the CLCI-PMT model are similar in adopting the PMT design, the eligibility criteria, variables, and scoring procedure differ.

The MOLSA PMT model was developed from the 2017-2018 Rapid Welfare Monitoring Survey, also known as the Survey of Well-being via Instant and Frequent Tracking (SWIFT) (Sharma & Poi, 2019).<sup>7</sup> The CLCI, however, has two PMT models, a legacy Vulnerable Assessment model (Legacy -VA) and the updated MPCA model (SEVAT). The Legacy-VA differed considerably from the MOLSA-PMT. It estimates the income-to-earnings ratio while the latter estimates household per capita consumption. The Legacy-VA uses a stepwise binary regression model to select the proxies and adopts a single model for all regions. The MOLSA-PMT, in contrast, uses continuous per capita consumption as the outcome variable in a postLasso procedure and has separate models for each region. The SEVAT, which replaced the Legacy-VA and uses the 2018 Multi-Cluster Needs Assessment (MCNA) survey, is more similar to the MOLSA-PMT. Both have regional models and estimate household per capita consumption per month as a dependent variable.

The variables included in the three targeting models – MoLSA, Legacy-VA, and SEVAT equally differ (Figure 1). The MOLSA-PMT variables are mostly geographic location, durable assets, housing, and other socioeconomic variables that are closely correlated to monetary poverty. Conversely, the variables included in the two CLCI-PMTs (Legacy-VA and SEVAT) are mostly coping strategies and employment, which are more likely to predict vulnerability to poverty. It should be noted that vulnerability to poverty accounts for poor households and households living on the edge of poverty (Dutta et al., 2011). Hence, it is expected that the shares of people identified by a vulnerable-to-poverty indicator could be larger than the share of households who are monetary poor (Pritchett et al., 2000). The implications for the analysis are: 1) that we may not find 100 percent cross-eligibility when matching the CLCI-PMTs with the MOLSA-

> 7 See Sharma and Wai-Poi (2019) for details.

9
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**Annex 3 – Mincerian earnings function estimated with the Heckman method**

||**Outcome**<br>**equation**|**Selection**<br>**equation**|
|---|---|---|
||Dependent:<br>Log<br>observed<br>market<br>wage|Dependent:<br>probability<br>of<br>employment|
|**Female**|-0.106***|-0.114***|
||(0.006)|(0.017)|
|**Age**|-0.000*|0.019***|
||0.000|(0.001)|
|**Secondary education**|0.162***|0.327***|
||(0.009)|(0.025)|
|**Tertiary education**|0.537***|0.550***|
||(0.012)|(0.033)|
|**Humanities and arts**|0.032**|-0.057|
||(0.013)|(0.041)|
|**Social sciences**|0.057***|-0.006|
||(0.009)|(0.028)|
|**STEM**|0.021***|0.123***|
||(0.008)|(0.023)|
|**Agriculture**|-0.060***|0.102***|
||(0.012)|(0.039)|
|**Health care**|0.107***|0.180***|
||(0.012)|(0.039)|
|**Married**||0.355***|
|||(0.021)|
|**Number of children**||0.016*|
|||(0.009)|
|**Constant**|4.356***|-0.733***|
||(0.018)|(0.038)|
|**Observations**|40,095|40,095|

_Source: Authors’ calculations. Note: *,** and *** denote statistical significance at the 10, 5 and 1% level, respectively. Standard errors robust to heteroskedasticity. Population weights_ _<u>accordingly used.</u>_ 27
developing countries in agriculture account for only 3 percent of world rice consumption. For wheat, where these countries account for 27 percent of world consumption, the benefit is likely somewhat greater. However, it is clear from these trade shares that the key trade policy influence on the stability of world markets is what happens in developing countries.

Within the group of developing countries, there are also very substantial differences in the extent of price insulation. As shown in Figure 4, it appears that domestic price rises for wheat in the 2006-8 period were restrained much more in Asia than in other world regions. This suggests that—in contrast with the case considered in Figures 1 and 2—price insulating policies may not have been completely ineffective in stabilizing prices in all regions. Rather, their effect may have been to reduce the volatility of domestic prices in some regions, while increasing this volatility in others.

Are rice and wheat representative of other farm products in terms of insulating behavior by governments? There is no global database for all farm products for the most recent spike period, but there is for the upward spike of 1974-6 and the slump of 1984-8. Anderson and Nelgen (2010a) decompose the nominal rate of assistance (NRA) estimates, for the overall agricultural sector of all 75 countries in the Anderson and Valenzuela (2008) database, into the various border and domestic measures for developing and high-income countries. The annual estimates summarized for the upward spike period of 1972-76, and the downward spike period of 1984-8, are reported in Table 2.

In both of these periods, export restrictions were the dominant instrument for developing countries; they became more and then less important in the upward spike period of 1972-76, and conversely in the downward spike period of 1984-88. In highincome countries there were virtually no taxes or other restrictions on exports, but export subsidies followed the same path as import tariffs over those spike periods: U- shaped during the upward spike, inverted U-shaped in the downward spike. Together these estimates suggest the experiences with rice and wheat were not inconsistent with the pattern for farm products in general, especially when bearing in mind that the NRA estimates in Table 2 include numerous nontradable products whose NRAs tend 11
# **2. On Wedding Celebrations6:**

Wedding celebrations in rural India can seem extremely lavish to an outsider, especially in contrast to the extreme poverty of rural Indian life, with large numbers of people invited for feasts and ceremonies that can go on for several days. Many weddings seem less influenced by norms in the village than by pattems in cities, and celebrations of poor families imitate the more extravagant patterns common in richer families. Take the case of a wedding of a small agriculturist with two acres of land that I observed: His teenage daughter was marrying a young man who had finished his BA and had ajob as a low- level clerk in the city. The groom was dressed in a crisp gray suit, the bride in a silk saree. A large number of guests, including a (minor) local politician had been invited, and the newlyweds were driven away at the end of the ceremony in a large white Ambassador car. When asked why he had spent so much money on a wedding that was obviously well beyond his means, the father said that his daughter had married into a "good family" (the groom's father was a relatively wealthy landowner from another village), and he wanted to have a "show." Thus, wedding celebrations have a lot to do with social status and prestige. Mobility within a village is often achieved by imitating the behaviors of families of higher social orders (Srinivas, 1989). For the parents of a daughter in particular, a marriage is potentially the most important source of mobility since marrying into a "good family" can greatly enhance how a family is viewed by its peers, and a prestigious match is an occasion for great celebration and status displays. This may explain why some weddings are particularly lavish. When a family marries into a rich family it is in their interest to demonstrate this to the rest of the village, particularly if the rest of the village does not know the new in-laws. The most effective way of signaling a family's newfound affinity-derived status is to have as lavish a wedding as they can possibly afford. On the flip side, if a family marries a poor local family - well known to everyone in the village - this may also be an occasion for celebration, but lavish displays are no longer necessary since not much can be gained by signaling.

To better understand the nature of marriage expenditures in India, it might help to outline the basic nature of Indian marriage "markets"':

> 6This section is culled from Bloch, Rao and Desai (1999).

> 7 Some of these features have become less restrictive in educated urban circles, but they continue to be a defining aspect of village life.

6
# **The Challenge of Reducing International Trade and Migration Barriers**

**Kym Anderson (Lead Author), L. Alan Winters (co-author)**

<u>kym.anderson@adelaide.edu.au l.a.winters@sussex.ac.uk</u> Draft paper for the Copenhagen Consensus 2008 (CC08) Project. This draws on Anderson’s earlier CC04 and CC06 papers which appear in the CUP books edited by B. Lomborg ( _Global Crises, Global Solutions,_ 2004 and _Solutions to the World’s Biggest Problems: Costs and Benefits,_ 2007). Helpful discussions with Will Martin, and computational assistance for Table 11 and the Figures from Ernesto Valenzuela and Johanna Croser, are gratefully acknowledged. The views expressed are the authors’ alone.

**Contact:** Kym Anderson, School of Economics, University of Adelaide, Adelaide, SA 5005, Australia, phone +61 8 8303 4712, fax +61 8 8223 1460 kym.anderson@adelaide.edu.au

**Key words:** trade policy reform, Doha Development Agenda, international migration

**JEL codes:** F02, F13, F22, F15, F17

**Contact author:**

## **Second author:**

Kym Anderson L Alan Winters School of Economics School of Social Sciences University of Adelaide University of Sussex Adelaide, SA 5005, Australia Brighton BN1 9QN Phone +61 8 8303 4712 United Kingdom Fax +61 8 8223 1460 L.A.Winters@sussex.ac.uk kym.anderson@adelaide.edu.au
**Table 8: Regression results with first-time issuer dummy variable** Dependent <u>variable: Log of spread-to-benchmark at launch</u>

||(1)<br>|(2)<br>|
|---|---|---|
||Pre-crisis|Post-crisis|
|IG dummy|-0.329***|-0.074|
||(-3.65)|(-0.53)|
|S&P Rating|0.135***|0.084***|
||(6.03)|(3.28)|
|Log(size)|0.057|0.052|
||(1.20)|(1.07)|
|Years to Maturity|0.012***|-0.003|
||(3.88)|(-1.00)|
|U.K. Law|0.025|0.214***|
||(0.26)|(2.86)|
|VIX|0.013**|0.025***|
||(2.17)|(4.92)|
|GDP growth (_t-1_)|-0.009|-0.014|
||(-1.26)|(-1.22)|
|First-time issuer dummy|0.051|0.207*|
||(0.60)|(1.89)|
|EAP|0.178*|-0.081|
||(1.79)|(-0.46)|
|ECA|0.187*|0.239|
||(1.72)|(1.16)|
|LAC|0.351***|-0.024|
||(4.58)|(-0.15)|
|SAS|0.368**|0.399***|
||(2.53)|(2.88)|
|SSA|0.488***|0.311*|
||(4.01)|(1.94)|
|Observations|328|162|
|R-squared|0.654|0.670|

Robust t-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

Finally, we tested the effect of SEC registration on spread differences using a dummy variable. The results in Table 9 offer a possible answer to the puzzle. In the pre-crisis period, neither jurisdiction nor SEC registration affect spreads. After the crisis it is really SEC registration that drives the spread difference. The U.K. law effect is not significant while SEC registration drives down spreads.

17
**Table 1: Bus Use in Response to Prices**<sup>**13**</sup>

||Adult use|d bus|Total Inbo|und Rides|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|MK 10|-0.06|-0.05|-0.28|-0.26|
||(0.06)|(0.05)|(0.24)|(0.22)|
|MK 20|-0.09|-0.08|-0.34|-0.33|
||(0.06)|(0.06)|(0.25)|(0.23)|
|MK 50|-0.18***|-0.15**|-0.63**|-0.56**|
||(0.06)|(0.06)|(0.25)|(0.23)|
|MK 100|-0.14**|-0.15**|-0.74***|-0.77***|
||(0.06)|(0.06)|(0.21)|(0.20)|
|MK 300|-0.41***|-0.43***|-1.20***|-1.26***|
||(0.05)|(0.05)|(0.19)|(0.20)|
|MK 500|-0.47***|-0.49***|-1.32***|-1.35***|
||(0.04)|(0.04)|(0.18)|(0.18)|
|Male||0.02||0.41***|
|||(0.04)||(0.14)|
|Head of household||0.14***||0.46***|
|||(0.04)||(0.17)|
|Married||0.09**||0.22*|
|||(0.04)||(0.13)|
|Adults in the household||-0.01||-0.08|
|||(0.02)||(0.06)|
|Children in the|||||
|household||-0.00||0.04|
|||(0.01)||(0.04)|
|Constant|0.47***|0.41***|1.32***|1.55***|
||(0.04)|(0.11)|(0.18)|(0.40)|
|Mean of dep. variable|0.339|0.340|0.844|0.847|
|Number of observations|936|933|936|933|
|R2|0.094|0.155|0.053|0.124|

note: This table presents OLS regressions on bus use for adults (18 years or older). Heteroskedasticity-robust household-clustered standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 13 Results are based on the individuals that participated in Phase 2.

9
# **Diagonal Cumulation and Sourcing Decisions**<sup>_∗_</sup>

Pamela Bombarda<sup>_†_</sup> and Elisa Gamberoni<sup>_‡_</sup>

## _JEL classification_ : F12, F13, F14, F15.

_Keywords_ : Intermediate Trade, rules of origin, rules of cumulation, PECS, input-output ta-bles.

> _∗_ Acknowledgments: We would like to thank Carlo Altomonte, and Paola Conconi for their insightful comments and discussions on this topic. Remarks from participants at the 2018 ETSG in Warsaw contributed to improving this paper. For their suggestions and comments, we would also like to thank Arup Banerji, Donato De Rosa, Vivek Suri, and Gallina A. Vincelette. We are also indebted to Davin Chor for kindly sharing with us the crosswalk IO1997 to IO2001. All errors are ours. Bombarda thanks support from the Labex MMEDII program (ANR-11-LBX-0023-01). The views, findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors or the countries they represent.

> _†_ Universit ́e de Cergy-Pontoise, THEMA, pamela.bombarda@u-cergy.fr.

> _‡_ World Bank.
# _Conditional direct operating subsidies_

An operating subsidy with some type of conditionality is the case of buses in Buenos Aires, Argentina. This experience is described in Box 4. The conditionality in this case is due to the fact that subsidy levels are partly dependent on the number of passengers transported and the kilometers supplied. Thus, it provides some incentives for performance improvements.

## **Box 4: Direct transfers to bus operators in Buenos Aires**

In Argentina, since the 2002 economic crisis, bus operators receive a direct transfer from the government to keep fares low. This subsidy is based on the number of passengers transported, the firm’s gross revenues and kilometers supplied. It is funded from a specific tax on diesel fuel (bus operators do not pay the tax on diesel oil, which in practice implies an additional indirect subsidy). It is interesting to note that the uncertainty regarding the continuity of the subsidy (it has been changed 26 times during the first three years it has been applied), affects investment incentives. (Krantzer, 2005, Bondorevsky, 2007).

Foster (2005) notes that given the excess capacity in the sector, a better choice might have been a demand side subsidy in this case. This would have allowed excess capacity to be eliminated in those routes were there is no demand for the service, increasing allocative efficiency. However, as noted by Krantzer (2005) the 2001/2002 crisis was so dramatic that given the available time frame the only reasonable choice to keep the public transport system afloat was to introduce this supply side subsidy. Furthermore, a demand subsidy would have been ineffective since only 2 million of the 6 million poor in the Buenos Aires Metropolitan Region would have been targeted had social security programs (such as Plan Jefas y Jefes de Hogar) been used as a targeting mechanism. The errors of exclusion would have been very high. However, Bondorevsky (2007) shows that the majority of subsidy resources go to households in the middle of the income distribution and are thus regressive. Moreover, a comparison between 2002 and 2006 indicates that the incidence of subsidies became more regressive, as the economic recovery process has led to a relatively more intense use of buses by households in the middle of the income distribution.

_Source:_ Foster (2005), Krantzer (2005) and Bondorevsky (2007).

# _Fuel tax rebate_

In the UK operators receive a rebate on tax paid for fuel by public transport vehicles. This subsidy is usually referred to as the Fuel Duty Rebate (FDR) but is now formally called the Bus Service Operators Grant (BSOG). This scheme refunds about 80% of taxes paid on fuel. One problem with the scheme is that subsidies are not conditioned on specific performance targets. In addition, as noted by Carruthers, Dick and Saurkar (2005), these subsidies bear no relation to income or poverty. In the DfT (2002) consultation, options were presented to modify the FDR in order to target it better to meet social, economic and environmental objectives.

19
Policy Research Working Paper 5322

# **Abstract**

This paper uses a dataset of more than 70,000 firms in over 100 countries to systematically study the use of different financing sources for new and young firms, in comparison to mature firms. The authors find that in all countries younger firms rely less on bank financing and more on informal financing. However, they also find that younger firms use more bank finance in countries with stronger rule of law and better credit information, and that the reliance of young firms on informal finance decreases with the availability of credit information. Overall, the results suggest that improvements to the legal environment and availability of credit information are disproportionately beneficial for promoting access to formal finance by young firms.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to study entrepreneurial and SME finance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lklapper@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table T3.CR:  Father's Education and Educational Expenditure on Children**

|||**RURAL**<br>**CFPS**<br>|**CHINA**<br>**2010**<br>||
|---|---|---|---|---|
||**6-22 Yea**|**r Children**|**8-23 Yea**|**r Children**|
|Father's Sch.|105.5***|79.6***|115.7***|85.6***|
||(12.8)|(12.1)|(15.0)|(14.0)|
|**Father's Sch. * Daughter**|**-31.8***|**-31.2***|**-21.7**|**-21.7**|
|**Dummy**|**(17.3)**|**(16.6)**|**(20.9)**|**(20.0)**|
|**Daughter Dummy**|**151.6**|**240.3****|**111.2**|**275.2****|
||(103.1)|(100.6)|(120.7)|(117.1)|
|Intercept|648.0***|1939.7***|705.7***|2173.6***|
||(71.0)|(117.9)|(83.3)|(132.7)|
|No. of Children||-716.8***||-838.0***|
|||(49.7)||(56.8)|
|No. Observations|4326|4326|3632|3632|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
Table 2: Effects of forecast errors and forecast growth on firm performance

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log sales|log exports|log (1+ inv.<br>fixed tangible<br>assets)|log (1+ inv.<br>intangible<br>assets)|log<br>employment|log value<br>added|log value<br>added per<br>worker|<br>log avg.<br>worker pay|
|Weighted forecast error|0.0701***|0.1189***|0.1224*|0.1224*|0.0226***|0.0574***|0.0329***|0.0130***|
||(0.0098)|(0.0188)|(0.0669)|(0.0645)|(0.0053)|(0.0097)|(0.0088)|(0.0045)|
|Weighted forecast growth|0.0320***|0.0785***|0.0500|0.0585|0.0092***|0.0248***|0.0138**|0.0048*|
||(0.0054)|(0.0123)|(0.0454)|(0.0454)|(0.0030)|(0.0058)|(0.0056)|(0.0026)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|22199|
|N (firms)|9306|9306|9306|9306|9306|9306|9306|9306|
|Adj. R<sup>2</sup>|0.0719|0.0707|0.0157|0.0131|0.0535|0.0345|0.0142|0.0306|
|RSS|1580|22750|129170|291606|858|2582|2378|465|

Notes: In each column, the dependent variable is the change between the average of each 3-year period. Standard errors are clustered at the firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
<!-- Start of picture text --> 70<br>61.5 58.5 59.8 61.3<br>60<br>.<br>49.9 52.6<br>50<br>40<br>30<br>20<br>0)<br>2008 2009 2010 2011 2012 2013<br>Mining sector (%) TM Other sectors (%)<br><!-- End of picture text -->
21 Table 3: Global market shares and net exports of cotton, and effects of GM cotton adoption as of 2001 on cotton output and exports, 2001

||_Share (%_<br>_of globa_|_by value)_<br>_l cotton:_|_Net expor_|_ts_<sup>_a_</sup>_($b) of:_|%<br>GM c|change fro<br>otton adopti|m<br>on in:|
|---|---|---|---|---|---|---|---|
|**_Adopters as of 2001:_**|_output_|_exports_|_cotton_|_textiles_<br>_and_<br>_clothing_|cotton<br>output<br>volume|value of<br>cotton<br>exports|value<br>added in<br>cotton<br>prod’n|
|<br>United States|_18_|_27_|_2.2_|_-60.7_|4.8|4.4|-0.1|
|China|_17_|_1_|_-0.1_|_41.9_|0.4|-4.3|-1.6|
|Australia|_3_|_13_|_1.1_|_-2.6_|7.2|4.3|2.1|
|South Africa|_0.1_|_0.3_|_-0.0_|_-0.2_|8.1|4.3|3.5|
|**_Non-adopters as of 2001:_**||||||||
|<br>Other high-income countries|_5_|_13_|_-1.7_|_-28.4_|-3.5|-5.7|-3.2|
|<br>Eastern Europe and Central Asia|_16_|_18_|_0.2_|_7.4_|-1.0|-4.5|-0.8|
|<br>Southeast Asia|_1_|_1_|_-1.5_|_18.4_|-2.3|-8.4|-1.4|
|South Asia|_21_|_3_|_-1.0_|_24.5_|-1.0|-8.8|-0.6|
|Middle East and North Africa|_8_|_7_|_0.3_|_-3.3_|-1.6|-7.8|-1.5|
|Sub-Saharan Africa (excl S. Africa)|_5_|_13_|_1.1_|_-1.5_|-4.6|-7.5|-4.4|
|<br>Latin America and Carib.|_6_|_4_|_-0.5_|_4.9_|-2.5|-8.9|-2.1|
|**World**|**_100_**|**_100_**|**_0.0_**|**_0.0_**|**0.2 **|**-1.1**|**-1.0**|

> a Exports minus imports, both valued at f.o.b. prices as in the GTAP database 6.05

Source: Authors’ GTAP model simulation results and (for columns 1 to 4) the GTAP database