rafmacalaba/datause-encoder-data huggingface dataset:
# **OA2. Robustness Check: Linear vs. Quadratic CEF Model**

**Table A.4: Full Sample Estimates with Linear CEF and Quadratic CEF**

||**CHI**|**NA **|**IND **|**IA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
|Mobilit CEF|||||||
|y<br>Specification|Linear|Quadratic|Linear|Quadratic|Linear|Quadratic|
|OutcomeVar|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|
|Father's Edu|-0.483***<br>(0.036)|-0.484***<br>(0.037)|-0.379***<br>(0.028)|-0.379***<br>(0.028)|-0.514***<br>(0.052)|-0.508***<br>(0.052)|
|Constant|18.823***<br>(0.424)|18.821***<br>(0.431)|20.134***<br>(0.144)|20.099***<br>(0.146)|16.784***<br>(0.429)|16.728***<br>(0.428)|
|Observations|94,159|94,159|86,748|86,748|18356|18356|
|R-squared|0.009|0.009|0.006|0.005|0.011|0.011|
|Mean ofY|16.83|16.83|18.76|18.72|13.587|13.569|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia.

40
- [17] Moravcik, I., 1965, ‘The Priority of Heavy Industry as an Objective of Soviet Economic Policy’. Soviet Studies, 17, (2), October, 245–51.

- [18] Rattsø, J. and R. Torvik, 2003, "Interactions between Agriculture and Industry: Theoretical Analysis of the Consequences of Discriminating Agriculture in Sub-Saharan Africa", Review of Development Economics, 7(1), 138-51

- [19] Sah, R. K. and Stiglitz, J. E., 1984, ‘The Economics of Price Scissors’. American Economic Review, 74, (1), March, 125–38.

- [20] Sah, R. K. and Stiglitz, J. E., 1987, ‘Price Scissors and the Structure of the Economy’, Quarterly Journal of Economics, 102, (1), February, 109–34.

- [21] Shleifer, A. and Vishny, R. W.,1994, ‘Politicians and Firms’. Quarterly Journal of Economics, 109, (4), November, 995–1025.

- [22] Shleifer, A. and Vishny, R. W.,1998, The Grabbing Hand: Government Pathologies and Their Cures, Cambridge, Mass.: Harvard University Press.

41
childbirth may truncate the continuation of a woman’s education and hence limit her labor market opportunities. The gap (in months) between marriage and first born and its evolution for women born in different years is shown in Figure 3, bottom left panel. This graph combines the trends in the two top panels of the figure. The gap is decreasing as women in younger cohorts are marrying slightly later but giving birth at the same age or slightly earlier than women in older cohorts.

Parental son preference is prevalent in South Asia and has been widely documented for India.<sup>18</sup> The DHS ask women about how many children they would choose to have in their whole life if they could go back to the time before they had children. A following question asked for the ideal number of sons, daughters, and ideal number of either sex. Son preference (as a reported choice and not the outcome) is estimated as the share of women who reported a higher number for the ideal number of sons than ideal number of daughters. The long-term trend of son preference across different birth years is shown in the bottom right panel of Figure 3. This graph paints a picture where son preference is decreasing in Bangladesh, India, and Nepal. While over 30 to 60 percent of women in these countries born in the late 1940s to early 1950s prefer to have more sons, less than 20 percent born in mid-1990s have this preference. For the Maldives and Pakistan, the trend is stagnant.

# **Intrahousehold power/negotiation**

Gender norms are intrinsically connected with roles and expectations for women and men that are played out inside the household and particularly in spousal power relations. These relations (and norms) are connected to issues such as women’s agency (Donald et al. 2020; Bussolo et al. 2021), which in turn responds to changes such as spousal gaps in education and age, differences between partners in terms of their desired and realized fertility, and experience and justification of IPV. As noted by Bernhardt et al. (2021), when men and women bear different costs of violating norms, intra-household bargaining will mediate the role that norms play in governing female labor supply.

Agency, according to Kabeer (1999), is the “ability to define one’s goals and act on them”. One of the ways in which researchers have measured agency is by considering the process of decision making inside households. The DHS collects data on self-reported participation in this process. In more detail, women

> 18 See Pande and Astone 2007 and Mitra, 2014 for India, Hafeez and Quintana-Domeque 2018, for Pakistan, Asadulla et al. 2021 for Bangladesh, Hattlebakk 2017 for Nepal.

17
the 2010/2011 national household survey as a baseline for this study. By chance, then, it is likely that one or two of the 16 randomly-surveyed households in our villages will be among those chosen by the GVH for the PWP. We term these households as “village chosen For this study, we randomly choose ten households from the 16 survey households in the village to be offered the program. To ensure that the experimentally-induced program offer did not affect the village selection process, the list of randomly selected households was distributed two weeks after lists of village chosen beneficiaries were submitted to district councils. We define these randomly-chosen households as “top up” households who are “treated” with the PWP program. Some small share of the “top up” households will also be village chosen beneficiaries. Additionally, given the coverage rate of the status quo program, one or two of the untreated households in PWP communities were likely to be included in the program through the village selection process.

In summary, our study has three groups of households: treated households in PWP villages (top ups who are randomly offered the program), untreated households in PWP villages, and households in non-PWP communities. By focusing on the random offer, we estimate the intent-to-treat effects of the program. By comparing the untreated households in PWP villages to households in non-PWP villages, we are able to measure the indirect (spillover) effects of the program.

# **3 Data**

The data for this study come from a panel household survey. The basis for the panel was the Integrated Household Survey 3 (IHS3) which was fielded in 2010/2011 by Malawi’s National Statistics Office. The IHS3 is a cross-section of 12,288 households in 768 enumeration areas (16 households per community) and has extensive household and agricultural modules.

The 16 IHS3 households in each EA included in the evaluation were interviewed in four additional rounds: before the public works projects started during the planting season (November 2012), after the first cycle, pre-harvest (February 2013), after the lean season cycle, post-harvest (April-May 2013), and finally after the completion of the 2012/2013 season (November 2013; see Figure 2). Our first survey (before PWP began in all but three villages) is, in effect, a second baseline to complement the IHS3. However, it could be tainted by anticipation effects if households in PWP villages modified their behavior before the program began in expectation of the employment opportunities or other changes it would induce. Twenty three enumeration areas (approximately 13 percent of the EAs in which the experiment was implemented) were incorrectly classified as included in the IHS3, and are therefore 8
**<u><mark>Tickets</mark></u>** -0.30*** -0.19** -0.20**

Note: CC and GE are inverted for clarity of interpretation. Based on 131 observations. Pair wise correlations. *** denotes significance at the 1% level, ** denotes significance at the 5% level, * denotes significance at the 10% level. Sidak test used to eliminate the possibility that robustness is due to multiple comparison fallacy. _Source_ : Author’s calculations using scores from 2010. When including the GCB data the sample is restricted to 27 observations. However, the indicator is highly correlated with the other measures.

_Source: The Author_

# **_Table 8 Correlation between Measures of Corruption (2000s data‐ OECD Sub‐ Sample)_**

|**Measure**|**CPI**|**CC**|**GE**|
|---|---|---|---|
|**CPI**|-|||
|**CC**|0.98***|-||
|**GE**|0.93***|0.95***|-|
|**Tickets**|-0.60***|-0.58***|-0.62***|

Note: CC and GE are inverted for clarity of interpretation. Based on 19 observations. Pair wise correlations. *** denotes significance at the 1% level, ** denotes significance at the 5% level, * denotes significance at the 10% level. Sidak test used to eliminate the possibility that robustness is due to multiple comparison fallacy. _Source_ : Author’s calculations using scores from 2010. When including the GCB data the sample is restricted to 27 observations. However, the indicator is highly correlated with the other measures.

_Source: The Author_ A second empirical test of the robustness of the different measures of corruption is the use of factor analysis. If the subjective and objective measures of corruption are measuring the same thing, we would expect them to load onto a latent (unobserved) measure of overall corruption. Specifically, we would expect the factor loadings of the subjective measures (given their scales, higher values denote less perceived corruption) to have factor loadings of the same sign and opposite to that of the objective measure (in which higher values indicate more corruption).

Table 9 shows that the factor analysis results yield consistent and significant values for both the pooled and the OECD sub-samples. Specifically, in both cases, the factor analysis yields only one significant (Eigen value >1.0) latent variable and in both cases the individual measures load onto this variable in the manner anticipated. Namely, the subjective indicators load onto the indicator positively, while the objective measure loads onto the latent variable negatively. This outcome is consistent with the expectation that higher scores on the subjective indicator and lower scores on the 26
# **_Appendix 2:_** _Balance check table_

||Standardize<br>|d differences<br>|Varian<br>|ce ratio<br>|
|---|---|---|---|---|
||Raw|Weighted|Raw|Weighted|
|Female|0.111|0.037|0.964|0.986|
|Age|0.051|0.013|0.778|0.695|
|Household dependency ratio|0.100|0.112|0.728|0.715|
|Head of household|0.129|-0.038|0.964|1.008|
|Completed secondary school|-0.019|0.001|0.989|1.000|
|Main income source: Agriculture|-0.033|-0.037|0.989|1.000|
|Main income source: Business|0.283|0.029|0.785|0.980|
|Main income source:|-0.314|0.016|0.544|1.024|
|Wage/Salary|||||
|Main income source: Other|-0.029|-0.055|0.841|0.697|
|Asset score|-0.401|-0.135|0.560|0.818|
|Bo|-0.031|-0.068|0.954|0.877|
|Bombali|-0.176|0.027|0.681|1.052|
|Kenema|-0.336|-0.095|0.356|0.790|
|Port Loko|0.227|-0.007|1.892|0.979|
|Western Area Urban<br>_Source_: Authors’ calculations from CIMS (202|0.199<br>0)._Note_: This Balanc|0.093<br>e check table includes R|0.999<br>ound 1 beneficiaries|0.999<br>in the largest|

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020).</mark> _<mark>Note</mark>_ <mark>: This Balance check table includes Round 1 beneficiaries in the largest estimation sample of our ECT-R1 analysis. “Raw” columns show the values prior to the inverse-probability weighting and “Weighted” columns show the values post weighting. The standardized differences should be equal to zero and the variance ratio should be equal to 1 if the samples are perfectly balanced.</mark> 33
20

**Table 4: East Asia and Latin America Intra-Regional Trade of Manufactured Goods and Parts & Components in Various Years**

||Share of<br>East A<br>|Intra-Regional<br>sia to the Wo<br>|Exports in<br>rld (%)<br>||Share of Intra-R<br>Latin America<br>|egional E<br>to the W<br>|xports in<br>orld (%)<br>||
|---|---|---|---|---|---|---|---|---|
|Year|ASEAN|NE Asia|East Asia|ANDEAN|MERCOSUR|LAIA|NAFTA|LAC|
|||||**Manufactured**<br>|**Goods**<br>||||
|1985|22.1|19.0|25.7|13.5|7.8|18.4|46.7|50.9|
|1990|19.9|25.5|35.4|14.6|10.5|17.0|43.1|47.0|
|1995|26.3|32.3|46.0|39.3|26.5|19.6|47.8|53.7|
|2000|23.3|33.0|44.9|33.2|27.0|13.0|55.5|59.8|
|2005|25.1|38.3|48.5|34.3|20.2|17.1|54.3|58.9|
|||||**Parts & Comp**|**onents**||||
|1985|22.8|23.2|31.2|9.5|8.0|17.3|41.3|46.1|
|1990|27.0|32.1|44.4|12.8|12.4|16.8|45.3|49.3|
|1995|32.1|34.3|51.9|40.1|34.2|17.5|45.9|51.3|
|2000|29.0|38.0|54.3|39.4|30.2|10.0|53.0|57.5|
|2005|29.6|45.8|59.3|39.5|22.6|14.0|53.0|58.0|

Notes: Manufactured goods is defined as SITC 5+6+7+8-68 in Revision 2. Parts & components include 75 items of parts in machinery, transport equipment and textiles & clothing. Cambodia and Vietnam are missing data in 1985 to 1995 in East Asia.

Source: Computations by authors based on UN COMTRADE Statistics.

Table 4 also shows that the intensification of intra-regional trade in Latin America started in the 1990s, while the pace of trade integration started prior to the proliferation of formal regional trade agreements in East Asia. In 1990, the shares of ASEAN intra-regional exports of manufactured goods and components and parts were 19.9% and 27.00%, respectively. The corresponding shares in Latin America were 14.6% and 12.8% for ANDEAN community and 10.5% and 12.4% for MERCOSUR countries.

Essentially East Asian integration was driven by market conditions. Integration via markets occurs before attempts to establish more formal regional trade agreements. For Latin America, _de jure_ trade agreements seem to have led to a slight acceleration of the pace of intra-regional trade.

Another way we can analyze the intensity of regional trade integration is through the use of the trade intensity index. The trade intensity index is defined as:
In Group 2, Hungary and Poland collected 150 percent of their predicted revenues. The (relatively) low predicted revenues for these two countries result, in part, from their relatively limited openness (with export share of only 18 percent of GDP), which, theoretically, reduces their potential to raise revenues. Nonetheless, Hungary's high tax effort index mainly reflects the very high level of actual revenue (the highest among the transition countries, and fourth-ranking in the world, after Sweden, Denmark and Netherlands). The only country from group 2 that shows a tax effort index below 1 is Bulgaria, whose predicted revenue level is quite high - owing mainly to strong pressure on raising revenue, which is predicted from servicing its high level of public debt.

Similarly to the previous groups, countries of **_Group_** _3_ have also, on average, collected revenues that seem to surpass their revenue capacities. There is, however, a great variation among them. Latvia, Estonia and Romania appear overstretched; whereas Lithuania and Moldova, do not seem to exploit their taxable capacity fully. Finally, as was hinted to in the discussion of the financial constraints, countries in **_Group_** 4, apparently lacking the ability to collect revenue and utilize their taxable potential.

Figure 5: <!-- Start of picture text --> General Goicrnment Revenue (4)<br>* Actual u Predicted<br>50 -<br>45-<br>40<br>20 -<br>15<br>30 ~.S and 1.23..contie ......<br>10<br>5 f<br>OBCD OECD w/o Westemn Transition Transition Tranition Transition Developing<br>U.S. aid Ruyop e 1 2 3 4 countries<br>lapin<br><!-- End of picture text --> 26
Lewis and Lockheed more than 70 percent of out-of-school girls in the developing world (figure 2). The regions with the highest enrollments – Latin America, Eastern Europe and Central Asia, and East Asia – are also those with the largest proportion of out-of-school girls from excluded groups.

**Figure 2: Excluded Girls as Estimated Share of All Girls Out of School, by Region**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40 Percent<br>Excluded<br>30<br>20<br>10<br>0<br>World SSA SA MENA LAC EAP ECA<br><!-- End of picture text --> Source: Lewis and Lockheed 2006

**_Gender gaps_** . Gender gaps are found in school enrollments, years of schooling attained and school completion. According to UNESCO, 40 percent of developing countries had not achieved gender parity in primary school enrollment as of 2002 (45 countries out of 113, although in five of these countries, the gender gap favored girls). At the secondary level, girls’ enrollment lagged that of boys in 46 developing countries, exceeded that of boys in 29 developing countries, and was at parity in the remaining 38 developing countries for which data were available (UNESCO 2005).

More recent data from UNESCO shows a sharp decline in the absolute number of girls who are not enrolled in school in developing countries, from an estimated 55.4 million in 1999 to an estimated 41.7 million in 2004. The number of boys not enrolled in school also dropped, from 38.6 million to 31.8 million over the same time period. The gender gap – 16.7 million more boys than girls enrolled in school in 1999 – dropped 40 percent, to 9.9 million more boys than girls enrolled in school in 2004 (UNESCO 2006). Much of the reduction occurred in South and West Asia, where the number of unenrolled children was halved, largely due to reductions observed in 7
**<u>Table A.28. The district fixed-effect RDD-DD regressions of households’ income using districts with the poverty rate from 35%</u>**

|||||Dependent variables<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program *Post-program period|0.0283|0.3115|0.7693**|-0.5190***|0.5714|0.8281***|1.0078***|
||(0.0821)|(0.4804)|(0.3213)|(0.1741)|(0.5429)|(0.2908)|(0.3853)|
|(Poverty rate – 50) *Post-program|-0.0070|-0.0471|-0.0620***|0.0457***|-0.0487*|-0.0443**|-0.0175|
|<br>period|(0.0057)|(0.0288)|(0.0234)|(0.0145)|(0.0287)|(0.0191)|(0.0215)|
|Program * (Poverty rate – 50) *Post-|0.0029|-0.0005|0.0472*|-0.0409***|0.0121|0.0322|0.0238|
|program period|(0.0066)|(0.0374)|(0.0282)|(0.0156)|(0.0380)|(0.0274)|(0.0297)|
|Age of household heads|0.0300***|0.0569***|0.0624***|0.1478***|-0.0475***|-0.1009***|-0.0696***|
||(0.0021)|(0.0131)|(0.0074)|(0.0115)|(0.0085)|(0.0089)|(0.0093)|
|Age squared of household heads|-0.0003***|-0.0009***|-0.0007***|-0.0014***|0.0007***|0.0013***|0.0009***|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0108|0.1242|0.2039***|0.7015***|-0.4569***|-0.1650***|-0.4123***|
|<br>female=0)|(0.0138)|(0.1177)|(0.0584)|(0.0614)|(0.0628)|(0.0551)|(0.0533)|
|Ethnic minorities (yes=1, Kinh=0)|-0.5423***|-0.1136|-1.8118***|0.7284***|-0.4677***|0.4781***|-0.5759***|
||(0.0369)|(0.1356)|(0.1192)|(0.1035)|(0.0974)|(0.0842)|(0.1036)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.4763***|2.8778***|1.5021***|3.5720***|4.9592***|2.1890***|3.2333***|
||(0.0742)|(0.3380)|(0.2094)|(0.2810)|(0.2691)|(0.2528)|(0.2262)|
|Observations|55,334|55,334|55,334|55,334|55,334|55,334|55,334|
|R-squared|0.369|0.163|0.104|0.143|0.246|0.140|0.092|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

78
6

||Rates of M|Table2<br>alnutrition<br>of Chi|ldren<br>0-|35 Months||
|---|---|---|---|---|---|
||Clinic<br>D<br> <br>|Clinicand Survey<br>Jamvlca,<br>1975-1<br>ata<br>GM=ZSTAND<br> <br>|Data<br>989<br> <br>ARD<br>|Household<br>Sur<br>|vev Data<br>|
|Year|Normal<br>Grade|Grade<br>Grade|Normal|Grade Gran|Grade|
||I|II<br>III||I<br>II|III|
|1976|*<br>73.2<br>22.2|3.9<br>0.7||||
|1977|-<br>-|-<br>-||||
|1978|*<br>-<br>-|-<br>-|60.2|32.4<br>6.5|<br>0.8|
|1979|-<br>-|_<br>_||||
|1980|74.4<br>21.6|3.6<br>0.5||||
|1981<br>|73.3<br>22.8<br> <br>|3.3<br>0.5<br>||||
|1982<br>|74.1<br>22.3<br> <br>|S.2<br>0.4||||
|1983<br>|74.3<br>22.1<br> <br>|3.1<br>0.6||||
|1984<br>|73.0<br>22.9<br> <br> <br>|3.6<br>0.5||||
|1985-<br>|<br>72.9<br>22.6<br> <br>|4.0<br>0.5||||
|Q1|-<br>75.0<br>21.1|3.4<br>0.5|63.3|29.3<br>6.2|<br>1.2|
|1986<br>|73.9<br>21.8<br> <br>|3.9<br>0.4<br>||||
|1987<br>|75.7<br>20.7<br>|<br>3.3<br>0.3||||
|19884*|*|||||
|1989<br>||||||
|Q.3<br>|||7'3.0|25.6<br>3.1|<br>1.3|
|Q.4|||71.2|25.6<br>2.5|0.7|
||<br>|UD S~ UDh3D<br> <br>|S<br>|||
||Nrtmal<br>M|oderate<br>**Sevre**|iNormal|Moderate|**Severe**|
|1989<br>||<br>||||
|Q.3<br>|93.6|6.1<br>0.3|90.3|8.4|1.3|
|Q.4|93.3|6.4<br>0.3|92.7|6.5|0.8|

> ***** 7 parishes only, for children 0-5§ months.

> **CC** Refers to the first quarter of the clinia data.

****** The reporting shifted from Gomez to WOO **_standards._** 9mause the timing of the stitch vas not uniform. and some confusion ensued, the data for 1988 mix the two measures and are not reliable estimates of either.

Definitions - Gomez Scale: Normal children are those vith weights over 90X of the reference standard. Grade I malnutrition Ls a weight for age from 75-89X of the reference standa ^rade II is 60-742 of the standards Grad. III is less thtAn 60 2 of the standard, WHO SCALR; Sormal _dren -. are those with weights for age over 80X of the reference standard. Moderate malnutritLon is a weight for age from 60-80S of standard, %evere malnutrition is a weight for age below 602 of standard. Both scales use the National Center Z;r Belth Statistics age and sex-specific growth standards.

Sourwas Clinic Data -- Ministrt of Health, Monthly Clinlc Recorda System, 1978 Survey -- _MOB_ (1978): 1985 Survey -- MOB (1985); 1989-Q3 Survey -- STATIN and PIOJ (1989), 1989-Q4 -- unpublished data fro Survey of Living Condltions.

the wealthiest, and therefore those less likely to be malnourished. Thus even considering the particular features of Jamaica's clinic reporting system, the direction of bias is uncertain.

19. Household Survey Data. The household survey data presented here are from high quality national household surveys. The sample selection, training of anthropometrists,4/ and data management are of good quality. The

- i./ Staff of the Nutrition Division of the Ministry of Health trained the anthropometrists for the surveys.
<!-- Start of picture text --> =,40*** ve<br>Provider’s Youth’s R2=0.56<br>{ abilities ae<br>| ~~ _B_=0.08***<br>} Se<br>|6=0.66*** B,=0.50*** a<br>=<br>R2=0.30<br>\ Youth’s<br>\ Youth’s B,=0.55*** Youth’s B,=0.16*** occupational<br>social origin ——_——— education . attainment<br>a<br>= B,=0.70*** _—<br><!-- End of picture text -->

<!-- Start of picture text -->
##* CFI0.979TLE0.9741=3,310 R2= 0.48 aweB,=0.32***<br>=p<0.001, **-= p<0.01, * 7 p=0.05 os Pa-<br>© Latent variable occupationirst =<br>oO Manifest variable<br><!-- End of picture text -->
# FIGURE 17. INFANT MORTALITY TRENDS AMONG URBAN AND RURAL POOR IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986)

<!-- Start of picture text -->
*-  S>~~~~~~~\  ,>t'  \\'----.*,,  RURAl-ALL<br>**,-  a*_.---'  S  (60% of the  population)<br>00  N.-  RURAL-POOR<br>0*0<br>75<br>so<br>1907  1975  m10<br>Year (1)<br><!-- End of picture text -->

NOTE: (1) IMR 5- YEAR MOVING AVERAGE. (2) POOR: POOREST 60 PERCENT.