rafmacalaba/datause-encoder-data huggingface dataset:
# **E.11 Dose-response Models**

**Table E.11:** Dose-Response with All Sections in Study Area

||To|tal|Totalp|er Clinic|Total|per 1k|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Proportion of Clinics Treated|0.173<sup>_∗∗_</sup>|0.159<sup>_∗_</sup>|0.165<sup>_∗∗_</sup>|0.169<sup>_∗∗_</sup>|0.052<sup>_∗_</sup>|0.053<sup>_∗_</sup>|
||(0.084)|(0.084)|(0.084)|(0.082)|(0.031)|(0.031)|
|Population (1000s)||||0.059<sup>_∗∗∗_</sup><br>(0.016)|||
|Number of Clinics||||||0.074<sup>_∗∗∗_</sup><br>(0.015)|
||Conf|rmed|Confrme|dper Clinic|Confrm|edper 1k|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Proportion of Clinics Treated|0.059<sup>_∗∗_</sup>|0.059<sup>_∗∗_</sup>|0.059<sup>_∗∗_</sup>|0.059<sup>_∗∗_</sup>|0.015<sup>_∗∗_</sup>|0.015<sup>_∗∗_</sup>|
||(0.024)|(0.024)|(0.024)|(0.024)|(0.006)|(0.006)|
|Population (1000s)||||0.007<br>(0.005)|||
|Number of Clinics||||||0.008<sup>_∗∗_</sup><br>(0.003)|
|Ebola Sample|✓||||||
|Full Sample||✓|✓|✓|✓|✓|
|Sections|160|205|205|205|205|205|
|Observations|5,440|6,970|6,970|6,970|6,970|6,970|

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

A44
# **Figure 7. Changes in share of agricultural and non-agricultural wage/salaried workers between 2005 and 2012, by consumption per-capita deciles of 2005**

# **a. Agricultural activities**

<!-- Start of picture text --> Rural areas Urban areas<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Consumption decile in 2005 Consumption decile in 2005<br>95% confidence intervals 95% confidence intervals<br>b. Non-agricultural activities<br>Rural areas Urban areas<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Consumption decile in 2005 Consumption decile in 2005<br>95% confidence intervals 95% confidence intervals<br>15 15<br>5 5<br>-5 -5<br>Change in share of wage/salaried agr. workers (as % of workers)<br>-15 -15<br>15<br>15<br>5<br>5<br>-5 -5<br>Change in share of wage/salaried non-agr. workers (as % of workers)<br>-15 -15<br><!-- End of picture text --> <!-- Start of picture text --> Source: IHDS, 2005 and 2012<br><!-- End of picture text --> Increases in remittances and benefits have been poverty reducing, with significant increases in both that have favored the poor. We do not find significant changes in the average amounts of remittances 22
12 The table shows that, with the exception of transport and coamunications, residuals are negative, and generally strongly negative. The other regressions are performed on non-Soviet data, with Soviet data --either aggregate or disaggregated by republic -- introduced in Figures 1-25 for comparative purpose.

Value added The share of total services in GDP is shown as a scatter against income in Figures 1 aud 2, which respectively include and exclude military personnel. As explained earlier, each observation is a decade average, with the time period in the OECD comparisons covering the 1960's, 1970's and 1980's. Typically, the 1960's observation is the furthest to the left, indicating lower **por** capita income; and the 1980's observation is the furthest to the right, indicating higher per capita income. In both figures, the USSR shows up as having a lower share of total services than expected, along with its fellow planned economy Yugoslavia. It would be necessary to reduce the estimate of USSR per capita income to that of Turkey to make its total service sector conform to the international pattern. 1<sup>4</sup> As described below, there is substantial variation in sub-sector activity levels.

Infrastructure. Figure 3 shows a comparison of the Soviet transportation share with the OECD category "transportation and storage", while Figure 4 shows the corresponding scatter for communications. The Soviet transport share is well above OECD norms, while the communications share is below OECD norms. The inclusion of storage in the OECD data makes the higher Soviet transport shares even more striking, although value added from storage is probably small. The 14 Of course, declines in GDP in recent years have resulted in dram'iatic falls in per capita incomes in Russia and other former Soviet states; but the analysis here predates this phenomenon.
# **1. Introduction**

Household consumption survey data that underlie monetary poverty estimates in low- and middle-income countries are often unavailable, unreliable or incomparable. To address these challenges, imputation-based methods have become increasingly more common not only to fill poverty data gaps in data-scarce and resource-constrained contexts, but also to identify project/program beneficiaries and evaluate development project/program impacts on poverty at low cost (World Bank, 2021; Smythe and Blumenstock, 2022; Dang and Lanjouw, 2023).<sup>1</sup> Building on the seminal technique that obtains small area estimates of monetary poverty by imputing from a household consumption survey into a census (Elbers _et al.,_ 2003), survey-tosurvey imputation builds an imputation model using appropriate predictor variables from an existing older consumption survey (base survey), which can be subsequently applied to the same variables in another non-consumption survey (target survey) to provide poverty estimates for the latter survey. The target survey can be either an existing, non-consumption survey, such as a Demographic and Health Survey (DHS) or a labor force survey (Stifel and Christiaensen, 2007; Douidich _et al._ , 2016), or a purposefully commissioned survey that only collects the requisite predictors. Recent applications also include sourcing the data for the requisite predictors from administrative records to impute poverty for hard-to-reach refugee populations <mark>(Altindag</mark> _<mark>et al.</mark>_ <mark>, 2021; D</mark> ang and Verme, 2023), or phone call detail records to target the ultra-poor (Aiken _et al._ , 2023).

Three key conceptual, but understudied, issues motivate our work. First, the literature on survey-to-survey imputation has long emphasized the requirement of having identical questions

> 1 Imputation techniques are widely used by international organizations and national statistical agencies to fill in missing data gaps such as education statistics (UOE, 2020) and income data (US Census Bureau, 2017). See also Dang and Lanjouw (2023) for a recent review of poverty imputation studies.

2
For a conditional social program to be successful in enhancing human capital formation, it is important that its presumably positive effect (through the conditionalities, intra-household allocation and an increase in disposable income) is complemented by private investments or, at least, not undone by changes in private expenditure decisions.

In this essay, we analyze how household expenditure decisions in nutrition, health and education change in the context of the Bolsa Fam ́ılia Program, the largest Conditional Cash Transfer program of which we have knowledge. We first model household behavior under a CCT program and show how intra-household preferences and their perception on the substitutability or complementarity character of inputs to human capital production influence their resource allocation decisions. Subsequently, we empirically test the theoretical implications with respect to private investments in child health and education, using the Brazilian household expenditure survey POF 2008-2009.

Overall, the results indicate an impact of the program which goes beyond the pure income effect of a cash transfer. We, then, investigate whether a gender effect could drive our results (i.e. different preferences between men and women together with increased bargaining power for women resulting from the program) and find no evidence in favor of this hypothesis. In line with our theoretical framework, we suggest that the conditionalities could drive the results through the perception of the nature of the human capital production function. To receive the cash transfer, beneficiary households are obliged to demand certain education and health services. Given the imposed demand for these inputs to human capital formation, households should adapt their expenditure behavior for the other inputs accordingly depending on whether they conceive of them as complements or substitutes. Indeed, we observe that households act in accordance with this theoretical prediction and adjust their expenditures in complementary inputs to health and education. The evidence is most pronounced for food; a positive impact (although less robust) is also found for education and school materials expenditures which households seem to perceive as complementary inputs to education and health services in human capital production. Our results with respect to health are not conclusive.

In conclusion, our findings have great policy relevance for the design of social programs. Firstly, we found that households react differently to a cash transfer provided with conditionalities compared to an expansion of the overall household budget. Secondly, we suggest the household’s perception of the nature of inputs to the human capital production function as one possible explanation. Finally, more research is needed to further disentangle the channels through which the conditionalities household decisions.

64
39 Tabte **11** (continued)

|**Country**or economy|**1985**|<br>1990|<br>1995|2000|2005|<br>2010|2015|2020|2025|
|---|---|---|---|---|---|---|---|---|---|
|Portugal<br>|10,157|10,271|10,408|10,548|10,677|10,775|10,846|10,901|10,948|
|**Ronunia**|22,740|23,235|23,760|24,334|24,909|25,361|25,706|26,003|26,312|
|Spain|38,523|39,373|40,255|41,061|41,701|42,135|42,438|42,738|43,023|
|Sweden|8,350|8,367|8,363|8,343|8,306|8,267|8,227|8,171|8,085|
|Switzerland<br>|6,458|6,494|6,496|6,478|6,435|6,372|6,294|6,202|6,090|
|**United**<br>Kingdom|56,618|57,205|57,641|57,913|58,010|58,077|58.173|58,250|58,218|
|YugoslavIa|23,123|23,816|24,461|25,066|25,561|25,941|26,229|26,463|26,667|
|Other Europe**(a)**|253|264|275|287|297|307|317|328|335|
|U.S.S.R.|278,618|290,977|302,059|312,232|321,408|329,778|338,008|345,262|352,319|
|Oceania|24,458|26,2"|27,896|29,381|30,763|32,060|33,300|34,420|<br>35,392|
|Australia|15,758|16,813|17,677|18,336|18,894|19,379|19,813|20,170|<br>20,444|
|FiJi|700|<br>750|805|860|912|<br>964|1,019|1,073|<br>1,123|
|French<br>**Polynesia**|175|204|235|<br>266|294|318|<br>339|360|380|
|Gum|123|<br>138|152|166|179|<br>191|<br>203|213|223|
|Kiribati|64|69|75|81|88|95|<br>lo|108|114|
|**New**<br>Caledonia|**15**|**0**<br>161|<br>172|<br>185|198|210|<br>221|<br>231|<br>241|
|New**Zeatand**<br>|3,254|3.354|3,450|3,548|3,639|3,715|3,790|3,855|3,907|
|**Pacific**<br>**Islands**|148|164|182|202|224|<br>246|265|283|<br>301|
|**Papua**<br>New Guinea<br>|3,329|3,740|4,186|4,652|5,113|5,579|6,037|6,468|6,856|
|**SolomonIslands**|273|<br>325|383|448|519|595|674|753|830|
|**Tonga**|97|104|113|122|132|<br>142|153|164|<br>174|
|Vaiujatu|131|151|<br>174|<br>198|<br>225|254|285|<br>317|<br>347|
|Western<br>Samoa|163|171|183|<br>199|216|233|251|268|286|
|Other Micronesia**(a)**|23|<br>25|28|<br>30|<br>33|35|38|40|42|
|Other Polynesia (a)|70|75|81|88|97|104|111|117|124|
|Wortd totaL|4,843,331|5,281,816|5,743,944|6.204,95<br>|8<br>6,645,894|7,064,022|7,472,393|7,876,265|8,262,434|
|Growth<br>rate (percent)<br>(b)|<br>1.73|<br>1.68|1.54|1.37|1.22|<br>1.12|1.05|0.96||

**(a)** For a listing of **countries** in the "other" categorie **s** , ee Table 12. **(b)** Average **annual** growth rate over each five-year period. The projected average **arwual** growth rate for 1985-2000 is 1.64 percent. The projected rate for 2000-2025 is 1.15 percent.
sense of belonging to the community (Rao and Walton, 2004). Additionally, distance _per se_ may not be the main barrier for educational enrollment decisions but rather related safety considerations (Lloyd et al., 2005). This issue can be particularly salient for young children in this context given that the civil strife that took place in Guatemala between 1960 and 1996 affected predominantly rural areas (Chamarbagwala and Morn, 2011).

On the other hand, the mentioned 0.5 kilometers minimum-distance restriction applies to the linear distance to _any_ community. In rural Guatemala the population typically lives in mountainous, rugged areas with poor access to infrastructure or public transportation. Therefore, the linear distance would dramatically underestimate a simple calculation regarding how long it would take to a child to walk to neighboring community to attend a pre-primary. Also, many communities in rural Guatemala are very small and they can be better interpreted as groups of households. For example, the community in the 25th percentile in the population distribution of the 2002 Census contained only 40 individuals. Finally, we note that the possibility of spill-over effects may be less of an issue for girls compared to boys, as existing empirical evidence suggests that the distance to an educational establishment may be a stronger barrier for girls (Burde and Linden, 2012). Hence, if spill-over effects are at play they will operate predominantly for boys, and, consequently, we should observe differences in effects across genders. However, Table 4 shows that effects are similar across genders suggesting a limited role for spill-over effects.

Notwithstanding these considerations, we empirically explored the robustness of the main findings to the minimum-distance restriction. In particular, we estimated heterogeneous treatment effects across communities that vary in their distance to its closest neighboring community. If spill-over effects play a role in the estimated effects, we should expect differential treatment effects across distance to neighboring communities. In practice, we divided the sample of rural communities under analysis based on whether the distance to the neighboring community was above or below the median distance (0.75 kilometers). We found that effects are qualitatively similar across the two sub-samples suggesting that spill-over effects are not affecting the main findings in the study (results are presented in Table 7).

# **6.2 Testing for differential pre-intervention trends**

Identification of the parameter of interest relies on the assumption that, in absence of the treatment, outcomes in the intervened primary schools would have evolved similarly to those from the comparison group. Although this assumption cannot be tested directly, we can provide some evidence on its plausibility by studying pre-intervention trends (Heckman and Hotz, 1989). To this end, we perform the following falsification tests. First, we restrict the sample to only the pre-construction cohorts (1988 to 1991 cohorts) - that is, 15
# Table A5. Distribution of Manufactruring Establishments by Region and Industry (SIC code) Category, Indonesia

## RegIon SIC Code frc mjor product

<!-- Start of picture text --> Frequancy<br>Percent<br>Rom Pct<br>Cal Pct 131.FoadI132.TsxtlJ33.Uood 134.Pperl35.Chil 36.Non-N137.BasicI38.Feb-N139.Oth.rI<br>1lsvro Ile/Leathl lcTl/Rubb1etaL Ninl-Netal lstat Elci I Total<br>................ . .. ............... ........... .... --------.--------.<br>Jakarta| 5.86 17 1 15.52 15. 45 1 4.8 4.13 1 3.79 I 9.31 2 27 20 07 .6 0.11 0. 7.9 722 0.34 I 1 48.97142<br>11.97 |31.69 9.15 I 7.75 19.01 4.23 0 00T 15.49' 0 70 '<br>36:96 1 67.16 1 54.17 1 55.00 1 45.76 33.33 0.00 47.83 14:29<br>................ ......................... __.___.............. ._..... ........ +. ..... ...<br>Botabek 15is 15 9 1 6 1 22 1 11 21 201 6 106<br>5.17 5.17' 3.10 2.07 t 7.59 3.79 0.69 6.90 2.07 36.55<br>3 14:15 14:15 3 8:493 5.66 1 20.753 10.381 1.89 18.87 5.663<br>- 32.61 1 22.39 1 37.50 1 30.00 1 37.29 i 61.11 66.67 1 43.48 1 85.71 '<br>Serang 1 141 71 21 3 10 1 1 1 4 01 42<br>4.83 1 2.41 0.69 : 1.03 3.45 1 Q.34 1 0.34 1 1.38 1 0.00 , 14.48<br>33.33 1667' 4761 7.14 ' 23.81 1 2.38 2.38 1 9.52 0.001<br>30.43 1 10:45 3 8:33 1 15.00 16.95 7 5.56 1 33.33 I 8.70 1 0.00<br>............... .. ....-- .- .- . . . -. . ....... -....-----.................<br>Total 46 67 24 20 59 18 3 46 7 290<br>15.86 23.10 8.28 6.90 20.34 6.21 1.03 15.86 2.41 100.00<br><!-- End of picture text -->
<!-- Start of picture text --> ©<br>ian]<br>=<br>5<br>c<br>Eu<br>g<br>6<br>|<br>=<br>a5<br>=<br>ao<br>5<br>><br>aan]<br>5°<br>oo]<br>S]<br>a<br>ou<br>0 4 2 3 4 5 6 F 8B ¥g 1<br><!-- End of picture text -->
Anxiety*Parenting <u>Stress*Depression</u> -.0005 <u>.000</u> -1.860 <u>.062</u> _Note._ (+) controlled for food insecurity (continuous 0-8; higher indicates more insecurity, flood impact (impacted in 2022 or 2023), fear of husband (never afraid=0, sometimes afraid=1, always afraid=2), asset index (1= poorest, 5= wealthiest), maternal education (1=less than primary up to 8= graduate degree), feeling safe while home (1= very safe up to 4= very unsafe), child gender (0=male, 1=female), refugee status, and child age (continuous in months) The same two linear regression models were run on ECD for 3- to 6-year-olds. In the unadjusted model only child age was controlled for, and in the adjusted model, the stressors and confounders were controlled for, with the addition of enrollment in early childhood education. A significant negative interaction was found between depression, anxiety, and parenting stress on child development for 36-72 months in both the unadjusted model at the p<.05 level and in the adjusted model at the p<.1 level (Table 10). That is, as levels of depression, anxiety, and parenting stress increase there is a significant negative association with levels of child development for 36-72 months.

# **Table 10**

_Linear Regression Results of Depression, Anxiety, and Parenting Stress Interaction on ECD - =_ _<u>Direct Assessment (36 72 months) (n 635)</u>_

|Outcome|Predictor|Beta<br>Coefficient|Linearized<br>Standard<br>Error|t-value|p-value|
|---|---|---|---|---|---|
|ECD Unadjusted||||||
|(R Squared= .0795)|Intercept|0.3299|.036|9.08|<.001|
||Anxiety|-0.0291|.009|-3.24|.001|
||Depression|-0.0135|.006|-2.15|.032|
||Stress|0.0095|.006|1.72|.086|
||Anxiety*Depression|0.0036|.001|3.86|<.001|
||Stress*Depression|0.0010|.001|1.43|.152|
||Anxiety*Stress|0.0012|.001|1.09|.274|
||Anxiety*Stress*Depression|-0.0003|.000|-2.54|.011|
|ECD Adjusted (+)||||||
|(R Squared= .2492)|Intercept|-0.1893|.0724|-2.62|.009|
||Anxiety|-0.0178|.0084|-2.11|.035|
||Depression|-0.0143|.0059|-2.41|.016|
||Stress|0.0114|.0047|2.41|.016|
||Anxiety*Depression|0.0030|.0009|3.34|.001|
||Stress*Depression|0.0009|.0007|1.26|.209|
||Anxiety*Stress|0.0001|.0011|0.06|.950|
||Anxiety*Stress*Depression|-0.0002|.0001|-1.85|.065|

_Note._ (+) controlled for food insecurity (continuous 0-8; higher indicates more insecurity, flood impact (impacted in 2022 or 2023), fear of husband (never afraid=0, sometimes afraid=1, always afraid=2), asset index (1= poorest, 5= wealthiest), maternal education (1=less than primary up to 8= graduate degree), feeling safe while home (1= very safe up to 4= very unsafe), child gender (0=male, 1=female), refugee status, ECE attendance (0= not currently attending, 1= currently attending) and child age (continuous in months) 26
to move via most convenient route, should be exempt from customs or transit duties, and should be free from unnecessary delays or restrictions.

The definition of custom environment used in this paper addresses the concerns of GATT article VIII. GATT article VIII states that in order to minimize impediments to trade due to customs procedures, fees charged by customs officials must be limited to the approximate cost of customs services. Also, there should not be substantial penalties for minor breaches of customs regulations such as clerical errors. Regulatory environment issues are addressed in GATT article X which discusses Publication and Administration of Trade Regulations. This article comes from the basic transparency obligation that requires prompt publication of laws and regulations affecting imports and exports so that foreign governments and traders may clearly understand them.

# **3.2 Defining and measuring trade facilitation indicators**

Trade facilitation indicators, in this paper, are constructed from the Global Competitiveness Report (GCR) produces by the World Economic Forum (WEF).<sup>6</sup> The GCR is an annual publication that aims to enhance global understanding of the factors influencing private-sector led economic growth and explains why some countries are much more successful than others at creating new employment opportunities and raising the income level of their respective populations. For participating countries, the GCR reports on performance and policy conditions affecting the ability of private sector firms to be globally competitive -- able to create and add value within the global marketplace. WEF has expanded its worldwide country coverage from 75 countries in 2001, to 125 countries in 2006's GCR. Particular attention has been paid to including more countries from Africa, as well as several from Central Asia, given the increasing importance of this region on the global stage<sup>7</sup> . This database offers then the opportunity for a focus on Africa in a manner that was not possible with previous data sets.

After developing the trade facilitation measures, the next stage in creating the trade facilitation indicators involves collecting the GCR's indexed inputs into the four specific

> 6   Our approach is therefore different from that followed by Wilson, Mann and Otsuki (2004) who used three survey (Kaufmann, Kraay,and Zoido-Lobaton (2002), World Economic Forum Global Competitiveness Report 2001-2002, and IMD Lausanne, World Competitiveness Yearbook 2002).

> 7 For example, the number of African countries has increased from 5 countries (Egypt, Mauritius, Nigeria, South Africa, and Zimbabwe) in 2001 to 25 countries in 2006.