rafmacalaba/datause-encoder-data huggingface dataset:
surprising; the impact of growth on poverty reduction has been well-known for decades (Kraay, 2006; Ferreira and Ravallion, 2009). On the other hand, ample evidence has found large inconsistencies between consumption measured in household surveys and national accounts within and across countries (Ravallion, 2003; Deaton, 2005; Ferreira _et al._ , 2010; Pinkovskiy and Sala-i Martin, 2016; Prydz _et al._ , 2021; Deaton and Schreyer, forthcoming) and noted the difficulty of measuring GDP in developing countries (Angrist _et al._ , 2022).

Several factors can explain why we nonetheless find GDP to be such an important predictor of poverty. First, discrepancies between levels of GDP and levels of welfare do not directly affect the accuracy of the method, which is based on growth rates from the two data sources. Second, the average discrepancy between growth rates in the two sources is accounted for by only allowing a fraction of in real GDP to to welfare as measured growth per capita ’pass through’ in household surveys. The preferred method has a passthrough rate of about 0.7 for consumption-based poverty estimates, and a passthrough rate of 1 for income-based poverty estimates. Third, GDP is probably the statistic in which the global community has invested the most amount of financing and capacitybuilding to improve quality and ensure cross-country comparability (see Angrist _et al._ (2022) for a discussion of this). Despite measurement issues and shortcomings, we would expect GDP to have more signaling properties for cross-country measures of average well-being than any other statistic. Fourth, although using growth in real GDP per capita is the best performing method, no method is able to explain more than a quarter of the variation of growth in mean welfare. Hence, a large part of the variation in poverty rates no model is able to predict, and may reflect a substantial amount of random noise in measured welfare, resulting from To our knowledge, this is the first paper comparing different ways of nowcasting poverty at a global scale. Other papers, such as Chi _et al._ (2021), Cuaresma _et al._ (2018), and Moses _et al._ (2021) have predicted global or near-global poverty rates but not with the purpose of testing different methods. For other indicators, nowcasting is a more established exercise, such as nowcasts of GDP (Giannone _et al._ , 2008), inflation (Aruoba and Diebold, 2010), and macroeconomic variables more broadly (Giannone _et al._ , 2012). Partially due to the SDG target of ending extreme poverty by 2030, many papers have focused on forecasting global poverty rather than nowcasting it (Edward and Sumner, 2014; Hillebrand, 2008; Ravallion, 2013; Lakner _et al._ , 2020; Sumner and Hoy, 2022).

Our findings contribute to the literature by pointing out that when predicting 4
past three months, labor income in the past five seasons, and an effective daily wage rate for work done in the past seven days. Per capita consumption is reported as a secondary outcome.

_Husbands._ Our analysis of husbands focuses on spousal quality and their attitudes towards women’s empowerment. The husband quality index, the first primary outcome, includes subcomponents that measure the husband’s highest grade completed and highest qualification obtained, his cognitive score on the Raven’s Colored Progressive Matrices, his employment status and wage, his HIV status, his marital fidelity (self-reported), and his mental health measured through the GHQ-12. The index of spousal attitudes towards women’s empowerment, also a primary outcome, includes sub-indices for attitudes towards their daughters’ schooling and marriage, their wives’ autonomy, domestic violence, as well as their divorce prospects and desired fertility levels. Components of these two indices are presented as secondary outcomes.

_Children._ The primary child outcomes fall under four domains: anthropometrics, health, parental practices and educational testing. For anthropometrics, we construct height-for-age z- score (HAZ) for living children younger than 60 months old. Our health outcomes include neonatal and post-neonatal mortality. For parental practices, we construct variables for exclusive breastfeeding in the past six months and an index of parenting practices. Finally, for educational testing we report MDAT and SDQ scores for all 36-59 month-olds.<sup>37</sup> The self-reported data on children come from complete birth and death histories collected at Rounds 3 and 4 from the mother or the primary caregiver.

> 37 The pre-analysis plan also indicates that we would report impacts for an indicator for child mortality, but there are only 22 child deaths in our entire sample during the study period, so we exclude this outcome. We also exclude weight for height, as the prevalence of wasting (weight-for-height z-score <-2) is negligible in Malawi. Finally, due to space considerations, we do not show impacts on secondary child outcomes, which include birth weight, vaccinations, and whether or not the child usually sleeps under a bed-net.

60
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# 8   References

Aschauer, D. A. 1989. “Is Public Investment Productive?” _Journal of Monetary Economics_ 23, pp. 177– 200.

Adam, C., and D. Bevan. 2004. “Aid and the Supply Side: Public Investment, Export Performance, and Dutch Disease in Low-Income Countries.” Department of Economics Working Paper 201, Oxford University.

- Armington, P. S. 1969. “A Theory of Demand for Products Distinguished by Place of Production.” IMF Staff Paper 16. International Monetary Fund, Washington, DC.

Bajo-Rubio, O., and S. Sosvilla-Rivero. 1993. “Does Public Capital Affect Private Sector Performance?” _Economic Modeling_ 10(3): 179–185.

- Decaluwé, B., A. Martens, and L. Savard. 2001. _La Politique Economique du Développement et les modèles d’équilibre général calculable._ Montreal: Agence Universitaire de la Francophonie– Presse de l’Université de Montréal.

- Dessus, S., and R. Herrera. 1996. “Le rôle du Capital Public dans la Croissance des Pays en Développement au cours des Années 80.” OECD Working Paper 115. Organisation for Economic Co-operation and Development, Paris.

- Dumont, J. C., and S. Mesplé-Somps. 2000. “The Impact of Public Infrastructure on Competitiveness and Growth: A CGE Analysis Applied to Senegal.” Unpublished paper, CREFA, Université Laval, Québec.

Dumont, J. C., and S. Mesplé-Somps. 2001, “Des retombées généralement bien appréciées: Infrastructure publique, croissance et compétitivité au Sénégal” in Eds. Decaluwé, B., A. Martens, and L. Savard _La Politique Economique du Développement et les modèles d’équilibre général calculable._ Montreal: Agence Universitaire de la Francophonie–Presse de l’Université de Montréal.

- Fay, M., and T. Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” World Bank Policy Research Working Paper 3102, World Bank, Washington, DC.

- Gramlich, E. 1994. “Infrastructure Investment: A Review Essay.” _Journal of Economic Literature_ 32(3): 1176–1196.

Munnell, A. H. 1990. “Why Has Productivity Declined? Productivity and Public Investment.” _New England Economic Review_ , January/February, pp. 3–22.

Savard, L., and E. Adjovi. 1998. “Externalités de la santé et de l’éducation et bien-être: Un MEGC appliqué au Bénin.” _Actualité Économique_ 74(3): 523–560.

- Yeaple, S. R., and S. S. Golub. 2007. “International Productivity Differences, Infrastructure, and Comparative Advantage.” _Review of International Economics_ 15(2): 223–242.

26
environment in which to understand the constraints adolescents face as they transition to adulthood.

# **2.2 Study Design**

Our study began by listing all eligible households within 176 Enumeration Areas (EAs) of the 550 EAs in Zomba District. This never-married, 13-22 year-old target population was then divided into two main strata: those who were already out of school at baseline ( _baseline dropouts_ ) and those who were still in school at baseline ( _baseline schoolgirls_ ). _Baseline dropouts_ comprised only 15% of target population, so were all recruited into the study. _Baseline schoolgirls_ were sampled into the study at probabilities increasing in age and rural status.

Treatment was assigned first at the enumeration area (EA) level; 88 to treatment and 88 to control. All _baseline dropouts_ in treatment EAs received conditional cash transfers (CCTs), while a further experiment was performed within the larger cohort of _baseline schoolgirls_ . For them, 46 EAs were assigned to CCTs, 27 were assigned to unconditional cash transfers (UCTs), and 15 were assigned to receive no transfers in order to study spillovers (from _baseline dropouts_ in those EAs). The amount of money received by the household head was randomized between $4 and $10 at the EA level, and the core respondents were assigned their own individual transfer amounts – ranging from $1 and $5 – in a public lottery.<sup>9</sup> The share of eligible girls offered cash transfers was randomly varied across clusters to estimate spillover effects. Offer letters were distributed in December 2007, payments began in February 2008 and continued through the end of 2009.<sup>10</sup> Four rounds of data took place: Round 1-Baseline (2007), Round 2 (2008), Round 3

> 9 The average total transfer to the household of $10/month for 10 months a year is nearly 10% of the average household consumption expenditure of $965 in Malawi in 2009 (WDI, 2010). This falls in the range of cash transfers as a share of household consumption (or income) in other countries with similar CCT programs. The transfers were offered to all eligible girls in our target demographic and were not targeted by poverty status. 10 In experiments like SIHR, it is important to try to understand what the beneficiaries expected as to the program’s timing and duration (Bazzi, Sumarto and Suryahadi 2015). When the initial offers were made, the beneficiaries were told that the program only had funding for one year, but that efforts were being made to extend it into a two-year

10
Table 13 Composition of Outstanding Bank Loans (Banking accounts, trillion yen)

||Total|Large<br>Firms|Small<br>Firms|Individuals|
|---|---|---|---|---|
|All loans|||||
|1975|88.0|53.4<br>60.7%|29.3<br>33.3%|5.2<br>5.9%|
|1980|134.6|66.1<br>49.1%|55.7<br>41.4%|12.8<br>9.5%|
|1985|222.8|103.6|103.1|16.0|
|||46.5%|46.3%|7.2%|
|1990<br>Equipment loans|376.0|111.5<br>29.6%|214.7<br>57.1%|49.8<br>13.3%|
|1975|23.7|12.6<br>53.1%|6.3<br>26.5%|4.8<br>20.3%|
|1980|36.7|13.0<br>35.4%|11.6<br>31.6%|12.1<br>33.0%|
|1985|53.5|17.1|21.1|15.3|
|||32.0%|39.5%|28.6%|
|1990|130.8|24.8|67.9|38.2|
|||18.9%|51.9%|29.2%|

Note: Small firms are those with capital of less than 100 million yen (3D million yen for wholesale and 10 million yen for retail trade) in 1975. Since 1977, firms with fewer than 300 employees (100 for wholesale and retail trade) are also classified as "small".

Source: Bank of Japan, _Economic Statstica Annual,_ various issues.

44
- 15 -

education data (testing of household respondents and local school questionnaire) in the second year of the Ghana survey.

# D. Organization and Implementation

The standard LSMS survey gathers data on three types of questionnaires. The largest and most time-consuming of these is the household questionnaire. It consists of 16 sections; sections 1-8 are filled in on the first visit to the household, sections 9-15 are filled in on the second visit, which occurs two weeks after the first, and section 16 (anthropometrics) is filled in in both visits. The information gathered in each of these sections is given in Table 2. For details see Grootaert (1986), Ainsworth and Munoz TABLE 2: Sections in LSMS Household Questionnaire First Visit

1. Household Roster

2. Housing Amenities and Expenditures 3. Education 4. Health S. Employment and Personal Activities 6. Migration 7. Selection of Respondents for Second Visit 8. Dwelling Characteristics Second Visit 9. Agricultural Activities 10. Non-Agricultural Household Enterprises 11. Expenditures on Non-Food Intras 12. Expenditures on Food and Consumption of Food Produced by the Household 13. Fertility 14. Other Income 15. Savings and Borrowing Both Visits 16. Anthropometric Data (Height and Weight)
# **_BECOMING FORMAL_**

# **_The effect of business entry reforms on number of firm registrations_**

A number of studies have examined the impact of business entry reforms on firm registration in different Latin American countries, exploiting cross-time and cross-municipality variation in the implementation of these reforms. A common element in these reforms is that they opened one-stop shop service points and thus eliminated the need to visit several different government offices for completing the registration process, lowering the time and/or cost needed to register a business. These studies find that a large reduction in the cost and time taken to register a firm leads to a modest increase in the number of formal firms.

A reform in Mexico, which was implemented in some of the most populous and economically developed municipalities starting in 2002, reduced the number of days taken to start a business from 30.1 to 1.4. Bruhn (2011) uses data on individuals’ employment status from the Mexican Labor Market Survey (ENE) to show that this reform increased the number of registered business owners by 5 percent. Kaplan, Piedra, and Seira (2011) find that the same reform increased the number of new firm registrations with the Mexican Social Security Institute (IMSS) by 5 percent, using administrative data.

The Colombian government implemented a one-stop shop that reduced the time required to register a business from 55 to less than 9 days and lowered registration fees by 30 percent. Cárdenas and Rozo (2009) use administrative data from Chambers from Commerce in six major cities to show that the reform led to a 5 percent increase in businesses registrations.

Entry reforms appear to have less of an effect on business registrations in less populous and more remote areas. Bruhn and McKenzie (2013) analyze the impact of the Minas Fácil Expresso program in the state of Minas Gerais, Brazil. This program aimed to extend the benefits of a one-stop shop to less populous municipalities, thereby removing the need for firms to register separately at their municipality and then travel to register with the state and federal governments in a larger town or city. They find this program actually led to a reduction in the number of firms registering during the first two months of implementation (perhaps due to officials learning to use the new system), with no subsequent increase.

The majority of these studies cannot disentangle whether the increase in registrations is due to informal firms registering or new firms being created by individuals who did not run a business before. The labor 6
below the cutoff ( _treatment_ ) with those of ineligible families with _Shombhob_ scores just above the cutoff ( _control_ ).

However the program implementation allowed us to construct several control groups to estimate the impact of the project. First, to derive the main results of the analysis we use the full sample of beneficiaries (i.e. the treatment group or T) and those who applied yet did not receive the program (i.e. the first set of control group, C1). T and C1 are expected to have similar scores and motivation: households in both groups belong to the bottom 50 percent of the income distribution and applied to the program. Second, we compare T with the group of households in the panel who did not receive any transfers under the _Shombhob_ . This second control group, C1+C2, includes C1 and the group of households who did not apply and thus did not get any _Shombhob_ transfer. This robustness check, while a “fuzzy RD design,” increases the statistical power of the estimates and gives insight on the impact of _Shombhob_ on a larger set of households. Third, we restrict the treatment and control groups to the households whose PMT is below 660: due to measurement error during the registration process, some rejected households actually record PMT scores below 660.<sup>16</sup> Lastly, we use the full sample (T versus C1+C2) with new weights inversely proportional to the distance between household PMT and the eligibility threshold _|PMTi-660|._ This fourth robustness check increases the power of the estimates – just like the second robustness check using the larger C1+C2 control group. It also allows us to give more weight to the observations closer to one another – i.e. those immediately above and below the PMT threshold, and less weight to those further away from the threshold – i.e. less comparable households.

> 16 We know what the “true” PMT score is from the preliminary census that was conducted prior to the implementation of _Shombhob_ . We believe that the baseline census records household level “true” information as trained enumerators were able to verify the information given to them by the households.

18
# C. <u>The mathematics and vocabulary tests</u>

We use three tests for our analysis. Two of these were applied to children at home as part of the household survey—regardless of whether they were enrolled in school; the third test is a mathematics test applied in school.<sup>6</sup> The first of the home-based tests is a mathematics test, which included 20 multiple choice items. Areas covered included basic algebra, geometry, and several questions which required using mathematical tools to answer simulated real world situations—including reading a simple graph or interpreting a bar chart. The survey protocol called for enumerators to find a quiet place where the applicant could fill out their responses; no time limit was given.7 The second home-based test we applied was a vocabulary test based on picture recognition. This test asked respondents to identify the picture corresponding to a word which the enumerator read out loud. For each word the respondent was then asked to select from a choice of four pictures. While the initial words are relatively easy to identify (“shoulder”; “arrow”; “hut”) the test is structured such that items become increasingly difficult (later words in the test we administered included, for example, “speed”; “selecting”; “adjustable”). There were in total 144 words that each applicant was asked to identify.

Last, we use a mathematics test that was administered in school to all students attending 7<sup>th</sup> grade in CSP program schools in June 2006. This was adapted from a test administered as a part of a separate national assessment exercise which tested mathematics skills at the end of 6<sup>th</sup> grade in a random subsample of primary schools (see Royal Government of Cambodia 2008). The CSP test was shorter, and focused on areas that would be reinforced during 7<sup>th</sup> grade. It consisted of 25 primarily multiple choice questions, and the areas covered were very similar to those in the mathematics test administered to

> 6 The tests administered as a part of the household survey were carefully piloted and adjustments were made, as necessary. (The classroom-based test had already undergone extensive validation.)

> 7 Average time was 45 minutes; with 10 percent taking less than 26 minutes, and  10 percent taking more than 75 minutes.

9
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30
Table 4. ‘Pure’ Profit Shifting

||(1)|
|---|---|
|Rate Diferential|1.832***<br>(0.480)|
|Host KF-METR|-0.003<br>(0.005)|
|Pure proft shifting (Ω)|-0.043<br>(0.033)|
|Observations|12,630|
|Pseudo R<sup>2</sup>|0.129|

_Notes:_ The dependent variable is _It/Kt−_ 1, and estimation is by PPML. Year and parent country fixed effects are included, as well as controls for host, parent and gravity variables. Standard errors, in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

Table 5. Taxation in the Parent Country: Worldwide vs. Territorial

||(1)|(2)|
|---|---|---|
|Rate Diferential _×_ Territorial Parent|3.208***<br>(0.524)||
|Rate Diferential _×_ Worldwide Parent|2.707**<br>(1.158)||
|Non-Host Component _×_ Territorial Parent||3.076***<br>(0.592)|
|Non-Host Component _×_ Worldwide Parent||2.982**<br>(1.163)|
|Host Component _×_ Territorial Parent||-3.212***<br>(0.525)|
|Host Component _×_ Worldwide Parent||-2.457**<br>(1.072)|
|Observations|11,208|11,208|
|Pseudo-_R_<sup>2</sup>|0.180|0.180|
|Controls:|Parent<br>Bilateral|Parent<br>Bilateral|
|FEs:|Host-Year|Host-Year|
||Parent|Parent|

_Notes:_ All specifications are estimated in PPML. The dependent variable is the _It/Kt−_ 1 ratio for FAI. All specifications include parent country and host country-year fixed effects, and the following characteristics of the multinational firm’s parent country: GDP (in log), capital account openness index, trade openness, inflation rate, government expenditure share, and exchange rates. Distance between the host and parent, and a dummy variable indicating whether they share a common language are also included. Standard errors, in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

53