rafmacalaba/datause-encoder-data huggingface dataset:
reference year. The mechanics of the extrapolation and interpolation are described in more detail in Ravallion (2003), Chen and Ravallion (2004) and Jolliffe et al. (2014).

For countries without any poverty estimates, extrapolation to the reference year is of course not an option, and PovcalNet does not report poverty estimates for these countries. Nonetheless, these countries do indirectly enter the regional and global poverty counts. The country-level poverty headcount used to account for them in regional totals is the product of the estimated poverty headcount in the remainder of the region to which they belong (e.g. subSaharan Africa) and the country’s own population, as reported by the WDI. So, when aggregating to regional and global totals, developing countries without any poverty measures are in effect assigned the average headcount for their regions. This procedure is obviously not intended to suggest any inference about poverty in those specific countries, which is why numbers are not reported for them. Rather, it is a compromise intended to avoid undercounting poverty in those regions with weaker data coverage.<sup>49</sup> High-income countries are assumed to have no people living in extreme poverty in our global count. Although some people in rich countries report household per capita incomes that are below the international poverty line, per capita consumption is above this threshold for nearly everyone. Drawing an example from the United States, Chandy and Smith (2014) find that 1 to 4 percent of the population live below $2 a day when using income measures, but that less than 0.1 percent live below this threshold when using consumption measure. While the assumption of zero people in extreme consumption poverty may not be fully supported, it is a useful simplifying assumption that appears to closely approximate the correct estimate.

In most cases, our estimate aims to be as complete as possible in terms of coverage; but there are cases where countries and regions are excluded. The World Bank previously reported global poverty numbers in 3-year intervals from 1981 to 2008. Since 2010, there have been annual updates for the reference-year poverty estimates, with this paper presenting estimates for the 2012 reference year.<sup>50</sup> For each reference year, we calculate a population coverage rate for regional poverty estimates. The coverage rate is defined by the share of the population in each region which is covered by a survey within plus or minus two years of the reference year. In

> 49 For an overview of countries missing poverty data see Serajuddin, et al. (2015).

> 50 Reference years used in PovcalNet are 1981, 1984, 1987, 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2010, 2011, 2012.

28
44 Table 6: Natural Resources, (De-)Industrialization & Vanitous Tall Buildings, 1960-2020

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|**Panel A:**<br>+ Urban Dvt Ctrls|**Long-Differ**|**ence:**Dep.|Var.: Log Non|-Residential|Height Densi|ty c. 2020|
|Buildings_≥_... Meters:|All|125|140|160|200|240|
|MFGSERV2020|0.01|-0.00|-0.03|0.06|0.03|0.01|
||[0.022]|[0.039]|[0.037]|[0.045]|[0.051]|[0.049]|
|NRXGDP1960_−_2020|0.05**|0.04|0.08**|0.09*|0.13**|0.14**|
||[0.023]|[0.035]|[0.036]|[0.043]|[0.063]|[0.062]|
|DEINDU1980_−_2020|-0.03|-0.06|-0.07*|-0.04|0.02|0.01|
||[0.024]|[0.044]|[0.038]|[0.047]|[0.065]|[0.061]|
|NRXGDP - MFGSERV|0.05**|0.05|0.11***|0.03|0.11|0.12*|
||[0.02]|[0.03]|[0.03]|[0.06]|[0.07]|[0.07]|
|DEINDU - MFGSERV|-0.04|-0.05|-0.04|-0.10|-0.01|-0.00|
||[0.03]|[0.06]|[0.05]|[0.06]|[0.08]|[0.08]|
|Obs; Controls|115; Y|115; Y|115; Y|115; Y|115; Y|115; Y|
|**Panel B:**<br>+ Urban Dvt Ctrls|**10-Yr Panel**|**(w/ 4 Lags):**|Dep. Var.: Lo|g Non-Resid|ential Height|Density_t_|
|Buildings_≥_... Meters:|All|125|140|160|200|240|
|Sum for MFGSERV Lags|-0.01|0.01|-0.03|0.01|-0.05|-0.15|
||[0.06]|[0.09]|[0.09]|[0.09]|[0.11]|[0.11]|
|Sum for NRXGDP Lags|0.04|-0.01|0.00|0.13*|0.13*|0.10|
||[0.04]|[0.07]|[0.07]|[0.08]|[0.08]|[0.08]|
|Sum for DEINDU Lags|-0.06|0.10|0.13|0.58**|0.46*|0.39|
||[0.07]|[0.13]|[0.12]|[0.26]|[0.27]|[0.25]|
|Obs; Cntry & Yr FE, Ctrls|346; Y|346; Y|346; Y|346; Y|346; Y|346; Y|
|**Panel C:**<br>**Long.Diff; Dep.Var.:**|<br>Vanity|Log No|n-Residential|Height Dens|ity c. 2020 Ba|sed on ...|
|+ Urban Dvt Ctrls<br>|(Tip-Occup.)|Steel|Concrete|Capital|Cap+Large|st<br>Other|
|MFGSERV2020|-0.06|0.13***|0.01|0.01|0.01|0.05|
||[0.080]|[0.040]|[0.038]|[0.024]|[0.041]|[0.038]|
|NRXGDP1960_−_2020|0.09|0.03|0.08**|0.05*|0.03|0.09**|
||[0.065]|[0.052]|[0.033]|[0.026]|[0.054]|[0.037]|
|DEINDU1980_−_2020|0.00|0.02|-0.04|-0.03|-0.03|0.09|
||[0.035]|[0.054]|[0.033]|[0.029]|[0.044]|[0.059]|
|NRXGDP - MFGSERV|0.15***|-0.10*|0.07**|0.03|0.03|0.04|
||[0.05]|[0.05]|[0.03]|[0.03]|[0.06]|[0.04]|
|DEINDU - MFGSERV|0.06|-0.11|-0.05|-0.04|-0.04|0.04|
||[0.07]|[0.07]|[0.06]|[0.03]|[0.06]|[0.07]|
|Obs; Controls|38; Y|115; Y|115; Y|115; Y|115; Y|115; Y|

_Notes:_ Robust SE in parentheses (clust. at the country level in Panel B).
# **6.    Conclusions**

In this paper we explore the short-run consumption expenditure dynamics and the interaction of public and private arrangements using a randomized sample of 749 rural households in Malawi. The context of rural Malawi represents an extremely interesting setting due to the high HIV/AIDS prevalence that has inevitably shaped the social structure of many households. Moreover, the cash transfer pilot project used in the analysis represents one of the first experiments of social protection policies based on unconditional cash transfers in Sub-Saharan Africa. We exploit the unique source of exogenous variation provided by the randomized component of the program in order to isolate the effect of cash transfers on consumption expenditures as well as the net crowding out effect of cash transfers on private arrangements. We find a large effect in the level of variation of consumption expenditures for those households receiving the cash transfer. The effect is statistically significant across all specifications. The magnitude of the coefficient varies on the basis of the distribution’s quantile, showing that households in the .25 quantile of the variation of consumption’s distribution (i.e. those households that, in the absence of the cash transfer, would have shown a smaller variation in consumption expenditures) are those who register the highest increase in their consumption expenditures, while households in the .75 quantile increase their expenditures less (as they would have probably increased it in any case). These findings confirm the rejection of the perfect risk sharing hypothesis and suggest that there might be an important role for public interventions that might help households to pool risk more effectively. Consequently, the government’s initiative of implementing a public cash transfer program seems to be well justified by the type of risk sharing arrangements and market imperfections existing at the village level.

However, in order to test how such public interventions interact with pre-existing private arrangements at village level we have also looked at the effects of cash transfers on private transfers in a context where the social fabric is already heavily compromised by the presence of high HIV/AIDS rates, and characterized by imperfect enforceability of contracts.

On the basis of the detailed information provided in the dataset, we could distinguish among three different types of private transfers: remittances, informal loans from friends or other family members and gifts from friends or other family members without the expectation of repayment. Results confirm the presence of crowding out effects on private arrangements when looking at gifts and, to a lesser extent, remittances, while informal loans seem to be completely independent from the cash transfer’s reception. The fact that the crowding out effect is much stronger in the case of 27
p p 2 He Of2 p p p p _ <u>o,2 1</u> Br-1=pt <u>o2,</u> ~~1p~~ Br-1 Be-1 OA22 Bea1LOWERLOWER _=p+=p++ <u>1</u> 2 <u>o2,</u> ~~1p~~ Br-1 O% Be-1 OA22 Bea1LOWERLOWER _=p+=p++ p Be-1 2 Ost p <( ~~—f~~ _ a2 > ~~(22)~~ (of2__ — P°or-472,2 p O}2 teET T 4 ~~—)~~ (of -— por =a Oo.2 ~~(—2)~~ 2__ a2,2 — 72 a ~~(G~~ C po.) A Br-1 BUPPERtt = min (4 pt, Of_-4 ~~of~~ 1-p1 ) BLOWERt-1 BUPPERt-1 2 2 A Of Ot-1 p Bft Be-1
# **<mark>Supplementary Materials</mark>**

# **<mark>Appendix A: Methods</mark>**

# **<mark>Study 1: Descriptive Findings: Agency in Context</mark>**

_<mark>Descriptive Surveys in Niger</mark>_ _Participants._ This data was from surveys collected among the full policy experiment sample at baseline (N=4,717) or otherwise from respondents assigned to the control group at the six-month follow-up (N=1,216). The reason for the latter choice was to obtain responses unaffected by the additional psychosocial interventions. The policy experiment sample is described in section ‘Study 2—Policy Experiment’ below.

_Procedure_ . For the descriptive data on models of agency _,_ we explored women’s understanding of drivers of and barriers to economic success through two questions asking them to rank four factors in order of their importance as drivers of women’s economic success and as barriers to women’s economic success. For both questions, two options were more independent factors taken from Western goal setting and entrepreneurial interventions (Campos et al., 2017). For the drivers of success question, these included “having self-initiative” and “hard work” and for the barriers question, these included “not planning for the future” and “not being persistent.” Other factors were derived from qualitative interviews. For the question on drivers, the options included “peacefulness” and “good social connections,” and for the question on barriers, these included “not respecting others” and “tension in the household.” In the question on drivers, participants were asked “of these 4 factors, which is the most necessary for the success of women in their economic activities?” and then asked to rank the subsequent qualities. The four options were: <mark>working hard, having peacefulness, showing personal initiative and being strategic, and having good social connections.</mark> For the question on barriers, participants were asked “of these 4 factors, which is the main reason for which women do not succeed in their economic activities?” and then asked to rank the subsequent qualities. The four options were: <mark>not respecting others, not persevering in the face of obstacles, having tension in the household, and not having a plan for the future</mark> .

_Empirical strategy_ . These analyses were not pre-registered as these measures were exploratory and descriptive in nature.

_<mark>Predictions study in the U.S.</mark>_ As a point of cultural comparison, we collected predictions of our Nigerien sample’s responses on models of economic agency by U.S. participants through an online convenience sample ex post.

_Participants_ . We recruited 302 respondents based in the U.S. from CloudResearch’s MTurk Toolkit platform to take a descriptive survey. The sample was on average middle income, with the modal annual household income being $40,001-$60,000; 57.6% identified as male, 41.7% as female, and 0.7% self-described; 42.4% had a 4-year college degree and 20.9% had some 32
Our paper addresses these issues in evaluating a rural roads program in Bangladesh, applying a dynamic panel data approach to household data collected under the program (Arellano and Bond, 1991, Jalan and Ravallion, 2002, 1998). The dynamic model also allows us to compare short-term with longer-term outcomes from the program, accounting for time-varying unobserved heterogeneity. We use new data collected by the Bangladesh Institute of Development Studies (BIDS) of project and control households under the Rural Roads and Markets Improvement and Maintenance Project (RRMIMP), spanning three rounds (pre-program and post-program) between 1997 and 2005. The RRMIMP project was a part of an LGED project financed by the World Bank. We examine whether rural roads generate increasing returns over time in poor areas, accounting for factors as pre-program community characteristics affecting road placement, presence of such complementary investments as electricity, institutional and market development, and other factors associated with road construction (e.g., quality and access).<sup>1</sup> Completed in 2005–06, RRMIMP is a road-paving project that targeted several villages across various districts of Bangladesh; control villages were also selected in separate districts. BIDS conducted the panel survey of households in project and control villages in three rounds: (i) prior to start-up of the RRMIMP in 1996–97, (ii) immediately following project completion in 2000–01, and (iii) in 2005 via a project follow-up survey. Khandker, Bakht, and Koolwal (2009) used the first two survey rounds to study the short-term effects of rural road development, finding positive effects on a range of household outcomes including higher per capita expenditure and lower transport costs.<sup>2</sup> The study used household fixed-effects estimation, controlling for a range of pre-program village

> 1 Our study closely follows Jalan and Ravallion (1998), who examine the impact of a poor-area development program on growth in household consumption using panel data collected from targeted and non-targeted areas across four provinces in China.  Using county yearbooks over 1980–87 and 1982 census data, they employ a Generalized Method of Moments estimation procedure on an AD(1,1) model for household consumption growth, including initial area conditions on the right-hand side, and using second and higher lags of consumption as instruments for lagged consumption to obtain consistent estimates of a dynamic growth model with panel data.

> 2 In addition to the RRMIMP, the 2009 study examined the effects of the Rural Development Project (RDP), another road-paving project, using a similar household panel survey of project and control villages; however the third survey round was limited to the RRMIMP project and control areas.

3
premium relative to European options, which allow the option holder to exercise the option only<sup>on</sup> the maturity date of the option. This in turn means that the use of the standard European option pricing formula of Black (1976) will result in ISD's which are overstated. The positive bias in<sup>the</sup> ISD occurs because the European formul3 assumes that the higher option price is due to<sup>nigher</sup> volatility (and therefore a higher estimated ISD is obtained), when il reality the higher price<sup>simply</sup> reflects the early exercise premium<sup>2</sup> . Unfortunately, no closed-form solution exists for<sup>American</sup> options, though several approximation methods exist. We adopt the method of Barone-Adesi<sup>and</sup> Whaley (1987), who provide an efficient approximation for pricing American options on commodity futures.

Suppose that the underlying futures price follows the stochastic differential equation (1) _dF/F = Cdt + odz,_ where _F_ is the commodity futures price, C is the iastantaneous expected relative price change of the commodity, _a_ is the instantaneous standard deviation, t is time and z is a Wiener process. Then if the- interest rate r is constant and no arbitrage opportunities exist, Black (1976) shows that the price of a commodity futures option, C, must follow the partial differential equation where subscripts represent partial derivatives of the variable with respect to the subscript. For a European option with no early exercise privilege, the boundary condition requires that the maturity value of the option be equal to max{O, _FT - X},_ where _FT_ is the futures price at maturity and X is the exercise price. This boundary condition is applied to (2) to get Black's European commoulty futures option pricing formula

# (3)

where T is timue to maturity, N(.) is the cumulative normal distribution, and

> **2 To de..o.rstrate the potential magnitude of the bias, consider the May/1985 soybean**<sup>**futures option selling on**Noverii</sup><sup>_.-r_</sup> **2, 1984 with an exercise price of** S600. The **futures price war 8664.75, the spot price was** 8627.50, **the call** price ' i% 876.00, **ani** the annualized interest rate **was** 9.737%. **Using** Black's (1976) formula **for** pricing<sup>Europeanfutures opLon.i</sup> e ISD **is** 0.2272, while **using** the Barone-Adesi **and** Whaley American option<sup>pricing approximation</sup> (which will t. cussed 3hortly), the ISD **is** 0.2174. The overstatement **using** the European formula **is**<sup>thu:</sup><sup>**about 5%.**</sup>

3
national poverty lines. The proposed relative poverty lines have a lower bound of $1.25, which applies to the aforementioned reference group, but then rise according to mean consumption with a gradient of one-in-three. This offers a very good fit with our data on national poverty lines.

In Chen and Ravallion (2008b), new global poverty measures for the period 1981-2005 are estimated from 675 surveys spanning 116 countries using the new international line of $1.25 per day in 2005 prices proposed in this paper, but otherwise using similar estimation methods to those outlined in Chen and Ravallion (2001, 2004). Chen and Ravallion also test robustness of the main qualitative results to the choice of poverty line, and provide estimates over the range $1.00-$2.50 per day.

24
**2 Data**

We combine information from three sources. Data on NTPs come from the World Bank Deep Trade Agreement Database (Hofmann _et al._ , 2019; Mattoo _et al._ , 2020). This includes information on all non-trade related provisions contained in 279 agreements signed between 1958 and 2015. The database distinguishes 14 “core” provisions that reconfirm existing WTO disciplines or impose additional (WTO-plus) obligations in policy areas that are covered by the WTO, as well as 38 provisions in areas that go beyond extant multilateral commitments (WTO-extra provisions). The dataset documents the growth in the inclusion of provisions on civil rights, environmental protection, and labor rights in trade agreements (Figure 1). In addition to reporting on the existence of provisions on a given subject, the database also provides information on their legal nature, including their enforceability.<sup>6</sup> We focus on three types of WTO-extra provisions in PTAs; those related to civil and human rights promotion, labor rights protection, and environmental protection. We reduce the original bilateral dataset to a panel defined at _country ∗ year_ level. For each country, we consider the year it signs a PTA containing the provisions of interest, the partner country (whether the PTA includes the EU, the US, or other countries), and whether the obligations arising from the agreement can be considered as binding (i.e., if they can be enforced through a dispute settlement mechanism). In case a country signs more than one agreement, we consider the first one in which a provision of interest is signed and assume that it stays in force even when additional agreements (possibly with different sets of partners and different sets of obligations) are signed. We allow for changes in legal enforceability over time with subsequent agreements.

> 6Information on legal enforceability is reported for 52 selected policy areas in total. An extended version of the dataset provides more detailed indicators for a subset (18) of these policy areas. We work with agreements signed in the period 1995-2010. See https://datatopics.worldbank.org/dta/about-theproject.html.

5
-2-

_Adjustment in Africa,_<sup>_1_</sup> which demonstrates, (1) that there is considerable variation in the economic results of adjustment programs among African countries, and (2) that this variation is explicable in terms of the quality and sustained application of adjustment policies.

This paper moves a step beyond the Bank's adjustment study by undertaking to identify the causal linkages underlying whether or not adjustment policies are sustained. It develops hypotheses which account for why some countries sustain their policies while others do not. In doing so it profits from the extensive literature in politics and political economy. It tests these liypotheses with evidence from seven case studies developed in relation to the Adjustment in Africe' project.

# The Seven Cases

The recent World Bank study _Adjustment in Africa_ assessed at some length the extent to which economic policy reforms were implemented in 29 countries that had ntroduced structural adjustment programs in the 1980s. The study found that the record of implementation was mixed: only six countries had brought about large improvements in macroeconomic policies, nine expenenced minor improvement and eleven deteriorated. Among the last group were several CFA countries which have since undertken a significant devaluation (January 1994). Countries that improved their policies, however, had a pay-off in terms of higher per capita GDP growth, large exports, faster agriculture and industrial sector growth. However, even in the relatively successfil countries progress in public enterprise reform, financial sector reform and public sector management has been less satisfactory. The case studies of adjustment experiences in seven African countries-Burundi, Cote d'Ivoire, Ghana, Kenya, Senegal and Tanzania-

**4/** _Adjuwe_ **_in Afnica._** _Rejfonns, Resuks and_ **_dc_** _Road Ahead,_ World **Bank,** Oxford **University Press,** 1994.

**_51_** Husain, L and **E'arnqee,** R., **_Ajmsemnt_** _in_ **_fica_** _Lecssons_ **_from_** _Country_ **_Case_** _Studies,_ World **Bank** Sector and Regional Studies, 1994.
30 up a nutrition package to 90% of the populations that are not accounted for in this framework. It should also be kept in mind that our results in the forward-looking exercise do not capture the effects of the program on mortality, which are estimated to be appreciable (Bhutta et al. 2013). Insofar as the program reduces child mortality, the initial effect will be to reduce the fraction of the population working, i.e. N�⁄ in eqn (1) will fall. This will cause per capita income to fall until the children grow N up and join the labor force. Reductions in child mortality are also likely, however, to lead to subsequent changes in fertility behavior, with families reducing their family size as children are more likely to survive childhood. This will push N�⁄ back up and hence dampen the downward pressure N on per capita income. Finally, we should keep in mind that childhood survival is valued in its own right – a more complete cost-benefit analysis would capture the intrinsic value associated with fewer children dying in childhood as a result of the nutrition program. All told, our estimates are probably underestimates of the effect of stunting on per capita income and on the rate-of-return to the Bhutta et al. package of nutrition interventions.

The fact that our underestimates are quite large despite likely being underestimates is quite striking. A GDP-per-capita penalty of 7% from extreme smallness in a child’s first 1,000 days seems like a big deal. It is true that at 17% our rate-of-return estimate is not as high as the 22% reported by the World Bank’s Independent Evaluation Group for World Bank projects over the period 2000-2008 (World Bank Independent Evaluation Group 2010). However, those estimates were not produced using the method that we used, and we suspect ours is more conservative. And as we have already said, our estimates are likely a lower bound, not least because we have not factored in the nonpecuniary benefits associated with better child health and improved survival prospects. A rateof-return of 17% therefore seems like a good investment.