rafmacalaba/datause-encoder-data huggingface dataset:
How big are these growth effects for a typical enterprise? Over the medium term, even a marginal improvement in the rural investment climate could be significant and lift the rural economy out of stagnation. Figure 17 builds on the simulations and plots the stylized enterprise employment growth process. The scattered lines assume that a broad improvement of the rural investment climate would result in a 0.1% increase in employment growth (much lower than the estimated impact of individual constraints ranging from 0.04 up to almost 0.3%, respectively). Even a marginal improvement of the investment climate could provide substantial gains for the rural economy. Over a 10-year period, a one-person enterprise would reach the two-worker category and experiences continued growth. After an initial period of stagnation, a five-person enterprise would generate on average up to four additional workers. Overall, this is in line with the findings presented in previous section. In relative terms, smaller rural enterprises would benefit most from an improved investment climate. Over a 10-year horizon, a one-person start-up firm could double its size while a five-person start-up enterprise could grow by 80%.

The analysis confirms much of the earlier descriptive evidence. Business constraints perceived by rural entrepreneurs are broadly consistent with objective and subjective measurements at the community level. They are also correlated with enterprise growth.

# **6. Conclusion**

The rural nonfarm private sector in Tanzania has grown too big to ignore. Tanzania encompasses more than one million of small rural enterprises. Results of this pilot survey suggest that some 28% of rural households have at least one family member working in a rural nonfarm enterprise. Moreover, evidence regularly suggests that rural nonfarm enterprise activity is a key source for income growth and diversification for the rural poor in Tanzania.

Overall, we describe a low-return rural enterprise sector struggling to compete in a difficult business environment. A central finding of our analysis is that even marginal improvements of the 28
1999, water coverage increases by 35 percentage points, sewerage by 15 percentage points, and (fixed plus cellular) telephone coverage quadruples from 2% to 8%. Unfortunately, the household survey data does not distinguish between fixed and cellular telephones. However, given that the coverage of fixed line telephones in El Alto was found to be static over this period (Figure 3.7), it can be inferred that these gains are largely attributable to the diffusion of cellular telephony. The expansion of electricity coverage, on the other hand, continues to increase at the same rate of about 2 percentage points each year, before and after the reform.

**Figure 3.10: Evolution of coverage for poorest quintile in El Alto**

<!-- Start of picture text --> 100<br>90<br>80<br>Water<br>70<br>60 Sewerage<br>50<br>Electricity<br>40<br>30 Telephone<br>(fixed plus cellular)<br>20<br>10<br>0<br>1989 1994 1999<br>Coverage rate (%)<br><!-- End of picture text --> _Sources_ : Encuesta Integrada de Hogares (1989, 1994); Encuesta Continua de Hogares (1999) A more indirect way of looking at the distribution of new connections between rich and poor households is to take geographical location as a proxy for poverty. The 1992 Census, which adopts a methodology of unsatisfied basic needs for the measurement of poverty, finds that by far the highest poverty rates are to be found in El Alto, the next highest in the ‘laderas’ of La Paz and the northern suburbs, and the lowest poverty rates in the central and southern suburbs of La Paz (Figure 3.11). In terms of absolute numbers, only a tiny fraction of the poor are to be found in the affluent central and southern neighborhoods. The remainder are evenly divided between El Alto and the northern neighborhoods and ‘laderas’ or La Paz (Figure 3.12).

**Figure 3.11: Poverty rates by geographical area**

<!-- Start of picture text --> La Paz<br>(Centro/Sur)<br>La Paz<br>(Norte/Laderas)<br>El Alto<br>0 10 20 30 40 50 60 70 80<br>Headcount poverty rate<br>18<br><!-- End of picture text -->
# **1. Introduction**

There is growing concern among economists and practitioners that economic conditions prevailing in early childhood may have a persistent effect on child health and socioeconomic outcomes later in life. The shock’s effect may be such that children cannot catch up even if they experience subsequent good years at later ages. Policy makers are aware of the distressing link between early childhood and adult well-being, making it one reason why the World Bank and non-governmental organizations view improvements in child health as a top priority.

In this paper, we focus on how exogenous shocks at birth lead to worse health outcomes in the short-run several years after the shock. In particular, we look at the effect of civil conflicts and crop failures on the health of Rwandan children born between 1987 and 1991. We use an integrated household survey, combining health and agricultural data with event data from reports by non-governmental organizations. We exploit the local nature of the crop failure (confined to provinces in southern Rwanda) and the civil war (confined to provinces in northern Rwanda) to identify the causal effect of these exogenous shocks on child health, and we use the variation across birth cohorts to capture the child’s exposure to the shock. Our approach is to estimate the effect of these shocks on height-for-age while controlling for province of residence, month and year of birth as well as household, infant, and mother characteristics. Our results are robust to using sibling difference estimators and for a sub-sample of children, we use rainfall and household production shocks to confirm the robustness of the crop failure results.

There is an extensive literature that attempts to measure the impact of economic shocks on future welfare outcomes.<sup>1</sup> Using Netherlands’ birth registry data, Van den Berg, Lindeboom,

> 1 This is related to research that explores the association between health and income and the link between childhood conditions and later welfare outcomes (Curry and Hyson, 1999; Case, Lubotsky, and Paxson, 2002).  In a paper exploring the role of shocks and income, Frankenberg, Smith, and Thomas (2004) find that richer households are able to smooth consumption and cope with the unanticipated financial crisis that occurred in Indonesia in 1998.

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proposed by the same authors in a later paper.

In this paper we propose alternative measures of jointness for Bayesian variable selection, based on probabilistic arguments and illustrate their application in the context of BMA. The next section discusses measures of jointness, which will be applied to two different growth data sets: the FLS data and the DW data in Section 3.

# **2. Jointness in Bayesian Variable Selection**

Consider two variables, _i_ and _j_ , and let _P_ ( _i_ ) denote the posterior probability of inclusion for regressor _i_ .<sup>2</sup> _P_ ( _i ∩ j_ ) is the posterior probability of including both variables _i_ and _j_ . These probabilities are simply defined as the sum of the posterior probabilities of all models that contain regressor _i_ (for _P_ ( _i_ )) or regressors _i_ and _j_ (for _P_ ( _i ∩ j_ )). Clearly _P_ ( _i_ ) _≥ P_ ( _i ∩ j_ ).

DW define jointness as (the logarithm of): There are several problems associated with this measure. First, since _Jij_<sup>_DW_</sup> = _P_ ( _i|j_ ) _÷ P_ ( _i_ ) = _P_ ( _j|i_ ) _÷ P_ ( _j_ ), it can be verified that as either _P_ ( _i_ ) or _P_ ( _j_ ) approaches unity, then _Jij_<sup>_DW_</sup> also approaches unity—regardless of the behaviour of the other variable. In fact, it is bounded from above as _Jij_<sup>_DW_</sup> _≤_ 1 _/_ max _{P_ ( _i_ ) _, P_ ( _j_ ) _}_ . This means that when the inclusion probability of one of the variables exceeds _e_<sup>_−_1</sup> = 0 _._ 37 this variable can never be a “significant complement” (corresponding to log( _Jij_<sup>_DW_</sup> ) _>_ 1) of any other variable in the data set according to the DW jointness measure.

Secondly, consider a case of extreme jointness where variables _i_ and _j_ always appear together; then _Jij_<sup>_DW_</sup> = _P_ ( _i_ )<sup>_−_1</sup> = _P_ ( _j_ )<sup>_−_1</sup> = _P_ ( _i ∩ j_ )<sup>_−_1</sup> so the DW measure equals the reciprocal of _P_ ( _i ∩ j_ ). This makes comparisons of DW-jointness across different pairs of variables quite difficult, and is not consistent with the fixed critical level used by DW. It also raises questions about interpretation, since while _P_ ( _i ∩ j_ ) could itself be considered a natural measure of jointness, that does not apply to its reciprocal.

Finally, contrary to the claim in DW, _Jij_<sup>_DW_</sup> does not correspond to the posterior odds of models including _i_ and _j vs_ models that include them individually. This ratio is properly defined in equation (6) below.

In subsequent work, Doppelhofer and Weeks (2006, DWa henceforth) propose an alternative jointness measure, based on the cross-product ratio of the binary indicators of variable inclusion. For two regressors _i_ and _j_ this measure corresponds to (the logarithm of):

> 2 To economize on notation, we shall not make the dependence on the sample explicit, but it should be understood that all posterior quantities are conditional on the observed sample.

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|Index of fatalities from violence (normalized 0-1)**|205|
|---|---|
|Index of homicides (normalized 0-1)***|90|
|_Social cohesion index (0-1)_|_68_|
|**Process Legitimacy**||
|Rule of law (normalized 0-1)|209|
|Control of corruption (normalized 0-1)|209|
|Government powers are effectively limited by the judiciary (score 0-1)|139|
|Equal treatment and absence of discrimination (score 0-1)|139|
|People can access and afford civil justice (score 0-1)|139|
|Government regulations are applied and enforced without improper influence (score 0-1)|139|
|The personal rights to life and security are effectively guaranteed (score 0-1)|139|
|_Process Legitimacy Index (0-1)_|_139_|

Source: Social Sustainability Global Database 2022; Social Sustainability Global Database 2022 Codebook. Notes: (*) Average set of assets that include computer, cellphone, radio, television, washing machine, sewing machine, car, fridge, motorcycle that is owned by a household in the country, normalized between 0 to 1 (1=having all assets considered). (**) Index of fatalities due to violence in a country. The indicator is normalized across the whole country sample using varvarmin/varmax-varmin (***) Index of the number of homicides per 100,000 inhabitants in a country normalized across all the countries in the sample using var-varmin/varmax-varmin

# **4.2 Linking social sustainability, poverty, inequality and income levels**

We use country-level data to scatter plot the logarithm of per capita GDP in PPP (international dollars 2017) and each of the social sustainability dimension indices. Additionally, we also plot poverty headcount, using national poverty lines of each country’s latest year with available information, against each of the indices. A third set of scatterplots looks into the relationship between each country’s social sustainability and income inequality.

Figure 3 below shows how richer countries present higher values for each of the social sustainability dimensions. This means that there is a <mark>positive correlation between</mark> per capita GDP and social inclusion, resilience, social cohesion, and process legitimacy. Although the relationship is quite strong for the process legitimacy and social inclusion indices (P4 and P1), this is not the case for the two other dimensions. When focusing on poverty levels, societies with stronger social inclusion, social cohesion, resilience, and process legitimacy are also less poor. See figure 4 (and online annex C). Thus, worldwide, societies with greater access to markets and services; more trusting communities; individuals, communities, regions and countries more capable of confronting different types of shocks; and societies where governments are fairer and more transparent are expected to be less poor. This is captured by the negative correlation between poverty headcounts and P1, P2, P3 and P4.

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particularly difficult to collect in transition countries, practice has shown that data on consumption can be gathered with considerable accuracy. Survey consumption modules have become more detailed over time and are better able to capture the various dimensions of consumption including, for example, informal payments.

Second, unlike the practice of simple aggregation undertaken by many statistical offices of the region, a distinction was made between different components of consumption. Since consumer durables and housing are consumed over a long period of time, it is customary to include the imputed value of the consumption flow associated with the possession of consumer durables (including housing) but to exclude the expenditure on the purchase of such goods. The lack of data, however, limits the application of this approach to all countries. It was therefore decided neither to include estimates of the flow of services of durables, nor durable purchases or rents.

Third, given the significance of spatial differences in the transition countries, an adjustment for spatial price differences was made, using Paasche price indices based on survey data in all countries. In cases where data were collected over a long period of time, it was also necessary to adjust for changes in prices over time. Quarterly CPI indices taken from IMF data were used to compute real values.

Fourth, households in the transition countries have coped with poverty by relying on an array of non-market strategies, including producing their own food and engaging in reciprocal exchange with other households and institutions. A consistent approach was used to assign a monetary value to these components of consumption.

Fifth, the same procedure, which conforms to methods used in other international household survey data depositories such as the Luxemburg Income Study, was used to clean the data of outliers across all data sets. Since a consistent approach was used across all data sets, one can be reasonably confident that differences across countries in the final consumption measure arise from differences in the primary data and are not owed to the method of aggregation.

Results for all countries with available primary records are presented in Table 2. The table clearly shows that there are discontinuities and that the evidence is of variable quality. However, the difference in country experiences regarding the evolution of inequality even with as comparable data as possible is striking. It dispels the notion that countries would converge to some common level of inequality that prevails in the long-run in market economies and provides motivation for the analysis undertaken in this paper.<sup>4</sup>

> 4 Ravallion (2001), quoting Benabou (2000) argues that countries are expected to converge to the same distribution and proposes a test for such convergence, but due to data limitations transition economies have not been fully incorporated in his analysis.

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_— —_ peer characteristics including previous labor market outcomes we merged in individual-level data from multiple administrative sources for Brazil and Ecuador.

# **Brazil**

**_Higher Education Census (HEC)._** This is the universe of higher education students, programs, and HEIs in Brazil and comes from the Ministry of Education. For a given academic year, and for every student enrolled in higher education, the HEC provides demographics (age, gender, and race), initial enrollment date, and dropout or graduation status by year’s end. Focusing on the programs in our sample of effective surveys (henceforth, surveyed programs), we selected the students who entered them in 2014, and used the 2015 and 2016 HECs to establish whether they had graduated. We define a student’s peers as those who entered her same program in 2014.

**_National Educational Entrance Examination._** The _Exame Nacional de Ensino Médio_ ,

(ENEM) dataset comes from the Brazilian Ministry of Education and includes student demographic and family background variables. Although it includes individual ENEM test scores, we do not use these because they are missing for 60 percent of the sample.

**_Labor Market Outcomes._** Their source is the Annual Reports of the Social Administration ( _Relaҁão Anual de Informaҁões Sociais,_ RAIS), a matched employer-employee dataset of all workers and firms in the Brazilian formal sector. It is constructed by the Brazilian Ministry of Labor based on a mandatory annual survey filled by all firms in the formal sector. RAIS contains information on earnings (gross monthly wages), employment, occupation, and demographics for all individuals who are employed by a formal firm in a particular year. For every individual selected from the 2014 HEC, we use RAIS to measure employment status and earnings in the 12month period before she starts the program (namely, in 2013) and the 12-month period following her graduation (namely, in 2016 or 2017) provided she graduates within three years.

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2. In the second stage, five interview partners responded to the indicative questions via email.

3. In a final stage, participants were followed up directly with further questions. Not every participant was able to answer the indicative questions within the time allocation of this project.

Table 2.1 provides an overview of the four GPCs and one RSMC most responsive to our survey. Each institution was questioned about its total operating costs and the marginal costs needed for providing the full suite<sup>2</sup> of global products to an additional country. The budgets of the agencies in the sample varied widely and often did not indicate details on specific costs of GPC and RSMC functions, necessitating broad assumptions when estimating the costs of global NWP provision.

**_Table 2.1_** _: Overview of interviewed GPCs and RSMC._

|**Name**|**Type**|**Inst.**<br>|**Fin**|**ancing**|**Prod**|**ucts**|**Additional**<br>|
|---|---|---|---|---|---|---|---|
|||**Status**|**Primary**|**Secondary**|**Forecasts**|**Other**|**Services**|
|ECMWF|GPC|Intergov<br>.|Member<br>dues|Projects (EU)|Global|Custom<br>forecasts|Training|
|DWD<br>(Germany)|GPC|NMHS|Central<br>gov.||Global &<br>regional|Regional<br>advisories|NWP model<br>support|
|Météo France|GPC|NMHS|Central<br>gov.|Projects|Global &<br>regional|Regional<br>advisories|NWP model<br>support|
|Met Office<br>(UK)|GPC|NMHS|Central<br>gov.|UK cont. to<br>WMO VCP,<br>projects|Global &<br>regional||NWP model<br>support, training|
|Met Service<br>(NZ)|RSMC|NMHS|Projects||Regional|Regional<br>advisories|Training|

The extent of service delivery also varied from one center to another. For example, the ECMWF is able to provide products, training and verification tools directly to NMHSs, while the Met Office and MetService provide a more extensive array of capacity building services in addition to their products. Furthermore, the Met Office and NOAA have already performed socioeconomic benefit studies (SEBs) for their services while others had not (ECMWF 2015a; Met Office 2015a: NOAA 2007).

# 3 Cost-benefit analysis

The costs and socioeconomic benefits (SEB) of GPCs providing the full suite of forecasting products to LICs’ NMHS are estimated and compared following the methodology described in (WMO et al 2015). The applied SEB framework consists of: establishing the baseline of the SEB analysis; identifying the relevant changes in services offered; identifying, screening, analyzing and comparing the costs and socioeconomic benefits; considering biases, omissions and uncertainties; conducting a sensitivity analysis; and communicating the results.

> 2 The “full suite” of products varies by GPC. As an example, ECMWF offers for up to 15 days in the future, as well as for monthly and seasonal outlooks, a wide spectrum of real-time parameters, levels and ranges; charts of weather parameters, meteograms and an Extreme Forecast Index (EFI); and archives of analyses, forecasts and climate re-analyses (ECMWF 2015b).

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high compared with the actual rates measured by the urban LFSs, especially for 1967-69.

The SPO estimates for years earlier than 1985 thus do not appear easy to reconcile with the LFSs. One way of approaching the problem of the national unemployment rates-for years before 1985 would be to accept the national LFS for 1985 with the urban LFSs for 1982-85 and 1967-69 at face value and assume that the ratio between national and urban unemployment rates in the wide sense for all years were the same as in 1985. With this assumption we would actually tend to overestimate national unemployment rates the further we go back in time, assuming the riwral rate to be proportionately lower than than the urban one, because the urban labor force has increased relatively over time. This leads to the following conjectures for the national rate as compared with the SPO estimates:

||Nati|onal une|mploym|ent rates|
|---|---|---|---|---|
||conj|ectured|SPO es|timates|
||pct|index|<br>pct|index|
|1985|12.7|389|12.7|259|
|1984|12.0||12.4.||
|1983|12.0|353|12.4|249|
|1982|10.6||11.8||

> - 15 -
incorporate the trade policy environment into our model.<sup>7</sup> We focus on this subcategory for two reasons. First, we want to have a relatively homogeneous product industry: woven apparel as a whole might be seen as too aggregated. Next, we want a fair number of ...rms to be present. Both requirements are met in this sector.<sup>8</sup>

# 2.1 The Trade Policy Environment

There are three main components of the trade environment: the trade policy of the US and EU, the trade preferences (if any) they grant to Bangladesh, and the Rules of Origin, or ROOs, upon which preferences are conditioned.<sup>9</sup> They can take a variety of forms. The important thing to note is that, whatever the form, if ROOs are binding, then the choice of inputs used in production di¤ers from their unconstrained levels. Thus, from an analytical viewpoint binding ROOs must raise the marginal costs of production. In addition, ROOs can also raise the ...xed cost of production as compliance with ROOs must be documented. A large part of these documentation costs involves learning the ropes and can be treated as ...xed. We explicitly allow for such costs of meeting ROOs in our model.

The US Environment In 2004, the US had tari¤s of about 20% applied on a Most Favoured Nations (MFN) basis, as well as Multi...bre Arrangement (MFA) quota restrictions in place for the imports of wovens from most developing countries, including Bangladesh. Quotas under the MFA were country speci...c, so exporting was contingent on obtaining origin: unless the good was shown to originate from Bangladesh, it could not enter un-

> 7The apparel sector of Bangladesh also produces non-woven products, but the production technique and the trade policy environment faced by the non-woven ...rms are vastly di¤erent from those of the woven producers. Hence, we exclude them from this paper to simplify the modelling and estimation. See Kee and Krishna (2008) and Demidova, Kee and Krishna (2012) for details. Both papers use the di¤erences in policies applied to woven and non-woven garment exporters to identify the sorting behavior of ...rms with unobserved heterogeneity in productivity and market demand shocks.

> 8This subcategory accounts for 31.3% of exports to the EU and 15.3% to the US. We have a total of about 800 ...rms that export to the US or EU in this category in 2004, and of the ...rms that export to the EU, 72.5% meet ROOs and invoke preferences.

> 9ROOs specify conditions on production that must be met in order to obtain origin and thereby qualify for country speci...c quotas or trade preferences. For a relatively comprehensive survey see Krishna (2006).

8