rafmacalaba/datause-encoder-data huggingface dataset:
# **Infrastructure, Economic Growth, and Poverty: A Review**

# **1. Introduction**

Policy makers, particularly in developing countries, often face a critical question: how much they should invest in physical infrastructure from their scarce financial resources, both resources generated domestically and provided by foreign sources (e.g., bilateral donors, direct foreign investment, multi-lateral donors). The same question is vital for international development financial institutions (e.g., World Bank Group, regional development banks) so that there is not just maximized finance, but also maximum impact on economic development and social welfare in the recipient economies.

Many efforts have been made in the literature to measure the economic growth and social welfare impacts of infrastructure development, both physical capital and human capital (see e.g., Calderón et al. 2015; Chakamera and Alagidede, 2018; Kodongo and Ojah, 2016; German-Soto and Bustillos, 2014; Sahoo and Dash, 2012; Calderón and Servén, 2010). However, there does not exist a consensus in the literature on the relationship between infrastructure investment and economic growth. Many studies, such as Chakamera and Alagidede (2018), Kodongo and Ojah (2016), Sasmal and Sasmal (2016), Sahoo and Dash (2012), Calderón and Servén (2010), Fan and Zhang (2004) and Démurger (2001), find that infrastructure development is significantly associated with economic growth, especially in low- and middle- income countries (China, South Asia, Sub-Saharan Africa). Some studies, such as Lall (1999), Roy et al. (2014) and Shi et al. (2017), however, find either no relationship or a negative relationship between infrastructure investment and economic growth.

Economic historians also debate the role of public investment in economic growth. For example, using 60 years of data (1853-1913) for the Netherlands, Groote et al. (1999) find that investments in new infrastructure (mainly railways and waterways) contributed to economic growth. On the other hand, Herranz-Loncán (2004) finds, using 90 years (1845-1935) of data for Spain, no relationship between infrastructure investment and economic growth. One can wonder if the findings of Groote et al. (1999) or Herranz-Loncán (2004) hold for other developed countries and developing countries.

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the main beneficiaries of migration as they are the main consumers of the services provided such as healthcare and household services.

Significant migration has already been occurring between the Mediterranean countries and the EU, driven by the forces discussed previously. Figure 4 presents data on the number of migrants in OECD members from the countries in the region. The largest source countries are Turkey (close to 2 million as of 2000), Morocco (slightly over 1 million), Algeria and Iran (around 500,000), and Egypt, Iraq, Lebanon and Tunisia (around 250,000).<sup>2</sup> Note that the data are plotted on a logarithmic scale, as otherwise the large source countries would visually dwarf the smaller countries. All countries have experienced an increase in migration during 1990-2000, the overall number of migrants increasing by some 40%. However, this includes the children of the migrants who were born in the destination countries and who are considered migrants according to the laws in most of the EU countries.<sup>3</sup> Smaller countries have experienced more rapid increases in migration. The impact of the post-2000 conflicts in the region is not reflected in the data.

If migration is measured as a share of the native labor force, Lebanese migration stands out, at 15% of the native labor force. This reflects decades of political instability and a long history of social and economic relations with Europe. Among other high outflow countries, Moroccan migration is partly explained by the relatively low cost of movement to France and Spain, reflecting historical links and geographic proximity. Other high emigration countries include Turkey, Tunisia and Algeria. As mentioned, these data underestimate total migration flows, as they do not include the significant migration to the Persian Gulf countries from other Arab countries, such as Egypt and Jordan.

> 2 The data used in what follows are from Docquier and Marfouk (2006). Their dataset is based on the national censuses of the receiving countries, and includes bilateral migration stocks in OECD countries from over 170 source countries for the years 1990 and 2000, by three education levels. Data is also compiled to allow a comparison of migrants’ educational attainment relative to the native populations. Although a major advance, the dataset has several shortcomings. First, it is limited to OECD members as destination countries. Thus it ignores several major destination labor markets such as the Persian Gulf, Singapore, and South Africa. Second, the dataset only includes employed migrants. Students are not captured, even though students, especially in post-graduate programs, are a major source of brain drain. 3 This is not the case in the US or Canada where the children of migrants receive automatic citizenship if they are born in the destination country.

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countries (World Bank 2015).

We use a randomized controlled trial to evaluate the program as designed and implemented by the Government of Malawi, and to test the hypothesis that changes to the timing of the program could increase its effect on food security, potentially at the cost of investment in fertilizer. The design takes advantage of oversubscription for the program: more villages request PWP activities than can be accommodated, and even in villages that have projects, not all eligible households can be included. The evaluation includes two levels of randomization, across villages and across households in treated villages. Villages that requested PWP projects were assigned to either a pure control condition (no PWP at all) or one of four treatment groups that offered the same wages and total number of days of employment, but differed in the schedule of work and the frequency of payments. Within treated villages, some households were randomly selected to be included in the program. Village treatment assignment was stratified at the regional level to enable estimates of the impact of the program across different agro-ecological and socio-economic settings within the country. We use household panel survey data to study the impact of the program (and design variants) on food security and use of fertilizer, as well as other measures of household resource allocation and wellbeing in order to understand the mechanisms through which the program affects these two key outcomes.

Malawi’s PWP was not effective in achieving its aim of improving food security during the 2013 lean season. Treated households do not have better food security than households in control villages, and we can rule out even moderate positive effects on a summary measure of food security. The program did not increase use of fertilizer or ownership of durable goods. The lack of impact cannot be explained by increases in prices or crowding out of labor to the casual wage market. The failure of the PWP to improve nutrition in either the short run (through consumption support) or longer run (because of increased use of fertilizer) is especially troubling because the MASAF PWP is the largest social protection scheme in one of the world’s poorest countries.

Not only did the program fail to improve food security for treated households, but also, there is some evidence of negative indirect effects on untreated households in villages with PWP. In the North, these spillovers are negative and meaningful: a summary measure of food security falls by one-third of a standard deviation for untreated households in villages with PWP, relative to households in control villages. In Central Malawi, there is a compelling pattern of negative but imprecisely estimated indirect effects on untreated households. These spillovers are not present in the poorest region, the South. However, the combination of no positive direct effect and some negative indirect effects mean that at best, this extensive program has no impact on food security.

For comparison, the two largest public works programs are the NREGA program in India 3
Table 3. Population Supply<sup>a,b,c</sup> (robust standard errors in parentheses)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||GMM-IV|OLS|GMM-IV<br>(1980)|GMM-IV<br>(1991)|GMM-IV<br>(2000)|
|Ln(incomeper capita)|2.370***|1.813***|1.830***|2.636***|2.886***|
||(0.683)|(0.378)|(0.569)|(0.704)|(0.933)|
|Ln(rural income opportunities:|-5.151***|-4.152***|-4.821***|-5.316***|-5.624***|
|marketpotential)|(1.454)|(0.819)|(1.457)|(1.354)|(1.824)|
|Ln(ruralpop. supplymarket|5.851***|4.878***|5.559***|5.978***|6.317***|
|potential)|(1.368)|(0.752)|(1.378)|(1.281)|(1.705)|
|time dummies|Yes|Yes|No|No|No|
|R<sup>2</sup>||0.745||||
|Hansen J statistic<br>(overidentificationtest)|1.909||1.297|1.148|1.655|
|(p-value)|(.591)||(0.730)|(0.765)|(0.647)|
|Average of Partial R<sup>2</sup>|0.657||0.691|0.644|0.662|
|Average of Partial F's|55.50||34.41|37.48|64.29|

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, ln(distance to São Paulo), ln(market pot. agric. land availability, 1970), port dummy, ln(per capita capital stock, 1970), southern region and time dummies.

- b. GMM estimates are from the two-step efficient GMM in the presence of arbitrary heteroskedasticity and arbitrary intra-group (within-state) correlation.

- c. OLS regressions are with robust cluster standard errors. We assume the observations may be correlated within states, but would be independent between states.

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The strongest correlation is between the Infrastructure Management index, which reflects quality of services, and the Accountability and Transparency index, which focuses on ease of access to information and transparency, at 0.33.

We also include data on the relationship between the survey measures and the APAs, in Appendix B. Since APAs were only collected at ULGSP LGAs, the sample is restricted to 18 observations. While for the household survey we observe overall positive correlations between outcomes measured at baseline, endline and changes over time using survey and APA indicators (Figure B3 and Figure B4), results for the government official survey are more mixed, with overall smaller correlation in magnitude and both positive and negative relationships (Figure B5 and Figure B6). We should treat these correlations with caution, not only due to the limited number of observations but also because in some indicators the amount of variation is quite limited. For example, almost all ULGSP LGAs had scored the maximum amount in the Oversight component of APA by our first-round survey, so variation between our first and second surveys is zero for the majority of them).

# **3.4 Evidence of successful project implementation**

An important initial question regards the implementation of the program: does the survey provide evidence that government officials were aware of the program and that they received training and funding as expected? The answer is a definitive yes: in the first round, officials in project LGAs recognize the project as the main source of capacity training and budget support for infrastructure. In Figure 2, panel A, we show that among officials in project areas, 65% report the project as the agency most supporting capacity building vs. 12% in comparison LGAs. For comparison LGAs, the main agencies reported as supporting capacity building are sector ministries (30%) and community-based organizations (18%).

Regarding budget support for infrastructure, panel B of Figure 2 shows that 50 percent of officials in treated LGAs choose the project as the main source for recent increases in budget for infrastructure vs. 0 percent in comparison. Twenty-six percent of project officials and 46 percent of non-project officials indicate own revenues as the most important source of finance, whereas 41 percent of officials in non-project LGAs indicate government grants as the most relevant source.

As discussed above, the first survey round was fielded after the 3rd APA, when government officials would have been aware of the project. The data above suggest that officials were not only aware of the existence of the program, but also report the project as being the most relevant source of capacity building and new finance for infrastructure. At the same time, panel B of Figure 2 shows that project LGAs still substantially rely on government transfers and own source revenues to finance infrastructure, which suggests that a redirection of central government resources from project to not-project LGAs is not happening, or at least not on a large scale.

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Though the results are not reported here in order to conserve space, one interesting result is that the ISD's from options on futures contracts relatively near maturity (30 - 50 days to maturity) provide long-run volatility forecasts that are similar in accuracy to those provided by options on futures contracts that are far from maturity (225 days to maturity). It seems that there are two offsetting effects here. The first effect is that the near maturity options are more heavily traded than the distant options and consequently are priced more precisely, implying a more accurate ISD. But this effect is countered by the fact that the ISD from the near to maturity option must be extrapolated to span the desired horizon, thus reducing its accuracy. In contrast, using distant horizon contracts eliminates the need for extrapolating, at the cost of using infrequently traded options.

But the true test of a model's ability to forecast can only be accomplished through out of sample forecasting. Therefore, the 225-day forecasts from each of **our** six models were computed for each day in the final eight weeks of each data set, using only data available up through but not including the fin.l eight weeks. This gives us a time series of 40 out-of-sample forecasts from each forecasting method,<sup>1 0</sup> for each commodity.

One additional forecast was prepared for the out-of-sample testing, which can be viewed as an alternative way of combining market-based forecasts (ISDs) and time series based forecasts (GARCH and HIST). Granger and Ramanathan (1984) argue that if a set of forecasts exists which are either based on different information sets or are based on the same information set but constructed differently, then a better forecast can be obtained by combining the existing forecasts. In our situation, we have forecasts which are constructed from different information sets (e.g., the GARCH forecasts are based on historical information, and the ISD forecasts are based on current market expectations), as well as forecasts constructed from the same information set but constructed differently (e.g., the HIST forecasts and the GARCH forecasts are both based strictly on historical information, but the forecasts are constructed differently). Therefore, combining these forecasts has the potential to generate an improved foft.ast.

One method of combining these forecasts, suggested by Granger and Ramanathan (1984), is to regress the true volatility on the set of forecasts to obtain weights, then weight all future forecasts by the weights obtained in this preliminary regression. To construct this combined forecast, we

> 10 The GARCH and COMB model parameters are not re-estimated as the 225-day forecast horizon moves through the 4,d day window. This biases the results against these **two** models since their results are only conditioned on the in-sampi, data and none of the out-of-sample data **was** used to update the model parameters.

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Table 7: CPIA components

|component|public admin.|budget mgt.|revenue mobil.|transp. & corrupt|
|---|---|---|---|---|
|GMM|.038|.029|.027|-.027|
||(.019)<sup>_∗∗_</sup>|(.008)<sup>_∗∗∗_</sup>|(.009)<sup>_∗∗∗_</sup>|(.080)|
||-.001|-.0005|-.0005|.008|
||(.0006)<sup>_∗∗_</sup>|(.0001)<sup>_∗∗∗_</sup>|(.0001)<sup>_∗∗∗_</sup>|(.010)|
|PSG _>_0|-.008|.004|.018|.006|
||(.025)|(.012)|(.008)<sup>_∗∗_</sup>|(.057)|
||.00009|-.0001|-.0004|.00007|
||(.0007)|(.0002)|(.0001)<sup>_∗∗∗_</sup>|(.006)|
|Closing year|.010|.034|.016|.068|
||(.025)|(.009)<sup>_∗∗∗_</sup>|(.011)|(.118)|
||-.0004|-.0006|-.0003|.004|
||(.0007)|(.0002)<sup>_∗∗∗_</sup>|(.0001)<sup>_∗∗_</sup>|(.020)|
|Excl. ARG|-.001|.028|.038|-.021|
||(.022)|(.014)<sup>_∗∗_</sup>|(.018)<sup>_∗∗_</sup>|(.082)|
||.0003|-.0005|-.001|.007|
||(.0008)|(.0003)|(.0007)|(.010)|
|Controls|.015|.017|.027|.043|
||(.018)|(.009)<sup>_∗_</sup>|(.009)<sup>_∗∗∗_</sup>|(.056)|
||-.0005|-.0003|-.0005|-.007|
||(.0005)|(.0001)<sup>_∗∗_</sup>|(.0001)<sup>_∗∗∗_</sup>|(.006)|
|Cross-sectional IV|.025|.065|.059|.089|
||(.018)|(.024)<sup>_∗∗_</sup>|(.032)<sup>_∗_</sup>|(.097)|

Note: Results from estimating regression (GMM) models on CPIA cluster D components: Quality of Public Administration, Quality of Budgetary and Financial Management, Efficiency of Revenue Mobilization and Transparency, Accountability, and Corruption in the Public Sector. Only coefficient estimates – linear and quadratic term – and cluster-robust standard errors of conditions variable are reported. * significance at 10%; ** significance at 5%; *** significance at 1%.

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# 6.1. Export shares

There have been various approaches developed to address these reclassi fi cation issues. One approach is to use HS 6-digit data concorded to the same 1992 product codes, but in general these categories might be too aggregated to be able to identify new products: by 1992, China was exporting in over 90 percent of these categories. To examine whether export growth is mainly from new goods with this aggregate data we follow Kehoe and Rhul (2005) by splitting exports into deciles by value in 1992 and calculate their share of exports in 2005. If export growth is mainly from new goods, we would expect rapid growth in the bottom deciles, where trade was negligible in 1992. Figure 10 shows the share of exports in 2005 that is accounted for by the products falling into each decile. The categories that accounted for the bottom twenty percent of trade by value more than doubled between 1992 and 2005, while the categories in the other deciles contracted or remained constant.<sup>4</sup> This points to a sizeable role for the extensive margin, as the least traded goods grew the fastest.

One problem with this method is that exports tend to be concentrated in a small number of categories. This can be clearly seen in Figure 11, where we divide exports into deciles according to the number of categories of trade in 1992. For example, the tenth decile is the top ten percent of product categories when products are ranked by value. The distribution in 1992 is highly skewed, re fl ecting that only 10 percent of categories accounted for nearly 80 percent of trade. The decline in the share of the top decile shows that there was a sizeable reallocation of trade, but it was not the bottom 50 percent of products that gained. Instead, gains in the trade share were in the four deciles just below the top.

In sum, the results imply that there was a signi fi cant reorientation in exports, and that the reshu ffl ing of export products during the expansion was mainly in the mid-upper rank products. These are products that were in the bottom 20 percent by value but in the midto-high range by product rank.<sup>5</sup> Taken with the previous results on specialization, this

> 4 Arkolakis (2006) develops a model consistent with this fi nding.

> 5 These fi gures and the estimates of the extensive and intensive margin are very similar if we use only manufacturing trade.

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|**Adjusted**|||||||||
|---|---|---|---|---|---|---|---|---|
|9-months|1154|31.37 (12.60)|1214|28.76 (12.34)|-0.28 (-1.16,0.60)||||
|24-months|996|35.33 (12.85)|1052|34.04 (12.75)|0.68 (0.22,1.58)|0.001|0.08 (0.06,0.12)|0.43 (0.40,0.47)|
|**Adjusted for predi**|**ctors of missin**|**gness**|||||||
|9-months|1131|31.49 (12.69)|1198|28.69 (12.36)|-0.18 (-1.07,0.70)||||
|24-months|974|35.57 (12.81)|1037|33.98 (12.77)|0.73 (-0.18,1.63)|0.003|0.08 (0.06,0.12)|0.43 (0.40,0.47)|

> ~~a~~ Mean score and sd at follow-up based on the data

> **b** The same class 1 spelling task was given at baseline, 9 and 24 months follow-ups, with different words used for the 24 month follow-up and was scored 0-20.

> **c** The same class 5 spelling task was given at baseline, 9 and 24 months follow-ups, with different words used for the 24 month follow-up and was scored 0-78.

d Mean difference (intervention-control) presented for continuous outcomes (scores on spelling task) and are obtained from random effects regression analysis accounting for school-level clustering and repeated measures on children.

e p-value for the comparison of the intervention effect at 12 months to 24 months

**Unadjusted** : All children with outcome measures, not adjusted for any baseline or study design characteristics.

**Adjusted** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available.

**Adjusted for predictors of missingness** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available. Additionally adjusted for parental education, SES and baseline educational level as measured by baseline spelling score (standardized by subtracting year-group baseline mean and scaled by yeargroup sd).

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