rafmacalaba/datause-encoder-data huggingface dataset:
contrary to the prevailing view in the household. In the case of the fourth hypothesis, we postulate that people who have a role in decision making within the household will be more likely to respond to the treatment as they have greater autonomy over their own choices. Due to the novelty of this research, these hypotheses extend well beyond clear guidance that is available from prior research. As such they were somewhat speculative when these hypotheses were pre-registered prior to the online survey experiment commencing.

# **2.3 Setting**

Zambia is a large, landlocked, resource-rich country with sparsely populated land in the center of Southern Africa. More than 58% (2015) of Zambia’s 16.6 million people live on less than the international poverty line of USD1.90 per day (compared to 41% across SubSaharan Africa) and three-quarters of the poor live in rural areas (World Bank 2022). The Zambian Ministry of Health has implemented successful mass vaccination campaigns targeted at children (Carcelen et al, 2021). In 2019, 93 percent coverage was reported for the first dose of measles-rubella vaccine (MR). Despite this progress with MR vaccines, coverage of other vaccines, such as hepatitis B, is far more limited (ibid).

The first COVID-19 cases in Zambia were reported in the capital city of Lusaka in March 2020 when most countries started grappling with the virus. There have been 319,755 (as of May 1 2022) cumulative confirmed cases of COVID-19 in Zambia in the past two years, since the beginning of the pandemic (WHO, 2022). In 2021, the total number of confirmed cases and deaths increased almost tenfold from 20,997 cases and 390 deaths in January 2021 to 305,047 cases and 3,734 deaths by the end of December 2021 (Johns Hopkins University, 2022) (see Figure 2).

The Government of Zambia launched the national COVID-19 vaccination program in April 2021. Zambia received its vaccines mostly through vaccine initiatives such as the African Vaccine Acquisition Trust (AVAT) and COVID-19 Vaccine Global Access (COVAX), a partnership between CEPI, Gavi, UNICEF, and WHO. In June 2021, amid a third wave 11
The price of intermediate goods from sector _s_ in country _j_ ( _Pj_ , _s_ ) is assumed to be the sum of production costs and a markup, that is, _Pj_ , _s_ = (1 + _ηj_ , _s_ ) _Cj_ , _s_ , where _ηj_ , _s_ is the markup: Substituting equation (21) for the price term in equation (20) gives: Equation (22) can be rewritten in matrix form as the following: where the elements within _A_<sup>�</sup> include the _α_ terms (i.e. input-output linkages among sectors) and the elements within Ψ<sup>�</sup> include the markup terms. The first item on the right-hand side of this equation calculates the dot product of _β_<sup>�</sup> and _W_<sup>�</sup> . Assuming that the mark-up Ψ<sup>�</sup> is stable over time and can be controlled for by a set of fixed effects, we can rewrite equation (23) as the following by dropping the markup term: Using the input-output relationship (matrix _A_<sup>�</sup> ), the factor intensities of all sectors (vector _β_ ) and a vector of input prices _W_ , we are able to compute the direct plus indirect cost of factor inputs. In the input-output extended model, we replace the direct cost terms (calculated as the interaction terms of factor intensities and input prices) with the direct plus indirect costs described in equation (24). It is worth emphasizing that by using inputoutput tables, we assume that intersectoral linkages are exogenous – a (small) change in input prices would not shift the input-output relationship drastically. We do not account for substitution possibilities among primary factors and intermediate inputs. In this way, we should interpret the results from the input-output extended model as an estimation of short-term effects.

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43 <!-- Start of picture text --> Prices with and without fund or options<br>40 -___<br>3 5 -<br>30-<br>-bo 25-<br>20-<br>1 5<br>86 87 88 89 90 9 1<br>Year<br>- -<br>Fund Only Fund and Options TJLight<br><!-- End of picture text -->

**Figure 6: Price with and without fund or options**
between savings and investment, and reform of inter-governmental fiscal relations that support greater public spending on health and education.

Kuijs and Wang (2005) calibrate a more capital-efficient growth scenario for China in which the economy grows at about the same rate as in recent years, but there is substantially less public savings (with the overall savings rate about one-fifth lower). So, in thinking about different savings scenarios, we consider one in which the status quo continues, and interpret this as a “stalled reform” scenario in which there continues to be a lot of inefficiency in the public sector and poor intermediation between savings and investment. In our “high reform” scenario we consider a decline in the aggregate savings rate of one-fifth.

# _What Will Happen to Productivity in China?_

Estimates of TFP growth in China are abundant, vary considerably.<sup>7</sup> In a recent very careful study Young (2003) concludes that various -- although not equally valid -- interpretations of Chinese data would result in TFP growth rates ranging from 0 to 6 percent per year! However, Young's preferred estimate is a very reasonable 1.4 percent per year, for the non-agricultural economy during the period 1978-1998. This estimate is in the same vicinity as Kraay (1996), who estimates a TFP growth rate of 1.8 percent per year for the period 1978-1994. These two estimates tend to be at the low end of those in the literature. For example, Borensztein and Ostry (1996) and Hu and Khan (1997) provide estimates of TFP growth of 3.8 and 3.9 percent per year over the period 19781994. In this subsection we make two points. The first is that relatively modest TFP growth rates for China are most likely higher than average productivity growth in the rest of the world, implying that China's level of productivity relative to the rest of the world has been rising over time. The second is that there is substantial scope for further productivity growth in China due to the continued reallocation of factors of production from low-productivity uses (agriculture, and the state sector) to high-productivity uses (non-agriculture, and the private sector). While this intersectoral factor reallocation is not pure TFP growth, it will contribute to higher productivity levels in China relative to the rest of the world.

> 7 In addition to those cited in the text, see for example Li (1992), Chow (1993), Woo (1995), Borenzstein and Ostry (1996), and Hu and Khan (1996).

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**Figure 3: Fiscal and Monetary Developments, 1996-2003**

<!-- Start of picture text --> 1 Structural De ficit (% of GDP) M one tary Conditions Inde x<br>2.5<br>0<br>2.0<br>-1<br>1.5<br>-2 1.0<br>-3 0.5 Monetary tightening<br>-4 0.0<br>-5 -0.5<br>-6 -1.0 Monetary easing<br>-7 -1.5<br>-2.0<br>1996 1997 S tru c tu ra l<br>1998 1999 2000 H e a d lin e -2.5<br>2001<br>2002<br>2003<br>Headline Cyclical Structural 96M1 96M8 97M3 97M10 98M5 98M12 99M7 00M2 00M9 01M4 01M11 02M6 03M1<br><!-- End of picture text -->

# **B. DID POVERTY RESPOND TO GROWTH?**

It is clear that the high growth rates of the mid-1990s to 1998 facilitated a decline in poverty in Poland, which suggests high responsiveness of poverty to economic growth. From 1999 onward, however, this responsiveness appeared to weaken; poverty began to increase even though growth remained at 4 percent for 1999 and 2000 and being modestly positive thereafter.

# **A Statistical Artifact?**

To fully evaluate the role of growth in poverty reduction, it is important to note that household consumption – as captured by the HBS – performed much more badly than the national accounts (NAS) would suggest, with average household consumption declining in the low – but positive -- growth years after 1998. This difference persists even when the figures for changes in aggregate private consumption from NAS are considered rather than GDP growth. This is not a phenomenon unique to Poland and may be explained by the unusually high investment accumulation over the 1990s, and by its relatively high export rate.<sup>3</sup> Reconciling these two series is beyond the scope of this report, but it is important to note that the HBS shows declining values of average per capita consumption for the period 1999 – 2002 while the NAS data show moderate positive growth.Thus, the estimates of poverty, which are based on the HBS, show increases in poverty in these years despite the positive consumption growth rates shown by the NAS data.

When the responsiveness of poverty to growth in average consumption (rather than to GDP growth) is considered, the analysis shows a very strong link between growth and poverty reduction. Over the last decade the average estimated elasticity of poverty with respect to growth in average consumption from the HBS data has been 3.6 with a peak of 4.11 in 1999.<sup>4</sup> Given Poland’s GDP level, this value compares very well with the estimates for other transition countries and with those typically found in countries outside of the region (Bruno, Ravallion, and

> 3 Adams (2003). 4 These estimates are in line with the value of 3.5 given in World Bank (2002).

5
therefore predicted more accurately than those further away. The plot reveals no systematic tendency for over or under prediction at any level of observed prevalence. The validation statistics (Table 1) further indicate a relatively well performing model, with reasonable correspondence between predicted and observed values (correlation 63%), precision (mean absolute error 11%) and accuracy (mean square error 2%). The latter two metrics are expressed in the same units as the predictions – i.e. prevalence on a scale of 0‐100%.

# _Pixel‐level predictions of stunting prevalence_

Figure 2 shows the predicted map of stunting prevalence in children under five years old, constructed using the mean of the predicted posterior distribution within each 1×1km grid cell. Many areas of the country are within the yellow part of the color scale, which is similar to the national mean stunting prevalence reported in the DHS survey of 34% [DHS, 2016]. Areas of notably lower stunting prevalence (shown in the greener shades) include the predominately urban areas around Dar es Salaam, Arusha and Dodoma, as well as a broader region to the south of Lake Victoria centered around the city of Tabora. Areas of consistently high stunting rates (red shades) tended to be more common in the more rural parts of the country, especially throughout the western and southwestern border areas.

Figure 3 shows how the uncertainty of the stunting prevalence predictions varies geographically. The uncertainty metric used is the width of the posterior 95% credible interval for each grid cell. Cells where prevalence could be predicted with high certainty had narrow posterior distributions around the point estimate, and thus the range of possible prevalence values between the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the distribution was small, and vice‐versa. Areas predicted with relatively low prevalence (green in Figure 2) tended to also have smaller uncertainty, represented by the blue shades in Figure 3, while areas of higher stunting tended to be more uncertain, shown in the purple and pink shades in Figure 3. Even in these regions, however, the 95% credible interval tended to be no wider than 10%, meaning the model could predict stunting prevalence with 95% probability to within a range of 10 percentage points.

_Aggregated estimates of stunting_ Figure 4 shows estimates of stunting prevalence and the number of stunted children aggregated to the Tanzanian 1<sup>st</sup> (Regions) and 2<sup>nd</sup> (Districts) administrative levels. While the pixel‐level map shown in Figure 2 provides the most granular, fine detailed, prediction of the landscape of stunting across the country, the aggregated prevalence maps (top row) provide a summary of this spatial pattern at different levels that may more directly inform programmatic planning. Both the District and Region maps highlight how high‐prevalence areas are concentrated towards the southwest, with the lowest 6
telecom effect merely reflects the time trend in relatively rich villages, then villages always with telecom access should have higher migration trends, but we find a similar difference in the migration levels for villages always with telecom access and those never with telecom access. Finally, if the telecom effect on outmigration reflects effects other than the information or psychological costs effect (as we hypothesize), telecom access will likely affect out-village, within-county migration, but we do not find that telecom access affects such short-distance migration.

We further test the two proposed mechanisms through which landline phones may increase outmigration, that is, information access and timely contact with left-behind family members. We find that the positive effect of landline phones on outmigration is greater for villages with a larger pool of previous outmigrants (a proxy for the information access through the network effect) and for villages with more young children (a proxy for left-behind family members).

Our paper is complementary to the existing literature on the determinants of migration in several ways. First, by using unique data on landline phone installation and by taking advantage of the predictive nature of landline phone installation at the village level, we have a relatively transparent and plausible strategy for identifying the effects of telecom on migration. The robustness of the results under a variety of specification checks testifies the plausibility of our identification strategy. Second, our evidence comes from the country that has experienced the largest migration in the world during which migration was in full swing, and it is useful to know whether modern telecom would have quantitatively important impact on migration. We find it is so. Third, there is little evidence of how family structure and psychological costs of migration affect migration, and in this paper, we find that modern telecom reduces the psychological costs of migration by allowing migrants to stay in touch with their children left behind in the villages.

The paper is organized as follows. In Section 2, we describe the rural-to-urban migration and the development of landline phones in China. In Section 3, we lay out the 5
For the benchmark years in between 1993 and 2008, there exists no unambiguous ranking of Lorenz curves. Hence the direction of change in inequality depends on the chosen inequality measure. For example, Figure 2 shows clearly that the Lorenz curves in 2003 and 2008 cross. This is also shown by the fact that the various GE measures move in opposite directions (with the exception of the change between 1998 and 2003).

Our results are unaffected by a number of robustness checks, such as using the original decile data, applying the 2005 PPP US$, and confining the analysis to the balanced sample (i.e. countries that are observed in both 1993 and 2008).<sup>30</sup> The results of these robustness checks are presented in the Appendix. First, using the original data set of decile groups without fitting a parametric distribution reduces the African Gini by about 0.5pp, while the time trend remains unchanged (Table A1, Panel ii). Of course, the fall in the African Gini is to be expected because every within-country distribution is now represented by ten instead of 100 points.<sup>31</sup> The changes in GE(0) over time and its within contribution are also robust to using the decile data. Second, the results from applying the 2005 PPP US$ remain comparable to the main results using the 2011 PPP US$, with the Gini index being 0.8pp lower on average, and increasing by about 9% over the 15-year period (Table A1, Panel iii). Third, we check whether the results are driven by changes in the sample of countries over time. The balanced sample raises the Gini increases by about 1.8pp in 1993 and 0.6pp in 2008 (Table A1, Panel iv). As a result, the Gini index increased by 6% between 1993 and 2008. This increase is slightly smaller than that observed in the main results but remains substantial.

> 30 These robustness checks are important. For example, Lakner and Milanovic (2015) who use a slightly different (and larger) sample of countries find a much smaller increase in the African Gini between 1993 and 2008 when they use the 2011 PPPs. Specifically, they tend to find a higher African Gini index than our estimates, while this gap diminishes over time. While it is difficult to determine the precise reasons for these differences, one difference is that Lakner and Milanovic use some income surveys, while we use consumption surveys throughout. Furthermore with the 2005 PPPs, the African Gini increases by 9% in Lakner and Milanovic, which is very similar to our findings.

> 31 To be precise, Yitzhaki (1994) shows that the (regional) Gini can be decomposed into (1) the between-country Gini and (2) a term which consists of the income share, the within-country Gini, and the overlap index between the country and the regional distribution. Replacing 100 points with ten points would reduce the last two components, while it would leave between-country inequality and the income share unchanged.

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includes regions in decline such as the old industrial regions in France and UK. Finally, there is a group of heterogeneous clusters which do not fall into a pattern. These include Dutch and Portuguese (excluding Lisbon and the North) regions. This shows that regions better able to adapt to changing global technological and market environments do better. However, it is left unclear if better performance is due to market friendly or interventionist policies.

The third strand has been very limited evidence showing that EU regional policy supports regional catch-up by poorer regions. Leonardi (2005) finds that Objective 1 regions have converged to EU mean, while non-Objective 1 regions have remained stable, suggesting that EU funds are effective in helping poorer regions catch up. Cappelen et. al. (2003) find EU support has significant and positive impact on growth performance of EU regions, however, with economic effects stronger in more developed regions. Most of the other evidence shows that EU funds have little or no impact (Boldrin and Canova, 2001; Canova and Marcet, 1995; Canova, 2004; Dall'erba and Le Gallo, 2007; Fagerberg nd Verspagen, 1996; Le Gallo and Dall'erba, 2006). According to Boldrin and Canova (2001), their analysis “does not show any indirect evidence that Structural and Cohesion Funds regions are behaving any differently from the remaining ones. In fact, time series data extending back to the pre-regional policies period suggest that much more convergence was taking place then than now.” The fourth strand that comes from the literature is that the national environment matters. Cuadrado-Roura (2001) finds strong national influence on the performance of regions, and that regions tend to cluster together both in terms of initial development level and per capita GDP growth rates. Quah (1996) also finds that national factors affect regional distribution dynamics. While Cuadrado-Roura et al. (2000) find that a fixed national effects model is not significant in comparison with the general model, the results of their fixed effects do suggest a common behavioral pattern amongst regions in the same country. Assuming policy and market environment is more similar within nations, this suggests that national policy and market environment matters more than EU regional policy. It also suggests that top-down development approaches from the supra-national level may have limited impact.

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role of linkages is stronger in manufacturing (Figure 6). The results indicate that an increase of 1 percentage point in the share of inputs sourced locally by MNCs is associated with an increase of 0.7 units in the measure of sales growth of indigenous high-growth firms in manufacturing and 0.5 units in services. Yet, an increase of 1 percentage point in the demonstration channel is associated with an increase of 0.2 units in the measure of sales growth of domestic high-growth firms in services, while this impact is not statistically significant for manufacturing firms.

**Figure 6: FDI Spillovers on High-growth Firms, by Sector**

<!-- Start of picture text --> 0.9<br>0.8<br>0.7 0.7<br>0.6<br>0.5 0.5<br>0.4<br>0.3<br>0.2<br>0.2<br>0.1 0.1<br>0.0<br>Linkages Demonstration Linkages Demo ns tration<br>-0.1<br>Services Manufacturing<br><!-- End of picture text --> Note: This figure shows the estimated coefficient of the role of FDI spillovers on high-growth firms in manufacturing and in services. Vertical lines denote 90 percent confident intervals. Regression results are presented in Annex 3. Source: Author computation using data from the World Bank Enterprise Surveys.

I also examine FDI spillovers across different factors that motivate MNCs’ decisions to invest in developing countries. I employ the taxonomy suggested by the seminal work of Dunning (1993), where FDI motivation can broadly be categorized as (i) natural resource-seeking investment, which describes a situation where a foreign investor establishes an enterprise in the host country to access natural resources; (ii) market-seeking investment, which is driven by the foreign investor’s intention to primarily serve the host country’s market; and (iii) efficiency-seeking investment, in which the investor chooses a host country in order to take advantage of some competitive factor, such as a labor force, utilities, services, or geographic location. Because the relationship between the pro-competitive effect of FDI and the potential for knowledge diffusion is different across FDI

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agents, as well as incentives to invest in innovation as the transport and communication infrastructure, and therefore access to market, change. Economies of scale due to network externalities are still another important explanation in the case of network industries.

As for more general types of externalities, the main candidate category is a broad one labeled “incentives”. Prescott (1998) mentions two specific historical examples. In the first one, drawn from Wolcott (1994), he argues that productivity differences between Indian and Japanese cotton mills, in the period 1920 to 1938, can be traced back to the ability of workers to resist organizational changes, itself due to differences in the composition of the population of workers, rather than to differences in technology. Similar reasons appear to explain the century-long pattern of productivity changes in strongly unionized coal mining in the US, with surges in productivity only occurring when changes in the environment (competition from low-price oil and from nonunionized mines) presented workers with an alternative between changing work practice to allow the use of more efficient technology or seeing the mines closed. Finally, Prescott argues that the large observed industry-level differences in productivity among developed countries such as the US, Japan, Germany, the UK and France, can hardly be attributed to differences in skills or the stock of useable knowledge, and must instead be related to constraints such as laws, regulations or union power.

Recent contributions that blend endogenous growth theory with contract theory are promising avenues to understand how the incentive structure prevailing in key areas such as R&D or government services affect the growth path of an economy (e.g. Martimort and Verdier, 2004; Sarte, 2002; Erlich and Lui, 1999). As for geographical linkages, Foster and Rosenzweig (2003) provide microeconometric evidence of changes in individual and geographical specialization patterns following the green revolution in India, with industrial investment clustering in low agricultural productivity regions where wages were lower. An intuitive question here is to determine to what extent the changes in regional specialization and the agglomeration of industrial and agricultural activities have been mediated or constrained by the availability of key infrastructure like roads and telecommunications, suggesting the relevance of potential correlations or 11