rafmacalaba/data-use-mentions-extended huggingface dataset:
# V. Extensions

## V.1 On the Sub-Components of Data Capacity and Transparency

Certain elements of the data ecosystem may matter more for forecasting growth than others. To test this, the SCI index is deconstructed into three subcomponents: (i) directly related to GDP forecasting; (ii) indirectly related through the macroeconomic framework; and (iii) other elements of the data ecosystem including the periodicity of micro and macro indicators, which capture overall quality of the data ecosystem.<sup>11, 12</sup> Direct elements include the periodicity of GDP growth data, import and export price indices, the industrial production index, and the updating of base years for national accounts and the consumer price index. The indirect elements related to the macroeconomic framework comprise standards for external debt reporting, government finance accounting, updated balance of payments manual, and subscription to the IMF’s Special Data Dissemination Standards (SDDS). Regression results show that of the three subcomponents, the “other elements” of the data ecosystem, are statistically significantly correlated with lower forecast errors and absolute forecast errors, while the direct and indirect elements are not (see Appendix Table A2). This finding implies that for countries to improve their forecasting accuracy, the whole data ecosystem needs to be upgraded, not just certain elements of it. A limitation of this finding is that there is far less variation in the direct element of the SCI indicator, which may explain the lack of statistical strength.

## V.2 Forecast Errors between Different Forecasters

We investigate the role of forecasters in explaining forecast errors. The type of forecaster may influence the magnitude of forecast errors or the optimism of forecasts. To take this into account, the data is restructured with each unit of observation being a forecaster for a country and year. Dummy variables are created to account for the different categorizations of forecasters.

Forecasters are divided into three categories based on the _institution_ sources. These include the IMF (WEO dataset), the World Bank (GEP
Fourteen of the 21 community radio stations in the sample depend on funds from foreign donors and three on funds from non-governmental organizations. Regardless of funding source, all of them describe the provision of information on health and education as a ―very important‖ objective. Consistent with this, the average number of health programs reported by station managers is large (138) and more than three times greater than for public or private commercial radio. Hence, access to a higher number of community radio stations is likely to be associated with exposure to more information about health issues.

To measure the number of radio stations to which households have access, enumerators first asked village-level key informants to list which radio stations they were able to receive. The enumerators also used their own transistor radios to verify and expand on this, if they received additional signals. We did not ask enumerators or village informants to describe the type of radio station or the nature of its programming. Instead, the radio codes from the village survey were matched with the radio survey data to examine the nature of programming available to the village.

Since community radio stations have low signal strength, no single station is accessible to more than a few villages in our sample. However, because the number of community broadcasters is large, at least one community signal is accessible to 93 percent of our sampled villages. Only a few villages in our sample (23 percent) have access to private commercial radio. Although private radio has greater signal strength, it is concentrated in the southern communes of Benin, outside our study area. Government-owned public radio has several relay transmitters allowing it to cover most of the villages in our sample (92 percent) with uniform programming. The information from religious stations is less reliable (for example, in the case of signal strength). However, only 13 respondents identified a religious station as their most preferred broadcaster.

The number of community radios to which villages have access ranges from zero to seven stations, averaging 2.4 and with a standard
ter, 2012 to the present.<sup>3</sup> We use the harmonized data produced by Li et al. (2020).<sup>4</sup> We clipped the night light raster files to include only Bangladesh’s territory and downloaded the shapefile at the national level from the Humanitarian Data Exchange, which is managed by the Office for the Coordination of Humanitarian Affairs (OCHA). Next, the spatial coordinates, originally measured in degrees using the (WGS 84) coordinate system, were projected to the EPSG 9678 coordinate system, which enabled the measurement of coordinates in meters. As a result, we divided the entire territory into grids consisting of cells of one square kilometer. Then, since the night light density raster files were in geographic coordinates, the grid cells were projected back to WGS 84. Night light density at the grid cell was then computed by calculating the simple mean of the night light luminosity across all pixels within each grid cell. The final data was restricted to the district of Cox’s Bazar and consists of an annual average measure of night light density for the period spanning 1992 to 2021 at a level of one square kilometer.

2. _Deforestation._ This study uses the collection of raster images from the Hansen Global Forest Change dataset, which is available in the Google Earth Engine database (Hansen et al. 2013). This dataset leverages Landsat images to assess and characterize global forest extent and changes over time. Specifically, it focuses on identifying forest loss, defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. For the analysis, forest loss was computed into 3Despite the availability of data, both sources of night light series are inconsistent due to differences in spatial and radiometric resolution, spectral responses, the spread function of the sensors, local overpass time at night, radiance range, and on-board calibration (Li et al. 2017; Sahoo, Gupta and Srivastav 2020). Furthermore, the DMSP-OLS sensor measures night
second measure ranks countries‘ regulatory quality, rule of law, control of corruption, political stability, absence of violence, and voice and accountability in descending order in terms of percentiles of the distribution based on the World Governance Indicators (Kaufmann _et al._ 2004). Low values characterize countries with poor governance. Finally, for land governance, we use a newly developed cross-country database assembled by the French Development Agency.

Key variables of the land governance indicator include tenure security and recognition of existing land rights (even if not formalized), the existence of a land policy, and levels of land-related conflict. We use the first component from a principal component analysis of these variables as an indicator of overall tenure security. Low values describe countries with high levels of tenure insecurity. Whether good (land) governance increases a country‘s attractiveness for land-related investment is difficult to anticipate the direction of the effect as there are two countervailing forces. On the one hand, given the long time horizon of any agricultural investment, security of property rights is likely to be a key determinant of long-term investment decisions as investors will not tie up large amounts of resources in a country where weak or unclear rights create a danger of opportunistic government behavior and creeping expropriation (Schnitzer 1999) once investments are sunk. The opposite could, however, also be true, i.e. large investors may find it easier to establish and defend property rights if (land) governance and the state‘s enforcement capacity and presence are weak. Some investors unfamiliar with customary tenure systems may indeed believe that it will be easier and more ̳secure‘ to acquire land directly from governments rather than by engaging in a dialogue with local rural populations. Others are quite outspoken about the perceived need and desire to enforce property rights through private militias--despite the problematic historical precedents.<sup>15</sup>

# **3. Data, econometric approach, and descriptive statistics**

Country as well as cross-country level data on large
W_</sup> _it_ �: From customs transaction data, we observe the total value of firms imports, _IMPit_ . Let’s define 0 _≤ κ ≤_ 1 equals the share of imports that is used for exports, such that _p_<sup>_W_</sup> _t_<sup>_M W_</sup> _it_<sup>=</sup><sup>_κIMPit_.Then</sup> (1) can be rewritten as Note that (2) is a structural relationship between the export value of a firm and the import value of a _κ_ . firm, which may allow for the estimation of the share of imports used for exports, As in Kee and Tang (2016), we define the domestic value added (DVA) in export as the sum of export-specific profits, costs of labor, capital, and domestic materials used for export: 8
, home heating, motor vehicles). However, locational economics inhibit CO2-intensive industrial activities in higherincome urban areas with higher land costs, fewer active resource mining sites, and stricter control of local air pollutants that are emitted along with CO2. In consequence, higher-income cities tend to import CO2-intensive goods and services from areas where the converse conditions hold. The net effect of urban economic development on CO2 emissions depends on the relative strength of direct income effects and indirect displacement and pollution control effects. Empirical research on the relationship between CO2 emissions and income has relied almost entirely on country-level emissions inventories. The results are mixed; some studies find a linear relationship, while others identify an inverse U-shaped relationship, or Environmental Kuznets Curve (EKC) (Ben Youssef, Hammoudeh and Omri 2016; Dasgupta et al. 2002). Where the EKC holds, domination passes from direct effects to displacement and pollution control effects as income rises. With satellitebased CO2 measures, the EKC investigation in this paper departs from conventional practice by avoiding emissions inventories and focusing on local rather than national CO2 emissions.

Climate comprises the third category. Other things equal, we would expect greater annual heatingrelated CO2 emissions from cities in cold climates.

We specify a linear estimation model because the atmospheric CO2 load should be additive in CO2 emissions from different sources. Spatially-referenced variables in the model are translated to

> 1 Recent research has used Google Traffic to produce high-resolution traffic congestion data for individual cities (Dasgupta et al. 2021; Heger et al. 2018).  Future research could extend this approach to all global cities.

5
Table 1 reports the socio-demographic characteristics of the sample at endline, together with the difference between treatment and control, and the level of significance of this difference. Sample households have large families with an average of over 8 members, including just under two children younger than 5 years old. Educational attainment is low, with 81% of household heads having received no formal schooling. The surveyed households are predominantly male-headed, with less than 1% being female-headed (only 17 households). Of the households in the sample, 42.3% are polygamous. There are a few imbalances between treatment and control: treatment households have fewer boys aged 6-16, are slightly more likely to have a household head that completed at least primary school, and are slightly less likely to have experienced a shock in the last year. We are limited on the range of variables we can test balance on given that we do not have baseline data in this experiment. However, this table also contains pre-program administrative data for a sub-sample of 86% of the cotton producers. Reassuringly, we do not find imbalances in the pre-program cotton area, which is around 3.65 ha in both experimental groups, nor in the cotton production, which is around 3770 kg.

The survey data provide a detailed picture of household agricultural tasks and cotton production in Cˆote d’Ivoire. While cotton producers are almost exclusively men, both men and women in these farm households are engaged in cotton production. Contrary to conventional wisdom detailing stark gender divisions in the performance of agricultural tasks, with men being more responsible for physically-intensive tasks like plowing and land clearing and women more responsible for weeding and sowing, Table 2 shows that there is little gender specialization in cotton-related tasks in our setting. The starkest gender difference is that household men are generally more involved than women in all tasks: over 80% of male producers perform each type of task and producers are over 10 percentage points more likely to conduct any given task than any other household member. Still, spouses are
acceptable (Farmer et al., 2011). The consequences of these teacher-to-student and studentto-student socialization experiences emerge in the classroom in various ways. Some teachers offer students many opportunities to work with peers whereas others do not. Peer interactions in some classrooms are positive (characterized by positive conversation and cooperation) whereas others are negative (characterized by name calling, teasing, or bullying).

Synthesis work on peer relationships in schools points to a few key features of effective peer relationships: warmth, structure, and autonomy support. Warm peer relationships give students an opportunity to listen to each other and talk openly. These interactions provide emotional support, establish a basis for respect, and give students a sense of belonging. Although the structure provided by peers differs from that offered by teachers, peer interactions offer structure by clearly signaling to students that they can rely on each other for help and guidance, by offering models of competent academic behavior, and by establishing clear and consistent expectations about shared materials. Peer interactions can also offer autonomy support in that working to understand each other’s viewpoints, explaining the relevance of school work to one another, or the healthy negotiation that comes in group work helps students develop a sense of autonomy (Furrer, Skinner, & Pitzer, 2014).

The quality of peer interactions and student collaboration skills has been linked to academic competence in both the short and long term. In one longitudinal study, Buhs, Ladd, & Herald (2006) examined longitudinal data from kindergarten to fifth grade to examine the extent to which peer rejection early on related to later classroom engagement and achievement. Further, the work considered the extent to which being excluded by peers or peer abuse (e.g., kids saying bad things about them) was the mechanism explaining the relation between early peer rejection and later outcomes. Findings showed that a student’s own perception of peer rejection was related to subsequent poor treatment by peers toward that child, resulting in the student’s withdrawal from classroom participation and
to the question of whether financial development was necessary for the real effects to be manifested.

We find a strong response in lending activity in districts adjacent to the GQ highway network, manifested in terms of both loan counts and larger loan sizes. Our results are strongest in districts where there was new construction (as opposed to upgrades) and dynamic specifications support the effect taking hold shortly after construction. Moreover, our results hold in straight-line IV frameworks and are also not present in 'placebo tests' with a second highway that was planned to be upgraded at the same time as GQ but subsequently delayed. Our results point to bank lending responding to the increase in real activity that arose from improved transportation infrastructure. Importantly, how- ever, we find our results are entirely concentrated in regions with strong initial financial development. Lending activity did not increase and in some specifications is seen to fall slightly in regions with initially low financial development, suggesting that while finance responded to help support increased real activity, the level of financial development played a critical role in determining where real economic activity grew. These results suggest that the initial level of financial development might be critical in shaping how (and where) infrastructure investment can jumpstart real economic activity.

Our study is the first to connect micro-level financial development with plausibly exogenous infrastructure development. This is not possible for the United States, where most research has traditionally focused, due to the older nature of the Eisenhower highway system. The later timing of the Indian investment and better collection of financial data over recent decades provide unprecedented platforms. Moreover, prior work mostly identifies how the existence of transportation networks impacts activity, but we can quantify the impact from investments into improving road networks compared to placebo networks that are not enhanced. This provides powerful empirical identification, and the comparisons are informative for the economic impact of road upgrade investments, which are very large and growing.<sup>3</sup> This project also contributes to important questions facing India as it seeks to build 3 Through
5 EPAs were contacted, and 93 answered positively.<sup>6</sup> In the fall of 2014, the ITC survey concentrated on EPAs in 14 European countries, all of which responded positively.<sup>7</sup> The study therefore was left with an unbalanced panel containing information on EPAs’ budget, sources of funding, governance, and activities for 94 countries.

The survey contains 19 questions in order to better understand the budget, sources of funding, governance, and activities of EPAs around the world.<sup>8</sup> The choice of questions was carefully designed with the the help of the best export-promotion experts in the World Bank and the International Trade Center, as well as practitioners from the European Trade Promotion Organization. The questions try to get to what are considered best practices in export promotion. Table 1 provides summary statistics for the variables used in this paper. It is important to note that this is an unbalanced panel so the averages are not necessarily for the same time period for each variable. Also, for non-European countries the sample stops in 2010, whereas for some European countries the sample only starts in 2010. The unbalanced nature of the panel is addressed using country and year fixed effects in the econometric specifications.

Also note that all questions regarding the share of the budget spent on different activities or type of firms, or coming from different sources vary in a scale from 1 to 6. It takes the value 1 if this share is 0, the value 2 if the share is between 0 and 10 percent, the value 3 if the share is between 10 and 25 percent, the value 4 if the share is between 25 and 50 percent, the value 5 if the share is between 50 and 75 percent, and the value 6 if the share is between 75 and 100 percent.

The share of the private-sector seats on the executive board is measured in percentage points by simply taking the number of seats in the hands of the private
measurement to these dimensions by asking about food coping strategies and anxiety over insufficient food access (e.g, Maxwell et al. 2014). However, there are significant additional challenges to incorporating anxiety regarding poor food access and other subjective measures. In particular, answers to subjective welfare questions depend on respondentspecific scales that: (1) may not be comparable across individuals or stable over time; (2) are potentially subject to frame-of-reference effects; and (3) suffer from measurement errors, over and above those affecting traditional welfare metrics (e.g., Ravallion 2013; Tandon 2021; etc.).

There have been extensive efforts to validate food access metrics by comparing them to the quantity of food consumed and to nutritional outcomes (e.g., Maxwell 1996; Weisman et al. 2009; Broussard and Tandon 2016; etc.). Yet measuring the quantity of calories consumed and the nutritional content of food consumption is difficult and nutritional outcomes are also strongly impacted by health and non-food factors. Therefore, food access measures have also been validated by comparing them to _other_ household characteristics (e.g., Maxwell et al. 2014). One such household characteristic is monetary poverty and low levels of expenditure, which are strongly correlated with a number of key food access metrics (e.g., Maxwell et al. 1999; Webb et al. 2002).

However, it is important to note that one cannot validate a new food access measure simply by comparing it to another previously-validated food access measure. If different food access metrics are poorly aligned, it might be that each food access metric is capturing different dimensions of food access (e.g., Maxwell et al. 2014), or it might be that the new metric is indeed poorly correlated with the underlying dimensions of food access that it purports to capture. Better understanding the degree to which different metrics complement each other and knowing which metrics might be poorly suited to specific contexts will help to weight metrics properly
property register, to infer the gender assigned at birth and the owners’ ages for over 100,000 properties annually. We then linked the gender indicator to the main property tax database to generate a sex-disaggregated analysis.

Following the strategy described above, we were able to correctly assign the gender to 74% of the 108,321 residential property owners using data provided by ARBA from 2018, while the remaining 26% were set to missing.

B-4
WPS3767

# **CHINA’S PATTERN OF GROWTH: MOVING TO SUSTAINABILITY AND REDUCING INEQUALITY**

## Louis Kuijs<sup>1/*</sup> and Tao Wang<sup>2/*</sup>

## **Abstract**

This paper studies the sources and pattern of China’s impressive economic growth over the last 25 years, and shows that key issues currently of concern to policymakers—widening inequality, rural poverty, and resource intensity—are to a large extent rooted in China’s growth strategy, and resolving them requires a rebalancing of policies. Using both macro level and sector data and analyses, the paper extends the growth accounting framework to decompose the sources of labor productivity growth. We find that growth of industrial production, led by a massive investment effort that boosted the capital/labor ratio, has been the single most important factor driving GDP and overall labor productivity growth since the early 1990s. The shift of labor from lowproductivity agriculture has been limited, and, hence, contributed only marginally to overall labor productivity growth. The productivity gap between agriculture and the rest of the economy has continued to widen, leading to increased rural-urban income inequality. Looking ahead, we calibrate two alternative scenarios. We show that continuing with the current growth pattern would further increase already high investment and saving needs to unsustainable levels, lower urban employment growth, and widen the rural-urban income gap. Instead, reducing subsidies to industry and investment, encouraging the development of the services industry, and reducing barriers to labor mobility would result in a more balanced growth with an investment-to-GDP ratio that is consistent with medium-term saving trends, faster growth in urban employment, and a substantial reduction in the income gap between rural and urban residents.

Keywords: Sources of growth; pattern of growth; labor productivity; labor relocation; income inequality; sustainability of growth; China.

**_JEL Classification_** : E22, E23, E24, J21, O11, O14, O15, O40, O53