rafmacalaba/datause-encoder-data huggingface dataset:
# 2.2 The RCRE Household Survey

The primary data sources used for our analyses are the village and household surveys conducted by the Research Center for Rural Economy at China’s Ministry of Agriculture from 1986 through the 2002 survey year. We use data from 88 villages in eight provinces (Anhui, Jilin, Jiangsu, Henan, Hunan, Shanxi, Sichuan and Zhejiang) that were surveyed over the 16-year period, with an average of 6305 households surveyed per year. Depending on village size, between 40 and 120 households were randomly surveyed in each village. Each village in the sample is in a different county, so county level policies affect each village in this sample differently.

The RCRE household survey collected detailed household-level information on incomes and expenditures, education, labor supply, asset ownership, land holdings, savings, formal and informal access to credit, and remittances.<sup>10</sup> In common with the National Bureau of Statistics (NBS) Rural Household Survey, respondent households keep daily diaries of income and expenditure, and a resident administrator living in the county seat visits with households once a month to collect the diaries.

Our measure of consumption is the sum of annual expenditures on non-durable goods and an imputed flow of services from household durable goods and housing. In order to convert the stock of durables into a flow of consumption services, we assume that current and past investments in housing are “consumed” over a 20-year period and that investments in durable goods are consumed over a period of 7 years.<sup>11</sup> We also annually “inflate” the value of the stock of housing and durables to reflect the increase in prices of durable goods over the period. Finally, we deflate all income and expenditure data to 1986 prices using the NBS rural consumer price index for each province.

There has been some debate over the representativeness of both the RCRE and NBS surveys, and concern over differences between trends in poverty and inequality generated from these surveys. These issues are reviewed extensively in Appendix B of Benjamin et al (2005), but it is worth summarizing some of their findings here. First, when comparing cross sections of the RCRE and NBS surveys with overlapping years from other cross sectional surveys that did not use a diary method, it is apparent that high and low income households are somewhat under-represented.<sup>12</sup> Poorer illiterate households are likely to be under-represented because enumerators find it difficult

> 10 One shortcoming of the survey is the lack of individual-level information. However, we know the numbers of working-age adults and dependents, as well as the gender composition of household members.

> 11 Our approach to valuing consumption follows the suggestions of Chen and Ravallion (1996) for the NBS Rural Household Survey, and is explained in detail in Appendix A of Benjamin et al (2005).

> 12 The cross-sections used were the rural samples of the 1993, 1997 and 2000 China Health and Nutrition Survey (CHNS) and a survey conducted in 2000 by the Center for Chinese Agricultural Policy (CCAP).

5
and J.P. Morgan; commodity prices from Goldman Sachs and the World Bank’s Development Prospects Group; bilateral investment treaties from UNCTAD; country risk and institutional indicators from the PRS Group’s International Country Risk Guide (ICRG); and technology and innovation indicators from the World Intellectual Property Organization. Depending on the specification, the dataset is an unbalanced panel that includes between 29,995 and 55,497 observations.<sup>10</sup>

# **4.2 Econometric specification and estimation**

The econometric model we use is an augmented gravity model that specifies that the number of cross-border M&A deals originating in country _i_ (“home”) and destined for country _j_ (“host”) at time _t_ , _Mijt_ , is a function of each country’s output in that period, _Yit_ and _Yjt_ , the (timeinvariant) bilateral distance between them, _Dij_ , and additional factors: where **X** and **Z** are vectors of home- and host-country characteristics, respectively, **B** is a vector of other variables capturing the bilateral economic relationship between the home and host countries, and **G** is a vector of additional controls representing global macroeconomic and financial conditions. To maintain parsimony, we nest the two possible host targets within (1), so that the various coefficients— _β_ , _γ_ , _λ_ , _φ_ , and _ψ_ —are allowed to vary by host-country class (advanced, _AD_ , or emerging, _EM_ ), so that _k_ = _{AD, EM }_ .

Variables considered within **X** and **Z** are informed by the different theoretical approaches outlined in Section 3. These include, _inter alia_ , GDP growth (corresponding to the factor proportions hypothesis), trade openness (corresponding to the proximity-concentration hypothesis), patents granted (corresponding to technology transfer arguments), and international reserve holdings (corresponding to political economy explanations). Additional variables included to account for the bilateral relationship between country pairs include factors such as the existing size of bilateral trade and the existence of a BIT between the two economies.

In our benchmark regressions, (1) was estimated using ordinary least squares (OLS) with two-dimensional clustering for standard errors (by country-pair and time), designed to correct for both heteroskedasticity across countries and serial correlation within countries. Because distance is time-invariant (and we are interested in the signs and magnitudes of the coefficient _β_ 3), we do not introduce country-pair fixed effects. Moreover, given the inclusion of global variables, we exclude time fixed effects from the benchmark in order to minimize the incidence of multicollinearity, although we explore this possibility in our robustness checks.

> 10The technical appendix provides detailed descriptions of each variable and its source in Table A.2, along with summary statistics for major variables of interest in Table A.3.

9
but is more effective in managing the low price shocks (which can be fully absorbed using storage alone) than in reducing high price shocks (for which trade policy is more effective).

**Figure 4. Asymptotic densities of availability and price in India under laissez-faire and optimal simple rules**

<!-- Start of picture text --> Laissez faire Laissez faire<br>Optimal simple rules - R-v=6 Optimal simple rules - R-v=6<br>Optimal simple rules - R-v=12 Optimal simple rules - R-v=12<br>75 80 85 90 95 100 8000 9000 10000 11000 12000 13000 14000<br>Availability (million tons) Price (Rs/t)<br>0.0020<br>0.12<br>0.10 0.0015<br>0.08<br>Density 0.06 Density 0.0010<br>0.04<br>0.0005<br>0.02<br>0.00 0.0000<br><!-- End of picture text --> To illustrate the complementarity between the two instruments, Table 7 displays statistics on Indian prices and the parameters of the policy rules when instruments are optimized separately. The price stability achieved by using the policy instruments separately is much lower than when they are combined. The optimal coefficient of insulation from world price � is very close to its value when combined with storage. This is not the case for the subsidy to private storage, which is lower than when combined with trade policy. This lower value can be explained by two things.

**Table 7. Optimal simple rules when instruments are considered separately**

||||**_R‐ν_**<br>|||
|---|---|---|---|---|---|
|**Variables**|**0**|**3**|**6**|**9**|**12**|
|||**_Optimal_**<br>|**_tradepolicy (wh_**<br>|**_en_**���**_)_**<br>||
|Insulationparameter���|‐0.17|‐0.40|‐0.48|‐0.52|‐0.56|
|Indiaprice||||||
|Mean(Rs/t)|9,728|9,631|9,602|9,587|9,575|
|CV(%)|12.79|11.24|10.92|10.79|10.71|
|RoWprice||||||
|Mean(US$/t)|201.0|201.6|201.8|201.9|201.9|
|CV(%)|21.40|22.40|22.75|22.95|23.13|
|||**_Optimal st_**<br>|**_orage policy (w _**<br>|**_hen_**���**_)_**<br>||
|Storage subsidy ���|‐0.09|0.49|0.73|0.85|0.93|
|Indiaprice||||||
|Mean(Rs/t)|9,809|9,802|9,805|9,815|9,823|
|CV(%)|14.46|13.61|12.85|12.28|11.85|
|RoWprice||||||
|Mean(US$/t)|200.5|200.3|200.2|200.1|200.1|
|CV(%)|20.72|20.56|20.41|20.27|20.16|

32
**Figure II-1: Spectral Density Function of Real Credit**

<!-- Start of picture text --> Saudi Arabia: Spactral Density Function of Real Credit Egypt: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram-2 log Periodogram -2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br><!-- End of picture text --> <!-- Start of picture text --> USA: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text -->

# **B.   Data**

To study the credit cycle, we obtain the annual data on different measures of credit and output from the World Development Indicators (WDI) for countries in MENA and five Western nations and Japan, during the period 1964 to 2017. To the best of our knowledge, this is the most extended and most trustable public source available for the full set of countries in MENA. Our data cover: the United States, Japan, Norway, the Netherlands, the United Kingdom, Spain, Saudi Arabia, Jordan, Bahrain, Algeria, the Islamic Republic of Iran, Kuwait, Morocco, Qatar (after 2000), Egypt, Lebanon (after 1990), Oman, Tunisia, West Bank and Gaza (after 2000), Yemen (after 1990), the United Arab Emirates, and Libya (after 2000).

In Table VI-1 we show the simple description of the data. This table presents the summary statistics of real GDP growth and growth of real private sector credit for all the countries in the sample for the period 1964-2017.

6
# **3. SDG gender indicator availability and country statistical performance**

Next we assess how a country’s overall statistical performance relates to the availability of gender data, and identify countries that may have strong systems overall but are underperforming on gender statistics. To do so, we compare the availability of gender statistics to scores on the World Bank’s Statistical Performance Indicators (SPI) (Dang et al 2023).

The World Bank’s Statistical Performance Indicators (SPI) measure statistical performance for 174 countries covering over 99% of the world population. The indicators are grouped into five pillars: (1) data use, which captures the demand side of the statistical system; (2) data services, which looks at the interaction between data supply and demand such as the openness of data and quality of data releases; (3) data products, which reviews whether countries report on global indicators;<sup>8</sup> (4) data sources, which assesses whether censuses, surveys, and other data sources are created; and (5) data infrastructure, which captures whether foundations such as financing, skills, and governance needed for a strong statistical system are in place. Within each pillar is a set of dimensions, and under each dimension is a set of indicators to measure performance. The indicators provide a time series extending at least from 2016 to 2020 in all cases, with some indicators going back to 2004.<sup>9</sup> The indicators are summarized as an index, termed the SPI overall score, with scores ranging from a low of 0 to a high of 100. We use the SPI data for 2019.

> 8 The data products pillar measures whether countries have recent SDG indicators across the 17 goals available in the UN Global SDG Indicators database.

> 9 The data for the indicators are from a variety of sources, including databases produced by the World Bank, International Monetary Fund (IMF), United Nations (UN), Partnership in Statistics for Development in the 21st Century (PARIS21), and Open Data Watch—and in some cases, directly from national statistical office websites.

21
estimates lie between 16 and 32 pp. In 1993, which is when income differences at the border must have been reduced to a minimum (see table 2 and above), BDs in early-age stunting are no longer observed. For Cˆote d’Ivoire, Cogneau and Jedwab (2012) found that the 1990 drastic cut in cocoa producer prices had a large impact on the height of two- to five-year-olds, less so on the youngest.

Finally, BDs for access to electricity seem large in magnitude for most estimates; they are always above 29 percentage points in 1986–8 but suffer from a lack of precision when the smallest 50 km bandwidth is used. BDs for access to safe water are positive but even less precise. With larger sample sizes on the Cˆote d’Ivoire side in 1992–3, both are found to lie in the same range and display higher statistical significance.<sup>10</sup> The results are not shown for the year 1998; despite a small sample on the Ivorian side (11 PSUs only), the results seem to confirm the persistence of the relative superiority of Cˆote d’Ivoire over Ghana in terms of utilities.<sup>11</sup>

# **The northern borders**

We now turn to the northern borders of Cˆote d’Ivoire with Mali and, secondarily, Guinea.<sup>12</sup> Although 1993 was a very bad year for Cˆote d’Ivoire, the macroeconomic figures in table 2 show that this country was still far above Mali in terms of household

> 10Using DHS data only and for the 75 km bw, the results are similar, although again less precise due to the smaller sample size (for electricity connection: +44**, +40*, +35; for water safety: +27*, +44***, +32**).

> 11For the 75 km bw and for 1998, the results for electricity connection are +81***, +63***, +48**; for water safety: +30*, +20, +19**.

> 12In previous versions of this work, we also looked at the border with Burkina Faso. However identification had drawbacks due to sample size and distribution. Borders _between_ northern neighbors were also studied. See supplemental appendix section S3.

25
Recruitment, effort, and retention effects of performance contracts for civil servants: Experimental evidence from Rwandan primary schools Clare Leaver,<sup>_∗_</sup> Owen Ozier,<sup>_†_</sup> Pieter Serneels,<sup>_‡_</sup> and Andrew Zeitlin<sup>_§¶_</sup> JEL codes: C93, I21, J45, M52, O15.

Keywords: pay-for-performance, selection, incentives, teachers, field experiment.

> _∗_ Blavatnik School of Government, University of Oxford; and CEPR (clare.leaver@bsg.ox.ac.uk)

> _†_ Deparment of Economics, Williams College; World Bank Development Research Group; BREAD; and IZA (owen.ozier@williams.edu)

> _‡_ School of International Development, University of East Anglia; EGAP; and IZA (p.serneels@uea.ac.uk)

> _§_ McCourt School of Public Policy, Georgetown University; and CGD (az332@georgetown.edu)

> _¶_ We thank counterparts at REB and MINEDUC for advice and collaboration, and David Johnson for help with the design of student and teacher assessments. We are grateful to Katherine Casey, Jasper Cooper, Ernesto Dal B ́o, Erika Deserranno, David Evans, Dean Eckles, Frederico Finan, James Habyarimana, Caroline Hoxby, Macartan Humphreys, Pamela Jakiela, Julien Labonne, David McKenzie, Ben Olken, Berk Ozler,<sup> ̈</sup> Cyrus Samii, Kunal Sen, Martin Williams, and audiences at BREAD, DfID, EDI, NBER, SIOE, and SREE for helpful comments. IPA staff members Kris Cox, Stephanie De Mel, Olive Karekezi Kemirembe, Doug Kirke-Smith, Emmanuel Musafiri, and Phillip Okull, and research assistants Claire Cullen, Robbie Dean, Ali Hamza, Gerald Ipapa, and Saahil Karpe all provided excellent support. Financial support was provided by the U.K. Department for International Development (DfID) via the International Growth Centre and the Economic Development and Institutions Programme, by Oxford University’s John Fell Fund, and by the World Bank’s SIEF and REACH trust funds. Leaver is grateful for the hospitality of the Toulouse School of Economics, 2018–2019. We received IRB approval from the Rwanda National Ethics Committee and from Innovations for Poverty Action. This study is registered as AEA RCT Registry ID AEARCTR-0002565. The findings in this paper are the opinions of the authors, and do not represent the opinions of the World Bank, its Executive Directors, or the governments they represent. All errors and omissions are our own.
garment exports of Malaysia, the Philippines and Thailand decreased by 2, 8 and 4 percent annually, respectively, on average.<sup>1</sup> In this environment, recommending a growth strategy based on labor-intensive exports was neither credible nor useful to the middle-income countries of the region.

At around the same time, theories of endogenous growth had entered the mainstream of policy debates. After the pioneering work by Romer (1986), Lucas (1988) and—a decade later—by Aghion and Howitt (1996), economists had started to unpack the technological black box of the Solow growth model. Competition, science and scalable technologies entered the mainstream of growth theory. These models seemed to better explain the phenomenon of “club convergence” (Baumol, 1986) where a select group of advanced countries appeared to converge, at least in terms of economic growth rates if not in terms of per capita income levels, while low- and middle-income countries, with only a few exceptions, got left ever further behind (Pritchett, 1997). And of course, technological breakthroughs and soaring valuations of technology companies suggested a new economics with important scale economies was at play in the 21<sup>st</sup> century.

There was considerable interest in ASEAN about transitioning to “knowledge economies”. The Republic of Korea had done this successfully after the Asian financial crisis in 1997/98. But we concluded that this would be premature for most of the middle-income ASEAN countries, given the mediocre quality of the higher education systems and low enrollment rates, the lack of domestic patents, low levels of innovation and technological diffusion, an absent venture capital eco-system, and assembly-type firms that were not moving rapidly up the value chain.

The Annual Meetings of the World Bank and International Monetary Fund being held in Singapore in 2006 provided an opportunity to reassess the issues faced by middle-income countries in the region, primarily those in ASEAN. So in early 2005, we started work that would be published in 2007 as _An East Asian Renaissance_ . _Renaissance_ introduced the concept of the “middle-income trap” into the development literature.

The concept was influenced by our experience working in Latin America where, although in different contexts and social and economic environments, rapidly growing economies like Brazil had suddenly stagnated. Empirical work by Easterly, Kremer, Pritchett and Summers (1993) had suggested mean reversion of growth rates was common, so East Asia’s past successful growth could not be projected forward in a mechanical fashion. Figure 1, which is an updated version of a graph in Gill and Kharas (2007), shows how five economies in Latin America—Argentina, Brazil, Chile, Colombia and Mexico—that had grown reasonably rapidly from 1950 to the mid1970s, then stagnated. We contrasted this experience with the growth pattern of the four East Asian NIEs and Japan, which showed continuous steady growth, and asked which path the five middleincome South-East Asian countries would follow.

> 1 UN Comtrade, retrieved 06‐17‐2015. http://comtrade.un.org/data/

2
|**Share of treatment households...**<br>||||
|---|---|---|---|
|(in parentheses: only compliant households)|%|**Pico‐PV lamp is mainly used by...**|**%**|
|using the kit at least once a day|86 (95)|Female adult >17 years old|49|
|...using the kit for lighting|85 (97)|Male adult >17 years old|23|
|...using the kit for listening to the radio|68 (79)|Female between 12 and 17 years old|10|
|...using the kit for charging mobile phones|10 (11)|Male between 12 and 17 years old|7|
|...use the battery pack|65 (71)|Collectively used by whole family|6|
|||Children between six and 11 years old|5|

_Source:_ Household data set 2012.

Most lamp users are satisfied with the lighting quality of the lamp. More than 70 percent of all lamp users report they are “always” or “often” satisfied with the lighting quality. Only 22 percent report to be satisfied only seldom and six percent are never satisfied. Satisfaction levels with traditional lamps are substantially lower. For wick lamps and hand‐crafted LED lamps, 94 percent and 91 percent, respectively, report to be satisfied seldom or never.

Since both treatment and control households are located within the same communities, spill‐over effects might occur. Especially children often meet and play with friends and there might be positive spill‐over effects on other households’ children. If among these ‘other’ households are households from our control group, it may even downward bias our impact estimates. Yet we did not find any evidence for spill‐overs. For instance, in the control group the share of children studying outside their home did not increase and is negligible at less than one percent. More generally, the qualitative interviews we conducted did not provide any indication for joint activities using the kits and hence spill‐overs of that sort.
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