rafmacalaba/datause-encoder-data huggingface dataset:
regional inequality—as well as serious resource and environmental constraints. The SARS crisis in 2003 also revealed a threat of unbalanced social development.

# **_II.1 The Current Employment Situation_**

China’s economic growth has not been labor intensive. According to an estimate of the World Bank, in the 1980s, when China’s economic growth was 9.3% a year, employment growth was just 3% a year, and in the 1990s, when economic growth was about 10.4%, employment growth was only 1.1%. By official statistics, in 2002, there were about 7.7 million unemployed people in the urban areas. 5 However, the widely perceived effective unemployment rate is about 10% or even higher, far above the officially registered unemployment level, and, during the late 1990s, each year around 12 million people leave unprofitable formal sector activities (mainly state-owned and collectives).6 Xue and Zhong estimated that the urban unemployment rate reached 11.5% in 2000 and had become a major cause of urban poverty and worsening inequality 7 (table 1).

**Table 1. Estimates of China’s Urban Unemployment Rate (%)**

|Year|Census<br>data|Survey<br>data|Official<br>registered<br>unemployment|Adjusted unemployment<br>(official + laid-off<br>unemployed)<br>a|Estimated<br>unemployment<br>b|
|---|---|---|---|---|---|
|1990|4.2||2.5|2.9|4.2|
|1991|||2.3|3.2|4.7|
|1992|||2.3|3.9|5.5|
|1993|||2.6|3.2|7.1|
|1994|||2.8|3.6|8.6|
|1995|7.7|9.6|2.9|4.3|7.7|
|1996|||3.0|4.8|7.3|
|1997|||3.1|5.5|9.2|
|1998|||3.1|4.5|10.8|
|1999||11.6|3.1|6.3|12.5|
|2000|11.5||3.1|8.1|11.5|

Source: _Population Census in China_ , 1990, 2000; _China Statistic Yearbook_ , various years, and surveys in 1995 (6,868 households and 21,533 individuals in 11 provinces and 60 cities) and 1999 (4,500 households and 15,904 individuals in 6 provinces and 13 cities) by the Chinese Academy of Social Sciences. Also see Xue & Zhong, “Unemployment, Poverty and Income Disparity in Urban China”, _Asian Economic Journal_ , V17, No. 4, 2003. Notes: a Laid-off unemployed = laid-off workers – reemployed. In official statistics, the laid-off workers are not counted as unemployed, regardless of their individual situations.

> 5 National Bureau of Statistics of China, _China Statistical Abstract 2003_ , China Statistics Press, Beijing. 6 The formal sector refers to state-owned and collectively owned enterprises, plus joint stock and shareholding companies and foreign funded enterprises but excluding township and village enterprises. In a broad definition, they also include non-enterprise units, such as government agencies, institutes, non-profit organizations, etc.

7 Three surveys conducted by the Chinese Academy of Social Sciences (CASS) in 1988, 1995, and 1999, respectively, show that the Gini coefficient for the urban area rose from 0.185 in 1988 to 0.225 in 1995 and 0.256 in 1999. The percentage increase was 22% for the period 1988-1995, 14% for the period 1995-1999 and 38% for the whole period 1988-1999. The 1999 survey also shows that the poverty rate was 13.2% for households with unemployed people and only 2.8% for households without unemployed people. See Xue & Zhong (2003).

4
advantage of the IFR data is the consistency and comparability across many jurisdictions which is typically lacking with firm level robot data, allowing us to consider 59 exporting countries.<sup>10</sup> We include country-industry controls for employment and labor productivity (real value-added per worker in constant price 2015 USD), which are obtained from the UNIDO Industrial Statistics Database.<sup>11</sup>

# **_ii. Measuring quality_**

Export quality is derived from detailed United States HS 10 digit import data which we match to the robotics data by country and industry over time. We exclude robot products from the trade data. We harmonize the HS import data, in order to consistently measure HS 10 digit products over time, following the procedure of Pierce and Schott (2012).<sup>12</sup> We use US import data, due to its availability at the 10-digit level, and so allowing the measurement of quality for detailed product varieties.<sup>1314</sup> Historically, the trade literature has used unit prices (trade values per unit quantity) as a proxy for quality (Schott, 2004; Hallak, 2010). However, the literature has recently recognized that a range of factors other than quality itself may influence prices such as transport costs, tariffs, competition and productivity. As a result, our main export quality measure for this paper follows the microfounded model of Khandelwal et al (2013) and Breinlich et al. (2016). Demand for each variety is given by: 10 Canada and Mexico have been dropped from the sample due to inconsistent data collection over the sample period. Before 2010, the United States, Canada and Mexico robot sales were classified jointly as North America. 11 Value-added in USD is deflated to constant prices using US CPI-U, consistent with the treatment of trade values. Other controls in the UNIDO Industrial Statistics Database, such as wages, capital investment or number of enterprises, are not widely available for our sample countries.

12 We gratefully acknowledge concordance code available until 2019 on Peter Schott’s homepage https://faculty.som.yale.edu/peterschott/international-trade-data/.

13 Allowing us to compare (Washington) apples with (Washington) apples.

14 We follow Amiti and Khandelwal (2013) and trim the top and bottom 1% of prices by year. We estimate quality only for manufacturing sectors, and for differentiated sectors using the Rauch (1999) liberal classification. The results are robust to using conservative differentiated sector classification.

12
the trend component. The larger the value of A, the smoother is the resulting trend series.

The HP filter has been subject to various criticisms. In particular, it has been argued that it removes potentially valuable information from time series (King and Rebelo (1993)), and that it may impart spurious cyclical patterns to the data (Cogley and Nason (1995)). Another important limitation is the choice of the value of A. The usual practice in the literature is to set A to a specific value (for instance, 100 for annual time series) derived from an examination of the properties of U.S. output data by Hodrick and Prescott. However, imposing this specific value in a multi-country study can be viewred as arbitrary, and may reflect an overly stringent implicit assumption about the degree of persistence in **xt.** As a consequence, two alternative approaches are also used. The first, as discussed by Ag6nor, McDermott and Prasad (1998) consists in choosing a value of A for each individual series, using a data-dependent method. Specifically, a method of _generalized cross-validation_ is used. The basic principle of cross-validation is to leave the data points out one at a time and to choose the value of the smoothing parameter under which the missing data points are best predicted by the remainder of the data. A priori assumptions about the appropriate value of the smoothing parameter are not required and the smoothing parameter does not have to be held constant across all countries.

Another approach, also discussed by Agenor, McDermott and Prasad (1998), is to use a nonparametric method. This technique uses a univariate nonparametric regression estimation technique to estimate the trend and cyclical components of a series without having to specify the functional form of the trend component of the underlying series or the degree of smoothing applied to the actual data. Specifically, it permits the modeling of trends that involve higher-order polynomials without imposing a particular functional form on the trend component.<sup>5</sup> The specification of the regression model uses private saving (calculated as the difference between gross domestic saving and government saving) in proportion to GDP as the dependent variable. Despite the relatively limited number of degrees of freedom available (as discussed below), the list of explanatory variables involves a fairly large group of control variables that have 5The method can also be extended to control for discontinuities or isolated change points in the series that may be interpreted, for instance, as level shifts in the underlying series.

10
|Salvador Social Multi-Sector Service DeliveryProject|Pablo Ariel Acosta|
|---|---|
|Salvador Social Multi-Sector Service DeliveryProject|Pablo Ariel Acosta|
|Salvador Social Multi-Sector Service DeliveryProject|Pablo Ariel Acosta|
|Salvador Social Multi-Sector Service DeliveryProject|Pablo Ariel Acosta|
|Investingin Maternal and Child Health|Djibrilla Karamoko|
|Investingin Maternal and Child Health|Djibrilla Karamoko|
|Western Balkans Trade and Transport Facilitation|Alberto Criscuolo|
|Western Balkans Trade and Transport Facilitation|Alberto Criscuolo|
|Western Balkans Trade and Transport Facilitation|Alberto Criscuolo|
|Smart Africa Alliance/Korea Partnership|CaseyTorgusson|
|MDTF Fundingfor Earthquake HousingReconstruction Project|Kamran Akbar|
|MDTF Fundingfor Earthquake HousingReconstruction Project|Kamran Akbar|
|MDTF Fundingfor Earthquake HousingReconstruction Project|Kamran Akbar|
|MDTF Fundingfor Earthquake HousingReconstruction Project|Kamran Akbar|
|MDTF Fundingfor Earthquake HousingReconstruction Project|Kamran Akbar|
|Nigeria- AcceleratingNutrition Results|Sangeeta Carol Pinto|
|Nigeria- AcceleratingNutrition Results|Sangeeta Carol Pinto|
|Nigeria- AcceleratingNutrition Results|Sangeeta Carol Pinto|
|Nigeria- AcceleratingNutrition Results|Sangeeta Carol Pinto|
|Turkey- Resilience,Inclusion and Growth DPF|Habib Nasser Rab|
|Jharkhand Power System Improvement Project|Amol Gupta|
|Jharkhand Power System Improvement Project|Amol Gupta|
|Jharkhand Power System Improvement Project|Amol Gupta|
|Gettingto Best in Education|Oni Lusk-Stover|
|Gettingto Best in Education|Oni Lusk-Stover|
|Gettingto Best in Education|Oni Lusk-Stover|
|Gettingto Best in Education|Oni Lusk-Stover|
|Baghdad Water Supplyand Sewerage Improvement Project|Amal Talbi|
|Baghdad Water Supplyand Sewerage Improvement Project|Amal Talbi|
|Baghdad Water Supplyand Sewerage Improvement Project|Amal Talbi|
|Baghdad Water Supplyand Sewerage Improvement Project|Amal Talbi|
|Baghdad Water Supplyand Sewerage Improvement Project|Amal Talbi|
|Pakistan HousingFinance Project|Namoos Zaheer|
|Pakistan HousingFinance Project|Namoos Zaheer|
|Pakistan HousingFinance Project|Namoos Zaheer|
|Pakistan HousingFinance Project|Namoos Zaheer|
|Implementation of the Open Data Action Plan in Serbia|Svetlana Vukanovic|
|Sao Tome and Principe: Institutional CapacityBuildingProject|Mazen Bouri|
|Sao Tome and Principe: Institutional CapacityBuildingProject|Mazen Bouri|
|Sao Tome and Principe: Institutional CapacityBuildingProject|Mazen Bouri|
|Sao Tome and Principe: Institutional CapacityBuildingProject|Mazen Bouri|
|Dominica Geothermal Risk Mitigation Project|Elvira Morella|
|Dominica Geothermal Risk Mitigation Project|Elvira Morella|
|MonitoringPost Ebola RecoveryFunds: A Focus on Service Deliver|yGloria Malia Mahama|
|Mauritanian Education Budget|Ann-Sofie Jespersen|
|Citizen Involvement in Municipal Service Improvement(CIMSI)|Andrianjaka Rado Razafimandimby|
|Citizen Involvement in Municipal Service Improvement(CIMSI)|Andrianjaka Rado Razafimandimby|
and public sector management and institutions. Individual policy scores are collected from the World Bank through surveys of its staff. For each policy, a score between 1 and 6 is provided, with higher scores indicating higher degrees of policy and institutional effectiveness, making the CPIA actually more a matter of resilience than of fragility. These scores are averaged at both the cluster and country level to provide an overall CPIA score falling between 1 and 6, with an unweighted average score of 3.2 or less generally seen as the threshold of state fragility. However, in our analysis, we treat state fragility as a continuum rather than a binary concept. We, therefore, refer to state fragility more broadly as countries with lower CPIA score without using the 3.2 cutoff point.

CPIA scores are available since 1977 for World Bank client countries though only those for low-income IDA-eligible countries are published. However, in our sample we focus on the period 1998-2019 to ensure comparability of CPIA scores to account for several changes occurred to the methodology over the years (see Gonzalez and Nishiuchi, 2018, for an overview). For the sample, we concentrate on countries without too many missing values for the CPIA. This means we leave out from the sample a number of World Bank client countries that have high-income status. As these countries would typically have high CPIA values, there would be little to explain from the covariates.

As a robustness check later in the paper, we also perform part of the analyses with another measure of fragility, namely the Fund for Peace’s Fragile States Index (FSI). This index consists of five groups of indicators measuring state cohesion, economic conditions, political legitimacy and public service provision, social conditions, and external intervention. The FSI is only available from 2006 onwards. We therefore do not use it in our baseline analysis, which spans a longer timeframe. The FSI also covers more countries than the CPIA in the sense that the latter has missing values for a fair number of developed OECD countries. In our robustness analysis, we therefore discard these countries as well in order to have similar samples for the CPIA and 10
its regions.

A SAM is a comprehensive, economy-wide data framework, typically representing a nation’s economy, linking production, primary factors, and institutions (typically, households, government, and the rest of the world) (Pyatt and Round, 1977; Reinert and Roland-Holst, 1997). Given their explanatory power, SAMs are widely used as data inputs in economics and computable general equilibrium (CGE) models. Consistency of input data ensures that an analysis of shocks and effects is not based on contradictory pieces of information (Cicowiez and Lofgren, 2017). Mainly, a SAM refers to a country’s economy for one year. A SAM is a square matrix in which a row and a column cross represent each account. Each matrix cell contains the payment from its column (expenditure) to its row (revenue) account. The main property of SAMs is to respect the principle of double-entry so that, for each account in the SAM, total revenue (row total) equals total expenditure (column total) (Lofgren et al., 2002). The SAM incorporates three macro balances for government deficit, trade deficit, and savings-investment balance. All SAM-based CGE models must specify how balance is achieved in the three macro accounts and incorporate Walras Law in some form. Usually, the SAM accounts are partitioned, separating endogenous and exogenous accounts so that the endogenous accounts are a function of the values of the exogenous accounts. The partition of the SAM accounts determines the macro “closure” of the model (Robinson, 2006).

One of the most critical parts of building such a regional model is the construction of the SAM containing data for each region. This point may be critical due to the lack of data, even if several authors in the literature have proposed techniques to overcome this issue. Research in the regional CGE models presents differences in their structure, but generally, a common aim is to simulate fiscal-economic shocks at a regional level. In the specific case of SAMbased regional CGE models, there is relatively little debate in the CGE community. Naqvi and Peter (1996) developed the MRF (Multi-Regional Forecasting) model. It simulates tax/environmental policy in Australia. Jean et al. (2004) presented Europe’s DREAMMIRAGE (Deep Regional Economic Analysis Model-Modelling International Relationships in Applied General Equilibrium) model by considering 119 different regions. Canning and Tsigas (2000) built a similar model for eight macro-regions in the United States. Concerning the purposes, there are works with the specific aim of addressing tax reforms (Shoven and Whalley, 1984; Powell and Snape, 1993; Jorgenson and Wilcoxen, 1997; Dixon and Rimmer, 2001), implementation of environmental policies and study of climate change impacts (Darwin and Tol, 2001; Bigano et al., 2008; Eurobarometer, 2008; Skjærseth and Wettestad, 2010), and trade liberalization (Anderson and Martin, 2005; Bouët et al., 2005).

In the tradition of Computable General Equilibrium (CGE) models, Mercenier et al. (2016) presented RHOMOLO, which relies on an equilibrium framework à la Arrow-Debreu, 3
**<u>Table 6: Determinants of illegal land reallocation at village level</u>**

||**Illegal real**<br>**Pro**|**location dum**<br>**bit**|**my - probit**<br>**Probit IV**|**Amoun**<br>**To**|**t of land real**<br>**bit**|**located**<br>**Tobit IV**|
|---|---|---|---|---|---|---|
|Reform dummy (_β_)|-0.020**|-0.004|0.088|-2.742**|-0.721|3.410|
||(2.30)|(0.30)|(0.78)|(2.38)|(0.40)|(0.50)|
|Director and secretary elected (_γ_)||-0.009|-0.048||-0.977|-6.262|
|||(0.86)|(1.04)||(0.76)|(1.02)|
|Director & secretary elected*Reform||-0.024|-0.126||-3.388|-9.451|
|dummy (_δ_)||(1.49)|(1.13)||(1.44)|(1.01)|
|Leaders’ knowledge of law|-0.019*|-0.018*|-0.019|-2.553*|-2.460|-2.735|
||(1.72)|(1.65)|(1.56)|(1.68)|(1.62)|(1.55)|
|Share of households with land|-0.015|-0.014|-0.011|1.182|1.189|-1.486|
|certificate|(1.43)|(1.36)|(1.01)|(0.84)|(0.85)|(1.05)|
|Village per capita income|-0.009|-0.005|-0.004|-1.070|-0.627|0.364|
||(0.85)|(0.51)|(0.36)|(0.74)|(0.43)|(0.22)|
|Share of agriculture|0.027*|0.023|0.017|3.285|2.908|2.022|
||(1.77)|(1.58)|(0.82)|(1.58)|(1.40)|(0.90)|
|_β_ +_δ_<br>||-0.028***|-0.038**||-4.109***|-6.041**|
|Wald Test of Exogeneity, χ<sup>2</sup>(2)|||3.31|||0.85|
|Observations|1917|1917|1785|1917|1917|1785|

Robust z statistics in parentheses `.` * significant at 10%; ** significant at 5%; *** significant at 1%. Province dummies included but not reported. An illegal land reallocation is defined as one that did not involve the village committee. The coefficients from Probit and Probit IV have been converted to marginal probabilities.

28
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# **Precipitation**

Since precipitation is an important determinant in runoff, a simple precipitation runoff model is calculated for the catchment study areas in order to compare accuracy and level of significance against the BWI runoff model. We hypothesize that the BWI regressed with gauging station flow is better than precipitation predicting flow pass a gauging station, which we test using a R<sup>2</sup> test. The hypothesis is based on the realization that runoff is compounded by many factors: such as snow melt, soil texture, irrigation, solar isolation, relative humidity, and wind speed. All these factors are difficult to measure separately and they vary spatially from _in situ_ observations. The satellite captures the spatial structure, and the BWI integrates all the above factors into one observation at each grid point (Basist et al. 2001).

# **Runoff**

We identify the correspondence between regional BWI and the rate of flow at various gauging stations. The findings allow us to evaluate the relationship between the two variables and determine if useful results can be observed from the statistical models of stream flow. In order to perform the estimates, the measuring station must have a sufficiently long observation period of record (minimum of four years – as long as possible) and the period of record corresponds with the SSM/I data, which is available from 1988 until present<sup>5</sup> , except mid-1990 through 1991.

# **Integrating BWI and runoff in GIS**

Since both the BWI and runoff are geo-referenced, the model calculates the BWI over the area upstream of the gauging station. The Geographic Information System allows for the spatial delineation of the upstream area, using HydroSHEDS<sup>6</sup> (Lehner et al. 2006) for a model of flow accumulation and direction. Since impoundments and/or diversions affect the amount and timing of water reaching the gauge, we used GIS to display the geo-referenced dam data

> 5 We have used data until 2009 in this analysis.

> 6 HydroSHEDS is a dataset in the public domain of conditioned Shuttle Radar Topography Mission (SRTM) elevation data (90m resolution) that used a series of processing steps that alter the elevation values in order to produce a surface that drains to the coast (except in cases of known internal drainages). Further steps include filtering, lowering of stream courses and adjacent pixels, and carving out barriers to streamflow.  Flow accumulation and flow direction grids (30 arc seconds) were downloaded at: http://gisdata.usgs.net/Website/HydroSHEDS/viewer.php.

9
allocation of property rights, public investment, services, credit, and subsidies. Although governments sometimes made efforts to redistribute land in response to rural unrest and violence, the programs were poorly designed and consequently achieved little. To the contrary, because they discouraged tenancy (by restricting property rights or by raising fear among landowners of future land reform), or explicitly prohibited tenancy, the land reform programs often led to the evictions of large numbers of tenant farmers, whose loss of income more often than not outweighed the gains achieved by poor farmers.

Although agricultural output grew rapidly in these countries until the mid-1980s, rural employment did not grow enough to keep up with population growth-because by subsidizing credit, governments encouraged large farmers to purchase farm machinery, which displaced labor. Agricultural growth largely stopped after governments stopped subsidizing credit in the mid-1980s with the onset of the fiscal and debt crises. Structural problems, manifested in widespread rural poverty and rural violence, again demand attention.

# **3.** IMPLICATIONS OF MATERIAL CONDITIONS AND MISSING MARKETS

It is impossible to consider the consequences of agricultural policies in isolation from general macroeconomi **c** onditions and policies affecting other sectors. But it makes sense to investigate the key factors influencing agricultural policies separately, since the material conditions of agricultural production and the specific imperfections of financial, insurance, and factor markets in rural areas can explain many observed regularities.4 We first identify the special characteristics of agricultural production and their consequences. We then investigate the way policies interact with market imperfections to produce efficiency- and growth-reducing outcomes. Finally, we explore whether and how material conditions can help explain policy choices and distortions.

> _4_ This section draws heavily from the large literature on tenancy and sharecropping recently reviewed for the _Journal of Economic Literature_ by Otsuka _et al._ (1992) and the literature on agrarian relations and land markets as reviewed by Binswanger, Deininger and Feder (1995). As the purpose of this section is to set the scene rather than provide a detailed discussion, we refer the interested reader to these references for a more elaborate argument and a borader set of citations.

9