rafmacalaba/datause-encoder-data huggingface dataset:
This paper has asked whether the censorship strategies employed by censors vary across individual targets, and whether punishment via censorship occurs when other forms of media capture through bribes or other economic inducements fail. Our leading theoretical hypothesis is that censors engage in a cost–benefit calculus of censorship targets based on the political threat posed by the audience size and transaction costs. This balance leads to economizing punitive censorship, by confining it only to segments of the media market that reach politically important constituencies, and only when economic inducements cannot effectively control information.

In order to test our theory of market segmentation, we constructed the first global data set that captures a full set of characteristics of salient punitive censorship events affecting individual media outlets and professionals, in 196 countries between 2001 and 2015. We found that, on average, foreign media, as well as outlets operating from abroad, are less exposed to censorship since on average they are less politically consequential than domestic media and entail higher transaction costs of suppression. One of the most interesting results suggests that the risk of being censored is strongly correlated with the audience reach of the media type. Even when controlling for the level of economic development that captures the prevalent types of media in a country, TV broadcasting, radio, and print are significantly more likely to be the targets of severe forms of censorship than media accessible mostly to elites, such as online news platforms, blogs or freelance journalism. The fact that traditional media reaches a wider audience renders censors more vulnerable to the perils of potential political dissent triggered by news.

The paper also investigated whether censors use different media capture tools simultaneously or in a substitutable manner. Besley and Prat’s (2006) seminal article suggests that coercion has been replaced with economic inducements, such as bribes or regulatory rewards for outlets. We show empirically that censorship is back, mirroring a general trend in democratic 45
percent higher to 44.3 percent lower than the real FDI stock. Especially in the cases of Qinghai, Sichuan and Inner Mongolia, the absolute values of residuals are less than 10 percent of total FDI stocks, or $10 million in real terms. Our analysis suggests that many of the coastal provinces, which have been important destinations for FDI," still have a long way to go before reaching the saturation point.

**Table 9: Comparing Actual and Predicted Values of FDI**

|_-Over-Investors'"_|||_F|"Under-Investor_|_s"_|I|
|---|---|---|---|---|---|
||Ratio|Value||Ratio|Value|
|Hainan*****|80.83|763.63|Ningxia|-547.53|-7.99|
|Shaanxi|76.05|349.13|Hebei*|-311.65|-956.08|
|Fujian*****|71.03|2,091.66|Zhejiang*****|-163.27|-924.29|
|<br>Guangdong*****|69.94|8,428.90|<br>Shanxi|-121.67|-100.91|
|<br>Guizhou|55.02|49.01|Gansu|-91.75|-10.98|
|l___________________||__________||_______<br>Shanghai*|-51.14|-991.27|

|_"Middle-Investors_|
|---|

||Ratio|Value||Ratio|Value|
|---|---|---|---|---|---|
|Xinjiang|39.65|21.46|Liaoning*****|-44.34|-690.11|
|Heilongjiang|25.99|75.53|Hunan|-44.20|-109.74|
|Anhui|22.82|35.81|Henan|-43.48|-106.64|
|Yunnan|20.31|13.71|Guangxi*****|-39.10|-175.30|
|Hubei|19.27|72.99|Tianjin*|-36.48|-222.61|
|Jiangxi|18.86|32.02|Jiangsu*|-22.75|-505.66|
|Jilin|9,88|17.85|Shandong*****|-17.23|-314.49|
|Qinghai|9.43|0.33|Inner Mongolia|-8.87|-3.79|
|Beijing*****|7.32|149.40|Sichuan|-1.82|-6.46|

_The "Over-Investors"._ Among the five Over Investors, the three coastal provinces come as little surprise. With an average annual FDI growth rate at 40 percent since 1988, Hainan's actual FDI accumulation is over 80 percent higher than its predicted level. The main reason for such an outcome lies more in the small predicted value of FDI for Hainan (the 11th smallest among all provinces) rather than its high actual FDI accumulation (the 8th highest). Despite a relatively extensive transportation system (the 6th highest transportation route density in China), Hainan has a very small economy (the 4th lowest in total GNP) and is poor in the quality of its labor force (the 11th highest illiteracy ratio in the nation). Its ability to attract such a large amount of FDI is mostly attributed to its unique geographical location and the favorable investment policies implemented there: the island's initial transformation to an SEZ was especially responsible for an upsurge in interest from the international investment community.

Fujian and Guangdong were also locales for the initial four SEZs. As seen from Table 6, these two provinces' FDI accumulations between 1985-92 were far ahead of all other provinces in China. Especially in the case of Guangdong, the amount of "excess" FDI it attracted was more than three times the total amount of FDI any other province ever accumulated in these eight years.

11 Recently, investment has been flooding into Jiangsu and Shandong provinces, where there are many rural enterprises. This trend has been taken as an indication that rural enterprises may fast become partners for joint ventures and joint enterprises; see China Newsletter (1993) 11
programs to assess the impacts of EGPP on poor beneficiaries, while EGPP beneficiaries’ sample data are also drawn to provide auxiliary information specific to the EGPP program.

The sampling design of the survey adopted a stratified approach in two stages. First, a number of _Unions_ as a Primary Survey Unit (PSU) were selected with a probability proportional to the budget allocated to the particular _Upazila_ (upper administrative unit above _Union_ ). The size of the budget was used as it reflects the _Upazila_ ’s 2010 poverty rates and number of the poor, and it is also directly associated with the number of beneficiaries in the _Upazila_ . Fifty _Unions_ (PSUs) were selected at the first stage. In the second stage, from the roster of beneficiaries of the selected _Unions_ , we randomly drew a representative sample of beneficiary households — 40 households in each _Union_ , which yields 2,000 households for the EGPP sample and 20 households in each _Union_ for four other traditional safety net programs, which yields a total of 4,000 households in the sample (hereafter Main sample).

In the second round, we construct a PSU level panel, where the same PSU was maintained although different households in the PSU were included in the sample with an updated roster. Faced with challenges in the field in locating the households on the administrative roster in the same PSUs, a few had to be replaced with adjacent PSUs in the same _Upazila_ . Keeping only the PSUs that appear in both rounds of surveys resulted in 10,792 (of 11,043) households being included in a balanced panel of 103 PSUs. Therefore, our data set consists of 6,812 households as the Main sample (3.5 percent attrition rate), supplemented by 3,979 EGPP households (0.6 percent attrition rate). The data contain very rich and detailed information on household characteristics including demographics, consumption, and savings and loans profile of the beneficiary, along with the labor market activities and earnings of adult individuals in the household. In addition, program participation and program-specific information was collected. The community module of the survey captures the information on each _Union_ . This includes general economic activities, agricultural production, access to infrastructure and social facilities, and local prices and wages.

7
disentangle the effects of robots on export quality at early, middle and later periods of the sample across developed and developing countries. Unfortunately, we are limited to OLS estimation as our instruments are weak when estimating effects across combinations of country income groups and three separate time periods.

The results in Table A6 in the Appendix show strong correlations between quality increases and robot diffusion in developed countries at the beginning of the sample period (2000-2005) and for developing countries at the end of the sample period (2011-2015). These results suggest there are quality gains from robot diffusion in developed countries as well as developing, but they were realized earlier and only in the first years of our data.

**Table 2: Effects of Robots on Export Quality**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Outcome:|O|Unit<br>LS|Prices<br>I|V|O|Qu<br>LS|ality<br>I|V|
|Robots|0.011*<br>(0.006)||0.023<br>(0.015)||0.030**<br>(0.012)||0.121***<br>(0.039)||
|Robots||0.023**||0.074**||0.052**||0.266***|
|– Developing||(0.010)||(0.032)||(0.021)||(0.089)|
|Robots||-0.001||-0.004||0.008||0.043*|
|– Developed||(0.006)||(0.010)||(0.010)||(0.025)|
|Kleibergen-Paap<br>F statistic|||115.8|32.64|||115.8|32.64|
|# Observations|897,260|897,260|897,260|897,260|897,260|897,260|897,260|897,260|

_Note: Robots reflects log robot stock. The instrument reflects robot diffusion in (initial) foreign export partners, in other world regions, defined using 2000 export flows. Quality refers log quality estimated via Khandelwal et al (2013), unit prices are in logs. Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The regressions also include exporter*10digit product and exporter*year fixed effects. The first stage has been omitted for brevity and is available in Appendix Table A5. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._

# **_ii. Heterogeneity across product types_**

The results of the previous section reflected regressions pooled across all product types. This section examines the extent to which there are heterogeneous effects of robots across different products. First, we explore if robot adoption has resulted in a catch-up or a divergence of product quality over time, by contrasting the quality growth of products based on their initial distance from 25
# XVi INTRODUCTION

Figure 3 Assumed Medium Trend and Alternative<sup>Slow and Rapid Trends in</sup> Total Fertility During the Fertiiity Transition <!-- Start of picture text --> . 5<br>4.0 4. 5<br><!-- End of picture text --> <!-- Start of picture text --> 2.5<br><!-- End of picture text --> <!-- Start of picture text --> 3.0<br>2 . 2.07 9 _<br>2. a -<br>2.4 -<br>2.6S<br>2.3-<br>2.2-<br>2.0-<br>1. -<br>12. 6 -t l wrItcDno<br>1.6<br>1. 7<br>1900-95 1990-95 2000-05 2012-15 2020-25 2030-35<br><!-- End of picture text --> Figure 5 Percentage of Total Fertility Assigned to Different<sup>Ages, by Level of</sup> Total Fertility <!-- Start of picture text --> 25 - Tr-R-8<br>26 -<br>24<br>22-<br>20-<br>16<br>14<br>12<br>10<br>4<br>2<br>15- 19 20- 24 25- 29 30- 34 35- 39 40-44 45- 49<br><!-- End of picture text -->
- 16 -

# B. Factors Explaining the Change in the Debt to GDP Ratio

We now can turn to the analysis of the effects of the early-1980s external shock and of domestic policies on the total public sector debt to GDP ratio. This will enable us to further understand the differences in debt strategy followed by etch country. In particular, it will enable us to understand how differently each of these six countries was affected by the external shock and how they responded. In addition, it will enable us to understand the factors that led the debt to GDP ratio to i icrease (or decrease) faster in some countries relative to others. The exogenous factors will be defined as the effects of the external shock on the debt to GDP ratio and the endogenous factors as the effects of domestic policies.

## External Debt

We can analyze the effects of the external shock and of the domestic policies on the external PS debt by breaking down the change in the external PS debt to GDP ratio as shown in equation (1): <!-- Start of picture text --> (1) [ b*t t 1 A[b6*t ' e 8 0 ] [Ab *.t e, - A b * .t]<br>Al _ -- +<br>rgdpjt rgdpt rgdpt<br>[Ab*t e, Abd*tn.8o] it- [b*t-l et 1<br>- -<br>rgdpt rgdJ rgdpt<br><!-- End of picture text --> b* is the stock of total net PS debt in current US dollars divided by a trade-weighted average international price index with weights given by the
# **Table A.1.6. Barth, Caprio and Levine (2004) survey questions** (continued)

Panel C: Legal action by deposit insurance authority and treatment of non-residents

|**Country name**|**Can the deposit insurance**|<br>**Has the deposit**|**Are non-residents**|
|---|---|---|---|
||**agency/fund take legal**<br>**action against bank**<br>**directors or other bank**<br>**officials?**<br>yes=1 no=0|**insurance agency/fund**<br>**ever taken legal action**<br>**against bank directors or**<br>**other bank officials?**<br>yes=1 no=0|<br>**treated differently than**<br>**residents with respect**<br>**to deposit insurance**<br>**scheme coverage?**<br>yes=1 no=0|
|Albania|1|1|0|
|Algeria|0|0|0|
|Argentina|1|1|0|
|Austria|0|0|0|
|Bahrain|1|0|0|
|Belarus|1|0|0|
|Belgium|1|1|0|
|Bolivia|1|1|0|
|Bosnia-Herzegovina|1|0|0|
|<br>Brazil|0|0|0|
|Bulgaria|0|0|0|
|Canada|1|1|1|
|Chile|0|0|0|
|Colombia|0|0|0|
|Croatia|0|0|0|
|Cyprus|0|0|0|
|Czech Rep.|0|0|0|
|Denmark|0|0|0|
|Ecuador|1|1|0|
|El Salvador|0|0|0|
|Estonia|0|0|0|
|Finland|1|0|0|
|France|1|0|0|
|Germany|1|0|0|
|<br>Gibraltar|0|0|0|
|Greece|0|0|0|
|Guatemala|0|0|0|
|Honduras|1|0|0|
|Hungary|0|0|0|
|Iceland|0|0|0|
|India|0|0|0|
|Ireland|0|0|0|
|Isle of Man|0|0|0|
|Italy|0|0|0|
|Japan|0|1|0|
|<br>Jordan|0|0|0|
|Kazakhstan|1|0|0|
|Kenya|1|1|0|
|<br>Korea|1|1|0|
|Latvia|1|0|0|
|Lebanon|0|0|0|

45
# i) Cost Effectiveness

Continuing the gasnoline example, suppose that there are two types of gasoline consuimers. Type I, who commutes to work from a location not served by public transport, finds it very costly to reduce gasoline consumption and would be willing to pay a high price to continue using gasoline for commuting purposes. Type II drives mostly for pleasure and would not be willing to pay a lot to continue using gasoline for this purpose. Pollution abatement achieved by curtailing type I's gae line consumption has high marginal cost while the abatement achieved by curtailing type II's consumption of gasoline has low marginal cost.

If the government mandates less use of gasoline by all consumers, say in equal amounts or proportions, pollution abatement would not be accomplished at least social cost. At the mandated total gasoline consumption level, letting type I consume an extra gallon and requiring type II to reduce by an extra gallon would leave total consumption unchanged but yields social cost savings. The least cost method of pollution abatement requires that the low abatement cost consumers reduce gasoline consumption more than the high abatement cost consumers. The least cost abatement policy requires that each type of consumer reduce his consumption of gasoline until the marginal cost of abatement, and therefore the marginal willingness to pay for a gallon of gasoline, is equal to that of every other type.

20
progressively narrower budget deficits, eventually realizing two years of budget surpluses. This allowed the government to reduce its debt stock (as a share of GDP) – a stated fiscal objective. Between 2009/10 – 2015/16 expenditure growth exceeded economic growth and revenue growth, resulting in sustained large budget deficits and an increased in its debt stock (as a share of GDP).

It is possible that the commodity boom period of 2001 to 2007 in South Africa would have seemed like a permanent shift in output. Government spending and revenues during this period increased rapidly. It is difficult to pinpoint whether this increase was due to an upward forecast bias in potential GDP, or whether it was intentional. There is some support that South Africa erred in terms of estimating potential GDP (Anvari et al. (2014)) after the financial crisis. Figure 8 illustrates this point, comparing the threeyear (and a few months) forecasts from the Budget Reviews and Medium Term Policy Statements (MTBPS) with actual growth outturn, i.e. it looks at the first outer year estimate/print published by the National Treasury. Figure 8a compares projections and outturn and Figure 8b looks at the cumulative forecast-outturn difference. Figure 8a demonstrates the over-optimism about future growth, following the global financial crisis and 2009 recession.

In Figure 8b, a flat line would suggest that there is no discrepancy between forecasts and outturns, a downward trend would suggest that projected growth was lower than observed and an upward trend would suggest that growth projections were larger than observed growth. The lines clearly point upwards both for Budget Reviews and MTBPSs after 2009, indicating an upward growth bias.<sup>4</sup> The optimistic bias from 2007/08 until 2016/17 in the graphs provides some evidence that potential GDP growth was perceived as much larger than it actually was.

# **Figure 8: Growth projections vs. growth realization**

_(forecasts vs. actual and cumulated projection growth differentials with actual growth)_ a) Outer-year forecast vs actual GDP growth <!-- Start of picture text --> 7%<br>Real GDP 3.25yr forecast Real GDP 3.5yr forecast<br>6% Real GDP growth Actual<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>‐1%<br>‐2%<br><!-- End of picture text -->

> 4 Investigating the potential biasness of the National Treasury’s GDP growth forecasts, the Parliamentary Budget Office found some statistical evidence of biasness for several forecast horizons, including the three-and-a-half and three-and-a-quarter year forecasts, with a tendency to overestimate GDP growth (Parliamentary Budget Office, 2016).

13
**Ψ** is defined to be the diagonal ( _p × p_ ) error covariance matrix with elements _σ_ 1 _, . . . , σp_ . Let the ( _p × p_ ) matrix **Σ** = _ββ_<sup>_′_</sup> + **Ψ** . Then, the likelihood function for the data is:

In (23) the parameter _α_ is simply the vector of the means of the _p_ variables in **X** . For observation _j_ the 1 _× p_ data vector is denoted _xj_ .

Denoting the ML parameter estimates with “hats”, the factor score for observation _j_ is computed as the conditional mean (conditional on _xj_ ) and the standard error of this estimates is computed as Where the EC method differs from the (random) factor method estimated by ML is in the specification of the likelihood functions. Whereas in the EC method the data likelihood is maximized over the parameters ( **A** _,_ **Σ** ), in the ML factor method the likelihood of the intercorrelations in the data is maximized over the parameters. In this sense, the EC method is still a variance method (driven by a squared error loss objective) while the ML factor method pays attention to the intercorrelations among the variables.

# **3. Data**

The Knowledge Assessment Methodology (KAM) database consists of more than 80 structural and qualitative variables to measure countries’ performance on how they perform as “knowledge economies”. We will use the subset of 12 variables that are used by the KAM 14
# **Figure 12 Fiscal balance in Indonesia and Argentina**

# **A. Indonesia**

# **B. Argentina**

<!-- Start of picture text --> Percent Index Percent Index<br>Fiscal balance Fiscal balance<br>Commodity export price (RHS) Commodity export price (RHS)<br>20 140 6 120<br>15 120 4 100<br>2<br>100<br>10 80<br>0<br>80<br>5 -2 60<br>60<br>-4<br>0 40<br>40<br>-6<br>-5 20 -8 20<br>-10 0 -10 0<br>1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 2021 1995 1998 2001 2004 2007 2010 2013 2016 2019 2021<br><!-- End of picture text --> _Sources_ : Arroyo Marioli and Végh (2023); International Monetary Fund; World Bank. _Note_ : EMDEs = emerging market and developing economies A.B. Fiscal balance as a percentage of GDP.

# **Figure 13 Institutional quality and fiscal procyclicality**

<!-- Start of picture text --> Index Correlation<br>Law and order Corruption control<br>7 Bureaucracy quality Correlation (RHS) 0.5<br>0.4<br>6<br>0.3<br>5<br>0.2<br>4 0.1<br>3 0<br>-0.1<br>2<br>-0.2<br>1<br>-0.3<br>0 -0.4<br>Australia Norway Chile EMDEs<br><!-- End of picture text --> _Sources_ : Arroyo Marioli and Végh (2023); International Monetary Fund; PRS Group (database); World Bank. _Note_ : EMDEs = emerging market and developing economies. The institutional quality indexes give higher scores to countries with better metrics. “EMDEs” shows the simple average of 68 commodity-exporting countries across the 3 indicators from 1990-2019. The correlation is calculated between the GDP and the real government expenditure of a country after using the Hodrick-Prescott filter to remove the trend component of the time series.

42
strictly prohibiting sale, exchange for other property or mortgage. However, a major improvement is that land leasing to a third party is allowed under the current system<sup>3</sup> .

But the policy is still unclear and land redistribution has taken place in some areas to provide land to new claimants. An instance of this is the 1997 land redistribution in the Amhara region. This redistribution affected land covered by perennials, and contrary to the stated policy compensation was not paid to the former owners (Holden and Hailu Yohannes, 2002). This has created fear among farmers that they will be subjected to possible land redistribution without compensation at any time in the near future. Based on a nationally representative survey of farm households, Deininger et al. (2003) found out that 9 percent of the farmers were affected by land redistribution in the 1991-98 period. Also, less than a third of the farmers expected that there would not be land redistribution in the near future even though there is an intent for policy to discourage such practices. In the data set used in this paper, these results are reiterated: about 7 percent of households in 1999 lost land during land redistribution in the last five years, while 11 percent of households expected to loose land themselves in the next five years due land reform, and 10 percent expected to gain.

Policy makers appear to state regularly that secure usufruct rights are crucial and some efforts have been made to formalize this, such as in the form of land titling exercises in Tigray. But the overall perception remains that recurring land reform is here to stay, contributing to substantial insecurity of tenure.

# **3. The Data**

This paper exploits household panel data from the Ethiopian Rural Household Survey (ERHS) covering the period 1994 to 1999. The survey initially covered about 1470 households in 18 Peasant Associations in 15 Woredas throughout the country. The villages were initially selected to reflect some of the diversity in agro-climatic conditions in Ethiopia. Geographically, these Woredas are located in Tigray (2), Amhara (4), Oromiya (4) and SNNP (5).

> 3 Informal arrangements in the form of sharecropping or fixed rent tenancy were taking place even during the Derg regime at the risk of losing land.

8