rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 7  Unemployment fluctuates with changes in business conditions, but remains high in CEE countries**

# **Unemployment Rate, 1992-2002 CEE**

<!-- Start of picture text --> 25.0<br>20.0<br>Czech R.<br>Bulgaria<br>Estonia<br>15.0<br>Hungary<br>Latvia<br>Lithuania<br>Poland<br>10.0 Romania<br>Slovakia<br>Slovenia<br>5.0<br>0.0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>percent<br><!-- End of picture text --> Source: ILO LABORSTA database (LFS data) In Russia and Ukraine the evolution of unemployment has been somewhat different from that in CEE. Unemployment reached its peak later, only in 1998 (i.e. four years later than in CEE). This is likely to reflect delayed restructuring and initially heavier reliance on wage adjustment (as opposed to employment adjustment) in response to the negative output shock. Since 1999 unemployment in both Russia and Ukraine has begun to decline. Again, it is too early to tell whether or not this decline represents a transitory movement, or a movement toward the equilibrium unemployment rate. Unemployment in Russia and Ukraine coincides with significant underemployment among workers in unrestructured firms or workers employed in the large informal sector. This is a still bigger problem in other CIS countries.

In the low-income CIS countries unemployment has also increased, but the problem is not so much open unemployment as underemployment. It takes the form of employment in subsistence agriculture, irregular or casual employment in the informal sector (e.g. petty trade), or employment in unprofitable and unviable enterprises, which often are not even able to pay wages. Underemployment is closely associated with low productivity and thus translates into lower output and social welfare, similarly as does unemployment
most other compilations of public spending data, this one includes all levels of govermnent. In this and other respects, considerable care appears to have gone into constructing the data. There is clearly little sign that social spending was protected from cuts. Indeed, there is sizable co-movement, with indications that (if anything) social spending was more volatile than other types of spending. One can see quite large proportionate declines in social spending in every year in which total spending falls. On the other hand, one often sees smaller (and sometimes negative) changes in social spending when total spending rises. Letting Gs denote social spending at date t, and _Gt_ total spending, one can test whether the elasticity to an increase in total spending differs from that for a decrease using the regression: AlnGs = _a + [yl,5, +_ Y2(1- 3,)]A ln _G, + et_ (8) where a is a time trend, yr is the elasticity when total spending increases, y2 is the elasticity when it falls, and **et** **___** I(AInG _G)_ takes the value unity when AlnG, > 0 and zero otherwise.

The data in Figure 1 yield an estimate of 0.14 for y1 . This is not significantly different from zero (the standard error is 0.37). On the other hand, the estimated elasticity to a decrease in total spending (r2) is 2.14, which is significantly greater than one (the standard error is 0.26). Social spending responds elastically to aggregate cuts, but the responsive to fiscal expansions is not statistically significant.<sup>7</sup> The constant term of 0.086, which is significant (t-ratio=2.83), indicating a sizable independent trend increase in the share of social spending. By contrast, non-social spending was well protected; the elasticity when total spending fell was 0.09, and not significantly different
Policy Research Working Paper 10862

# **Abstract**

With about 80 percent of poor households in Timor-Leste dependent on agriculture for their livelihood, it is widely acknowledged that growth in the agriculture sector is an important channel for poverty reduction in the country. That notwithstanding, the country’s agricultural production system is one of the least developed in the world, with the sector’s productivity being well below that of other small island developing states and below the average for other low-income states. Using data from the 2007 and 2014 waves of the Timor-Leste Surveys of Living Standards, this paper provides insight into the extent to which increases in agricultural productivity can contribute to poverty reduction, and assesses the drivers of and constraints to increasing agricultural productivity in the country. The findings show that improvements in agricultural productivity reduce the probability of being poor among agricultural households. The paper also finds that for agricultural productivity to increase, the following factors should be considered: facilitation of farm mechanization, improvement in the use of chemical (fertilizer, pesticides, and herbicides) inputs, enhancement of access to credit and extension, encouragement to farmers to join farmer groups, expansion of the commercialization of farm produce, and reduction in the gender gap in agricultural productivity.

This paper is a product of the Agriculture and Food Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fdarko@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are
surveys are not specifically aimed to be nationally representative, due to the three dimensions of stratification, these samples are likely to cover a substantial fraction of total exports. Freund and Pierola (2015) have documented the remarkable concentration of trade in very few enterprises for a sample of 32 developing countries. They show that, on average, the top 5 firms account for one third of aggregate exports, and this fraction is even higher for manufacturing products.

Using the information in the UN Comtrade dataset, we calculate total exports and total manufacturing exports for each country-year in which we observe a firm-level survey. In Table 1, we report the ratio of the sum of exports of the sampled firms divided by aggregate exports. The average fraction across all surveys is 19%. In only 23 of the 94 (country-year) surveys is the average below 5%, while in 24 cases it exceeds 25%. It underscores that by surveying a relatively small number of firms, a significant fraction of a country’s exports can be accounted for. We therefore expect our results to be broadly representative of the experience of exporters and importers in many these countries.

> 7 Only counting the years with survey activity between 2002 and 2017, there is an average of 6.6 surveys in each year.

6
statistical “universe”) and thus sample appropriately to achieve representativeness. In EMDEs, by contrast, we expect most transactions to occur informally and outside of what is observable publicly. Through web scraping, we constrain our analysis to the formal housing market and to what is transacted online. With this approach, we are able to obtain house prices for the formal housing market but are unable to infer the degree the estimations apply to – in some EMDEs admittedly large – informal housing markets. We aim for representativeness of the formal housing market only, and to achieve this, we scrape entire websites to cover all available listings. Despite these efforts, we encounter smaller sample sizes in some countries, which are likely to stretch margins of error (cf. Section 3.3). Annex 1 provides an overview of the sample coverage and percentage of formal households scraped.

# 3.2 Data Processing: Data Cleaning and Outlier Removal

As with any data set that is obtained from user-inserted data, the scraped data is prone to incorrect, inconsistent, or missing information. Most online listing platforms do not run quality checks on the listed properties or require fully populated identification of property features. Preparing the data for analysis, we diligently cleaned the web scraped data removing data entry errors, duplicates, and outliers. Given the issues outlined in the previous section, which are inherent to EMDEs, data cleaning is more tedious and timeconsuming than for more structured data likely to be obtained in developed economies. We describe the data processing steps in detail below, illustrating descriptive statistics of the various stages of the process (Table 1).

## Duplicates

The first step in processing the data is identifying and flagging repeat data and duplicates. These mostly arise for two reasons: first, many EMDEs’ property websites allow for the re-submission of the same property within some days’ interval. Realtors mainly use this option to restore the property at the top of the search results list to improve visibility on the website. Second, duplicates may also arise
the _Doing Business_ report for a specific year and (b) the day prior to the press release date of the _Doing Business_ report for the subsequent year. For instance, for _Doing Business_ 2017, the time period for counting Factiva articles is between 10/27/2015 (the day of the launch of _Doing Business_ 2016) and 10/24/2016 (the day prior to the release of the _Doing Business_ 2017).

Most of the media articles that discuss _Doing Business,_ especially in India and Russian Federation, tend to highlight increases in countries’ rankings, comparing them either with the preceding years or with those of neighboring economies. Many articles also focus on recent accomplishments of incumbent governments. India’s media, for example, discusses the ambitious plans and strategies of the government to secure a position on the _Doing Business’_ top-performers list. In Mexico, _Doing Business_ related media coverage often outlines recent reforms and investment attractiveness of the economy, while also highlighting remaining

> 6 Primary data collection on media coverage of _Doing Business_ has been done through the use of the Factiva database. Factiva is an online comprehensive database primarily designed to provide data to business communities, researchers and specialists, aggregating content from both private and public sources. It pulls data from more than 32,000 information sources, including newspapers, journals, magazines, television and radio transcripts. Factiva has an almost universal reach, allowing to filter data by local and international sources.

> 7 Media outlet is considered to be a written publication either in print or electronic. If the same outlet produces both print and online outputs, only one is counted.

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particular aspect of governance is better than in the world as a whole (e.g. BERI, which covers primarily developed countries). Suppose further that this source tends to score countries relative to each other, so that the worst (best) country in the sample receives the lowest (highest) possible score? of zero (one). This would be reflected in a relatively high value of ,8(k), since relatively small differences in true govemance are magnified into relatively large differences in observed scores.

## **The Conditional Distribution of Governance**

Our objective is to summarize our knowledge about governance in each country j by the distribution of governance conditional on the observed data in country j. This task is greatly simplified by assuming that both g() and the disturbances so,k) are jointly normally distributed. In this case, go) and y(,k), k=1,...,Ko) are jointly normal, and the conditional distribution of g() given the data is also normal, with mean and variance given by:.

# (3) VE[(g)ly(j)] = 1y- c

where y() is a Kj)xl vector which stacks the K(j) data points for country j, a is the corresponding K()xl vector of a(k)s, B and , are K&)xK() diagonal matrices with the corresponding ,B(k)s and _a,(k)_<sup>_2_</sup> _s_ on the diagonal, and t is a K()xl vector of ones. We refer to the conditional mean in (2) as the estimated value of that aspect of govemance in country j. With a slight abuse of terminology, we refer to an interval from the (o/2)t percentile to the (1-8/2)th percentile of the conditional distribution of **go)
Almost 20 percent of Cambodians experience fiscal impoverishment. This means that: 1) some individuals whose pre-fiscal incomes were below the poverty line were net contributors to the fiscal system such that this reduced their net cash position, and 2) some individuals whose pre-fiscal incomes were above the poverty line were net contributors to the fiscal system such that their post-fiscal incomes ( _consumable income_ ) was below the poverty line. The average reduction in income as a proportion of market income among Cambodians fiscally impoverished was 3.7 percent.

Conversely, some of the poor gained through the fiscal system. Fiscal gains to the poor (FGP) traces _prefiscal_ poor households to determine how many experienced an income increase following fiscal policy execution. Fiscal policy made only about 2 percent of pre-fiscal poor better off; that is, net beneficiaries of the Cambodian fiscal system. Among pre-fiscal poor beneficiaries, the average increase in market income was 10.6%.

Table 5: Fiscal impoverishment and fiscal gains to the poor

|Market income|Change in poverty|Fiscally|Fiscal|Fiscally better-|
|---|---|---|---|---|
|poverty|headcount|impoverished (%|impoverished (%|off (% of market|
|headcount|(percentage points)|of population)|of consumable|income poor)|
|(%)|||incomepoor)||
|17.7|2.1|19.5|98.4|1.9|

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **Contribution of Individual Interventions to Poverty and Inequality Reduction**

In addition to overall effects of fiscal policy, policy making must assess the contribution of each fiscal intervention to poverty and inequality. We can analyze an intervention in terms of its progressivity and marginal contribution to poverty and inequality. To do this, we use the Kakwani index (Kakwani 1977) to analyze whether a tax or transfer
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
increase in the growth of finance is associated an increase in employment growth by 0.89 percentage points among MSMEs and only

> 21 Micro firms with access to a loan increase employment growth at a rate of almost 5 percentage points larger than that of micro firms without a loan, by far the sub-sample with the highest correlation. 22 All analysis using ORBIS data is done using the full sample of firms available even when data for a particular firm is not available for all years. As robustness check, we repeated all our analysis over a balanced panel that drops firms not available over the entire 2004-2011 timeframe. Although the number of countries and the sample size drops substantially, all results carry through. The regression tables are available on request.

20
individual, how long the person spent there and the length of time in the destination. I make two assertions:

1. The likelihood a person, _pt_ , is infected is based on the fraction of the month spent in the origin district j, _Tjp_ , and the monthly incidence rate in j, _xjt_

2. The contribution of an imported case to a new location is calculated as a fraction of time spent in the destination location i, _Tip_ The contribution of person _pt_ , who enters i from j at time t, to imported cases is calculated based on the incidence of the origin district and the length of time spent in origin district j and destination health post catchment area i. Only up to 15 days in the origin and up to 15 days in the destination are considered since the human incubation period is 15 days. Therefore, _Tip_ is the proportion of 15 days _pt_ spends in _i_ after entering _i_ and _Tjp_ is the proportion of the month up to 15 days that _pt_ spent in _j_ with monthly malaria incidence _xjt_ in month _t_ .<sup>19</sup> Due to the complicated nature of the imported incidence variable, I break down what explains the variation in this variable. Imported incidence combines information on travelers, the incidence where they are coming from, the timing in the destination and the origin, and the population in the destination. Based on a partial _R_<sup>2</sup> of .44 the month explains a large portion of the variation, while .33 of the variation is explained by the health post catchment area. Jointly, they explain about half of the variation ( _R_<sup>2</sup> of 0.56), implying that the other half of the variation in the variable of interest is coming from a combination of the month and the location receiving imported cases. This shows how the identication comes from the unique combination of detailed data on travelers and the incidence in the origin. The matrix _Zit_ includes zero
Table B1: Time series used in model estimation

|Real Total Gross Domestic Product, quarterly (demeaned growth rate)|
|---|
|Real Private Final Consumption Expenditure, quarterly (demeaned growth rate)|
|Real Gross Fixed Capital Formation, quarterly (demeaned growth rate)|
|Real Government Final Consumption Expenditure, quarterly (demeaned growth rate)|
|Real Exports of Goods and Services, quarterly (demeaned growth rate)|
|Real Imports of Goods and Services, quarterly (demeaned growth rate)|
|Real public investment expenditure, quarterly (demeaned growth rate)|
|Real private investment expenditure, quarterly (demeaned growth rate)|
|Rate of capacity utilization, quarterly (demeaned)|
|Gross Domestic Product defator infation, quarterly (double-sided HP fltered)|
|Harmonized Index of Consumer Prices: Overall Index Excluding Energy, quarterly (demeaned growth rate)|
|Harmonized Index of Consumer Prices: Energy, quarterly (demeaned growth rate)|
|Employment rate: number of people employed/total labor force, quarterly (double-sided HP fltered)|
|Real compensation of employees, quarterly (demeaned growth rate)|
|Total Hours worked, quarterly (demeaned growth rate)|
|Low-skilled hourly wage infation, quarterly (double-sided HP fltered)|
|High-skilled hourly wage infation, quarterly (double-sided HP fltered)<br>Discount rate, quarterly (double-sided HP fltered)|
|3-month short-term interest rate, quarterly (double-sided HP fltered)|
|Real Broad Efective Exchange Rate, quarterly (double-sided HP fltered)|
|Total fnal household (residential+public+half of transportation) energy consumption, annual (demeaned growth rate)|
|Total fnal frm (industry+half of transportation) energy consumption, annual (demeaned growth rate)|
|Renewable energy supply, annual (demeaned growth rate)|
|Fossil energy supply, annual (demeaned growth rate)<br>CO2 emissions, annual (demeaned growth rate)|
|Domestic fossil fuel producer prices, quarterly
awareness campaigns**

To see if measures to improve awareness and decentralize service delivery affected the volume of registered land transfers, we exploit quarterly data from 2014 and 2015 on the number of registered transactions for the country’s 2,148 cells. Locally weighted regressions of the number of registered transactions per quarter by distance to the DLO and year or presence of SLMs point towards a clear increase in volumes of registered sales but not non-monetary transfers over time (figure 3) and with the presence of a SLM (figure 4).

Cell-level quarterly data for 2014 and 2015 also allow us to explore determinants of registered transfers of agricultural or residential land. We denote cells by _c_ , quarters by _q_ and year by _t_ to estimate a regression of the form where _Tcqt_ is number of registered residential or agricultural land transfers (total, monetary or nonmonetary) for cell _c_ in quarter _q_ in year _t_ ; _Dc_ is the distance of cell _c_ from the DLO in km, _Scqt_ is an indicator variable for whether or not cell _c_ is located in a sector where a SLM had been appointed at _q_ in _t_ ; _Lcqt_ is a dummy indicating past coverage of the sector where cell _c_ is located by land week events at _q_ in _t_ ; _Rc_ is the number of registered agricultural or residential land parcels in _c_ ; _Uc_ is the share of urban population at sector level from 2012 census estimates; _Pt_ is a province-year dummy; _εcqt_ is a random error term, and _β_ s are coefficients to be estimated.

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**Figure 6: Prices and Volumes of Non-Traditional Exports 1993-2009**

<!-- Start of picture text --> (indices 1994=100)<br><!-- End of picture text --> <!-- Start of picture text --> 700<br>Price<br>600<br>Volume<br>500<br>400<br>300<br>200<br>100<br>0<br>Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-07 Jan-09<br><!-- End of picture text -->

# **4.  Trends in export diversification**

Although the performance of Peruvian exports has been notable during the last decade, one large concern is the diversification of the range of export products, as the economy has suffered historically from strong swings in its terms of trade due to excessive product concentration. This section presents an analysis of the evolution of the degree of concentration of Peruvian exports since the start of the economic reform phase in the early 1990s until 2009. The analysis relies on the computation of annual time series of diversification indicators between 1993 and 2009<sup>12</sup> , as well as a more disaggregated comparison of the degree of export diversification at the product level in two years: 1993 and 2009. The empirical evidence obtained from both approaches indicates that over these two decades, Peru has seen a trend towards a greater concentration of its exports, especially in the post-2000 period. The silver lining is that this greater overall concentration of aggregate exports has gone hand in hand with a greater diversification of the non-traditional export category, which is reflected in the emergence of a large number of new export products.

## **Concentration indices**

This section looks at the evolution of diversification using two different methods of measurement. The first is the proportion of the value of exports of the major export products, a widely used simple and intuitive indicator. The second one