rafmacalaba/data-use-mentions-extended huggingface dataset:
This version: 15 Feb 2024 9 transformation fro <mark>m a plantation economy to a manufacturing exporter,</mark> driven by labor-intensive export-oriented industries such as textiles and electronics assembly. Yet in 2018 various opinion polls documented widespread social discontent over lifequality (World Bank, 2019). In this section, we offer an account of this paradoxical pattern in Malaysia’s development trajectory using a variety of quantitative measures.

**Figure 1** provides a brief timeline of Malaysia’s economic and political journey over the past 65 years. A race riot in 1969 led to the launch of the New Economic Policy (NEP), a 20-year long affirmative action program targeting the majority Bumiputera population. Since then, Malaysia’s GDP per capita rose steadily, helping it to leave its ASEAN neighbors behind. This trend is remarkable not only because the country successfully survived two major external shocks (both regional and global crises), but also because poverty fell sharply from 50 percent in 1970 to single digits by the 1990s. In other words, macroeconomic success coincided with a steady fall in the population below the government’s poverty line income (PLI). Based on official statistics, extreme poverty is no longer much of an issue. The national poverty rate declined from 49.3% in 1970 to just 0.4% in 2016.<sup>6</sup>

Some social groups and geographic locations were historically much poorer than others. Yet income poverty fell everywhere, and among all ethnic groups, along with visible improvements in living conditions. Not only was poverty reduction a

> 6  Trends for recent years based on an updated PLI are also consistent with the past pattern of poverty reduction. The official PLI uses a single reference consumption bundle, with the monetary value varying by state and urban/rural area according to average local prices.

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subsidies may also lead to a failure to supply the right amount of fertilizer at the right time, a problem that seems to be pervasive in developing countries, where technical know-how is low (Duflo, Kremer and Robinson 2011). This is particularly so in the case of nitrogen fertilizer, which is more complex than other fertilizer types when it comes to determining appropriate timings and scales for application. This is partly because the nitrogen forms available for crops are highly volatile and can leave the soil very quickly.

Subsidized or distorted prices, combined with farmers’ inability to gauge amounts needed, may result in overapplication and incorrectly timed application (Islam and Beg 2021). Several studies show that fertilizer subsidies have increased both fertilizer use intensity and overuse (Huang, Gulati and Gregory 2017 and references therein). Some studies suggest that China’s maintenance of low and stable fertilizer prices has contributed to its overuse to a “moderate” or a “significant” extent (Cassou et al. 2017).

# **3 Data**

We use three datasets to examine the distributional impact of output subsidies. We take disaggregated crop-production data from the Spatial Production Allocation Model (SPAM) (IFPRI 2019). SPAM estimates the spatial distribution of crop production for 42 crops by combining crop production statistics from national or subnational administrative regions with crop-specific suitability information based on local landscape, climate, and soil conditions. In doing so, it disaggregates coarse data from countries or 5
different routes to female headship, and the changes in the household composition more broadly, impact poverty risks and expenditure levels.

This paper explores level and distributional changes in expenditure among Syrian refugee households in Jordan between 2013 and 2018 using a gender lens. By the end of 2018, UNHCR had registered more than 671,000 Syrians<sup>1</sup> who had fled their homes and settled in Jordan. At the end of the time period covered by this research, UNHCR’s 2019 Vulnerability Assessment Framework (VAF)

> 1 UNHCR’s data portal records 671,551 registered Syrian refugees residing in Jordan on January 13, 2019. See UNHCR (2022b) available at https://data.unhcr.org/en/situations/syria/location/36.

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rating of an institution's overall condition and performance by assessing six components: capital adequacy, asset quality, management quality, earnings, liquidity, and sensitivity to market risk. Asset evaluation is of paramount importance: it measures to what extent the financial institutions are exposed to credit risk, usually the most important driver of bank losses which is present in the trading book (e.g. in OTC credit derivatives) but most prominently in the loan portfolio (broadly defined, including off-balance sheet exposures). These examinations can be streamlined using the information in PCRs.

The assessment of bank loan portfolio quality is usually based on samples of borrowers. It is not feasible that supervisors conduct a one-by-one review of every credit, particularly when dealing with large banks. Although samples of credits can be obtained from the banks themselves, they can also be produced with PCR data. By knowing beforehand the number and amount of credits that each bank has granted by type of borrower, type of credit and risk rating at the minimum, samples can be customized and supervisory efforts better allocated. Samples may be stratified to ensure representativeness of the banks’ activity in the geographic regions, business

> 19 On-site inspections and off-site monitoring are closely interrelated components of bank supervision. However, for exposition purposes we will here refer to them in separate sub-sections. 20 World Bank Survey on Public Credit Registry for Central Banks.

> 21 Argentina, Bangladesh, Chile, Colombia, Honduras, Hong Kong, India and Kenya are a few examples of countries that have adopted the CAMEL rating system - or similar approaches - as an element of their supervisory toolkit. For a review of the models used by G10 countries, see Sahajwala and Van den Bergh (2000).

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(Table A.1 in Appendix A). Since 2017, the EPHC has been collected on a quarterly basis, while previously, the EPHC was collected only on the fourth quarter of each year. While data for the years 20202022 is available, the observed increases in poverty or decreases in incomes during the pandemic and subsequent economic recession may not be primarily attributed to weather shocks, and including such data may introduce contamination in our estimations. Therefore, our primary analyses in this study focuses on pre-pandemic years. It is also important to note that the poverty estimates used in this study are based on weights derived from national projections, 2015 revision. In 2022, the statistical office conducted a Population and Housing Census, and updated the weights for household surveys, but at the completion of this study, data for 2022 and 2023 were only available. Therefore, the historical series used in this study are not strictly

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<!-- Start of picture text --> Crop Productivity Index<br>28<br>Range of all sample paths of stochastic model<br>Deterministic-Baseline<br>26 average<br>10% quantile<br>50% quantile<br>24 90% quantileone sample path<br>22<br>20<br>18<br>16<br>14<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year<br><!-- End of picture text --> Figure 2: Crop Productivity Index _At_ at time _t_ , so they are not realized sample paths, but Figure 2 also displays one realized sample path of _At_ which is the dotted line.

# **5 Model Results**

This section describes the results of the impact of crop yield uncertainty on the optimal path of global land use based on the dynamic stochastic model simulations. While it is unlikely that the world’s land will be optimally allocated in the coming century, knowledge of this path can provide important insights into how uncertainty and irreversibility shape the desired path for global land use decisions. We solve the model over the 400 years with 5-year time steps and present the results for the first 100 years to minimize the e↵ect of terminal period conditions on our analysis.<sup>25</sup> We first present the results of the perfect foresight model, wherein the optimal land allocation decisions are made based on the values of the crop productivity index in the absence of climate and technology shocks. This deterministic analysis is a useful reference point for further discussion when the uncertainty in food crop yields is introduced. We then present the results of the dynamic stochastic model, where the impact of the intrinsic climate and technology uncertainty is brought into the model optimization stage. Specifically, we generate 1,000 sample paths of optimal global land use under di↵erent realizations of the stochastic crop productivity index. The results are presented
<br>20 ETH 20 LSO 20 LSO<br>BFATCD LSOAGO BFA AGO BFAAGO<br>10 GABGINSENMRTCIVGNBGHA 10 GABGINSENMCIVRTGNBGHA 10 GABMRTGINSENGHACIVGNB<br>10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

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able to disentangle genetic and environmental effects. But if the placement of the adoptive child is not random, then the break is not as clean and estimates will be biased. I believe, however, that using the data from the EICV that include information both about the type of relationship between the child and his new family and about the education of the biological parents can control for non-random placement and remove the bias associated with it.

The adoption strategy also, implicitly, assumes that adoption occurs just after birth, so that all the environmental effects can be attributed to the adoptive parents. In the current study, there is no way to know when the “adoption” occurred, but it can be assumed that in most cases adoption did not occur immediately after birth. Indeed, as suggested by the higher orphanhood rates for children born before 1994 in figure 1, many of the children might have lost their biological parents during the Genocide in 1994. HIV/AIDS orphans are also not very likely to have lost their parents immediately after birth. Although the duration is difficult to estimate for each child, it is likely that many children lived for some years with their biological parents before being placed in a new household. In that case, the environmental effect should be assigned both to the biological parents, for the first pre-adoption years, and to the “adoptive” parents for the subsequent years. In that situation, in a regression including both the schooling levels of biological and “adoptive” parents, the nature component, if represented by the coefficient for the biological parents, would be upwardly biased, while the nurture component, if represented by the coefficient for “adoptive” parents would be downwardly biased. Since most of the coefficients on the education of “adoptive” parents are nevertheless positive and significant, despite this potential downward bias, it actually reinforces the conclusion from this study that there is a nurture component in the intergenerational transmission of human capital.

The third objection to the internal validity of estimates derived from the adoption
these task measures. Particular task measures and RTI are standardized using their mean and standard deviation in the United States.

We use hourly wages in US dollars, adjusted for purchasing power parity, with a 99% winsorization. Wage data are available for 38 of the 47 countries in our sample (Table A4), we adjust the sample accordingly in the wage analysis.<sup>6</sup> The country-sector level measures of GVC participation are based on the EORA database (Lenzen et al., 2012, 2013) and computed following the methodology of Borin & Mancini (2015, 2019). In particular, we use both backward and forward GVC participation measures. Both quantify value-added flows that cross at least two country borders. Backward GVC participation measures the share of imported inputs used in export production (% of total exports). Forward GVC participation captures the share of domestic value added embodied in a country’s bilateral partners’ exports (% of total exports).<sup>7</sup> Finally, we follow Blinder & Krueger, 2013), dividing occupations into offshorable and non-offshorable. We assign occupations to groups starting at the 4-digit ISCO-08 level, depending on data availability. Most of the countries report occupations using 3- and 4-digit ISCO-08 codes. For clarity, Table A3 in Appendix A lists occupations with assigned offshorability groups (at the 2-digit ISCO level).

> 6 We use the full sample of 47 countries in the first part of the analysis to maximize variation across countries. 7 This measure avoids a double-counting problem prevalent in alternative measures of forward GVC participation.

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# Productivity and the Welfare of Nations<sup>�</sup>

Susanto Basu Luigi Pascali Fabio Schiantarelli Boston College and NBER Pompeu Fabra University Boston College and IZA Luis Serven<sup>y</sup> World Bank April 4, 2012 JEL codes: D24, D90, E20, O47 Keywords: Productivity, Welfare, TFP, Solow Residual.

> �This paper builds on Basu, Pascali, Schiantarelli, Serven (2009). We are grateful to Mikhail Dmitriev, John Fernald, Gita Gopinath and Chad Jones for very useful suggestions and to Jose Bosca for sharing with us his tax data. We would also like to thank seminar participants at Bocconi University, the Federal Reserve Bank of Richmond and Clark University for their comments.

> yBasu: Susanto.Basu@bc.edu; Pascali: Luigi.Pascali@upf.edu; Schiantarelli: Fabio.Schiantarelli@bc.edu; Serven: Lserven@worldbank.org

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outcome variable will be the change in log night lights between the year the disaster takes place and the following year.

In addition to the publicly available imagery, NOAA has produced, especially for this paper, monthly frequency composites for the 2004 to 2013 period. These satellite-month datasets are derived using the same process and raw images as the annual composites. In addition to calculating log night lights at the municipal and state level as we did with the annual composites. We will construct two additional sets of night light measures from monthly composites. The first will take advantage of the overlapping coverage of the F15 and F16 satellite during the 2004 the 2007 period, and use as source data pixel level averages from the two satellites. The second will calculate our log night lights measure from composites where we have excluded top coded pixels, that is, pixels with a DN of 63.

Unfortunately, late sunset during summer months leads to missing log night lights data for some parts of Mexico, every year, between June and August. In addition in 2009 due to degradation of the sensor on board the F16 satellite we have up to 7 months of missing night light information. In order to overcome the constrains created by missing data, we will take averages over monthly log night lights. In our preferred specification the key outcome variable is the log difference between the average for the 12 months before the disaster and the average at various points in the post disaster period. One important implication of using these averages in conjunction with stable night light imagery is that our results will not be affected by transient phenomena 7
predict the average value of outcome _Yi_ over the full sample but conditioning on both size and whether the business is led by a man or a woman.<sup>12</sup> We show these estimates in the following sections and report whether the gender difference is statistically significant at the 95% confidence level, but the full set of results from ordinary least squares and the Probit estimations is available in the appendix.<sup>13</sup>

# **5. The shock**

This section chronicles the impact of COVID-19 across five important dimensions of firm-level outcomes: business closures, disruptions in supply channels, sales revenues, financial risks, and the owners’ expectations about the future. For each outcome, we first report unconditional gender gaps, followed by a more detailed discussion of conditional gender gaps, including industry- and firm-size specific results.

## **5.1 Temporary business closures**

At the onset of the COVID-19 shock (spring of 2020) an estimated 70% of businesses were temporarily closed but 6 weeks after the peak (proxied using Google mobility trends around transit stations) this fraction averaged 25%; it then declined to 10% around week 15 (Apedo-Amah et al., 2020). The data suggests that women-led businesses resumed operations at a slower pace relative to men-led businesses (Table 2). The unconditional average predicted likelihood of operating regularly 6 weeks or more after the peak was 85.9% for men-led businesses and 84.5% for businesses led by a woman, a statistically significant difference of 1.4 percentage points (pp). This gender gap declines but remains statistically significant if we control for firm size and sector (for a conditional difference of -1.1 pp), which shows that some of the difference between men- and women-led businesses in the probability to remain closed six weeks after the peak crisis reflects gender differences in firm size and the fact that male and female entrepreneurs tend to operate in different industries.
origin as an instrument for investor protection, they then demonstrated a strong link between those protections and measures of financial development. We therefore include dummy variables for English and French legal origins in the set of potential instruments. We expect English (French) legal origin to be positively (negatively) linked to financial development.

> 9 The measure is based on information from the World Values Survey.  See Knack and Zak (2001) for details of its construction.

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government service is predictable, regardless of the actual size of the bribe demanded or how many firms pay them. In the latter case, however, the lack of a higher authority to provide constraints mean that officials may set bribes at any level that they desire and the market can bear.

As no central authority coordinates these actions, different bureaucrats are likely to demand different bribes – depending on their evaluations of the market – and the bribes needed to secure services should become more unpredictable. The unpredictable nature of such corruption, in turn, creates uncertainty about regulatory enforcement, since firms are unsure of who will be forced to make informal payments, for how much, and to whom in order to receive basic services. Whereas differences in _de jure_ and _de facto_ law can arise for benign reasons, such as changing regulator priorities or differing interpretations across regions, however, variation in corruption stems mostly from the desire of officials to rent-seek and the inability (or unwillingness) of higher level officials to centralize these practices. In other words, variation in administrative corruption takes on the form of a direct, predatory cost of doing business.

To approximate regulatory uncertainty we computed the regional variation of “bribe tax” or the percentage of annual sales spent on bribe payments taken from questions on the RR BEEPS 2012 survey from those firms that answered the question.<sup>12</sup> Our indicator is calculated as a standard deviation of the aggregate bribe tax

> 12 The RR BEEPS question read as follows: _“It is said that establishments are sometimes required to make gifts or informal payments to public officials to” get things done” with regard to customs, taxes, licenses, regulations, services, etc. On average, what percentage of total annual sales, or estimated total annual value, do establishments like this one pay in informal payments or gifts to public officials for this purpose?”_

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Italy as well (61.8% for a closed sample of 972 large private firms over the 1982-90 period). Nevertheless, the differences between the two financial systems are profound. In particular, both the stock and the bond market play a very limited role in Italy, as the aggregate data on market capitalization we have reported suggest. Very few companies are quoted and, furthermore, public companies in the conventional AngloSaxon sense do not exist. On the contrary, family groups hold controlling interests through a pyramidal hierarchy of financial and operational firms and cross-shareholding abounds. Firms get a substantial proportion of external financial sources (both long and short term) from banks or from other financial institutions (for the same sample of Italian firms bank debt represented in 1990 about 75% of total financial debt with 8 intra-group financial debt covering much of the rest). In spite of recent privatizations, the influence of the Government on banks' behavior is still large, and it was even larger in the period covered by our panel (1977-90) when the largest banks were directly owned by the State. According to Italian banking law, until the early nineties financial intermediaries were segmented in two separate categories: "Banche Ordinarie" (Commercial Banks) and "Istituti di Credito Speciale - ICS" (Investment Banks), specialized in supplying short-term and long term loans respectively. One of the reasons why firms did not rely more on direct bond issues is that ICS could issue I Notice that the ratio of domestic bank credit to the private sector relative to GDP is higher for the UK than for Italy, but this indicator is misleading to judge the importance of **banks** for firms' financing. In fact, domestic bank credit includes also credit to consumers and Italy is notorious for the narrowness of its mortgage and consumer credit markets.

**1 0**
4 How did these growth rates impact on employment? The most important developments were a marked slow-down in employment in agriculture from around 1990 and an acceleration in the rate of growth in job creation in manufacturing, construction and transport and communications. Employment in all non-agricultural sectors grew strongly in the 1990s except government, community and personal services.

Higher rates of employment growth in non-agricultural sectors are consistent with more rapid rates of output expansion in individual sectors after 1987. They are also consistent with the changing structure of manufacturing in favour of labour-intensive sectors such as garments and footwear.

The apparent sharp decline in agricultural employment growth from around 1990 was unexpected, however. The expansion of job opportunities in agriculture was relatively robust at around two per cent per annum in the 1980s, although rates of growth began to decline in the second half of the decade (Table 2).6 The National Labour Force Surveys suggest that growth rates were negative - close to two per cent per annum - from 1989-90. Nearly **half** of all new jobs taken up in non-agricultural sectors in the early 1990s represented a shift in employment away from agriculture.

Even more unexpected, agricultural employment fell absolutely in the Outer Islands as well as on Java from around 1990 (Figure 1). The decline in absolute numbers was slight outside Java - compared with a reported three and a half per cent rate of decline annually on Java from 1989/90.7 But this was a marked turnabout in agricultural labour absorption Outside Java compared with the previous decade.<sup>8</sup> The pattems and possible causes of agricultural employment decline outside Java will be discussed further in the analysis of regional labour market change below.

What were the main features of non-agricultural employment growth on a national scale?. Viewed in historical perspective, a major shift had already occurred the

> 6 It is not appropriate to compare agricultural employment growth rates in the early 1980s with the second half of the decade owing to different procedures adopted for enumerating female unpaid family workers