rafmacalaba/data-use-mentions-extended huggingface dataset:
large firm. Therefore, a rise in market interest rates will lead banks more than capital market lenders.

In a crisis situation, during which the relationship between monetary policy instruments and nominal income changes drastically, assessing the monetary policy stance becomes intricate. Consequently, it may be misleading to focus solely on key indicators of monetary policy when evaluating the monetary policy stance and credit conditions in order to detect "a _credit crunch"._ Evaluation of monetary and credit aggregates must be accompanied with a more detailed investigation of the channels through which firms, banks, and households are affected by changes in monetary policy.

# **Box 2. Illustration of the Relevant Spreads**

The following expression presents the set of interest rate spreads which capture the credit channel effects of the transmission mechanism of monetary policy: SPRo = LR-TB = SPRI + SPR2 where LR = lending rate; TB = Treasury bond rate; SPR1 = corporate bond rate - Treasury bond rate; SPR2 = lending rate - commercial paper rate; The difference between rates on corporate and on Treasury bonds (SPRI) measures the general risk premium as it is perceived by the market. If the balance sheet effect is at work, we expect that this spread will increase after the monetary restriction, reflecting the fact that private sector debt has become relatively riskier vis-a-vis sovereign debt.

The spread between the lending rate and commercial paper (SPR2) is a proxy for the lending chainel effect. The difference between lending rates and commercial paper rates quantifies the premium that bank-dependent borrowers must pay in order to raise external finance relative to those firms able to issue debt on the market. The lending channel effect contends that this spread will increase in the aftennath of the monetary squeeze.

Source: Doma,c and Ferri (1998) As widely acknowledged, a decline in either bank loans or a decline in their growth following a monetary tightening is not sufficient to pin down an adverse movement in banks' loan supply. This is because the decline could be induced either by the corporate sector
all countries in the world at five year intervals (United Nations 2006).<sup>19</sup> The missing end decades are calculated for 116 countries for which data are lacking, most of them for the 1960s and 1970s.<sup>20</sup> Trends in International Migrant Stock database thus can be used to estimate growth rates by estimating missing totals in years for which censuses are not available, and it provides a consistent set of totals over time for countries for that have data of insufficient quality.

An important difference between the matrices presented in this article and the Trends in International Migrant Stock database is the treatment of refugees. While refugees are generally enumerated in developed country censuses, this is not always the case for developing countries. Refugees interned in camps are less likely to be surveyed at the time of census. Making allowances for these refugees, the Trends in International Migrant Stock database adds to the number of migrants refugees reported by the United Nations Refugee Agency and the United Nations Relief and Works Agency for developing countries that are not likely to have included the refuges in their census data. Since the majority of developed countries record refugees alongside other migrants on a bilateral basis, there are normally no remedial measures for removing them. Similarly, for developing countries for which no census data are available, it is impossible to know whether the numbers contained in Trends in International Migrant Stock database include refugees. For the cases that rely on the Trends in International Migrant Stock database, the number of refugees is subtracted from the totals, with the intention of removing refugees in camps from the total, since the focus is on economic migration.<sup>21</sup> COUNTRIES WITH VERY POOR DATA. For the 59 destination countries for which there are two or fewer census data points, it is impossible to meaningfully interpolate missing census totals or bilateral numbers. In these cases the census totals detailed in the Trends in International Migrant Stock are used. This has the advantage of ensuring consistent totals
These observations are described as fuel stacking, in which households use several fuels, adding extra fuels as incomes increase (Masera, Saatkamp, and Kammen 2000).

Bacon, Bhattacharya, and Kojima (2010) reported that, out of six countries for which information on the primary cooking fuel was available, more than half of the top two urban quintiles in two countries and more than half of the top two rural quintiles in five countries named biomass as their primary cooking fuel. These findings indicate that availability and affordability, although very important, may not be the sole determinants of fuel choice and use, and that the use of solid fuels for household energy is not always a result of poverty but occurs over a broad income range in developing countries. Gaining a better understanding of the factors that determine household fuel use patterns is important for setting appropriate policies to shift households away from biomass to LPG and other cleaner-burning options.

The foregoing discussion raises two separate questions: (1) What factors influence the household’s decision to use a gaseous fuel or electricity (where it is used for cooking and heating) to meet household energy needs? (2) For those households that have decided to use cleaner forms of household energy, what determines the amount of their use? National household expenditure surveys provide a large amount of information on households as well as their total expenditures and patterns of expenditure on energy sources. To carry out a comparative analysis of different fuels, fuel use would need to be standardized on a common basis—for example, gigajoules of _usable_ energy delivered by each fuel—because bundles of firewood and kilograms (kg) of LPG are not directly comparable nor can they be added for those using both fuels. Further, a number of surveys do not ask for information on the quantities of dung or collected firewood consumed. Calculating the amount of usable energy requires not only the energy
the firms had a decline in sales. In unreported logit regressions, we find that the firms who had a positive increase in sales tend to be larger firms, in manufacturing (SIC codes 20-39), and with lower pre-crisis (in 2007) levels of short-term and long-term debt.

In panel B of Table 5 we repeat our analysis of Table 3 for the sample US firms using different measures of changes in financing as reported on the Balance Sheet and Statement of Cash Flows in Compustat. The Data Appendix provides details on how each of the variables is

> 21 The Compustat data download was in July 2011. So we may be missing data on some firms that have fiscal year ends in January-May2011 (which would count as fiscal year 2010 data) that may not have been updated in Compustat. Hence, we use a balanced sample because we are looking at aggregate sales and debt figures in the economy and we don’t want the year to year variation to be biased by non-missing data. However, to ensure that our results do not change if we were to include the non-missing data we re-do our analysis using quarterly statement data and do find a recovery in sales across all the firms in second quarter of 2009.

24
the geographic units are a reasonable choice to measure LLM impacts. It shows that exposure to local and US automation (through exports) did not have a significant impact on net migration at the area level. Thereby, it is unlikely that automation-induced labor mobility introduces a bias to our estimates of employment impacts. Table 21 tests for other confounding factors that may affect the estimations. It shows that controlling for overall investments, and investments in machinery and ICT, as well as exposure to Chinese imports and off-shoring, dropping LLMs with only one municipality and weighting by the working age population do not affect the main findings.

Finally, Table 22 shows the impacts of automation on wage inequality, defined as the ratios of average monthly wages from different percentiles of the wage distribution (in log points). It shows that more exposure to domestic automation increases the wage ratio between the 90th and 10th percentile. The size of the coefficient implies that an increase in one robot per thousand workers raises the 90-10 wage ratio by 0.29 percentage points ( _≈ e_<sup>0</sup><sup>_._00294</sup> _−_ 1).

# **6 Conclusion**

This article examines the link between automation in high-income countries and trade with developing economies, and the associated labor market impacts. Using trade data by municipality, sector and destination from 2004 to 2014, it provides new evidence that increasing exposure to robot adoption by US industries results in lower export growth from Mexico to the US. The decline in export growth was driven by intermediate goods, which is consistent with the hypothesis that automation in rich countries may induce reshoring, or lower the pace of offshoring to developing economies. Exposure to US automation also reduced exports of consumption goods from Mexico. In contrast, higher exposure to US automation was accompanied by an increase in imports of raw materials and intermediate goods, which is consistent with US automation contributing to increase the economic efficiency of Mexico’s 26
I rely on commitment and disbursement transactions on loans extended by official creditors to developing countries, as recorded in the DRS database. Official creditors include a range of multilateral lenders such as the World Bank, the African Development Bank, the Asian Development Bank, and the European Investment Bank. My dataset also includes loans issued by bilateral official creditors. The majority of these are major OECD aid donors such as Japan, Germany and the United States, but the dataset also includes a number of non-OECD creditors such as Kuwait, Saudi Arabia, Russia, and the former Soviet Union (in the earlier half of my sample).<sup>9</sup> The dataset, retrieved from DRS in January 2012, contains 60,192 loans issued by 188 distinct creditor countries and organizations. A large number of creditors represented in DRS account for only a handful of loans each, and usually for very small amounts. I discard a total of 768 loans issued by 113 creditors who have fewer than 50 loans each in the DRS data (and on average fewer than seven loans each), leaving a total of 59,424 loans issued by 75 distinct major official creditors. Loan commitments are reported in the currency of origination of the loan, and subsequent disbursements are recorded in DRS in current US dollars. I discard a further 36 loans for which data on the exchange rate used to convert the disbursements denominated in the currency of origination into US dollar could not be retrieved from DRS. This reduces the sample further to 59,388 loans.

The first step in the development of my instrument is to construct a disbursement profile for each loan, i.e. the fraction of the original loan commitment that is disbursed in the commitment year and each subsequent year. I calculate these by converting the US dollar disbursements on each loan back to the currency of denomination of the original commitment, using the corresponding disbursement-year exchange rates, and then express this as a fraction of the original commitment. Roughly 10 percent of loans have accumulated
(1), (2) and (3) together;

> 2 It is also possible that the survey loses representativeness through changes in the population resulting from inmigration, although this is not believed to be a major threat in the LSMS-ISA survey context.

> 3 Exceptions to the general methodology described above include a rural-only sample design for the Ethiopia Rural Socio-Economic Survey, which also includes additional second-stage stratification, and not tracking split-off household members in the panel component of the Nigerian General Household Survey.

2
**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp-yr + imp-yr|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|33748|33748|33748|33748|33748|33748|
|P-R2|0.985|0.967|0.958|0.961|0.938|0.972|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex 33
, those laid off from formal sector jobs may be entitled to unemployment benefits, but the benefits expire rapidly.

50.The reference to "no skills" in this paragraph should not be confused with the reference to difficulty finding a "job to match their skills" mentioned in the previous paragraph. The latter, in the Mongolian language, implies in possession of specific, marketable, skills for which the respondent could not find a job. The former means the lack of any marketable skills.

51. The official poverty line at the time of the survey was 8,000 togrogs per household member. However, the official poverty line tends to be changed very infrequently, such that the real poverty line declines over time followed by a large increase when the official poverty line is changed. If unadjusted, this would result in an apparent decline in the poverty rate over time, followed by a sudden jump in poverty. But the phenomena would only be an artifact of inflation. So, for the analysis of poverty in this section, a poverty line of 9,600 was used for October 1996, and 9,800 was used for November, 1996. These levels were determined by simple interpolation between two months during which the official poverty line was adjusted: February 1996: 8,000; February 1997: 10,400.

The estimates presented in the Box refer to November, 1996. In October, 1996, 38% were below the official poverty line (adjusted as above), but 54% would have been if they didn't have informal income.

The estimation of the number of children raised from poverty is computed by multiplying the actual number of children in the marginal households (those above the official poverty line only because of informal income) by the inverse of the proportion of Ulaanbaatar households sampled.

Using a Living Standard Measurement Survey (LSMS), the World Bank found an incidence of poverty in Ulaanbaatar in June 1995 of 35 percent. See World Bank 1996 for a detailed discussion of poverty
of public revenues and medium-term growth.

## **Model characteristics**

In the MFMod framework, country-specific models are designed to reproduce the flow of funds across the whole economy by mapping out the main identities of the national accounts, balance of payments, labor markets and financial sectors (Burns et al., 2019 provides a detailed description of the model structure and equations). Structural relationships are consistent with both economic theory and the observed dynamics of the economy. The short-run dynamics are data-driven, with estimated parameters reflecting the actual behavior of the economy. Most equations are estimated using error-correcting models and parameters were estimated using, or calibrated to, Chadian data. In the MFMod framework, the overall equilibrium growth path of the economy is determined by potential output, which is calculated using the production-function methodology which is a function of total factor productivity, the level of capital and labor. Deviations from equilibrium, as well as convergence to equilibrium, are determined by the historical behavior of the Chadian economy.

The data used in MFMod-TCD were obtained from the World Bank WDI database for national accounts data, the IMF for fiscal and balance of payments data and the ILO for labor market data. Detailed data on the fiscal accounts were sourced from the Ministry of Finance, and include:

- Oil revenues, grants and other non-oil-related revenues.

- Expenditures on goods and services, public wages and compensations of employees, interest payments, transfers and capital expenditures.

6
we study here are changes that were undertaken during major policy reform episodes (both in the late 1980s and under NAFTA), and many observers have argued that the lowering of trade barriers was mainly used by the Mexican government to signal its commitment to overall policy reform (Tornell and Esquivel, 1995). Second, and somewhat related to the first point, in our data virtually no industry experienced a rollback of the liberalization effort once tariff rates had been reduced. Finally, we note that such pattern of endogeneity would only cause a bias against our reported findings. That is, if such bias exists, the true short-run effect of trade policy changes on income risk is even larger than what we report in this paper. However, it also means that our finding that trade liberalization has no long-run “level effect” could be the result of two opposing effects canceling each other out.<sup>27</sup> Estimation bias could, of course, also arise if systematic changes in non-tariff barriers reversed the effects of tariff reductions, but these changes in non-tariff barriers were not taken into account by us. To ensure that this is not the case, we studied the patterns in the use of non-tariff barriers (NTBs) in Mexico in the years included in our sample. NTB use in Mexico primarily took the form of anti-dumping duties in these years and the anti-dumping duties were concentrated entirely in the ‘Basic Metal Products’, ‘Chemicals’ and ‘Textiles’ industries.<sup>28</sup> Studying the link between trade policy and income risk using data from the remaining industries did not alter qualitatively or quantitatively any of the reported estimates (see Table VIII).

Our estimation results could also be biased if there is unobserved heterogeneity among work-

> 27Notice also that despite the work by Alesina and Drazen (1994) and others, major trade policy reforms are in general rather difficult to understand theoretically once policy is treated as being endogenous. The dominant theory of endogenous trade policy determination - the
reforms targeting SSI repealed the historical product reservations protecting small businesses (Tewari and Wilde, 2015). The staggered removal of these international and domestic protections boosted competition.

We use a long-difference strategy similar to the one applied in Ghani, Goswami and Kerr (2016a) by analyzing differences in the timing of the implementation of reforms across industries. We conduct a panel analysis for a total of 50 industries that allows us to exploit this industry level timing variation and differences in traits across industries. Industries are defined at the three-digit level within the manufacturing sector. Specifically, to measure the direct effect of any industry level reform, we consider the following reduced form estimating equation: where ∆�� is the outcome variable of interest relating to female activity or gender segmentation for industry _i_ . Female activity is measured through two alternative indices: (i) share of female-owned plants and (ii) share of females in total employment. Segmentation, broadly defined, is the extent to which people of the same gender tend to segregate and work together. Thus, segmentation in a workforce could be measured from two perspectives: (i) the gender of the owner and (ii) the gender 17
incentives? Is the reported performance reliable? Does the institutional context matter for bureaucrat behavior in performance reporting?

Besides shedding light on bureaucrat behavior, understanding GDP measurement is important on its own. As the key indicator of economic activity, GDP is at the core of macroeconomic analysis and policy making. The veracity of GDP data is vital to international and interregional comparisons as well as policy decision-making. It is believed that the quality of GDP figures in developing countries is lower mainly for technical reasons such as lower statistical capacity (Johnson et al. 2013; Hu and Yao 2019). However, perhaps due to the lack of full accountability of the government, GDP figures in developing countries are also more susceptible to manipulation.

The Chinese context is particularly relevant for understanding bureaucratic performance for several reasons. _First_ , China has had millenniums of history of centralized states, as well as relatively strong state capacity. Though there might be some accountability in China (Besley and Kudamatsu 2
by Haslett et al. (2014), Bangladesh Bureau of Statistics (BBS) and the World Food Programme (WFP). The estimates combine survey data from the Child and Mother Nutrition Survey of Bangladesh 2012 (CMNS) and the Health and Morbidity Status Survey 2011 (HMSS) which some additional data from the BBS Census 2011.

|_Table 2: HCFs at ea_<br>_Water_|_ch WASH Tie_<br>_Freq._|_r, by category_<br>_%_|
|---|---|---|
|0|7,690|64.67|
|1|3,692|31.05|
|2|509|4.28|
|_Total_|_11,891_|_100_|
|_Sanitation_|Freq.|Percent|
|0|3,539|30.03|
|1|6,402|54.33|
|2|1,586|13.46|
|3|183|1.55|
|4|74|0.63|
|_Total_|_11,891_|_100_|
|_Handwashing_|Freq.|Percent|
|0|1,697|14.27|
|1|4|0.03|
|2|10,190|85.7|
|_Total_|_11,891_|_100_|

# Method

In addition to descriptive statistics, the primary objective of this analysis is to have a upazila level spatial snapshot of the state of WASH in CCs. To this end, we first create tiers for each category of WASH using the available survey data. The questions in the survey are not comprehensive enough to create the complete JMP tiers. However, we follow the JMP guidelines to the extent possible with the available data. Specifically, each of the three category tiers are coded as shown in Table 1.<sup>5</sup> Table 2 shows the number and proportion of CCs that fall under each
sharing venture rather than one of risk-transfer. However, this reason alone is not satisfying in explaining the access to finance gap among the MENA countries in this report.

Figure 2. Export Indicators, 2012 <!-- Start of picture text --> A. Logistics performance index, other B. Delay to export, other<br>IRQ<br>ARE<br>QAT<br>TUN MLT SAU<br>MAR BHR<br>EGY<br>YEM OMN<br>KWT<br>JOR LBN<br>IRN<br>DZA YEM<br>IRN<br>IRQ LBN<br>DZA KWT QAT<br>MAR TUNEGY JOR OMNBHR MLTSAUISR<br>ARE<br>6 8 10 12 6 8 10 12<br>Log GDP per capita (2012,USD) Log GDP per capita (2012,USD)<br>C. Documents to export, other D. Cost to export, other<br>IRQ<br>EGY DZA<br>IRN BHR OMN KWT IRQ<br>YEM<br>MAR JOR MLT SAU QAT<br>TUN LBN ISR<br>ARE IRN<br>DZA<br>YEM EGYMARJORTUN LBN MLTOMNBHRSAUISRKWTARE QAT<br>6 8 10 12 6 8 10 12<br>Log GDP per capita (2012,USD) Log GDP per capita (2012,USD)<br>Source: WDI<br>4 80<br>3.5<br>60<br>3 p<br>40<br>y<br>2.5<br>g<br>20<br>2<br>1.5 0<br>15 8000<br>6000<br>10<br>p<br>p<br>4000<br>5<br>2000<br>0 0<br><!-- End of picture text -->