rafmacalaba/data-use-mentions-extended huggingface dataset:
There is vast fiscal literature on the factors leading to the deterioration of state finances in the late 1990s. Factors considered critical to the fiscal deterioration include the impact of the wage revisions; inability to contain wasteful expenditure, including subsidies; reluctance to raise additional resources; and competitive reduction in taxes. Mohan (2000) pointed to the increasing debt service payments and inadequate returns on government spending as important factors behind the deterioration in states‘ fiscal conditions. Acharya (2001) and Rao (2002) attributed the worsening of revenue (current) balance during this period to the implementation of the Fifth Pay Commission recommendations.<sup>22</sup> The RBI Study of State Budgets, 2002–03, while drawing attention to the growing fiscal and revenue deficit and high debt levels of states, pointed to the following causes of the deterioration in states‘ fiscal condition: (a) an inadequate increase in tax receipts, (b) negative or negligible returns from public investments due to losses in public sector undertakings, (c) large subsidy payments, (d) increased expenditure on salaries due to pay revisions, and (e) higher pension outgo. Another study, by Prasad, Goyal, and Prakash (2004), concludes that interest payments played a prominent role in the deterioration of state finances.

Until the mid-1980s, interest rates on government borrowing were highly subsidized, indicative of the degree of financial repression. After the 1980s, the rates on government bonds became progressively aligned with market interest rates; during the 1990s there were increases in both bank deposit rates and policy rates (table 2). During the 1990s, average interest rates rose, and those on state government bonds averaged over 10 percent during the 1990s (RBI). At the same time, the reliance on market borrowing to finance the fiscal deficits increased from 11 percent in the 1980s to 16 percent in the 1990s. Significant changes in the structure and cost of state government debt contributed to a sharp increase of about 60 percent in the
returns hence lower than in lower-income countries. On the other hand, it is interesting to see the most negative and positive parameter estimates for inflation and infrastructure for Europe in the regional breakdown of Figure 3. Taken together, this suggests that the applied regional classification is probably too broad. Finally, it is worth highlighting that temperature volatility negatively correlates with growth mostly in low-income countries, which often heavily rely on agriculture.

Taken together, our results suggest that using growth regressions for country-specific analysis should actively test for parameter heterogeneity. In case significant parameter heterogeneity is found for the country (group) of interest, it remains to be seen whether it also translates into quantitatively relevant differences in country-specific growth correlates in the sense of section 6.1.

**Figure 3: Parameter heterogeneity across regional groups**

<!-- Start of picture text --> lagdependent<br>linflation_na<br>lrer<br>ltraderesid<br>infra index<br>fincrisis<br>sd(temp)<br>dltot<br>lkg<br>sdgrowth<br>durbanpop<br>-2 -1 0 1<br>Africa Americas<br>Asia Europe<br><!-- End of picture text --> Note: Reported parameters are partial correlation coefficients, not standardized beta coefficients.

-19-
**Table A.1.2: Child Labor Statistics by Survey Assignment and Season: 7-day Recall Period**

|||**Pooled Dat**<br>|**a**|**Shor**<br>|**t Raining S**<br>|**eason**|**H**<br>|**arvest Sea**<br>|**son**|
|---|---|---|---|---|---|---|---|---|---|
||Self-<br>reported|Proxy-<br>report|Diff|Self-<br>reported|Proxy-<br>report|Diff|Self-<br>reported|Proxy-<br>report|Diff|
|**_Partic_**|**_ipation in_**|**_Household_**|**_Farm Activities (_**|**_%)_**||||||
|All|0.629|0.609|0.020|0.489|0.484|0.004|0.773|0.734|0.039|
||[0.017]|[0.012]||[0.025]|[0.017]||[0.022]|[0.016]||
|Boys|0.677|0.688|- 0.010|0.567|0.575|0.008|0.792|0.800|-0.008|
||[0.022]|[0.015]||[0.031]|[0.022]||[0.025]|[0
19 V. Using Regression Analysis to Identify the Determinants of Income Inequalitv in EgYpt The preceding approach can be implemented by using regression analysis to estimate the determinants of the various sources of income in this study. Since the goal here is to quantify the contribution of land to the determination of different kinds of income, this analysis cannot be applied to Jordan because the 1997 HIES Survey did not collect any data on landownership. The remainder of this section therefore focuses on the 1997 data from rural Egypt.

Two hypotheses are to be tested in this section. First, since land is distributed so unevenly in rural Egypt,<sup>29</sup> and land is such a vital component of agricultural production, it can be hypothesized that the close relationship between land and agriculture "causes" agricultural income to go mainly to the rich. Second, it is possible that nonfarm income is an inequality-decreasing source of income in rural Egypt precisely because nonfarm income has no relationship with size of land owned.

The challenge in using regression analysis to test these hypotheses in rural Egypt is twofold: first, to identify those exogenous household-level factors (including landownership) which somehow "cause" income to be produced; and second, to pinpoint the relative importance of each of those factors in producing different types of income (such as agricultural and nonfarm income).

In the strictest sense, most of the relevant income-producing variables that can be identified in rural Egypt reflect a series of endogenous rather than exogenous choices made by the household. However, the management and taste factors that affect such choices should be fixed, and, therefore should not seriously bias the regression estimates.
17-2061|10|5||
|1234|17-2061.02|No|17-2061||5|5%<br>|
|1234|17-2063.00|No|17-2063|40|40|=5/(5+5+40+50)|
|1234|17-2064.00|No|17-2064|50|50||

Source: Own elaboration based on O*NET database (Green Occupations & O-NET-SOC 2010 Occupation List), U.S. Bureau of Labor Statistics correspondence between SOC 2010 Codes and ISCO-08 categories, and 2018 Occupational Employment and Wage Statistics.

> 9 Estimates for 2018 are used since it is the most recent year in which survey data was collected using the 2010 SOC classification. After this year, estimates use a hybrid of the 2010 and 2018 SOC systems, which makes the link with the O*NET-SOC 2010 classification of green jobs less direct. While employment estimates from this program are not perfect since they exclude selfemployment, they are the only source of publicly available data with information at the 6-digit level of the SOC.

> 10 This assumption had to be used in other cases in which more information was not available: 1) for six SOC occupations at sixdigits for which U.S. employment data was only available at five digits (in these cases employment at 5-digits was equally distributed among the SOC occupations at six-digits corresponding to the 5-digit code); 2) to estimate employment in some of the six cases in which the SOC classification from O*NET and OEWS were different: SOC 211018 in OEWS (Substance Abuse, Behavioral Disorder, and Mental Health Counselors) was equally distributed between SOC 211011 (Substance Abuse and Behavioral Disorder Counselors) and 211014 (Mental Health Counselors) from O*NET; SOC 512028 in OEWS (Electrical, Electronic, and Electromechanical Assemblers, Except Coil Winders, Tapers, and Finishers) was equally distributed between SOC 512022 and 512023 from O*NET (Electrical and Electronic Equipment Assemblers + Electromechanical Equipment Assemblers)
.3%|
|poor among<br>Men<br>Women|31.8%<br>30.1%|
|Average HH size|5.49|
|Average number of dependents|2.60|
|Fraction of poor HHs whose head has low education|95.0%|
|Fraction ofpoor HHs whose head is not currentlyworking|23.8%|
|Fraction of poor HHs that have some welfare-relevant assets|-|
|Fraction of poor HHs that have a private car/motorcycle or bike<br>Fraction of poor HHs that have any washing machine (electric, semi-automatic,<br>or automatic)|14.9%<br>-|
|Fraction ofpoor HHs that have a smartphone|34.6%|

**Source** : Authors’ calculations based on HIECS 2017/18. **Note** : “Low education” includes all illiterate individuals. “Currently not working” includes the unemployed and people not actively looking for a job. “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

Overall, the previous tables show that poverty increased over the period 2015 to 2017/18 but that the profile of the poor did not radically change. However, the interpretation of this increase in poverty is complicated by the hybrid nature of the official poverty measurement methodology. We now turn to the interpretation one can give to the increase in poverty.

# **3.3 Decomposition of the poverty trend over 2015 to 2017/18**

We address the difficulty associated with the interpretation of a hybrid definition of poverty lines by decomposing the evolution of poverty into the effect of changes in real total expenditures and the effect of changes in real poverty lines.

To correctly interpret the poverty trend, we apply a Shapley-Owen-Shorrocks (SOS) decomposition (Shorrocks 1999). To simplify its exposition, assume that all households have the same composition and 8
19

# **Figure 8.  Welfare loss associated with not having one additional year of schooling, Pakistan (upper bound)**

<!-- Start of picture text --> 60<br>50<br>40<br>% 30<br>20<br>10<br>0<br><!-- End of picture text -->

# **Alternative Pakistan simulation**

The Mincerian function was fitted to just the time-series observations for Pakistan, 19502010. The number of observations is only 13 because the education data are available in 5-year periods (see Table 8): (16) Ln _Y_ = 6.319 + 0.290 _S_ , R<sup>2</sup> = 0.89, N = 13 (t = 9.41)

**Table** **<u>8.  Alternative Pakistan simulation using time series</u>**

|Schooling/loss|1900|1950|2010|2050|
|---|---|---|---|---|
|Inertia_S_|0.1|0.9|5.6|7.0|
|Enhanced_S_|1.3|1.9|6.6|7.8|
|Upper bound loss (%)|42.0|34.0|34.0|26.0|
|Loss lower bound (%)|21.0|17.0|17.0|13.0|

The results are largely consistent with the 13 percent micro-estimated social rate of return to investment in primary education in Pakistan in 1975 (Psacharopoulos 1985: table A-1).
remains unclear whether low-skilled emigration reduces incentives to invest in education. Similarly, while high-skilled migrants have been shown to accumulate human capital while they are overseas which ultimately benefits home labor markets, there is still limited evidence on whether low-skilled migrants experience human capital gains. Second, the vast majority of the literature has been treating temporary migration has a one-time event. Yet, the impacts of repeated/circular migration for the home country are much less understood. This is partly driven by data availability as one would need detailed historical data on all the migration episodes undertaken by workers and their outcomes to analyze repeat migration. Third, even though policy makers in migrant-sending countries are often concerned about the possible discouragement effect of past migration on economic participation in home labor markets, this question has 33
Figure 2: The proliferation of NTMs over time and development level of destinations <!-- Start of picture text --> Products Countries<br>2000 2003 2006 2009 2012 2015 2018 20 22 24 26 28 30<br>Years GDP of destionation country (log)<br>(a) Proliferation of NTMs (b) The use of NTMs by income status<br>1200 100 2<br>1000<br>80<br>800 1.5<br>600 60<br>1<br>400 40<br>Number of countries imposing an NTM<br>Count of NTMs applied by destionation (log)<br>Number of HS6−digit products attracting an NTM 200 20 .5<br><!-- End of picture text -->

# **3.2** **_Firm-level export data_**

Our source of firm-level customs transaction data for the three selected African countries is the Exporter Dynamics Database, described in Fernandes et al. (2016). The data is collected by the Trade and Integration Unit in the Development Research Group at the World Bank from the customs agencies and statistical institutes of the respective countries. For each transaction, the firm-level customs data contains unique information on the exporting firm, the country of origin, the export destination, the product classification at the HS6-digit level, the year of the transaction, and the Free on Board (FOB) value and quantity of export. As such our unit of analysis is at the firm-product-destination-year level.<sup>13</sup> We make use of firm-level customs transactions for three selected African countries due to data availability. These include Burkina Faso (2005-2012), Malawi (2006-2019), and Senegal (2000-2019). To merge the NTM panel data with the firm-level trade data at the HS6 product level, we convert the HS6 product codes in both the firm-level trade data and the NTMs data into a consolidated version, following the procedure
This discussion should take into account three main issues concerning the implementation of a poverty mapping for improving the design of household surveys: i) the capacities of National Statistical Offices to perform poverty mappings; ii) the problem of performing a poverty mapping when the census has been conducted too long ago; and iii) the variability affecting the mean per capita consumption per PSU in the poverty mapping exercise.

Concerning issue i), the poverty mapping exercise was initially quite an intensive procedure, which needed high econometric capacity and, therefore, only highly skilled World Bank consultants were able to implement it. In order to apply the ELL approach, the World Bank research team has developed a user-friendly software that can easily be implemented inside National Statistical Offices. PovMap version 2.1, the latest version as of 2015,<sup>3</sup> provides computational solutions to all stages of poverty mapping activities. However, the new methods mentioned earlier are developed only in a Stata module produced by a team of researchers at the World Bank.<sup>4</sup> This software is valid for use when there is a recent census available and a recent Household Budget Survey (or World Bank format of Living Standard Measurement Survey, LSMS) also available.

Concerning issue ii), often the question may be: “What should be done when the poverty map is too old?” In some countries or cases, the poverty map may be too old or not updated enough in order to properly implement the methodology proposed in this paper. This may occur primarily because a census is usually conducted every 10 years. There are two possible solutions depending on the type of recent household survey that is available. In the case of a recent HBS or LSMS, the proposed methodology could take into account the so-called “updated” or “further-updated” poverty maps that are prepared with recent methodologies developed under World Bank projects. In particular, Dabalen and Ferrè (2008) have shown how to implement the Lemieux (2002)
When the economy is constrained by the zero-lower bound, reforms may even have negative impact by fueling expectations of deflation, which may push up the real interest rate and further depress aggregate demand. Similarly, Cacciatore et al. (2016) showed that although less sensible, product market reforms may have an ambiguous impact due to the interaction with the business cycle Using a sample of large OECD countries, Bordon et al. (2016) focused on labor and product market reforms and found a lagged but positive impact on job creation that remained even after controlling for the endogeneity of the decision to reform. However, the effects of labor market reforms undertaken during periods of economic distress tend to be negative. More recently, Campos et al. (2017) also stressed the importance of timing when assessing the impacts of all kinds of structural reforms. Using firm-level data, de Almeida and Balasundharam (2018) found that structural reforms do have a positive effect on output and employment, particularly over the medium term, but different reforms have different payoffs, with labor market reforms having the least impact. This may be because most labor market reforms had occurred during economic downturns. Aiyar et al. (2019) also discussed the interaction between structural and cyclical policies and showed that structural reforms are needed to enhance the resilience of countries, and the monetary union as a whole. Recently, IMF (2019) demonstrates that some reforms, including easing job protection regulation and liberalizing domestic finance, may entail significant short-term costs if carried out in bad times. In line with other research, this study shows that structural reforms may have less effect on product markets if implemented during periods of significant slack.

Finally, this paper is also related to the literature on the effects of EU accession, with two main views prevailing: (1) EU accession may result in large upfront costs of structural reforms or in reduced incentives to reform, especially for small countries joining the monetary union, as they are unlikely to affect union-wide inflation bias. Duval and Elmeskov
<mark>Becker, Bettina (2013). “T</mark> <u>he Determinants of R&D Investment: A Survey of the Empirical Research,” Discussion Paper Series</u> <mark>2013_09, Department of Economics, Loughborough University.</mark> Becker, Bettina. 2015. “Public R&D Policies and Private R&D Investment: A Survey of the Empirical Evidence.” Journal of Economic Surveys 29(5): 917–42.

Bernard, A. B., & Jensen, J. B. (1999). Exceptional exporter performance: Cause, effect, or both? _Journal of International Economics, 47_ (1), 1-25.

Bloom, N., & Van Reenen, J. (2007), “Measuring and Explaining Management Practices Across Firms and Countries,” _Quarterly Journal of Economics_ 122 (4): 135 – 1408.

Bloom, Nicholas, Heidi Williams and John Van Reenen. 2019. “A Toolkit of Policies to promote Innovation” Journal of Economic Perspectives 33(3): 163–184.

Blundell, R., Griffith, R., Van Reenen, J. (1999). Market share, market value and innovation in a panel of British manufacturing firms. Review of Economic Studies 66: 529-554.

Broadberry, S. and Crafts, N., 2001, ‘Competition and Innovation in 1950’s Britain,’ _Business History_ , 43, pp. 97–118.

Brown, J.R., Fazzari, S.M and Petersen, B.C. (2009) Financing innovation and growth: Cash flow, external equity, and the 1990s R&D boom. _Journal of Finance_ 64: 151-185.

Brown, J.R., Martinsson, G. and Petersen, B.C. (2012) Do Financing Constraints Matter for R&D? _European Economic Review_ 56: 1512-1529.

Brouwer, E., Kleinknecht, A.
empirically derived inventory demand function using historical data on prices and inventory. Formally harvest, , is a function of past period crop prices , where and such that denotes the set of crops and denotes a region. Assuming prices follow a random walk, then suggests that the end of period inventory is only a function of current and past prices, as well as the beginning stocks in period , i.e., . Consumption demand for crops comprises of demand for food ( and demand for biofuel production ( . Both demand for food/feed and demand for biofuels are a function of the price of crops ( and the price of energy ( . In addition, demand for food and feed at region at time is a function of GDP per capita, .

With inventory, the equilibrium price does not need to equate harvest, , plus imports, , with consumption, , plus exports, . However, it should equate world supply, , plus global beginning stock, , with world demand, , plus global ending stocks, : The left-hand side can be called total availability, , at time . The equilibrium condition can now be written as Knowing , and the shape of demand functions , one can determine the effect of different levels of biofuel mandates on crop prices.

26
comply with other obligations of the agreements and are immune from trade disputes in the WTO Dispute Settlement Body. China and other WTO members can decide not to base their domestic standards on international standards. In case a member implements standards considered highly protectionists, it must provide specific justifications for the deviation according to the SPS agreement<sup>5</sup> (Du 2010). Thus, TBT and SPS Agreement do not mandate the use of international standards but provide strong incentives for China and other WTO members to harmonize their domestic standards.

Despite the incentives to adopt international standards, the number of Chinese standards based on international norms remains low compared to other countries. From the total number of standards in our database only 14 percent are based on international norms. In addition, China tends to adopt only parts of international standards, as 70 percent of all harmonized standards in our database are modified versions of international standards. Only one-third of standards in our database are identical to international standards. In the next section, we analyze empirically the impact of standardization and harmonization on Chinese food exports.

# **4. Empirical Model and Results**

## **_Data Sources and Descriptive Statistics_**

In this section, we describe our dataset and provide descriptive statistics based on the data. Export volumes are compiled from the COMTRADE database at the Harmonized System (HS 1992) and tariffs are compiled from the TRAINS data base. Consumption variables are computed from the Food and Agriculture Organization of the United Nations‟ statistical database FAOSTAT.<sup>6</sup> We deflated our data US Bureau of Labor Statistic's HS Import Price Indices. The indices are available for the whole period under consideration (1992-2008). The US Bureau of Labor Statistic has no import price deflator for sugar and cereals. We deflate import data for the two product groups with the import price indicator for foodstuffs.

Our dataset covers seven product groups meat (HS-02), fish (HS-03), vegetables (HS-07), cereals
1.93)|1,421|
|FR Alertness (Scale 1-7)|-0.21|(0.36)|[1.00]|3.08|(1.99)|1,421|

**_Notes_** :

- The table shows the regression results on pre-specified mental health outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Center for Epidemiologic Studies Depression Scale (CES-D) is a measure for depressive symptoms and includes 10 items asking about the past week. It is defined as a mean-effect index of 10 items scored 0-3 in the past week (I was bothered by things that usually don’t bother me, I had a problem in concentration on what I was doing, I felt depressed and troubled in my mind, I felt that everything that I did took up all my energy, I felt hopeful about the future (reversed), I felt afraid, I had difficulty in sleeping peacefully, I was happy (reversed), I felt lonely, I lacked the motivation to do anything). It is then normalized by subtracting the control group’s mean and dividing by its standard deviation. The Perceived Stress Scale (PSS-4) is a mean-effect index of four items measured over 1-5 in the last 30 days (how often have you felt that you were unable to control the important things in your life?, how often have you felt certain in your ability to overcome your own personal problems?, how often have you felt that things were going your way?, how often did you feel that the problems were too much for you to manage?). It is then normalized by subtracting the control group’s mean and dividing by its standard deviation. Higher scores for CES-D and PSS indicate better outcomes. Predeterminsim is measured over 1-10 where 1 means “everything in life is determined by fate” and 10 means “people shape their fate themselves".