rafmacalaba/datause-encoder-data huggingface dataset:
port measures from the government and may need to maintain some means of livelihood to cover basic necessities (Franklin 2018).

We do not observe job quality directly in the HFPS data, so we rely on several proxies to analyze the quality of net employment changes. In particular, we examine transitions between sectors and types of employment (wage employment vs. self-employment) across different population groups before and after the onset of the pandemic. In the case of agriculture, in poorer countries the sector oftentimes acts as a shock absorber and workers unable to find work elsewhere may resort to jobs in the agriculture sector. As the economic fallout caused by the pandemic lingers over a longer period, households may rely on agricultural employment as a coping strategy. Since we observe the prepandemic sector of employment for each respondent, the analysis here relies on the plausible assumption that the movement from non-agricultural employment immediately preceding the COVID-19 pandemic to agricultural employment after the onset of the pandemic is a push factor related to the need to mitigate income losses, rather than a pull factor associated with better remunerated employment in agriculture. Similarly, self-employment which is oftentimes in the informal sector can serve as a buffer against the labor market shock, particularly among households who do not have access to formal or informal safety nets that would allow them to wait it out for a better job. As such, it could be argued that transitions from prepandemic wage employment to pandemic self-employment are primarily push factor transitions into immediate, but more precarious and lower-quality employment vis-`a-vis the jobs held previously.

HFPS data record a significant share of workers changing jobs during the COVID-19 pandemic. In the early phase of the pandemic, a period also characterized by substantial work stoppages, an additional 9 percent of workers reported working in jobs different from their prepandemic employment. Over time, this figure rose to more than 20 percent of workers in the second half of 2020, before slowing in 2021, at which point around one in four workers reported having a different job than before the pandemic (Table A6.6). The incidence of job switches is particularly high in LICs, primarily on account of SSA countries, with 40 percent of workers reporting having changed jobs by 2021 since before the pandemic.

We construct transition matrices that trace changes in individuals’ sector of activity or employment type. Because of limitations in the HFPS data, job changes within the same sector or within the same employment type are not captured. For example, the transition matrices would not capture workers who had a job in the services sector in February 2020 and switched jobs to a different services sector in 2021, but they would record a movement from services to agriculture, or from wage employment to self-employment. As such, job change estimates in the transition matrices are lower bound estimates of the total incidence of job 20
# Trade Wars: What do they mean? Why are they happening now? What are the costs?

Aaditya Mattoo and Robert W. Staiger<sup>∗</sup> JEL No. F02, F13. Keywords: trade wars, WTO, multilateralism, cooperation

> ∗Development Economics Research Group, World Bank; and Department of Economics, Dartmouth College and NBER, respectively. Thanks to Treb Allen, Emily Blanchard, Chad Bown, Davin Chor, Swati Dhingra, Caroline Freund, Penny Goldberg, Judy Goldstein, Henrik Horn, Doug Irwin, Petros C. Mavroidis, Emanuel Ornelas, Nina Pavcnik, Alan Sykes, Nick Tsivanidis and Ben Zissimos for many helpful comments. All remaining errors are our own. The views expressed in this paper are those of the authors and should not be attributed to the institutions to which they belong. Research for this paper has been supported in part by the World Bank’s Umbrella Facility for Trade.
Figure 3: Densities of Weeks Pregnant at 1st Prenatal Visit Notes: Densities estimated using an Epanechnikov kernel with optimal bandwidth. P‐vales of Kolmogorov‐ Smirnov tests of equality of distributions between groups reported below figure. The two vertical lines indicate weeks 13 and 20 of pregnancy. Source: Authors’ own elaboration based on data from the provincial medical record information system.

29
We employ two measures that capture different dimensions of concentration. First, we construct a Herfindahl index using export data disaggregated at 4-digit SITC. The index ranges from zero and one and increases with concentration.<sup>6</sup> This index is widely used in studies that focus on general indicators of economic concentration (e.g., Antweiler and Trefler 2002). Figure Ic suggests a downward sloping relationship with growth.

Second, we employ the share of natural resources exports in total exports. This was employed by Sachs and Vial (2002), again, as a measure of resource abundance and found to be very robustly negatively related to growth in a panel specification in differences. Again, we would argue that this measure has intrinsic interest, but as a specific measure of concentration of exports in one particular industry. Figure Id also suggests a negative relationship with respect to economic growth. But it also shows a significant re-ranking of countries compared to the previous resource measures. Papua New Guinea, Malawi, Nicaragua, Togo, among others, now appear as high value cases while Finland and Singapore have fallen among the lower value cases.

# _Intra-Industry Trade_

The final trade measure we employ is the Grubel-Lloyd (1975) index of intra-industry trade (IrT).7 The scale economies arising from IIT are thought to lead to more rapid productivity gains and hence faster growth (see for example, Krugman 1979). Because

**6** The index is defined as: _H ,_ where subscript i' stands for a particular product and 'n' is _tEXs_ p

the total number of products. When a single export product produces all the revenues, H=1; when export revenues are evenly distributed over a large number of products, H approaches 0.

total number of products. This index varies between 0 and 1, and it shows the share of total trade that is conducted among identical products (i.e., imports and exports of the same product category).

_5_
<!-- Start of picture text --> 200<br>150<br>50<br>GUO baseline = 1<br>SSgEe Tez Tae ERY<br>errreererery452" Soe juee%g2 2 v se a isP50a5 RabbisSESEoieees26 225fac 5c Se20s 22 2EEG<br>gees Eee Es cep S25 e202 oe ga<br>eee He See § ina : a = goa 368 #3 i ro | 22 § -<br>:<br><!-- End of picture text -->
0.06 and 0.10, the magnitude of the effect is small, however: only between six and ten cents of an additional dollar earned in profits are invested.

Subsequent research based on RPED data confirms that investment is not particularly sensitive to changes in profits. In an in-depth analysis of the manufacturing sector in Zimbabwe 1992-94, Fafchamps and Oostendorp (1999) show that the cash-flow sensitivity of investment is low, even among small firms. Söderbom (2002) obtains a similar result for Kenya. Mazumdar and Mazaheri (2003) use a sample of six countries (adding Côte d’Ivoire and Zambia to the set of countries considered by Bigsten et al., 1999a), and report an estimated profit coefficient is 0.08, thus very similar to that of Bigsten et al. Mazumdar and Mazaheri also split the sample according to firm size, and obtain a profit coefficient equal to 0.09 in the subsample of small firms. They interpret the larger profit coefficient among small firms as evidence that small firms are more credit constrained than large ones. Reinikka and Svensson (2001) obtain a profit coefficient equal to 0.08 based on a sample from 1996-97 of Ugandan manufacturing firms. These authors too find a larger profit effect among smaller firms, which is consistent with the notion that credit access is more of a problem for small firms. The point estimate of the coefficient on profits among small firms is 0.11, and so quantitatively the effect is not particularly large even for small firms.

Now consider the effect of uncertainty on investment. There is hardly any second-hand market for used machinery in Africa, which means that investments largely have to be treated as sunk cost (i.e. they are “irreversible”). This is problematic in an environment where uncertainty about future rules of the game as well as market development is a standard feature. Theories of irreversible investment under uncertainty predict that investment will be slower to respond to demand shocks if uncertainty is relatively high. As mentioned in Section 3.1 testing empirically for the effects of risk is difficult, as risk is not easily measured. In this context, the design of innovative survey questions has been a major step forward. Pattillo (1998) uses panel data on Ghanaian manufacturing firms from 1994 and 1995 to test various hypotheses from models of irreversible investment under uncertainty. Based on data on entrepreneurs’ subjective probability distribution over future demand, Pattillo calculates the variance of demand and uses this as the measure of uncertainty. Empirical results indicate that uncertainty has a negative effect on investment and this effect is more pronounced for firms with more irreversible investment. A similar 26
Policy Research Working Paper 8308

# **Abstract**

This paper assesses the effects of selected structural reforms on labor productivity growth for 37 developing countries over 2006–14. It combines newly constructed reform indexes using the International Monetary Fund’s Monitoring of Fund Arrangements data set and firm-level productivity from the World Bank Enterprise Surveys. The paper highlights the following results. Structural reforms under consideration in this study—financial, fiscal, real sector, and trade reforms—significantly improve productivity at the firm level. Interestingly, real sector reforms have the most sizable effects on firms’ productivity. The relationship between reforms and productivity is nonlinear and shaped by certain characteristics of firms, including financial access, a distortionary environment, and firms’ size. The pace of reforms matters, since being a “strong reformer” is associated with a clear productivity dividend for firms. Finally, except for financial and trade reforms, all the macroeconomic reforms considered are bilaterally complementary in improving firms’ productivity. These findings are robust to several sensitivity checks, including alternative methodologies and measures of productivity, and a counterfactual experiment based on unsuccessful reforms.

This paper is a product of the Office of the Chief Economist, Africa Region. 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://econ.worldbank.org. The authors may be contacted at wkouame@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 entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure 5 China Source of Financing Health Expenditure (Pecentage Share) <!-- Start of picture text --> percent<br>100 ,;-_ _=_ _<br>90<br>80-<br>70 - ~ s~ ' 3⁄4~<br>60-<br>50-<br>40-<br>- 1 li 1<br>30- 10_<br>20<br>1980 1985 1990 19 3<br>Year<br>Central Goverment GIESb Rural Collective Fund<br>Local Govemment a Labor Patient Fees<br>a Local Govemment = aggregate expenditure of province + county + municipality<br>bGEIS: Govemment Employees Insurance System<br><!-- End of picture text --> _Source:_ Table 7 (Annex ).
(or even spouses)”.

# 4 Testing the predictive power of the models

Before investigating the e¤ect of the randomized capital grants on sample ...rms, we brie‡y examine the baseline data for evidence consistent with predictions made by the models presented in Section 2. The evidence we o¤er is non-experimental, but since we are not trying to draw causal inference, observational data remains useful.

The Ramsey model predicts, other things being equal, that more patient entrepreneurs have a higher steady state capital stock. More talented entrepreneurs should also have larger ...rms and thus larger capital stock. The time inconsistency model further predicts that entrepreneurs that are more hyperbolic in their discounting should also have a lower steady state capital stock.

We investigate these predictions using the baseline capital stock of all respondents. The results are shown on Table 2. Consistent with the predictions of the model, we ...nd some evidence that more able entrepreneurs have larger capital stocks. The regression in column 1 shows that years of schooling, results from a Raven test of non-verbal reasoning, and results from a forward digitspan test all have positive measured e¤ects, though only the digitspan score is statistically signi...cant.

The column 1 regression also includes measures of the owner’s short-term discount rate and an indication of hyperbolic attitudes. Hyperbolicity is proxied by answers to questions asked in the baseline survey involving discount rates over the two di¤erent time horizons, and patience is measured by whether a respondent has a discount rate above or below the sample median. Firm owners were asked hypothetical questions to elicit the amounts that would leave them indi¤erent between an amount today, and 100 cedis in one month; and between an amount in ...ve months and 100 cedis in six months. From this we construct two proxies: an indicator variable of whether the respondent has a discount rate above the median for the one month versus today comparison (the median person would take 90 today instead of 100 in one month); and an indicator of being a hyperbolic discounter based on comparison of discount rates over the two horizons (28 percent of the sample are classi...ed as hyperbolic). We ...nd that hyperbolic discounters have signi...cantly smaller businesses, consistent with the theory. Those with below median one month discount rates have insigni...cantly larger businesses.

Our surveys include two additional measures which each measures of self-control and the 20
sectors. Template 10 collects all the data related to spending on public infrastructure by any level of government (central or local). _This template with all its occurrences is the core of the SPI exercise._

# **Macroeconomic parameters for budgetary context of infrastructure spending (template 9)**

The data for template 9 should be gathered from the government. It must be kept in mind that even if the definitions used by a government are different from the standardized definitions suggested in this manual, a proxy for infrastructure spending based on institutions that provide services (rather than infrastructure services provided by infrastructure and non-infrastructure specialized institutions) can always be found. This back-of-the-envelope estimation will guide the user in positioning infrastructure within the overall fiscal framework.

# **Template 9. Macroeconomic parameters for budgetary context of infrastructure spending**

|**Policy**<br>**code**|**Series**<br>**code**|**Variable**|**Definition**|Unit|
|---|---|---|---|---|
|re||Total budget||LCU per year|
|ructu<br>)||Infrastructure||LCU per year|
|frast<br>(NIN||Education||LCU per year|
|onin<br>||Health||LCU per year|
|N||Other sectors||LCU per year|

_Source:Authors’elaboration_ . _Note:_ LCU = local currency units.

# **Functional and economic classification of central and local government expenditures (template 10)**

Template 10 is probably the single most relevant output of the SPI exercise. Its purpose is to provide, for all relevant sectors (4-digit categories or classes—see below for definitions), expenditures according to the GFSM 2001 functional and economic classification. It should be filled in for the central government to cover the most recent 5 years of available data for:

- Budget estimates

- Budget releases

- Actual expenditures (audited expenditures if available)

Thus, ideally, 15 occurrences of this template (estimates, releases, and actuals for 5 years) should be completed for central government expenditures. The collection of separate data on estimates, releases and actuals allows for the subsequent calculation of various budget execution ratios and provides a much richer understanding of expenditure patterns and processes. However, in some cases it may not always be possible to do so.

Template 10 should also be completed for local governments following the same approach: budget estimates, releases, and actuals for the most recent 5 years of data available. Depending on the country, local government data may be available in an aggregate form provided by the central government. Where this is not the case, the data collection exercise should aim to cover the most important local authorities in spending terms, collecting data individually from each local authority and then aggregating it into a first order estimate of local government expenditure.

41
region (this is the share of total regional income in total national income): it ranges from near zero in Lakshadweep in India<sup>39</sup> to 0.83 in England (U.K.) Using these household income data, we compute income Gini coefficients for each region, measuring within-region inter-personal income inequality. Economic regions in Armenia and Slovak republic all have low levels of inequality and the highest Gini coefficient values are in Brazil, Colombia, and some regions of Nigeria, Senegal, and South Africa.

# **_Natural Resources_**

The relative abundance of natural resources across regions is another important variable in the model. Ideally, we would want to know the value of all territorially concentrated tradeable resources in each region. Any number of primary commodities could be important for the local or national economy (coffee in Burundi and Colombia, diamonds in Sierra Leone and South Africa, natural gas in Indonesia and Russia). The ideal measure is thus the share of total national resources for each region, but such a measure can only be computed for a few countries and a small number of agricultural commodities, minerals, and precious stones due to the paucity of data on the location of different commodities and the lack of region-level breakdowns of the total dollar-value of production or trade in these commodities. Thus our analysis is inevitably limited. We collected data on oil and natural gas and identified with a binary variable all the regions that have substantial amounts of these resources (exploited or unexploited reserves are coded; see supplement for coding details and geological maps for each country).

# **_Regional Ethnic Difference_**

To measure regional ethno-cultural differences from the rest of the country, we identify all groups larger than 1% of the country’s population and collect data on the size of as many of

> 39 Data for India are for rural areas.  Urban and rural income data are reported in separate surveys. We selected the rural data as more directly relevant to the question at hand.

23
gaps over time.<sup>25</sup> Taken as aggregates, there is considerable room both in the composition of employees and how they are compensated for differences by subgroups to arise. We turn to this next.

# III. Descriptive Findings

In 1987, the mean salary of a female staff member at the WBG was 52% that of a male staffer. By 2015, this had increased to 77%. The male-female difference can be further separated by country-part and a clear ordering emerges with the highest salaries for Part 1 males throughout the period of our data (Figure 5). The salaries of Part 2 males are lower, but the large differences are between men and women, with salaries of both Part 1 and Part 2 females significantly below those of males. Among females, salaries of Part 1 are again higher than those of Part 2 employees throughout the entire data period.

**Figure 5: The Aggregate Gap: Mean Salaries by Subgroups over Time**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>Women: Part 2 Women: Part 1 Men: Part 2 Men: Part 1<br>Salary differences relative to Men Part 1 (%)<br><!-- End of picture text --> In Section IV, we develop a dynamic accounting framework to decompose this gap into its respective components---differences in staff composition (both historical and present); differences in entry salaries; differences in salary growth over time and; differences in attrition. Here we treat each of these individually to understand how they could contribute to the aggregate and career gaps at the institution.

> 25 It would be useful to understand these changes in the context of changes in other institutions, but this is easier said than done. The WBG does have some claim to exceptionality and includes some of the best trained and educated staffers in any institution from multiple nations in the same location. In addition, data from private firms are usually not available. Finally, the IADB and IMF have not assembled their HR data in a similar fashion.

16
deflators). See the Appendices for more information on the construction of our data and key explanatory variables.

# **5. A Bird’s Eye View of Reallocation: Job Flows and Aggregate Productivity Dynamics**

# **5.1  Job Flows, Entry and Exit**

Table 1 and Figure 2 present aggregate entry, exit and employment growth statistics. Average exit over the entire period is 8.8% while average entry is 11.1%. Firm exit spiked during the crisis - in 1997 10.8% of firms exited, while in 1998, 11.2% of all firms exited - and dropped precipitously during the recovery in 1999 and 2000, to peak again in 2001 (see Appendix A4). Employment growth followed a similar trend, but did not spike in 2001; before the crisis, manufacturing employment grew quite rapidly. The crises induced substantial job losses; on average firms shrank employment by 1.4% in 1997 and by 3.7% in 1998. Employment growth recovered in 1999 and 2000, but dropped in 2001.

The high job losses during the crisis were driven both by a slowdown in job creation and by a spike in job destruction, and predominantly accounted for by employment adjustment by incumbents (see figure 2). The share of job flows accounted for by firm entry and exit is likely to be underestimated, however, since we only observe firms with at least 20 employees. The amount of gross reallocation far exceeded the amount required to achieve net employment adjustment, leading to enormous excess churning. The increase in excess churning attests to the importance of heterogeneity across firms: even during the crisis, almost a third of all firms reported expanding employment. Nevertheless, a striking feature of the data is how persistent employment is; over the entire period considered on average 21% of firms did not change their labor input each year. The figure also shows a longer trend towards lower job creation, both in aggregate and in net creation rates.

# **5.2 Decomposing Aggregate Productivity Growth**

17