rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Industrial Country Imports of Manufacturing Goods from Developing Countries during 1991/92 to 2001/02 and 2001/02 to 2007/08**

||Share of<br>Imports in|Developing<br>Domestic De|Country<br>mand (%)|Annual Imp<br>from Develo|ort Growth<br>ping Co. (%)|<br>1991/92 -|Annual Impor<br>2001/02|t Growth due to<br>2001/02 -|<br>2007/08|
|---|---|---|---|---|---|---|---|---|---|
|Country|1991/92|2001/02|2007/08|91/92 -<br>01/02|01/02 -<br>07/08|Demand<br>Change|Market<br>Share<br>Change|Demand<br>Change|Market<br>Share<br>Change|
|Canada|3.5|7.6|11.7|10.9|16.8|2.7|8.2|8.6|8.2|
|France|4.3|5.0|9.6|6.5|19.1|3.1|3.4|6.8|12.3|
|Germany|3.8|9.9|13.9|8.5|17.2|-1.3|9.8|10.6|6.6|
|Japan|1.5|4.8|8.2|11.1|12.3|-1.3|12.4|2.4|9.8|
|United States|4.3|9.9|13.1|12.8|10.4|3.7|9.1|5.4|5.4|
|Total: above<br>Industrial Co.|3.3|8.1|11.8|11.3|12.7|1.6|9.7|5.8|6.9|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Source: Computations based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Table 4 shows the shares of imports from developing countries in the total gross absorption (demand) of the five industrial countries, growth rate of these imports, and the decomposition of the import growth between demand and market share changes. Import growth rates from developing counties (about 11 and 13 percent p.a.) are much higher than their import growth rates from the rest of the world shown in Table 2. Import growth rates from developing countries are almost identical for the two periods while world trade growth has doubled during this time. Especially during the first period when the demand increases in industrial countries amounted to only 1.6 percent per annum, imports from developing countries increased by more than 11 percent p.a. Most of the import growth was caused by market share changes rather than demand increases. During the second period, despite higher demand growth, market share changes are still greater than demand changes. For this group of industrial countries, market share changes explain the bulk of the growth of imports from developing countries.<sup>11</sup> Of course, market penetration of almost 10 percent p.a. is much more disruptive and politically more sensitive.

Despite the rapid import growth caused by market penetration, by the end of the second period, the share of imports from developing countries in domestic absorption of this set of

> 11 EU-12, the countries which joined European Union after 1990 and Mexico for US and Canada are included in the developing country numbers. They do not change the general picture as shown in Annex table 2.

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A qualification related to our analyses is that cross section data only shows a static snap-shot of the revealed preferences of all households. This static analysis cannot cast any light on the dynamics of biomass depletion and the rationale of the households for doing so. Thus, the picture we present below highlight the state of the equilibrium at a given point in time. This picture, though incomplete, adds valuable insight to the present conditions of the households and their relationship with biomass scarcity.

In this paper, our focus is on the increase in time spent on collecting fuelwood, which is a frequent and important response to biomass scarcity (Cooke 1998a, 1998b, Kumar and Hotchkiss 1988, Dewees 1989). Fisher _et al._ (2005) show that households in Malawi increase labor allocated to forest extraction as returns to these activities increase. They argue that with increasing wood scarcity, the returns to forest labor and time allocated to forest product extraction are likely to increase. As elsewhere, Malawian households use other coping strategies as well: they switch to lower quality wood, economize on wood use and increase the number of collectors (Brouwer _et al._ 1997). In this paper, we use this broad understanding of household responses to fuelwood scarcity to carefully examine how forest-dependent households allocate labor. We focus on time spent on fuelwood collection and ask whether scarcity has an effect on collection time and on labor allocated to agricultural tasks.

The paper makes two contributions to the literature on forests and poverty. First, there are few studies that combine spatial information with household data (Dasgupta 2005). Most existing studies that focus on degradation and household behavior use fuelwood prices or other selfreported measures of scarcity (Cooke 1998b). By linking biophysical data with household data, we think we are making a significant contribution to existing literature. Second, we try to examine the poverty-environment nexus at three levels: a) the district level to understand the distribution of biomass availability; b) at the household level to assess welfare implications of biomass scarcity; and c) at the individual level to examine responses to scarcity. By taking a three-tier approach, we try to provide a broad and simultaneously nuanced understanding of the complex interactions between forests and the poor. Further, our paper uses a large-scale survey to quantify the impacts of biomass scarcity on poverty in Malawi. Policy makers in Malawi have been struggling with deforestation and fuelwood loss for decades. We hope our analyses can point to the extent of the problem in welfare terms and its regional manifestations.

5
|There are many popular<br>search engines in the<br>world, with different<br>designs and functions. Search<br>engines are used every day by<br>over 1 billion people<br>worldwide.|<br>When government money is misused, it<br>is very important to find and punish<br>those responsible. Your government<br>has a national agency, the [AGENCY<br>NAME], that helps to punish the<br>misuse of government funds.|<br>You have been selected to be part of the<br>Online Citizen Assembly: a national<br>conversation about how the government<br>of [COUNTRY NAME ] should spend<br>money. The results of the Online<br>Citizen Assembly will be presented to<br>the government.|
|---|---|---|
|Many people say they are<br>annoyed by all the<br>advertising on search engines.<br>How much does this apply to<br>you?|<br>The [AGENCY NAME] has<br>investigated many cases of government<br>corruption. Many people who misused<br>government funds have been punished.<br>Do you think it is good to have an<br>agency that investigates government<br>corruption?|<br>What should your government spend<br>more money on? The results of the<br>Online Citizen Assembly will be<br>presented to the government.|
|Not at all<br>A bit<br>Quite a bit<br>Very much|Yes<br>No|Defense and Police<br>Education<br>Transportation<br>Welfare<br>Health<br>Environment|

To measure tax morale in the survey, we use two outcome questions.

- 1) The tax morale question from the General Social Survey (GSS):

_If a taxpayer does not report all of their income in order to pay less income taxes do you feel it is: (Not wrong / A bit wrong / Wrong / Seriously wrong)_

# and,

- 2) A tax fine attitude question:

_If a taxpayer does not report all of their income in order to pay less income taxes, what percentage of their income should they pay as a penalty? (None (0%) / 1-10% / 11-20% / More than 20%)_ Although we think of both of these questions as tapping tax morale, note that there are important differences here between the first question, which is framed as a moral question, and the second question, which is more specific and boils down to an actual policy parameter. Also note that, while many might agree that increasing tax morale is desirable in most contexts, it is not necessarily clear that increasing punitive preferences in society is desirable in and of itself.

We also ask questions about basic covariates (age, gender, labor market status), as well as “manipulation check” and “mechanism” questions that allow us to look at whether respondents absorb the information given to them and investigate potential mechanisms. The full survey can be found in the appendix.

This survey was served to internet users in the designated countries using an online survey platform (RIWI) which delivers anonymous opt-in surveys to random Web users who are surfing online and land upon inactive domains. This means that respondents do not expect to take the 7
agricultural, urban and small areas under 10 km2.<sup>14</sup> The distance from these areas serves as a proxy value for transport costs.

As a non-renewable decentralized power supply option, we include diesel generators, which are already widely used throughout the developing world. ESMAP (2007) and KMOE (2008) provide information on prices and energy conversion rates for power generation for conventional diesel fuel. Generators can be deployed at the household level, or, far more efficiently, as a minigrid system. Appendix 1 provides details.

# **_Cost of decentralized generation_**

Meeting each household’s demand requires a given system size for each standalone and minigrid option that depends on local renewable energy potential. For solar PV this is the number of panels necessary to produce enough power to meet demand. Power supply from wind turbines at a given wind speed depends on the hub height and the size of blades. Diesel and biodiesel generators exist in many different configurations. Small, household level systems typically have a size of only a few kW. Minigrid systems have larger capacity and can serve 50 or even 100 households at a time. In this study we compare the costs of single-household and minigrid options to understand how scale might play a role in increasing coverage rates.

Each of these power options is also associated with an efficiency rating that dictates the amount of energy that is actually produced. In this study we simulate both high and low scenarios to see the relative cost impact of adopting higher efficiency technologies, but to keep comparisons manageable, we report results based on today’s average efficiency. Efficiency ratings and power configurations for each of the technologies is described in Appendix 1.

> 14 Specifically, areas defined by the World Wildlife Fund ecoregions database as biomes of Deserts/Xeric Shrublands & Tropical/Subtropical Grasslands, Savannas, and Shrublands. See Buys et al. (2007).

15
one category; and temporarily closed by mandate and choice into a second one. Similarly, we group plants that granted leave without pay and with pay into one group.

Change in sales is only available for businesses open or partially open, or that closed less than 4 weeks prior to the time of the survey (temporarily or permanently). We set change in sales -100 for businesses that have been temporarily closed for more than four weeks at the time of the interview.

For comparison purposes, size and sector in each country is obtained from the pulse survey data, even if in some countries these variables are available from the sampling frame. In some countries where the survey was a follow-up from the Enterprise Survey, size excludes part-time workers. To compute the percentage change in employment, we subtract workers laid off from workers hired, but we exclude observations with measurement error in the question on workers hired (number identical or higher than the size of the firm).

We trim the top 1% in the number of workers hired and in the percentage change in sales relative to the same period of last year. We also trim the top and bottom 2% in the predicted changes to sales in the three scenarios (pessimistic, regular, optimistic). To study expectations and uncertainty, We only use subjective probability distributions where the probabilities for the three scenarios total 100.

# **B.5 Predicting mobility trends**

For countries not covered in the Google mobility reports (Google, 2020), we use the stringency of lockdown restrictions in Hale et al. (2020) to predict mobility trends. Mobility around transit stations is highly correlated but not only explained by the stringency of lockdown restrictions (Figure A4). We fit a linear regression model of mobility on stringency of lockdown restrictions including a dummy for whether the observation is before or after COVID-19 was officially declared a pandemic by the World Health Organization (March 11th), the interaction of the two variables, and country and date fixed effects. We then smooth the predicted time series using moving averages. Figure A5 illustrates the goodness of fit of the prediction for a group of selected countries, whereas Figure A6 shows the relation between the stringency of lockdown restrictions and mobility trends in Algeria and Tunisia, which are not covered in the Google mobility reports.

45
**Figure 2** **_:_ Types of hazards threatening households in urban and rural areas (% of respondents) – (20212023)**

<!-- Start of picture text --> 65.9 64.6 66.4 68.5<br>6157.9<br>54.4 52.5 52.6 51.9 50.3<br>45.7 44.9 45.8<br>35.731.3 33.8 37.332.9 37.1 36.6<br>27.3 27<br>17.2 14.111.7 18.6 19.6 15.121.4 12.3 9.6 14.6<br>3.6 5.2 4.7<br>Heat waves Hurricanes Extreme rainfall Drought Severe storms Floods Earthquake Wildfires Tsunami Landslides Storm surges Other<br>Urban 2021 Urban 2022 Urban 2023<br>57 58.6 64.4 66.559.8 61.7 61 57.6 63 69 68.2 71.1<br>52.9 53.4<br>47.5<br>42.6 41.1<br>36.7 36 35.2<br>25.5 29.4 29.7 25.2 27.1 22.625.5 29.5<br>18.4 15.8 13.6 11.3 13.1<br>7.1 3.7 8.2<br>Heat waves Hurricanes Extreme Drought Severe Floods Earthquake Wildfires Tsunami Landslides Storm surges Other<br>rainfall storms<br>Rural 2021 Rural2022 Rural 2023<br><!-- End of picture text --> Note: Results are from entire sample of wave 2, wave 3 and wave 4 households.

Most natural hazards threaten in greater proportion those in the bottom poorest quintiles compared to households in richer quintiles. To examine whether exposure to hazards differs by socio-economic status, the data was disaggregated by household consumption quintile.<sup>8</sup> By the end of 2022 and by early 2023 a larger share of individuals in the poorest two quintiles reported facing threats to most natural hazards compared to those in the middle- and high-consumption quintiles (Table 1). For example, while 75 percent of individuals in the bottom quintiles reported living in a household exposed to earthquakes, this was 8 The SWIFT methodology was used to simulate consumption expenditure. Specifically, wave 1 of the HFS (collected in JulyAugust 2021) included questions on poverty correlates to distinguish relatively poorer households from relatively richer households. These questions were drafted following Haiti’s ECVMAS 2012 household survey and were then used to predict the position of respondent’s household in the consumption distribution and to subsequently create wealth quintiles. To assign wealth quintiles to households in the wave 2 data, we restrict the sample to the panel of households that were interviewed in both rounds 1 and 2 and we assume that households maintained their quintile position in wave 2. Using this approach, of the 2,361 observations in wave 2 only 1,487 observations were panel and could be assigned the consumption quintile. For this and subsequent sections where data from wave 2 is disaggregated by quintile, the sample is restricted to only these panel households. Given that all observations in wave 3 and wave 4 were new, we applied the same methodology used for wave 1 data, to assign wealth quintiles to the households. Out of a total of 1,530 observations in the wave 3 data we could assign wealth quintiles to 1,529 households. Data disaggregated in this note by consumption quintile for wave 3 refers to these 1,529 observations. For the fourth round, consumption quintiles were assigned to all 1,532 observations.

4
# **4.4 Differences across firm capabilities (pre-pandemic)**

An important factor associated with the digital divide in response to the pandemic is the gap in digital readiness and overall firm capabilities pre-pandemic. Figure 9 shows the distribution of the information about increasing use and investment in digital technologies based on three groups of firms classified by the levels of digitalization pre-pandemic.<sup>12</sup> The figure shows significant persistence in digitalization, with a ratio close to 3 in likelihood of using and investing in digital technologies between ex-ante highly digitalized companies compared to those that had prior low digitalization. These results are aligned to previous findings combining the BPS data with very granular information on pre-existent digital technologies, from the Firm-level Adoption of Technology for firms in Brazil, Senegal, and Vietnam, as described by (Comin et al., 2022).

<!-- Start of picture text --> 80<br>68<br>60<br>52<br>45<br>40<br>28 29<br>20 16<br>0<br>Low Medium High<br>Digital readiness score (pre-pandemic)<br>Increasing use of digital tech.<br>Investing in digital solutions<br>Predicted probability<br><!-- End of picture text --> Figure 9: Predicted probability of digital response to COVID-19 by digital readiness (pre-pandemic) A potential explanation for this persistence in digitalization might be associated with the capabilities for digital upgrading and awareness. When comparing similar firms in terms of size and sector but differences in management practices, Figure 10 shows that among firms that did not increase the use of digital technologies in response to the pandemic, those adopting better managerial practices pre-pandemic were less likely to do report that they did not need digital upgrading.<sup>13</sup> . The role

> 12 Groups are created using the number of digital technologies used by the firms before the pandemic from the following list: digital platforms for sales or digital payments; use of online chat, social media or big data for marketing and product development; software for customer or supply chain relationship management; and enterprise resource planning for operations or business administration. Low are those firms with 0 or 1 technology, medium those with 2, and high those with 3 or 4.

> 13Groups are created using the number of good management practices implemented by firms before the pandemic from the following list: compared sales to targets at least once per month, advertised the business, or made a promotion based solely on performance and ability factors. Low group are those firms with 0 practices, medium those with 1, and high those with 2 or 3.

13
# **Financial Constraints, Working Capital and the Dynamic Behavior of the Firm**

Rosanna Chan<sup>_∗_</sup> Nov 31, 2008

## **Abstract**

Financial constraints are widespread in developing countries, where even short-term credit is limited. Finance held by firms as working capital is a substantial proportion of sales revenue, yet the role of working capital is largely neglected by existing models of financial constraints. This paper presents a dynamic model of the firm that incorporates working capital by introducing a delay between factor payments and the receipt of revenue. In contrast with previous models, the working capital model predicts that firms under binding constraints will substitute between labor and capital in response to demand shocks, causing investment to be countercyclical. For firms near the margin of being constrained, constraints bind when positive production opportunities arise. Output growth is therefore constrained in response to positive shocks but not to negative shocks. Simulations suggest that models without working capital may understate the predicted effects of financial constraints on production efficiency, firm profit and growth over time. The predictions are tested with the Bangladesh Panel Survey data for manufacturing firms. Consistent with the theory, there is evidence that constraints bind when output price increases, that investment by constrained firms is countercyclical, and that output response to positive shocks is dampened for firms that are sometimes constrained. The results also are important for policy. In order to maximize growth, efforts to relieve credit constraints should be focused on periods when demand shocks are high.

> _∗_ Economist, South Asia Finance & Private Sector Development, World Bank. Email rchan@worldbank.org I am grateful to Albert Park, Dan Silverman, Jim Levinsohn, Jan Svejnar, Chris House and Andrei Levchenko for their advice, Mary Hallward-Driemeier at DECRG, World Bank for data and institutional background, Aneeqa Aqeel, Oli Coibon, Jane Dokko, Chris Kurz, Sara Lalumia, Rahul Mukherjee, Joo Youn Park, Bill Powers, Heidi Schierholz, Mine Senses, Lina Walker for their insightful comments and encouragement. I would also like thank seminar participants at the Universities of Michigan, University of Melbourne, Australia National University, University of Wisconsin and the Minnesota International Economic Development Conference for their valuable input. JEL: D01, D2, D21, D22, D24, O12 Keywords: financial constraints, working capital, firm behavior, real effects. All errors are my own.

1