rafmacalaba/data-use-mentions-extended huggingface dataset:
affect the firm size distribution across regions and sectors.

# **3. Data**

In this paper we use a newly available firm-level data set from the World Bank Enterprise Surveys. The surveys cover more than 100,000 firms across more than 120 economies and six regions during 2006–10. We use a sample of 39,538 firms in 98 countries for which data are complete. The unit in the sample is the establishment; one firm may have more than one establishment. For simplicity, we use the term firms throughout the paper, though the analysis is based on establishment data.

Our outcome variable of interest is employment growth, measured by the number of permanent employees. Our policy interest is in understanding the determinants that are important to the longterm business operation and employment growth of firms.<sup>2</sup> Because there are no data on temporary

> 2 Like other researchers, we use employment growth rather than sales growth, for several reasons. Sales growth is more volatile and is also more prone to reporting and measurement biases, especially when survey respondents are reporting sales realized three years before. Moreover, for tax reasons, firms may not choose to report actual sales.

6
conflict count across departments, the dummy conflict indicator may not adequately explain the variation in average years of education across departments. As a robustness check, the last two columns report the estimated coefficients of years of education when the war intensity variable is measured as the actual number of conflict events. The outcome suggests that an increase in the war intensity by one additional event of conflict lowers the average years of education for the young age-cohort (aged 10 to 22 years) by .01 years compared to old age-cohort (aged 23 to 32).

Table 4.1.2 Effect of War on Education using 2008 household survey data <u>(Dependent variable = Years of Education)</u>

||**War int**<br>**dummy (=**<br>**was at lea**<br>**eve**|**ensity =**<br>**1 if there**<br>**st one war**<br>**nt)**|**War intens**<br>**number**<br>**eve**|**ity = actual**<br>**of conflict**<br>**nts**|
|---|---|---|---|---|
|War intensity× Young Cohort|-0.940***|-0.499***|-0.011***|-0.008***|
|Control variables|||||
|Birth fixed effects (department)|Yes|Yes|Yes|Yes|
|Birth fixed effects (Age Cohort)|Yes|Yes|Yes|Yes|
|Household controls|No|Yes|No|Yes|
|Constant|8.355***|4.677***
5,182|
||(0.187)|(0.209)|||
|treatment x post x Goal|0.292<br>(0.269)||||
|With boyfriend|||||
|treatment x post|-0.147***|-0.015|0.212|5,182|
||(0.055)|(0.049)|||
|treatment x post x Goal|0.059||||
||(0.057)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. See notes for Table 2 on table structure and control variables. The outcomes are the hours the female reports spending in each category on the day prior to survey. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel.

66
Policy Research Working Paper 5619

# **Abstract**

One side-effect of the Global Financial Crisis of 2008–09 was the resurgence of a debate over exchange rates. The conventional wisdom dictates that real-exchange rate adjustments are needed in order to bring about changes in trade balances across countries. However, the literature on the effect of exchange rate fluctuations and currency under-valuations on exports is surprisingly ambiguous. This note explores for the first time the potential role of foreign direct investment as an intermediate variable in the process of trade adjustment after large realexchange rate changes. Real-exchange rate devaluations might result in increases in foreign direct investment inflows, as investors can take advantage of changes in the foreign-currency value of domestic assets. If so, the response of exports will depend to some extent on the nature of such foreign direct investment inflows, with inflows motivated by “horizontal” foreign direct investment associated with negligible changes in export growth after devaluation. The author utilizes quarterly data on real effective exchange rates, foreign direct investment inflows and exports to explore the effects of large devaluations (defined as the largest observed quarterly real effective exchange rate devaluation) on foreign direct investment and exports from 1990 to 2010. The admittedly speculative evidence suggests that there were heterogeneous experiences regarding the timing and magnitude of subsequent changes in foreign direct investment and exports, but on average foreign direct investment inflows tended to precede export surges within two year horizons.

This paper is a product of the Trade and Integration Team, Development Research Group. 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 author may be contacted at dlederman@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
Policy Research Working Paper 9344

# **Abstract**

The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase output or consumption when a country is under heavy debt burdens because it sends positive signal about the country’s solvency situation and long-term economic wellbeing. Empirical tests of this hypothesis have suffered from identification concerns due to data sources and empirical methodology. Using a sample of OECD countries between 1978 and 2014, this paper combines new IMF narrative data and the proxy structural Vector Auto-regression (SVAR) method to examine whether fiscal austerities can be expansionary when debt levels are high. Fiscal austerities are measured as 1) narrative fiscal shocks and 2) structural shocks from a proxy SVAR. Additionally, this paper uses a model-based approach to determine the cutoff debt level beyond which EFC is expected to be observed. This paper finds empirical evidence in support of the EFC hypothesis for OECD countries: results for output are driven by changes in tax rates and are robust to how one defines a high-debt regime and how one measures austerity.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at onie@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
had to restrict our sample to 89 countries. Included in the following analysis are all countries for which data on regulations, the dependent and all independent variables are available.<sup>6</sup> Base year for the regulation index is January 2003. For our country sample, the combined indicator ranges from -4.24 to -0.42, with a mean of -2.1 and a standard deviation of 0.85.

> 3 For an extended discussion of the advantages of the Doing Business indicators, see World Bank (2004). In general, the Doing Business database is widely recognised (and used) as a high-quality measure of regulations across countries. For instance, Bolaky and Freund (2004) and Pica and Mora (2005) have used the Doing Business indicators in their empirical work.

> 4 Before taking the average for this indicator, all three sub-components are rescaled to 0-1 by dividing all observations by the highest figure in each of the three sub-components.

> 5 Before combining the five regulation indicators, we have taken the logarithm of each of them. This procedure yields negative values for the regulation index for all countries.

> 6 See Appendix C for the country sample.

7
%|7.93%|

Source: (Havlík et al. 2015) In the model, we will work with the numbers for the net impact, because the private adaptation is reported in the table is effectively the kind of optimization behavior embedded in the model. For the intermediate years, we interpolate linearly.

_Model modifications_ In order to account for climate damages ) to agricultural output (note that it has a negative sign), we include it explicitly in the calculation of value added in the agricultural sector: tt (DD K K K K 1 ) This equation derives value added for the crop sector, a sub-sector of agriculture, from the value in the Δ log (YYVVGGDD,tt) = cc −θθ<sup>VVGGDD</sup> ∗log (YYVVGGDD,tt−1) −log ((1 + DDtt−1) ∗YY<sup>�</sup> VVGGDD,tt−1)) + Δ log ((1 + DDtt ) ∗YY<sup>�</sup> VVGGDD,tt social accounting matrix (SAM). reflects the damages caused by climate change in year . Potential GDP is not modeled bottom up from sectoral values. Instead potential GDP is calculated from DDtt SS the aggregate production factors. The agricultural damage has the effect of reducing TFP, because the same amount of capital and labor produces a reduced amount of output compared to previous years. The modification to potential GDP ∗ is tt ∗ ∗<sup>αα</sup> K YY YY ββ γγ where is the weight of agriculture in total value-added. It would have been ideal to map damages YYtt = (1 + DD∗ωωVVGGDD)AAttCCtt KKtt−1YYEE,tt ∗ ∗ K where is the weight of agriculture in total value-added. It would have been ideal to map damages in the agricultural sector to changes in informal employment and wages, since they are most likely to be YY VVGGDD affected. ωω

# 3.8.5 Heat impacts
from firms’ constant expectations, capturing yearly variations in weather conditions. This assumption of constant expectations is reasonable for several reasons as firms typically base their planning and operational strategies on long-term climate averages and historical weather patterns, which tend to remain relatively stable over short time horizons.

# 3.4 Grid-level aggregation

In our study, the primary unit of observation is the grid, aligning with how weather data is measured and recorded. For the firm-level data, we aggregate this data at the grid level.

We implement two distinct levels of aggregation in our analysis:

> 13For the daily temperature measurement, three variables are available from the BERKEARTH dataset at the Climate Data Store: minimum, mean, and maximum daily temperatures. We do not consider the minimum temperature in this study. Between the mean and maximum daily temperatures, there is a difference in data availability. The mean temperature data is accessible only up to June 2018, whereas the maximum temperature data extends through 2019. Due to this disparity in data coverage, we have chosen the maximum temperature variable as our primary variable for analysis. Both the mean and maximum daily temperature variables are highly correlated.

> 14The smoothing spline is fitted using the `smooth.spline()` function from the base R package. The default method for estimating the tuning parameter ( _λ_ ) uses generalized cross-validation (GCV), which balances the trade-off between smoothness and fidelity to the data.

9
only. If _X_ can be partitioned into two not necessarily disjoint sets ( _Q,T)_ such that _Q_ affects the outcome equation while _T_ determines the selection part it is possible to estimate the following model: Outcome equation _: Y_ = _g_ ( _Q_ ) + δ _D_ + _u_ Selection equation: _D_ * = _h_ ( _T_ ) + _v_ where _D=1_ if _D*>0_ and _D=0_ otherwise. Selection bias is then treated as an omitted variable problem. As is common, linearity is assumed in the _g_ and _h_ functions. This set up imposes a much stricter parameterization on the outcome equation than matching does and than we would prefer since we think that treatment may be accompanied by changes in the production process. In our case no justifiable exclusion restrictions can be found to separate Q and T and therefore semiparametric estimators cannot identify g(.)<sup>8</sup> . Imposing assumptions about the joint distribution of _u_ and _v,_ i.e. that they follow a bivariate normal distribution offers another alternative although Heckman, Tobias and Vytlacil (2000) have shown that distributional misspecification in this setting can impart important bias.

# **III. Data**

We employ the National Survey of Microfirms (Encuesta Nacional de Micronegocios, ENAMIN) for the years 1992, 1994, 1996 and 1998. This survey covers a sample of individuals who declare that they are self-employed in a broader labor survey, the Encuesta Nacional de Empleo Urbano (ENEU). It is restricted to micro-firms with at most 6 workers (including owners) for all economic sectors except manufacturing where the range is from 1 to 16 workers. We restrict the sample to micro-firms with 1 to 6 workers to be consistent across industries. The ENAMIN allows a relatively precise construction of a wide variety of variables that represent basic firm and entrepreneur characteristics: profits, employment size, capital stocks, time in business, and engagement with a wide variety of societal institutions, including those for which treatment effects
Is it then possible to use these estimates to avoid the assumption of conditional independence. In the absence of such data, one can rely on at least one matching variable that is highly correlated with the target variables. If X and C are perfectly correlated, we can rely on the regression function in the donor dataset between both variables to predict C in the recipient dataset. While the set of matching variables that are

> 7 Examples are information on the number of household members, the age and gender of household members, or their labor market status.

8
Tanzania, Uganda, and Zambia, national level semi-autonomous revenue authorities are in charge of collecting a limited number of taxes. The Ugandan Revenue Authority (URA), for example, collects all major indirect taxes and some direct taxes (income tax, but not property or social security tax). Even with the presence of national revenue authorities, there is often little or no coordination in setting and collecting taxes between the various levels of government in Africa (Fjeldstad and Rakner, 2003, vi).

The Afrobarometer asks respondents whether they have paid five different types of taxes and fees within the year preceding the survey: fees for a government service, such as education or health care; licence fees to local government, such as a bicycle, cart, or market stall; property rates or taxes; public utility fees; and, income taxes. The majority of respondents, 68 percent, have experience with paying a fee for a government service. 47 percent of respondents have had experience with paying a public utility fee. One-quarter of respondents have experience with paying a license fee to a local government. 24 percent of respondents have experience with paying a property rate or tax. Only 22 percent of respondents have had experience with paying an income tax.

Table 2 shows a large amount of variation between countries in the percentage of respondents who report having paid a tax or fee to government. For example, 83.7 percent of respondents from Senegal have experience with paying a fee for a government service compared to 38.4 percent of respondents from Mali. Forty-two percent of respondents from Ghana have experience paying a license fee to their local government compared to 3.5 percent of respondents from Mozambique. Although the percentage of respondents who have experience paying an income tax is low, there is a substantial difference between Liberia and Mali. Forty percent of respondents from Liberia have experience paying an income tax compared to 10.3 percent of respondents from Mali. I control for respondents’ experience with paying an indirect tax, an income
452***|-0.0266**|0.0977***|
|(0 to 100)|(19.15)|(-2.37)|(11.45)|
|Time trend|0.274***|0.0273|0.0280|
|(=0 in 2000)|(5.61)|(1.17)|(1.37)|
|n|1,286|1,232|1,128|
|R-squared|0.279|0.080|0.102|
|RMSE|7.73|3.90|3.05|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

8
total area of the district is classified as forest land, compared with the state average of around a third of total land area. Sambalpur district has a total population of just over 800,000 (1991 census) of which nearly 75 percent live in the rural areas. The scheduled caste (17 percent) and scheduled tribe (35 percent) population together constitute more than half of the district's total population. The terrain is generally rocky and undulating, which makes it difficult to regulate the flow of water. Only 52 percent of the net sown area in the district is irrigated. Soils are generally deficient in nitrogen and phosphate which limits crop productivity.

**Khurda,** lying in the coastal plains region of Eastem Orissa, was carved out of Cuttack district in 1993. According to the 1991 census, its total population was just over 1.5 million. More than one-third of the people live in urban areas including the state capital Bhubaneswar. The scheduled caste and scheduled tribe population together make up less than 19 percent of the district's population. The Bengal tenurial system was prevalent in Khurda. Only 21 percent of the district land area is classified as forest land. Soils are fertile loams, and a high proportion (84 percent) of the net sown area is irrigated by canals.

**Ganjam** is the largest district in Orissa, with a total population of 2.7 million. Only 15 percent of the total population reside in urban areas. Ganjam formerly fell under Madras Presidency. Scheduled castes (18 percent) and scheduled tribes (3 percent) constitute 21 percent of the district's population. The forested area of Ganjam district is the same as the state average (36 percent), and the share of net sown area that is irrigated is high (94 percent).

10
most volatile in the case of poor non-LAC countries followed by LAC and high-income countries. This pattern is closely followed with some exceptions for the whole GDP and nonagricultural GDP.

Table 7 presents results from the fixed effects IV and fixed effects without instruments estimators. The results indicate that agriculture and not-agricultural outputs reduce volatility in poor non-LAC and LAC countries. In high-income countries, the coefficients indicate that agriculture contributes to volatility while non-agricultural output decreases it.

The results indicate that the volatility-reducing effect of the non-agricultural sector increases with the level of income for this elasticity is smaller in magnitude for poor-non LAC countries, followed by LAC and high-income countries. The elasticity that captures the effect of agriculture on volatility follows the opposite pattern. The first set of results is consistent with existing analyses of the relationship between economic development and diversification. Imbs and Wacziarg (2003) and Klinger and Lederman (2004) find a consistent pattern related to stages of diversification, based on production, employment, and export data. This pattern follows an inverted U curve with respect to the level of development, whereby countries seem to reach a pinnacle in terms of diversification around $9,000-10,000 dollars of GDP per capita measured in purchasing-power parity terms. Thus our results concerning the agricultural sector are consistent with the finding that poor countries’ growth is associated with greater diversification, which in turn reduces macroeconomic volatility. In contrast, the results for the non-agricultural sector seem puzzling in the light of Imbs and Wacziarg (2003), although it is plausible that the agricultural specialization effects predominate over other diversification effects, especially given the fact that our estimated coefficients are quite small indeed. In other words, it is also possible that the Imbs and Wacziarg results might not hold for all economic activities.

27
# **4. Data and estimation procedure**

We use satellite data from NASA’s MODIS collection (publicly accessible via Google Earth Engine; see Gorelick et al. (2017)) to construct a panel of monthly grid-cell data on forest fire incidences for each of Burkina Faso’s 77 protected forests in the period from January 2003 to December 2019. The MODIS collection has a resolution of 1 km<sup>2</sup> and provides global coverage every one or two days.<sup>4</sup> Our main measure of forest fire incidences is the number of days in a month on which a forest grid cell was detected to be on fire, averaged over all grid cells in a forest. This measure thus simultaneously captures two dimensions of forest fires – the number of grid cells that were on fire in a month, but also the number of days each of the grid cells was on fire in that month. Following the terminology of Giglio et al. (2018) we will refer to this measure as “forest fire occurrences”, which is thus forest- and month-specific.

The resolution of 1 km<sup>2</sup> is small enough to assume that if a fire is detected in a grid cell, most (if not all) of the vegetation is burned (Burgess et al., 2012; NASA, 2019). However, false positives may still arise. MODIS addresses this concern by reporting a confidence level (between 0 and 100%) for each detected fire. Figure 3 presents the pre-intervention fire occurrences, averaged over the pre-intervention period 2003-2013, for each month of the calendar year, using three different confidence thresholds – 30%, 50% and 80%. Consistent with Figure 1, the three panels of Figure 3 show that fires occur mostly in the dry season (November-February), and especially so in November and December, the first two post-harvest months. The panels also show that the mean number of forest