rafmacalaba/datause-encoder-data huggingface dataset:
# **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 fire occurrences is similar in the (then yet to be) treated forests and in the 65 other classified forests (depicted by the continuous and dashed lines, respectively). Also note that the variance around these means is quite substantial (as indicated by the

> 4 We preferred MODIS over NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) as the latter data are only available from 2012 onwards. Hence, using FIRMS would not allow us to properly train the SCM because of the paucity of pre-intervention observations.

13
homogeneous group than the world countries as a whole, Figure 1 (bottom panel) suggests that there are regional factors that should be able to explain the low FPR in the region. Hence, the behavior of regional grouping is something we should consider more closely below.

# **Figure 1 - Female Labor Participation and GDP per capita (World and Mena countries, age 15 and more)**

<!-- Start of picture text --> World 1990 World 2000 World 2010<br>MA MA MA<br>4 6 8 10 12 4 6 8 10 12 4 6 8 10 12<br>MENA 1990 MENA 2000 MENA 2010<br>ISR QAT<br>ISR<br>KWT AREKWT<br>ISR<br>QAT BHR<br>BHR ARE<br>DJI<br>YEM DJIMARSYREGY TUN LBN OMNBHRSAU ARE YEM MARJORSYREGY IRQDZATUNIRN LBN LBY OMNSAU YEM MARSYRJORIRQEGYDZATUN IRNLBN OMNSAU<br>JOR DZAIRN<br>7 8 9 10 11 7 8 9 10 11 7 8 9 10 11<br>Log GDP per Capita<br>100 100 100<br>50 50 50<br>0 0 0<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br><!-- End of picture text --> Source: ILO-EAPEP database (Female LFP) and World Bank, World Development Indicators (GDP per capita).

Figure 1 depicts MENA countries as a group of countries that are transiting through the saddle point and towards the upper part of the U curve. This picture is also consistent with the level of GDP per capita. As we saw in the literature review, historically the transition from the downward to the upward slope occurs at levels of GDP per capita around 2,000-3,000 USD. If we exclude resource rich countries for which GDP per capita is a poor indicator of progress, many of the MENA countries considered found themselves around that threshold at the beginning of the period considered (1990). For example, Egypt, Morocco, Jordan, Syria, Yemen and Djibouti had all a GDP per capita in PPP 2005 terms below 4,000 USD in 1990.<sup>4</sup>

> 4 World Bank open data. See data section for more details.

8
**Table 2:** Supply-side Measures and Community Support

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>Difference|(6)<br>Joint<br>_F_-test (_p_)|(7)<br>N|
|---|---|---|---|---|---|---|---|
|Clinic quality|0.000|0.104|_−_0.004|0.213|_−_0.216|0.929|254|
||(1.000)|(0.149)|(0.175)|(0.176)|(0.184)|(0.397)||
|||[0.395]|[0.649]|[0.237]||||
|Health service delivery|0.000|0.039|0.070|0.027|0.043|0.507|2877|
||(1.000)|(0.059)|(0.071)|(0.062)|(0.059)|(0.603)||
|||[0.395]|[0.266]|[0.583]||||
|Community support|0.000|0.007|0.027|_−_0.013|0.040|0.062|508|
||(1.000)|(0.095)|(0.112)|(0.109)|(0.116)|(0.940)||
|||[0.891]|[0.564]|[0.619]||||
|CDPE|0.000|0.231|0.202|0.261|_−_0.059|3.849|508|
||(1.000)|(0.085)<sup>_∗∗∗_</sup>|(0.102)<sup>_∗∗_</sup>|(0.101)<sup>_∗∗_</sup>|(0.110)|(0.023)<sup>_∗∗_</sup>||
|||[0.034]<sup>_∗∗_</sup>|[0.095]<sup>_∗_</sup>|[0.032]<sup>_∗∗_</sup>||||

_Notes:_ Treatment effects are estimated using ANCOVA, controlling for the community-level average of the outcome family index at baseline and matchingtriplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets. Column (5) presents an _F_ -test for the joint significance of CM and NFA, with the associated _p_ -value in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.
Figure A.2: Validation with SICONFI data: verification phase, by function <!-- Start of picture text --> CE MG<br>10000 100000<br>7500 75000<br>5000 50000<br>2500 25000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PR<br>30000<br>8000<br>6000 20000<br>4000<br>10000<br>2000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>RS SP<br>30000 60000<br>20000 40000<br>10000 20000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data % difference from SICONFI data<br>Frequency<br>Frequency Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage difference of verification data from the budget execution dataset in relation to data on verified expenses from SICONFI, as described in Section 3.

Figure A.3: Validation with SICONFI data: payment phase, by function <!-- Start of picture text --> CE MG<br>6000 80000<br>60000<br>4000<br>40000<br>2000<br>20000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PR<br>10000 20000<br>7500 15000<br>5000 10000<br>2500 5000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>RS SP<br>30000 60000<br>20000 40000<br>10000 20000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data % difference from SICONFI data<br>Frequency<br>Frequency Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage difference of payment data from the budget execution dataset in relation to data on paid expenses from SICONFI, as described in Section 3.

37
# **TABLE 15. Total Employment**

||F|E|FE‐|2SLS|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|0.076***|0.073***|0.60***|0.60***|
||(0.011)|(0.011)|(0.14)|(0.14)|
|ICT|0.0072|0.014|0.50***|0.50***|
||(0.014)|(0.014)|(0.15)|(0.15)|
|ICT * High Growth|0.12***|0.10***|0.22***|0.22***|
||(0.015)|(0.015)|(0.037)|(0.037)|
|Observations|3,571|3,571|3,571|3,571|

Dependent variable: change in total employment. Regressors: ICT investment dummy, ICT investment dummy interacted with a firm‐level indicator variable that is equal to one when the growth in revenue during the sample period is above the median (High Growth). Instruments defined as in Table 3. Robust standard errors in parenthesis.

39
43

|**Sectors**|**Modes**|**Categories**|**Measures**|**Key restrictions**|
|---|---|---|---|---|
|||Air passenger international|BASA publicly available||
|||Air passenger international|Total number of BASA-s in use|Information not<br>available from the|
|||Air passenger international|Air transport agreements with seat and/or frequency limitation|survey. Policy<br>|
|||Air passenger international|Air transport agreements granting 5th freedom rights|information and<br>restrictiveness index|
|||Air cargo international|Open regime|came from the<br>|
|**TRANSPORTAT**|**MODE 1**|Air cargo international|Regulated through BASAs|WTO's QUASAR<br>database.|
|**ION (AIR**||Air cargo international|Other conditions||
|**PASSENGER,**<br>**CARGO**||Maritime shipping international|Quotas for private cargo: liner|YES|
|<br>**DOMESTIC**||Maritime shipping international|Quotas for private cargo: bulk|YES|
|**AND**<br>**INTERNATION**||Maritime shipping international|Quotas for government cargo: liner|YES|
|**AL, MARITIME**||Maritime shipping international|Quotas for government cargo: bulk|YES|
|**SHIPPING, AND**<br>||Maritime shipping international|Exemption of carrier agreements from competition law|YES|
|**MARITIME**<br>**AUXILIARY**<br>**SERVICES)**||Maritime auxiliary services|Are foreign shipping firms permitted to establish their own facilities in the main<br>port and supplythe followingservices for their own ships?|Not covered|
|||Maritime auxiliary services|Cargo handling|YES|
|||Maritime auxiliary services|Storage and warehousing|YES|
||**MODE 3**|Maritime auxiliary services|Container station|YES|
|||Maritime auxiliary services|Maritime agency|YES|
|||Maritime auxiliary services|Customs clearance|YES|
|||Maritime auxiliary services|Freight forwarding|YES|
# **1. Motivation**

East Asia is undergoing a deep structural change with employment in the region rapidly changing from agricultural activities into manufacturing and services, and from resource-based products to labor-intensive low technology products. Moreover, over the last decade, the share of skilled workers has also been increasing significantly, even within narrowly defined sectors. In some rapidly-growing economies, emerging skill shortages threaten to undermine the competitiveness of export-oriented firms with rising skills premiums. This paper investigates the extent to which the increasing openness (captured by exports and foreign direct investment) and technological innovation have increased the demand for skills. We explore a large cross-sectional micro data set for firms in the East Asia region.

Our results are consistent with greater openness and technological innovation increasing the demand for skills especially in middle-income countries. However, this is not the case for firms operating in China and in other low-income countries. There, a stronger integration of firms into the international markets is still associated with a greater specialization in low skilled intensive goods. These findings strongly suggest that international integration and the technology transferred to middle-income countries tends to be more skilled biased than for low-income countries. This is also consistent with middle-income countries having a higher absorptive capacity and, thus, being recipients of more advanced technological innovations than low-income countries.

The recent decades have been characterized in many developing countries by an increased income inequality favoring the most skilled workers. In particular, income inequality between skilled and unskilled workers increased in several countries after trade liberalization (e.g., Harrison and Hanson 1995, 1999, 1999a; Revenga, 1997; Robbins 1996). This has become a particularly severe problem in developing countries because of the potential negative social consequences associated with high poverty levels and income inequality. Moreover, a large chunk of worker reallocation has occurred within industries (between low and high skilled workers), rather than across industries (e.g., Harrison and Hanson 1995; Robbins 1996). This evidence suggests that there has been a simultaneous increase in the share of skilled labor (in total employment) and an increase in the returns to skills (e.g., Harrison and Hanson 1995; Robbins 1996) following 2
<!-- Start of picture text --> South Africa<br>Change in TFP<br>8<br>7*<br>2<br>8e<br>e.<br>weight Employment<br><!-- End of picture text --> <!-- Start of picture text --> South Africa<br>Change in TFP<br>=3<br>5<br>53<br>e<br>z= —_f = @<br>S<br>°<br>°<br>#2 € @ 3 to GoGo 4<br>@: =2 e 8 as 338 a3 é<br>= 5g 3s2 a<br>weight Employment<br><!-- End of picture text -->
# **Figure 48: Chinese Corporate, Government, and Household Savings to GDP**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>Government savings Household savings Corporate savings<br><!-- End of picture text --> Source: Flow of Funds Account Tables, China Statistical Yearbook, various issues; National Bureau of Statistics of China.

101. First, large state-owned enterprises have kept their profits as corporate savings.<sup>78</sup> In 1994, during the reform of taxation, a decision was taken that the government would not collect profit from SOEs for an unspecified period of time. In this context, a number of factors may have played a role. For one, this decision seemed a natural extension of the logic prevailing in SOE reform in the 1980s, which emphasized the independence of SOEs from the government. Second, the central theme of SOE reform during 1992-1994 was to increase managerial autonomy and reduce government intervention, as was evident in the State Council decision on ―Regulation on Transformation of Management Mechanisms of SOEs‖ in 1992. Collecting dividends from SOEs would appear naturally as moving in the opposite direction. Third, without corporatization and some minimum progress in corporate governance, it would be practically difficult for the government to determine an acceptable rate of dividend without reopening the negotiation with the management of each SOE on profit division. Fourth, SOEs as a whole were in a poor financial situation in early 1990s. What the government could collect was not much, while the need for new capital injection was perceived as urgent and large. In 1994, the total profit of industrial SOEs was only 1.8 percent of GDP, which compared with 3.3 percent of GDP in 2003.

102. Second, China‘s financial structure is dominated by four big state-owned banks (Figure 49). Those big banks and China‘s equity market only provide access to financial services to large enterprises, either owned by the state or by wealthy individuals. Small and medium enterprises, which are very labor intensive, do not have access to any financial services.<sup>79</sup> This situation generates two implications for income distribution. On the one hand, China has an abundant

> 78 Kuijs, Mako and Zhang (2005)..

> 79 Lardy (2007).

65
|||Mean|||Difference||
|---|---|---|---|---|---|---|
||grid|SHS|solar|grid|grid|SHS|
||||lamp|vs.|vs.|vs.|
|||||SHS|solar|solar|
||||||lamp|lamp|
|Log<br>of<br>monthly<br>expenditure<br>in|5.41|5.24|5.04|0.17<sup>***</sup>|0.37<sup>***</sup>|0.2<sup>***</sup>|
|constant 2011 international $ (PPP)|||||||
|Expenditure quartile 2|0.23|0.24|0.27|‐0.01|‐0.04|‐0.03|
|Expenditure quartile 3|0.23|0.27|0.24|‐0.04<sup>*</sup>|‐0.01|0.03|
|Expenditure quartile 4|0.31|0.26|0.20|0.05<sup>**</sup>|0.11<sup>***</sup>|0.06<sup>***</sup>|
|Rwanda|0.43|0.54|0.58|‐0.11<sup>***</sup>|‐0.15<sup>***</sup>|‐0.04<sup>*</sup>|
|Senegal|0.25|0.17|0.17|0.08<sup>***</sup>|0.08<sup>***</sup>|0|
|Log of the initial bid in constant 2011|3.39|3.12|2.65|0.27<sup>***</sup>|0.74<sup>***</sup>|0.47<sup>***</sup>|
|international $ (PPP)|||||||
|Number of household members|7.93|7.13|7.03|0.8<sup>***</sup>|0.9<sup>***</sup>|0.1|
|Head of householdʹs age|41.74|42.19|41.11|‐0.45|0.63|1.08|
|Head of household is a female|0.22|0.24|0.31|‐0.02|‐0.09<sup>***</sup>|‐0.07<sup>***</sup>|
|Head<br>of<br>household<br>has<br>formal<br>education|0.48|0.56|0.54|‐0.08<sup>***</sup>|‐0.06<sup>**</sup>|0.02|
|Household has a bank account|0.34|0.36|0.29|‐0.02|0.05<sup>*</sup>|0.07<sup>***</sup>|
|Household has contracted formal<br>credit|0.34|0.32|0.27|0.02|0.07<sup>***</sup>|0.05<sup>**</sup>|
|_N_|481|757|684|1,238|1,165|1,441|

Note: *** _p_ <0.01 ** _p_ <0.05 * _p_ <0.1 denote statistical significance.

# **4. Results**

# _4.1 Households’ Stated WTP_

Figure 3 presents the WTP of households by country and electricity access technology. We can see that WTP increases in the intensity of electricity access technologies: households are willing to pay the most for the grid access ($9.6 to $22.3), followed by the small solar home systems ($7.1 to $15.3), and solar lamp ($4.5 to 14.3). In absolute terms, the average household WTP is higher in Senegal 20
# **<u>3.5.2. Teacher pay and other aspects of the economy</u>**

How do teacher earnings differ as structural aspects of economies differ? We explore the correlations between teacher earnings differentials and key macroeconomic indicators like growth in GDP and GDP per capita; labor market indicators like formal sector size and female labor participation; and political economy factors such as the level of teacher unionization.<sup>35</sup> None of these indicators shows a statistically significant association with either the monthly or the hourly earnings differentials for teachers. We also examine the correlation between teacher earnings premia and teachers as a fraction of the labor force, and the change in teachers as a fraction of the labor force and find no statistically significant relationship. (All these correlations and their statistical significance are reported in Appendix Table A23.) Thus, we see no evidence that teacher wage differentials are more pronounced in richer economies, more formal economies, economies with more women in the labor force, or even with teacher unionization. The lack of association between teacher unionization and teacher wage differentials is consistent with evidence from the United States (Frandsen 2016; Paglayan, 2019).<sup>36</sup>

# **4. Conclusion**

Teacher pay makes up the vast majority of education budgets in many African countries (Ugandan Ministry of Education and Sport and UNESCO 2014; World Bank 2018). In this paper, we present new evidence on teacher earnings across a set of 15 Sub-Saharan African countries. We document key descriptive statistics about the level of teacher earnings and compare this level to earnings for other workers of comparable education and experience in the same economies. Our results may explain part of the contention between those who argue that teachers are paid too little and those who argue that teachers are paid too much (Das, 2017). In several countries in our sample, teachers earn less than comparable workers in terms of their monthly earnings, but they earn more than others per hour. In several countries, the earnings gap between teachers on fixed term and permanent contracts is large. Teachers are much more likely to take a second job than other

> 35 The macroeconomic indicators and labor market indicators are drawn from the World Development Indicators database (World Bank, 2019c). The level of teacher unionization is derived from the survey data used in this paper. A teacher is considered to be a union member if she answered yes to a survey question like “Do you belong to a union?”

> 36 Frandsen (2016) does find that increased unionization reduces teacher hours in the United States.

21
The results confirm the positive and statistically significant innovation wage premium, and the fact that this exists irrespective of size. The premium is larger for large firms, but positively and significant at 1% level for all size groups, even after controlling for within size group firm size and worker effects. Workers in micro firms, with less than 10 workers, also have a wage premium when working in innovative firms. The results, therefore, provide evidence of both an innovation premium and large firm wage premium.

Table 6: Private Sector: Innovation Premium per firm size, 2014-2017

|||Dependent va|riable: (_wijkft_)||
|---|---|---|---|---|
|Firm size|(1)<br>Micro (_<_10)|(2)<br>Small (10-49)|(3)<br>Medium (50-99)|(4)<br>Large (_>_99)|
|Innovation|0.0713***|0.132***|0.139***|0.171***|
||(0.00476)|(0.00710)|(0.0198)|(0.0258)|
|Age Squared|0.106***|0.171***|0.242***|0.358***|
||(0.00214)|(0.00258)|(0.00796)|(0.0113)|
|Tenure|0.0167***|0.0229***|0.0244***|0.0294***|
||(0.000101)|(0.000132)|(0.000384)|(0.000989)|
|Temporary|0.00796|-0.00490|-0.0372***|-0.0325***|
||(0.0138)|(0.0247)|(0.00688)|(0.00540)|
|Firm size|0.0242***|0.0368***|0.0588***|0.0172***|
||(0.000224)|(0.000498)|(0.00265)|(0.00166)|
|Individual-Year|Yes|Yes|Yes|Yes|
|Observations|20453784|24511458|7462266|57445724|
|R-squared|0.914|0.919|0.940|0.940|

_Note:_ Innovative firms is measured by the share of PoTec workers in total employment for firm _f_ in year ** _t_ . Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

# **4.4 Innovation premium by skill level**

The literature on skill-biased technical change has emphasized the role of technology in increasing labor polarization and depressing the incomes of low-skilled workers (Acemoglu and Autor, 2011). This empirical literature suggests that the returns on high skill workers should be larger on average than for other skills. One exception to this evidence is Aghion et al. (2019), who develop a model where low and medium-skilled workers that possess soft skills that are complementary to high skill workers in innovative firms. The authors using data from the United Kingdom find empirical support to the hypothesis of large complementarities, and a larger wage premium associated with working in an innovative firm for low and medium-skilled workers. Thus, identifying how the wage premium is related to the worker’s skill level is critical to understand the distributional impacts of innovation, especially in a 20