rafmacalaba/data-use-mentions-extended huggingface dataset:
irregular components of compensation. In addition, the information on schooling and detailed occupation makes it possible to examine heterogeneity in the wage effects of revenue shocks across workers in different positions of the wage distribution, and how these effects are shaped by the skills of top executives.

In the empirical analysis, we find that unexpected demand shocks have a significant impact on sales, employment, investment and average wages. Using employer-employee panel data, we find that firm revenue windfalls, in the form of unexpected demand shocks, are partly transmitted to workers in the form of higher average wages, but in a highly unequal way, with most of the wage increases occurring close to the top of the within-firm wage distribution. Interestingly, firm adjustments are much larger for negative demand shocks. More specifically, a demand increase leads to higher sales, exports, investment, employment and wages when actual demand falls short of expectations, while we do not find similar statistically significant responses to demand increases above the expected level. This suggests the presence of short-run capacity constraints that make output harder to adjust upwards than downwards, and our results are consistent with firms setting short-run capacity according to expected demand.

Although parts of the aforementioned wage increases consist of increased overtime payments in exchange for more hours worked, we also find that unexpected demand shocks lead to significant increases in base wages and other wage components. This suggests that that firms tend to share the unexpected changes in revenue with their workers, which is inconsistent with perfectly competitive labor markets, and also suggests that firms have some degree of product market power. Our analysis reveals indications of several different channels of rent sharing. First, we find that increases in base wages are much stronger for firms with firm-level (as opposed to more centralized) wage agreements, which we take as an indication of collective bargaining power. Second, we also find somewhat stronger and more significant wage effects for longer-tenured workers, which might be caused by hiring or training costs that give individual bargaining power to this type of workers. Finally
confident about the accuracy of the LULC types classified as forests and settlements. Forest cover, as used in this study, refers to dense, moderate, and sparsely forested areas as well as woodlands, grasslands, shrublands, orchards, and fruit trees within the study area. The spectral characteristics of areas shown as forests, including grassland and other forest cover types, are quite different from other LULC types’ spectral features. Similarly, as presented below, the NDVI results also validate our confidence level in forest area classification.

# **4.4 Panel Data Construction and Descriptive Figures**

We generate a random data set of 10,000 pixels across Cox’s Bazar. With our LULC classified maps and other GIS inputs (Section 2.1), we derive zonal statistics for each pixel. We use this initial sample to determine an analytical approach that allows us to estimate a valid counterfactual scenario and hence account for land cover changes that would have taken place in the absence of the large population influx. This is especially important since camp locations are not randomly determined (Maystadt & Verwimp, 2014) and since we observe considerable deforestation across the district in earlier years.

Figure **_2_** provides the percentage of pixels classified as forest in our sample by year and distance to the nearest refugee camp border. Prior to camp construction in late 2017, pixels 0-5 km from the nearest camp tended to be heavily forested. To a large extent, this is because several of the camps sit on the border of the Teknaf Wildlife Preserve, a highly forested area (see Figure 1). We also observe high forest cover 15-20 km away: at this distance, many pixels fall within Himchari National Park. Pixels that are 5-15 km from the nearest camp, or 20-40 km from the nearest camp, tend to have lower forest cover on average. From 2010 to 2020, the time series shows a steady decline in forest cover in all the distance buffers.

There appears to be a small rise in
of 2.4. The public system continued to expand slowly but with a growth rate below that of the population.

The period of abject relative decline of the public school system corresponds closely with the reign of the dictator François “Papa Doc” Duvalier, who came to power in 1957. After his son Jean-Claude “Baby Doc” Duvalier took over in 1971, the public system continued to stagnate, but the growth rate of non-public schools accelerated, in part due to a rule instituted by Baby Doc that religious missionaries were required to build an affiliated school with any new church.<sup>8</sup> The expansion of private schools increased further in the late 1980s, after the end of the repressive Duvalier regime in 1986. The years 1994-1999 were a peak period for school construction. This coincides with the first democratic transition, a period of relative political stability, and a national campaign to promote the importance of education. The period was also marked by large investments in social sectors from external aid funds.

As of the 2003 school census, 92 percent of Haitian schools were private, accounting for more than 80 percent of school enrollment, as shown in Table 1. Of the 15,223 schools in the country, 33 percent were in urban areas. Fifty-four percent of schools were “multigrade,” i.e. had more than one grade in the same classroom. Although this policy has emerged out of necessity due to the limited number of classrooms and teachers, it may contribute to poor student performance, due to the low time of attention that every teacher has to give to the students. The School Census also shows that in 2003 only 8 percent of private schools functioned with a license, a government credential which certifies that minimum facility and quality standards are met. The difference in licensing between urban and rural areas reinforces the geographical differences in schooling: 17 percent of the schools located in urban areas are licensed, compared to only 3 percent of rural schools. This may also be a
achieved a post-secondary education level whereas only 7.5% did not start a secondary education course (see Table 1). People with higher education tend to earn higher wages; average salaries in the public sector may be higher as a result of the differential of the education in the labor forces. To take this issue into account, we distinguished between people with higher or lower education while comparing public and privates sector wages. If higher education is one of the drivers of higher wages in the public sector, wage differentials should shrink once we control for the level of instruction. Results in Figure 2 corroborate this hypothesis.

The analysis of the statistical distributions of compensation for each education levels provides additional information (see Figure 3).<sup>10</sup> The public sector distribution for the three highest

> 8 The issue of formality has been considered under the legalistic definition based on the right to a retirement pension linked to employment. Even if data on the right to a retirement pension are not directly available, the existence of a formal contract between employer and employee is considered to be a reasonable signal of formality.

> 9 The analysis of the distribution of public sector and formal private sector compensation allows to compute and compare wage compression ratios in Peru. The compression ratio, computed as the ratio between the 90<sup>th</sup> percentile and the 10<sup>th</sup> percentile of the population, is much higher in the formal private sector (9.4) than in the public sector (3.1).

> 10 Female and male pay distributions for each education level are presented in Annex 1 (Table A1.2 – Panel A and Panel B).

6
the free text field. Additional helpful context for the _Study sampling method: Description_ field could include that, even though the targeted and actual sample sizes are equal, five of the originally selected villages were replaced prior to the randomization for various reasons.

The other elements of the Study Design section provide additional information relevant for the variability and external validity of the treatment effect estimates. First, contributors can select the types of _covariates_ available in the data from a CV, with the decision to indicate types of available covariates rather than variable-level information aiming to not overly burden contributors. After testing different CVs, we propose a CV adapted from GESIS (Hoffmeyer-Zlotnik, 2016) to describe individual-level covariates, and a new vocabulary to describe covariates at the cluster level, such as household or other group-level characteristics. Information on covariates is needed for meta-analyses and can also be useful for methodological research. For instance, Tabord-Meehan (2018) uses data from an experiment on increasing charitable donations by Karlan and Wood (2017) to demonstrate a new method of adaptive stratification that uses information from a first experimental wave to select “stratification trees” in the second wave.

The next field of the schema includes a new optional field that describes which forms of treatment effects _the study was designed to analyze_ . This includes the “intent to treat” effect, average treatment effect, and local average treatment effect or average treatment effect on the treated. Note that the latter two imply that compliance with the treatment assignment is known (see below). A study that is designed to 15
Global Poverty Monitoring Technical Note; no. 7. Washington, D.C.: World Bank Group.

de Soyres, F., Mulabdic, A., and Ruta, M. (2018). The Belt and Road Initiative: A Structural Analysis, Unpublished Working Paper, World Bank.

De Soyres, Francois Michel Marie Raphael; Mulabdic, Alen; Murray, Siobhan; Rocha Gaffurri, Nadia Patrizia; Ruta, Michele. 2018 _. How Much Will the Belt and Road Initiative Reduce Trade Costs?_ . Policy Research working paper; no. WPS 8614. Washington, D.C. : World Bank Group.

Devarajan, S., J.D. Lewis, and S. Robinson (1990), “Policy lessons from trade-focused, two-sector models,” _Journal of Policy Modeling_ , 12(4): 625-657. DOI:10.1016/0161-8938(90)90002-V.

Hummels, D. and Schaur, G. (2013), “Time as a Trade Barrier,” _American Economic Review_ , 103(7): 2945-59.

International Trade Centre (ITC). Market Access Map (MAcMap). 2015. “Tariff Rates for 2014– 2031 between TPP Member Countries absent the TPP Agreement.” Prepared for the Global Economic Partnership Agreement Research Consortium.

Jolliffe, D., & Prydz, E. B. (2016). Estimating international poverty lines from comparable national thresholds. Journal of Economic Inequality. https://doi.org/10.1007/s10888-016-9327-5

- 20 -
_References_ Afridi, F., Dinkelman, T., Mahajan, K., 2018. Why are fewer married women joining the work force in rural India? A decomposition analysis over two decades. _Journal of Population Economics, 31(3),_ 783– 818 Alan, S., Boneva, T., Ertac, S., 2019. Ever failed, try again, succeed better: Results from a randomized educational intervention on grit. _The Quarterly Journal of Economics_ , _134_ (3), 1121-1162.

Altonji, J.G., Pierret, C.R., 2001. Employer learning and statistical discrimination. _The Quarterly Journal of Economics_ , _116_ (1), 313-350.

Andrabi, T., Das, J., Khwaja, A.I., Farooqi, D., Zajonc, T., 2002. Test feasibility survey Pakistan: Education sector. Available at: <u>https://www.researchgate.net/publication/349377986_Test_Feasibility_Survey_Pakistan</u> Andrabi, T., Das, J., Khwaja, A.I., Vishwanath, T., Zajonc, T., 2008. Learning and educational

- achievements in Punjab schools (LEAPS): Insights to inform the education policy debate (Research Report No. 43750), _The World Bank, Washington, D.C_ .

Andrabi, T., Das, J., Khwaja, A.I.,2022, The Learning and Educational Achievement in Pakistan Schools (LEAPS) Longitudinal Dataset, 2004-2011, version 11-2022, released November 1st 2022.

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Lack of innovativeness is also reflected in the small proportion of innovating firms introducing new innovation outputs such as patents (Table 11), although regional innovation leaders present values above the average for the ECA and Africa regions. However, laggards are below international standards. Other innovation outputs such as trademarks or licensing are more important than patents for all the countries considered.

<u>Table 11. Innovation outputs (% of innovating firms)</u>

||**Bangladesh**|**Pakistan**|**India**|**Nepal**|**SARaverage**|**ECA**|**Africa**|
|---|---|---|---|---|---|---|---|
|Patent|8%|2.2%|8%|1.3%|5%|2.5%|6%|
|Other than patents|18%|5%|32%|1.7%|14%|-|17%|

Source: Author’s elaboration from Enterprise Survey (2014) <u>Table 12. Links between innovativeness and firm characteristics</u>

|||**Bangladesh**|**India**|**Nepal**|**Pakistan**|
|---|---|---|---|---|---|
|No innovators|age|18.70|22.54|13.65|22.15|
|Imitators|age|18.42|20.39|18.21|34.24|
|radical|age|16.46|20.51|16.57|28.25|
|No innovators|size|162.09|95.99|12.89|168.
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31
households and Muslim households within geographic areas.<sup>5</sup> This paper estimates that, while there is a substantial unconditional association between religion and sanitation practices, a comparison of Hindu households and Muslim households within similar locations finds more similar sanitation practices. The estimates suggest that household religion itself has less influence on sanitation practices, as compared to characteristics of household location. Sanitation practices are substantially better in urban settings, with better access to sanitation infrastructure, for both Hindus and Muslims.

The paper proceeds as follows. Section I describes the data, and Section II presents descriptive statistics. Section III describes the estimating equations, and Section IV presents the results. Section V concludes.

# **I Databases on Indian Households: IHDS, DLHS-3, NFHS-3**

The analysis uses data from three nationally-representative surveys of households in India: the first wave of the Indian Human Development Survey 2004–05 (IHDS), the third wave of the District Level Household and Facility Survey 2007–08 (DLHS-3), and the third wave of the National Family Health Survey 2005–06 (NFHS-3). These data sets each contain different information on sanitation practices, and provide complementary insights on the relationship between sanitation practices and religion. The IHDS includes the most data on various sanitation practices, but is a smaller sample. The DLHS-3 has more households, but has fewer detailed questions on sanitation practices. The NFHS-3 provides an opportunity to follow migrants’ behaviors, though district-level identifiers are not available.

Three sanitation practices are studied: (1) whether an individual or household owns or uses a latrine, (2) whether the respondent washes hands with soap after defecation, and (3) whether the interviewer observed fecal matter (human or animal) around the dwelling of the respondent at the time of the survey.<sup>6</sup> The analysis is limited to households in which the

> 5There is also evidence of differences in contraceptive use among Muslim women and Hindu women who report not wanting
# **3. Implications of public investment quality for sovereign risk and debt sustainability**

The objective of this section is to test the fiscal implications of public investment quality.

# **3.1. Conceptual framework**

One can envision several transmission channels through which public investment quality may impact public debt sustainability and sovereign risk dynamics in the context of the conventional (r−g) framework. Consider the standard debt dynamic equation: where B and Y are public debt and GDP levels, respectively; _r_ and _g_ are the real interest rate and real output growth rate; and pb is the primary balance as a share of GDP.

Public investment boosts both short-run aggregate demand, and high-quality public investment also boosts long-run aggregate supply via improvements in productivity. Large public investment multipliers for countries with greater public investment efficiency are documented by Adarov (2024), Daude (2011), Furceri and Li (2017), Gupta et al. (2014), Izquierdo et al. (2018), Leduc and Wilson (2012), and Leeper et al. (2010).

There can also be important fiscal transmission channels because scaling up of high-quality public investment may decrease sovereign risk (and the other way around for low quality public investment). Thus, better public investment quality may improve debt sustainability by boosting g and reducing r. There can also be a positive impact on the primary balance because high-quality public investment is likely to yield infrastructure that is less costly to maintain, and vice versa.<sup>19</sup>

# **3.2. Data and sample**

We use the PIQ index to gauge whether public investment quality affects the relationship between public investment and sovereign risk in EMDE countries.<sup>20</sup> We conduct this exercise by using two measures of sovereign risk: sovereign ratings and 5-year CDS spreads.<sup>21</sup> Sovereign ratings reflect an independent assessment by a credit rating agency of the — creditworthiness of
7 Figure 1: Modern input use in SSA is not uniformly low <!-- Start of picture text --> % %<br>100 Share of cultivating households using 100 Share of cultivating households<br>inorganic fertilizer in main season using agro‐chemicals<br>90 90<br>77<br>80 80<br>70 70<br>56<br>60 60<br>50 41 50<br>40 35 40 31 33<br>30 30<br>17 17 16<br>20 20 13 11<br>8<br>10 3 10 3<br>0 0<br><!-- End of picture text --> <mark>Note: Agro‐chemicals include pesticides, herbicides and fungicides</mark> <mark>Source: Sheahan and Barrett, 2017.</mark> One in six farmers also uses agro‐chemicals, rising to one in three in Nigeria and Ethiopia—related to the more widespread use of herbicides in these countries.<sup>9</sup> These rates are substantially higher than the available numbers in the literature, and come somewhat as a surprise. It prompted follow up work, also using the LSMS‐ISA data, which suggests a strong correlation of pesticide use with increased value of the harvest, but also with increased health expenditures and time lost from work due to sickness. This draws attention to the health implications of increased agro‐chemical use in SSA and the need for effective regulatory policies as areas for future attention (Sheahan, Barrett, and Goldvale, 2016). There are also signs of improved seed use, especially for maize, with 24 to 41 percent of maize growers reporting seed purchases, though these are likely lower bounds.<sup>10</sup> Input use appears also no longer confined to traditional cash crops, with input intensification (fertilizer, pesticides, improved varieties) now equally (and
<!-- Start of picture text --> 3,000 4,000<br>2,500<br>2,000 3,000<br>1,500<br>1,000 2,000<br>500<br>0 1,000<br>-500<br>-1,000 0<br>-1,500<br>-2,000<br>-1,000<br>1 2 3 4 5 6 7 8 9 10<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of per capita household PRE-FISC income<br>Deciles of per capita household PRE-FISC income<br>In-Kind Health Transfer Health User Fee Net health transfer<br>In-Kind Edu.Transfer Edu. User Fee Net Edu Transfer<br>Yuan RMB Yuan RMB<br><!-- End of picture text -->

# C. Average Spending on Private Education by Decile

<!-- Start of picture text --> 250<br>200<br>194<br>156<br>150 135 134 131<br>116 114 115 116<br>106<br>100<br>50<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of per capita household PRE-FISC income<br>Yuan RMB<br><!-- End of picture text -->

**Third, cash benefits are low but are essential for those at the bottom of the distribution** . For the poorest households, 20 percent of their final income comes from Residents’ pension benefits (11 percent) and cash transfers (9 percent). The average amount received by the first decile is also higher than for the rest of the deciles, in absolute terms. Despite the relatively modest cash amounts of both residents’ pension and cash transfers, they do help lower
49

**Table 5.3: Evolution of (Real) Per Capita Expenditure by Type and Geographical region (Poverty Line=N395.41)**

||**NORTHE**|**RN BELT**|**MIDDL**|**E BELT**|
|---|---|---|---|---|
|**Expenditure Type**|**85/86**|**92/93**|**85/86**|**92/93**|
|Own Consumption|54.72|168.24|49.77|344.92|
|Food Cash Expend.|309.14|271.23|324.37|142.50|
|All Food Expend.|363.86|439.47|374.14|487.42|
|Non Food Expend.|174.54|194.44|177.50|266.14|
|P. C. Expend.|538.40|633.91|551.64|753.56|
||**SOUTHE**|**RN BELT**|**ALL NI**|**GERIA**|
|Own Consumption|39.46|304.18|44.74|263.55|
|Food Cash Expend.|371.10|317.15|346.89|267.27|
|All Food Expend.|410.56|621.33|391.62|530.82|
|NonFoodExpend.|245.86|313.15|201.19|261.82|
|P. C. Expend.|656.42|934.48|592.81|792.64|
|Source: NCS, 1985/86 and|1992/93.||||