rafmacalaba/data-use-mentions-extended huggingface dataset:
∆log _yi,t_ = _α_ + _β_ ∆ _satellitedatai,t_ + _νi,t_ where the operator ∆denotes the change between _t_ and _t −_ 1 and _νi,t_ = _εi,t − εi,t−_ 1 is the potentially serially correlated error term. The key questions of interest are whether the parameter _β_ is stable between the cross-sectional regressions in Section 3 and the analysis in the time series and whether the reduced variation in the satellite measure has enough predictive power for economic activity at the commune level.

The data contains the number of enterprises and employment for the years 2004 to 2012, and expenditure for the years 2004, 2008, 2010, and 2012. However, inconsistencies in the commune boundaries and changes in the sampling frame of the surveys lead to problems in the comparability of the data. The analysis therefore explores the predictive power for changes of different horizons. Potential serial correlation is dealt with by estimating equation (2) both in a panel framework as well as in long differences of different length.The analysis focuses on medium-run changes of enterprise counts and employment from 2004 to 2012. For the change in expenditure, results are reported for two time frames that correspond to the sampling frameworks of the VHLSS: a four-year period between 2004 and 2008 (based on the 1999 population census) and a shorter horizon from 2012 to 2014 (which both were based on the 2009 population census).

# **4.2 Empirical Results**

## **4.2.1 Nightlights and Landsat Indices**

Table 4 summarizes the results for the different specifications of equation (2) for the combined nighttime lights and the simple Landsat indices. In contrast to the cross-section regressions, the results show very low predictive power and parameter instability for predict20
uses quarterly data, adjustments are conducted to correct for seasonal fluctuations, except for the US federal fund rate and domestic lending interest rate. CPI is used to convert nominal GDP into real GDP.

# **4.2 Unit root tests**

As mentioned above, we need to apply unit root tests to the variables under consideration to identify their integration orders. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests are employed. The results are provided in Table 1. From the table, while _z_ , _gygap,_ and _infl_ are I(0) (i.e. variables having integration order of 0), _ffr_ , _bmgdp_ , _ttbgdp_ , _tbeugdp_ , _tbusagdp_ , _lendint_ , and _intresvgdp_ are I(1) (i.e. variables having integration order of 1).

> 9 The sample periods are different by specification, depending on data availability.

10
1 2 3<br>Score Score<br>18-24 yrs old 25-30 yrs old 18-24 yrs old 25-30 yrs old<br>31-40 yrs old 41-50 yrs old 31-40 yrs old 41-50 yrs old<br>. .<br>.5 .5<br>.4 .4<br>Density .3.2 Density .3.2<br>.1 .1<br>0 0<br>.6 .6<br>.4 .4<br>Density Density<br>.2 .2<br>0 0<br>.4 .5<br>.4<br>.3<br>.3<br>.2<br>Density Density .2<br>.1<br>.1<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Note: The only densities that are significantly different from others are those of consciousness for individuals aged 18 to 25 years old compared to any older group and for individuals aged 25‐30 years old compared to individuals older than 40 years old; those of kindness for all age groups when compared to individuals older than 40 years old; those of extraversion, emotional stability, and perseverance of effort for individuals aged 18‐24 compared to any older groups; those of openness between individuals aged 25‐30 years old and older than 40; and those of consistency of interest for individuals in the age range of 25‐30 compared to older groups.

**28 |** P a g e
of Madagascar, that isolation (defined as travel time during dry season from the commune center to the nearest urban center) implies lower agricultural productivity, increased transport and transaction costs and increased insecurity. The authors found a major jump of per capita consumption from the least remote quintile to the second quintile and therefore a negative relationship between isolation and poverty.<sup>5</sup> So far, most development partners and governments in SSA have mainly relied on the assumption that most households in rural areas in Africa are not connected to markets by paved or passable roads for motorized transport and therefore need a road passable for a truck/bus. Many investments in rural roads seem to be built on the assumption that they will lead to market provision of transport and thus, poverty reduction and income generation. Estache (2010) points out a lack of rigorous evidence on these assumptions; namely there is a lack of randomized impact evaluations in the field of investments in roads, which call into question these assumptions and limit the ability to quantify the economic and even social benefits of the provision of rural roads.

In fact, there is some empirical evidence that calls into question current transportation strategies in Sub-Saharan Africa. Using the second Cameroonian national household survey (Enquête Camerounaise Auprès des Ménages II, 2001) and the Cameroon case study, Gachassin et al. (2010) demonstrate that investing in tarmac roads is likely to have a low impact on poverty. For example, isolation from a tarred road is found to have no direct impact on consumption expenditures in Cameroon. The paper reasserts the fact that access to roads is only one factor contributing to poverty reduction (and not necessarily the most important). Considering that increase in non-farming activities is the main driver for poverty reduction in rural Africa, the authors suggest that emphasis on roads investments should be given to locations where nonfarming activities could be developed, which does mean that the last mile in rural areas should not be probably a road with a high road level of service (except
Another geographic aspect of migration is to look at the issue of whether inmigrants are from same province or other provinces. Table 7 suggests that in Zhejiang, about 41 percent of towns in-migrants were from other provinces. In contrast, in Henan and Sichuan, majority of migrants were from same province, and there was only about 8 percent from other provinces.

Table 7 Town in-migration by province and region: 2000

|||Zhejiang|Henan|Sichuan|
|---|---|---|---|---|
|||(%)|||
|From same p|rovince|59.08|91.93|92.68|
|From other p|rovinces|40.92|8.06|7.32|
|||100.00|100.00|100.00|
|Of which:|coast|11.03|38.33|22.79|
||central|60.74|34.00|15.47|
||west|28.23|27.67|61.74|
||total|100.00|100.00|100.00|

________________________________________________________________________ Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table L703b, and Table L704b.

# Note: regional classification:

Coast includes following provinces: Heilongjian, Jinin, Liaoning, Beijing, Tianjian, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujiang, Guangdong, Guanxi and Hainan.

Middle includes following provinces: Nei Mongol, Shanxi, Henan, Anhui, Hubei, Hunan and Jiangxi. West includes following provinces: Xingjiang, Qinghai, Gansu, Ningxia, Shaanzi, Sichuan, Guizhou, Yunnan and Tibet.

Table 7 further shows that among those of inter-provincial in-migrants in Zhejiang, majority (89%) came from central and western regions. Thus, many inmigrants in fact crossed provincial lines and traveled a quite long distance
# **E. Change in fiscal balances three years after major events**

# **F. Change in government debt three years after major events**

<!-- Start of picture text --> Percentage points of GDP<br>1.5 Small states EMDEs excl. small states<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>Natural disasters Global recessions<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points of GDP<br>8 Small states EMDEs excl. small states<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>Natural disasters Global recessions<br><!-- End of picture text --> _Sources:_ EM-DAT (database); WEO (database); World Bank.

_Note:_ EMDEs = emerging market and developing economies. Panels show differences in average fiscal balance and debt as a percent of GDP in EMDEs compared with those in the year preceding events occurring in year _t=0_ . Events include global recessions (2009 and 2020) or natural disasters resulting in damages of at least 5 percent of GDP. Natural disasters include droughts, earthquakes, extreme temperatures, floods, storms, volcanic activity, and wildfires. In five cases where natural disasters coincide with global recessions, they are attributed to either natural disaster or global recession events and not both, to avoid double counting. Results are robust to alternative assumptions.

A.-B. Difference in average fiscal balance and debt as a percent of GDP in small states compared to year preceding a natural disaster occurring in period _t=0._
This is however just one aspect of the story. The effects of mobile money on the formal private sector is still ripe for research. Several questions remain unanswered regarding various aspects of firm outcomes including productivity and profitability. Much of this research has been restricted by the lack of reliable data that hampers inferences both at the country-level and across countries. Thus there is an urgent need to promote data collection ventures to provide nationally representative data following a consistent methodology across countries. The Enterprise Surveys is an ideal example of the type of data needed.

Finally, policy makers may be able to draw larger inferences about technology in general from studies on mobile money and the formal private business sector. The introduction of mobile money may have similar attributes as other forms of technology, and the findings garnered from the mobile money literature could provide useful in informing the potential impact of new technologies. Given the importance of technology in development, research on the interaction between technology and the private sector is crucial. We hope this study encourages further research in this area.

15
# **3. ANALYSIS**

Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years.

The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “recent migrant” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources. In that respect, the regression dos not suffer from the small sample size or any other limitations relating to the estimation of poverty rates for recent migrants.

The year 2013 is selected since there is available administrative information on the stock of Syrian refugees across regions. The choice of 2011 is rather arbitrary since we assume no foreign-born population in the year 2011 or before. The explanatory variable of interested is the proportion of SUTPs relative to the population (���). The assumption is that the proportion equals zero for all regions in 2011. This is a simplistic but reasonably accurate estimate since the majority of SUTPs arrive in 2012. Moreover, official estimates of the number of Syrians in Turkey were also not available until starting in 2012. Overall, in 2013, Syrians were still very concentrated in only a few regions. Out of 26 regions, Syrians comprised more than 1 percent of the population in only 7 regions. Estimations
XX uu in the same community in the same manner, and i captures the indirect effect of covariate shocks. Equation (4a) may be rewritten as equation (4b) below whereby the term 1ii i , the indirect uu XX effect of covariate shocks, introduces a random component (varying by j) to the coefficient of 1ii i : uu XX l i 00 01 10 i 11 i i (4b) XX ii 1ii ii 0ii cc = γγ + γγ ZZ + �γγ + uu �XX + γγ ZZ XX + uu + ee

> 8 A toolkit and a manual for the implementation of this two-level hierarchical model of consumption for the estimation of vulnerability to poverty based on cross-sectional data from other countries can be accessed at <u><mark>Equity Policy Lab – Intranet.</mark></u>

8
, going from 18.17% for high-risk firms to 2.05% for low-risk firms (Panel A, Column 4).<sup>27</sup> We then calculate a measure of total credit risk (i.e., expected loss) for each bin size by multiplying the dollar value of program loans by the predicted default probability. As a proportion of GDP, the total credit risk corresponds to an expected credit loss of 0.27% (Panel A, Column 8). The contribution to expected loss is similar across risk categories because the larger amounts granted to low-risk firms and their smaller default probability compensate across buckets. When considering all guaranteed loans to formal firms and natural persons, the expected credit loss is 0.44% of GDP (Panel B, Column 8), which corresponds to a 9.56% default probability of the guaranteed credit (0.44%/4.6% = 9.56%) (Panel B, Column 4). This estimate of expected loss seems large relative to the credit granted because it is 27A negligible fraction of firms do not have sales data and therefore cannot be classified by size. Likewise, a small fraction of firms do not have data to be classified in a risk category. To be conservative, we assume that firms with no sales or risk data (including naturals persons) have the default probability of the riskiest group of 23
For both the Malawi and Uganda, the entire sample of households from the latest round of the LSMSISA which had contact information served as the HFPS sample. In Malawi, the initial HFPS sample consisted of 2,337 households while for Uganda it consisted of 2,421 households. The crude contact and response rate was very high in Uganda where 93 percent (2,259) of sampled households were successfully interviewed. Malawi’s contact and response rates were slightly higher than Nigeria and Ethiopia with 74 percent (1,729) of sampled households being successfully interviewed.

# <mark>3.1.2 Sampling Weight and Bias Adjustments</mark>

Sampling weights were calculated in all four surveys following similar methods. The sampling weight for the most recent round of the LSMS-ISA survey served as the starting point in all four countries since these weights produce representative estimates from the full sample of households in those F2F surveys. These weights were then inflated through basic ratio adjustments at each stage of the selection and interview process from the frame to the interviewed sample (depicted in Figure 3.1). These ratio adjustments preserve the original sum of weights from the full sample of the LSMS-ISA survey. These naïve ratio adjustments do not take into account any nonrandom bias contained in the sample.

The ratio adjustments were then followed by the weighting adjustments to reduce bias in the interviewed sample relative to the F2F representative sample. The weighting adjustment implemented in each country serves to counteract both coverage and nonresponse bias simultaneously. However, the approaches taken at this stage vary in the four countries. For the Ethiopia, Malawi, and Uganda HFPS, the bias adjustment was conducted using a propensity model approach following Himelein (2014). The probability of household response was modeled using a logistic regression. The characteristics included in the logit response probability module are presented in Table 3.2 and were selected due to observed bias in the interviewed sample along these dimensions. Full results of the logit model are provided in Appendix Table 3.1.
# **5. Conclusion**

Monitoring and enforcement issues have been the objecl: of a very limited number of empirical analyses at plant level. In developing countries, the difficulties of accessing data may explain this state of fact. Given the very limited number of empirical analyses of the nature conducted in this paper, we hope to have provided, a better understanding of the impact of the regulator's monitoring and enforcement act:ivities on the behavior of industrial polluters. Our analysis of plant-level environmental performance for the city Zhenjiang suggests that inspections do have a statistically significant impact on firms' environmental performance, and are a better determinant of the firms' performance than is the variation in pollution levies. However, it cannot be concluded from this analysis that pollution levies do not have an impact on firms' environmental performance. Only a similar dataset covering numerous cities across China would allow us to delienate the effective impact of inspections and pollution levies on firms' environmental performance in China. This is the object of ongoing research. Our results also demonstrate that citizens' complaint are an important determinant of inspections, and therefore, via inspections, of environmental performance. Hence, this would suggest a greater role for the regulator to embark on information and education policies.

Whether or not these results support that additional resources be devoted to monitoring activities cannot strictly be addressed with the data currently available. Indeed, in order to do so, there would be a need to compare the costs of performing additional inspections with the net benefits of reduced pollution emissions. However, given the high level of air and water pollution currently observed in Zhenjiang, given the impact that these may have solely on health and productivity loss, it is reasonable to estimate that inspections do increase social net benefit in Zhenjiang.'<sup>7</sup> Further research in this field will prove to be crucial and provide key policy implications. In particular, it does appear of interest to examine more thoroughly the Dasgupta et al. (1997) provide
|11-100<br>100+|||-0.00942<br>(0.133)<br>-0.109|
|---|---|---|---|
||||(0.138)|
|40-49 hrs||-0.482***|-0.359***|
|||(0.108)|(0.122)|
|50+ hrs||-0.482***|-0.231|
|||(0.127)|(0.143)|
|Constant|-0.0858|0.223|0.303|
||(0.160)|(0.325)|(0.362)|
|Observations|5326|5280|4695|

Source: Authors’ estimation using RLMS Note: Columns (1) and (2) include all observations, Columns (3) excludes self-employed informals. This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their likelihood of being in the bottom quintile of the per capital household income distribution, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent,
Table 8: EFFECTS ON RECIPROCITY OF ULTRA-POOR’S TRANSFERS

||Reciprocity<br>of transfer<br>links|Re<br>ultra<br>poor|ciprocity<br>other<br>poor|of transfers<br>middle<br>class|with<br>upper<br>class|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Treatment|0.076***|0.044*|0.094***|0.086***|0.117***|
||(0.018)|(0.024)|(0.020)|(0.020)|(0.026)|
|Mean in control|0.510|0.627|0.560|0.449|0.267|
|Adjusted R-squared|0.217|0.189|0.208|0.235|0.192|
|Observations|12774|2792|4823|9474|3135|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is the fraction of transfer sources within the village who are also reported as recipients of transfers (of last 12
laws, however, also allow incompetent managers to keep their jobs. Therefore, the bankruptcy regime should encourage the behavioral incentives that require that all responsible parties incur some costs for the firms’ poor performance. These costs usually take the form of a reduction in creditors’ claims value or negative consequences for the managers.

These disincentives become extremely important in preventing imprudent investments, enhancing contractibility, and distributing loans with too high probability of default (Rajan and Zingales 1995, White 1993). However, when the personal costs are too high they discourage public bankruptcy filings in favor of private negotiations. For instance, studies of distressed firms in the United States (Gilson 1989, Gilson and Vetsuypens 1994) find that after a bankruptcy filing managers receive significantly lower salaries and bonuses (on average, managers receive only 35% of their previous gross income), and more than half of the sampled managers are fired. Since new management may be unfamiliar with the company and unable to ensure a smooth transition, the creditors will suffer higher costs of resolution during distress.

In addition to reputational and financial losses, entrepreneurs and managers suffer psychological costs—referred to as _stigma_ — when filing for bankruptcy (Shepherd, 2003). Individuals from different countries are likely to differ in the level of stigma they experience upon bankruptcy (Hofstede, 1980). For instance, in a study of eight countries, individuals that reported an aversion to ‘uncertainty’ were significantly less likely to be business owners, presumably because of fear of failure (McGrath, MacMillan, and Scheinberg, 1992).

The importance of efficient bankruptcy laws is highlighted by evidence showing that creditors and debtors change their behaviors when there are improvements in bankruptcy procedures, such as laws dictating each side’s rights in resolving disputes stemming from contract violation. In the absence of bankruptcy law, even when coordinated liquidation would 9
.1***|
|Share classes in school with full-time<br>day (7 or more hours)|0.0|0.0|0.0|0.0|0.0|0.0|
|Total duration of school day (hours)|4.3|4.4|-0.1***|4.3|4.5|-0.2***|
|N|611|17,263||555|13,404||

_Sources: Prova Brasil,_ School Census, and INEP. _Note:_ Table reports the average characteristics in 2007 for the schools that joined _Mais Educação_ in 2008 and remained until 2011 (columns (1) and (4)) and for nonparticipating schools (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.