rafmacalaba/data-use-mentions-extended huggingface dataset:
The rest of this report is organized as follows: Section 2 describes the fiscal system in the West Bank and Gaza, providing context on each of the fiscal instruments modeled in this assessment and an overview of the share of taxes and revenues modeled. Section 3 describes the fiscal administrative and microdata sources used for our model and the Commitment to Equity (CEQ) framework used in the analysis. Section 4 describes methodologies used to allocate each of the fiscal instruments, while Section 5 analyzes headline results. Section 6 presents the marginal contribution from each fiscal intervention to poverty and equity, while Section 7 presents distributional analysis by decile. Section 8 analyzes how fiscal interventions in the West Bank and Gaza compare to other countries in terms of distributional effects. Section 9 provides macro-validation, totals allocated in the survey compared with the official fiscal administrative data and reflects on the main limitations of the analysis. Finally, Section 10 provides concluding remarks.

9
||x|
|Nber. Obser.|4049|3726|827|736|3654|3700|745|748|
|R squared|0.23|0.23|0.32|0.33|0.23|0.23|0.32|0.32|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. Robust standard errors 22
costs, although it is much closer to zero than with Pareto (-0.3 rather than -0.8). In any case, this is just a ”proof of concept” that lognormallydistributed productivity can by itself improve the performance of the model relative to the data. In the next subsection we present the results obtained with the estimated full Melitz-lognormal model.

# **Full Melitz-lognormal model**

To estimate the parameters of the full Melitz-lognormal model we use firm-level data from the EDD and China for the year 2007.<sup>30</sup> This entails 39 different origins, but we had to drop 2 origins from our analysis due to convergence issues discussed in the Online Appendix F. As mentioned above, we consider 15 destinations: USA, Germany, France, Japan, and the 11 largest destinations by export value for each origin. Finally, we use the estimates of Bas et al. (2015) and set _σ_ = 5.

Before presenting the results of the estimation and discussing their implications for the IME,

> 28  ̄ See the section in the Appendix titled QQ-Estimation of _σφ_ for a discussion of these estimates and their relation to the estimate in HMT.

> 29Using Head et al. (2014) estimate of  ̄ _σφ_ = 2 _._ 4 we get IME of around 0.12.

> 30For computational reasons, for China we considered only a random sample consisting of 5% of exporters.
the legal expertise to corroborate that those were aligned to the definition proposed in section 2. Once those legal forms were verified, we identified the companies with legal forms associated to state ownership by law as non- corporatized SOEs. In addition, some of these companies can have subsidiaries. Hence, for these non- corporatized SOEs we explored whether those companies are shareholders in other subsequent companies using the algorithm in Figure 7.

_Annex 3._ _Table A.2 Complementary data sources for the global BOS database beyond ORBIS_

|**Database**|**Vendor/Source**|**Coverage**|**V**<br>**Financial**<br>**Information**|**ariable coverage**<br>**Ownership information**|
|---|---|---|---|---|
|EMIS Intelligence|EMIS|5.5 million listed and unlisted firms in 125<br>emerging markets|Yes|Yes (name of shareholders).<br>SOEs can be more easily<br>identified for countries with<br>centralized SOE ownership.|
|Factiva/Factset|Dow Jones/Factset<br>Research|Listed and unlisted companies|Only for listed<br>companies|Yes (binary variable on SOE)|
|Worldscope|Thompson<br>Reuters/Refinitiv|Listed companies|Yes|Yes (common shares<br>outstanding with information<br>on as “government owned<br>company or majority owned by<br>government”.|
|Global 2000|Forbes|Largest 2000 firms worldwide measured by<br>sales, profits,assets and market value|No|No|
|PMR|OECD/WBG|OECD & 40+ developing economies|No|Yes|
|PitchBook|PitchBook|3.3 million firms worldwide covering public<br>listed and private (unlisted)
085|
||(0.061)|(0.118)|(0.124)|(0.057)|(0.112)|(0.118)|
|Role Model Edu Sq|-0.004|0.002|-0.006|-0.003|0.002|-0.004|
||(0.004)|(0.005)|(0.006)|(0.003)|(0.005)|(0.006)|
|Constant|5.988***|6.389***|-0.4011|4.722***|5.406***|-0.684|
||(0.285)|(0.682)|(0.675)|(0.272)|(0.683)|(0.672)|
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|8292|4111|12403|8292|4111|12403|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). A role model is someone who was born in the respondent’s district but is within _±_ 5 of respondent’s father age and belongs to top 50 percent education level within that age group of that district. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls
a randomized trial in Mozambique. _American Journal of Agricultural Economics_ , _95_ (5), 1345-1351.

Carter, M, Lybbert, T and Tjernström, E (2015), "The Dirt on Dirt: Soil Characteristics and Variable Fertilizer Returns in Kenyan Maize Systems" Working paper, USAID Feed the Future Innovation Lab for Assets and Market Access, Davis, CA Carter, M, Laajaj, R and Yang, D (2019), “Subsidies and the Green Revolution in Africa: Direct and Social Network Effects of Randomized Input Subsidies in Mozambique”, NBER Working Paper 26208.

Cassou, E., S. M. Jaffee, and J. Ru. 2018. _The Challenge of Agricultural Pollution: Evidence from China, Vietnam, and the Philippines._ Washington, DC: World Bank.

Chatterjee, S., Kapur, D., Sekhsaria, P. and Subramanian, A. 2022. Agricultural Federalism: New Facts, Constitutional Vision, Economic and Political Weekly, 57(36) 2022 Chilimba, A. D., S. D. Young, C. R. Black, M. C. Meacham, J. Lammel, and M. R. Broadley. 2012. “Agronomic Biofortification of Maize with Selenium (Se) in Malawi.” Field Crops Research 125: 118–128 Cole, S., & Sharma, G. (2017, July). The promise and challenges of implementing ICT in Indian agriculture. In _India Policy Forum_ (pp. 11-12).

Cox, Cindy L and Kwon, Ho Young and Koo, Jawoo, 2015, The Biophysical Potential for Urea Deep Placement Technology in Lowland Rice Production Systems of Ghana and Senegal. IFPRI Discussion Paper 1448, Available at SSRN: https://ssrn.com/abstract=2631732 17
945.00|-3,463.00|
|||Import value in 2008||716,175.40|94,106.72|
|||% Change in import value||1.53|-3.68|
|330300|Perfumes and toilet waters.||France|Belarus||
|||Change in import value||18,613.00|-5,530.00|
|||Import value in 2008||378,971.80|50,324.80|
|||% Change in import value||4.91|-10.99|
|640291|Other footwear with outer soles and uppers of rubber or plastics, covering the ankle.||China|Kazakhstan||
|||Change in import value||22,574.00|-986.00|
|||Import value in 2008||437,397.20|6,763.98|
|||% Change in import value||5.16|-14.58|
|730890|Other structures (excluding prefabricated buildings of heading 94.06) and parts of structures (for example, bridges||Germany|Ukraine||
||and bridge-sections, lock-gates, towers, lattice masts, roofs, roofing frame-works, doors and windows and their<br>|Change in import value||9,939.00|-8,397.00|
||frames and thresholds for doors|Import value in 2008||170,398.90|100,947.20|
|||% Change in import value||5.83|-8.32|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 45
_Source_ : Author’s tabulations using the Encuesta de Hogares de Propósitos Múltiples 2015 (EHPM).

_Notes:_ Numbers represent the distribution of women's employment outcomes conditional on their spouse's (column percentage). The category "Other" includes wage employment, domestic work, and unemployment. The following outcomes for men were not included due to small sample size: public sector wage employment, unpaid status, unemployment, and inactive. This analysis is limited to households with a male head and a female spouse present. They represent 67 percent of households and 73 percent of working-age individuals in the rural sector.

The use of proxy respondents can also elicit biased responses since they do not always provide accurate information for the other household members, which can result in an underestimation of women’s participation in work activities (Dixon 1982; Shah 1985; Rives and Yousefi 1997; Franck and Olsson 2014; Bardasi et al 2011). In the case of the 2015 household survey in Honduras, however, men and women report female participation at the same rates.

In 2015 few Honduran women in the rural sector reported being employed with most reporting being out of the labor force. From analyzing the household survey, several hypotheses can be drawn to explain this. First, intensity of household tasks is likely very high limiting the extent to which women can engage in activities outside the home. Second, it is likely that agricultural activities performed by women are not being accurately captured by the survey. In fact, some of these may be considered household tasks. This is further supported by the observation that women are particularly unlikely to report being in the labor force if they live with a husband who has an agricultural enterprise. Third, the lack of difference by gender of respondent suggests that both men and women are inaccurately reporting activities of women.

# **Methodology**

To better understand female employment and economic activity in rural areas and how well this is measured in the household survey, qualitative research consisting of a mix
**Figure 9: Performance Ratings of National Government and Changes over Time**

<!-- Start of picture text --> Overall Ratings of the Performance of National Government<br>Uzbekistan<br>Tajikistan<br>Sweden<br>Azerbaijan<br>Georgia<br>Turkey<br>Belarus<br>Montenegro<br>Kazakhstan<br>Hungary<br>Albania<br>Estonia<br>Mongolia<br>Bulgaria<br>Moldova<br>Great Britain<br>France<br>Macedonia<br>Slovenia<br>Kyrgyzstan<br>Russia<br>Armenia<br>Germany<br>Ukraine<br>Czech Republic<br>Poland<br>Bosnia-Herzegovina<br>Serbia<br>Italy<br>Latvia<br>Slovakia<br>Lithuania<br>Croatia<br>Romania<br>0 20 40 60 80 100<br>Percent of respondents<br>Very Good Good Neither Bad Very Bad<br><!-- End of picture text --> <!-- Start of picture text --> Perceptions of change in performance of national government<br>Georgia<br>Uzbekistan<br>Azerbaijan<br>Tajikistan<br>Turkey<br>Bulgaria<br>Armenia MontenegroHungary Sweden MacedoniaKazakhstanMongolia Belarus<br>Albania<br>Estonia Moldova Poland Kosovo<br>Slovenia<br>Great Britain<br>Ukraine Czech RepublicSlovakiaSerbiaKyrgyzstan<br>Russia<br>Latvia Bosnia-Herzegovina<br>Lithuania CroatiaItaly GermanyFrance<br>Romania<br>-10 -5 0 5 10<br>GDP growth 2009-2010<br>Source: LITS 2006 and 2010, IMF. 2010 growth numbers are estimates (IMF WEO October 2010 Update).<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source
# **Figure 14 TFP growth by region**

(percent a year) <!-- Start of picture text --> 1998~2013 1998~2007 2007~2013<br>Central East<br>North NorthEast<br>NorthWest South<br>SouthWest<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: Authors’ estimates based on NBS’s Industrial Enterprise Survey.

# **2.4 The declining efficiency of aggregate investment and credit**

As TFP growth deteriorated, GDP growth has relied much more on investment in physical capital, but such investments have brought much lower returns to growth than in the past. In response to the global financial crisis, China implemented a large public sector stimulus which concentrated on investments in infrastructure and housing. Given that public infrastructure is no longer a major constraint on growth (see next section), government investment now faces the challenge of diminishing returns. China’s incremental capital-output ratio (ICOR) has risen significantly in recent years, pointing to a declining impact of investment on growth (Figure 15).

# **Figure 15 ICOR and real GDP growth**

(yoy, percent) <!-- Start of picture text --> GDP growth ICOR<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 1999 2003 2007 2011 2015 2018<br><!-- End of picture text --> Source: NBS; authors’ calculations.

Note: The ICOR is calculated as the ratio of average real gross fixed capital formation in periods _(t-1)_ and _(t)_ to the change in real GDP between _(t)_ and _(t-1)_ .

Much of the increase in the economy-wide ICOR
12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total sales in the last year** , in Naira, winsorized at the 99<sup>th</sup> percentile. For firms not answering the exact value, the midpoint of the sales range they give, for those answering sales are in range above 10,000,000, sales are coded as the median of sales for other firms with sales in this range, or 12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total profits in the last month** , in Naira, winsorized at the 99<sup>th</sup> percentile. This is asked via a direct question. For those not answering the exact value, the midpoint of the profits range they give, for those answering profits are in range above 10,000,000, profits are coded as the median of profits for other firms with profits in this range, or 12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total profits in the last year** , in Naira, winsorized at the 99<sup>th</sup> percentile. This is asked via a direct question. For those not answering an exact value, the midpoint of the profits range they give, for those answering profits are in range above 10,000,000, profits are coded as the median of profits for other firms with profits in this range, or 12,000,000 if no other firms are in this range.

**Standardized profits and sales index** is obtained by aggregating these different effects as a standardized z-score.

62
all restrictions in five priority sectors – air transport, eASEAN, Healthcare, tourism and logistics. By 2015, ASEAN investors should be allowed to hold up to 70% of the equity in Indonesian firms in these sectors. These targets are subject to some flexibility (15%) in the case of sub-sectors of national sensitivity. Implementation of these targets, moreover, requires domestic legislation, including amendments to both the Negative Investment List and relevant sectoral legislation.

This Negative Investment List (NIL) is probably the most important reference for prospective foreign investors outside Indonesia. It details those sectors from which foreign investment is proscribed, and those sectors that are open, but carry certain restrictions such as location, company size, special permits and foreign equity limits. This list has undergone a number of incarnations. Both before (1994) and after (2000) the East Asian crisis the Negative Investment Lists lacked sufficient meaningful detail. With the landmark Investment Law of 2007, a new List was developed which was supposed to be the single, comprehensive reference point for

> 4 A recent study by Borchert, Gootiiz and Matoo (2012) reveals that the higher the GDP per capita of a country, the lower the services trade restrictions they impose.. Interestingly, when the set of countries is restricted to those in the East Asia Pacific region, the relationship is inverted: countries with higher GDP per capita tend to impose higher restrictions in services trade..

> 5 Instead, at GATS it remains less active. The World Bank’s GATS Restrictiveness Index, that measures the extent of GATS commitments toward liberalization of a given country, shows Indonesia with a score of 9.52, while the average for East Asia-Pacific is 22.08 (0 indicates no commitments, 100 indicates fully liberalized).

6
we have multiplied each grid squares employment by 2.761 to make up for the employment deficit.<sup>5</sup> The implicit assumptions in allocating growth and under-counting of employment to grid squares are obvious.

For Nairobi in 2009, we can quite accurately infer population of the grid square, based on fine-scale EA data. However, we do not know total employment, nor its distribution. We infer total employment based on Nairobi’s 2009 population, and labor force participation and age distribution numbers for urban Kenya. Since there is no economic census for

> 5The World Bank estimates that labor force participation of people aged 15 or more in Uganda is 0.71. There are 1,704,604 people of age 15+ in Kampala from the 2011 census. Thus, approximately 1,210,267 people should work out of the total city population of 2,957,505. The economic census only captures 438,374 of these.

6
. The FEPB provides compensation for any kind of death as long as the migrant had obtained permits from DoFE, died within the duration of his contract, and the family files a claim within a year of the date of death with necessary documents. The necessary documents would typically be issued by local authorities after verifying that the claimant is indeed related to the deceased migrant worker. This verification process may also aid in the transmission of the news of a migrant death in addition to the word-of-mouth diffusion of news among potential migrants. The records at FEPB are considered quite comprehensive counts of the death of registered migrant workers abroad.<sup>6</sup> The data used in this paper contains all such claims made with FEPB for deaths that occurred from January 2009 to December 2013. For each deceased (anonymized), I observe the date of death, the country of death, district of residence in Nepal and the cause of death reported in the death certificate. Figure 3 shows the total number of deaths for every month in each of the major destination countries. The figure shows an increase in the reported number of deaths in most destinations akin to the increase in migrant outflow. Because migrant deaths are rare events, the numbers look sporadic for countries with smaller migrant flows, and therefore a small migrant stock. I aggregate this data up to the district-destination-month cell for analysis. Each district-destination cell experienced 0.1 deaths in a month, with the variation across destinations resembling the migrant stocks in the destination countries (Column 2, Table 2).

Though considered an accurate source of information on the number of migrant deaths, this data

> 6Note that it is not the complete census of all deaths of Nepali workers abroad as it relies on the claims made by the family of the deceased.

6
, Log of Total Population, Log of size, Direct exports, Foreign ownership, checking or savings account, sector are controlled for. Survey weights are used.

# 4. Summary and Conclusions

The Covid-19 pandemic and the ensuing financial tightening due to the inflationary environment has raised public debt as governments grapple with public health investments to curb the pandemic and collapse in revenues due to slower economic activity. The rise in debt may further disrupt the formal private sector in developing economies. Using two to three waves of panel firm-level data across developing economies, this study finds that higher public debt is correlated with low investment by formal private sector firms. The finding is largely driven by small and medium-size enterprises, domestic firms, and non-exporters – 17
Figure 6: Distribution of share of local suppliers across different states <!-- Start of picture text --> MG PR<br>40<br>75<br>50<br>20<br>25<br>0 0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>RS PB<br>40 30<br>20<br>20<br>10<br>0 0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>CE PE<br>30<br>20<br>20<br>10<br>10<br>0 0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>Share of same-municipality suppliers Share of same-municipality suppliers<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the distribution of the percentage of suppliers located within the same municipality where the tender process occurs, regardless of whether it is a headquarters or a branch, for each state. Data are drawn from the tender-participant table, and encompass all types of purchases, including tender waivers, for both products and services. Here, we consider only the winners’ (suppliers) information. We match this dataset with the _Cadastro
framework.<sup>9</sup> We interact time trends and country fixed effects to produce country-specific time trends. Standard errors are clustered at the country-law (e.g., the CSL) level, which is the level of treatment assignment. We estimate Equation (2), the

> 6We further Winsorize educational attainment at the 1 percent level, to minimize outlier bias and address spurious coding in the ESS data of extreme values. With Winsorization, we have a minimum of two years of schooling and a maximum of 22 years. Without Winsorization, 414 respondents or 0.11% of our sample report at least 30 years of education, which clearly does not map to our standard notion of years of full-time education, even for individuals with a PhD, motivating Winsorization. Otherwise, the maximum reported education is 60 years of schooling which exerts undue leverage on the rest of the data. Nevertheless, our results also hold when using raw years of education or topcoding at 20 years of education instead.

> 7This is defined based on each respondents’ birth year and starting school age to derive when the reform would first take effect for a given individual.

> 8Higher order time trends consume too much of the variation caused by the instrument, leaving insufficient identifying variation. We believe that the model with a quadratic time trend is misspecified, as evidenced by the fact that such a model yields _negative_ and highly statistically significant effects of education on household earnings, contradicting the economic consensus that education causally increases earnings (Angrist and Krueger, 1991; Ashenfelter and Rouse, 1998; Card, 2001). In contrast, our model with linear country-specific time trends produces estimates more consistent with the literature.

> 9Omitted indicators are the earliest laws in each country, such that the earliest laws take the value of the country fixed effect, and each subsequent law has a positive _β_ estimate as long as the reform i increased education relative to the country’s time trend.

6