rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 1 Intergovernmental Tr**|**ansfers in C**|**hina**<br>**Central-P**|**rovincial **||**Provincial**|**-Prefecture**|**Provinci**<br>**C**|**al/Prefecture-**<br>**ounty**<sup>**1**</sup>|
|---|---|---|---|---|---|---|---|---|
||**20**|**04**<br>Percentage||**2003**<br>Percentage|**20**|**03**<br>Percentage||**2003**|
||Amount<br>(billion<br>yuan)|of<br>Total<br>Transfers|Amount<br>(billion<br>yuan)|of<br>Total<br>Transfers|Amount<br>(billion<br>yuan)|of<br>Total<br>Transfers|Amount<br>(billion<br>yuan|Percentage of<br>Total<br>Transfers|
|**General Purpose Transfers**<br>|||||||||
|Revenue Sharing Transfers<sup>2</sup>|469.5|31.6|355.6|30.6|231.0|28.8|111.4|20.6|
|Tax Rebate<br>|405.0|27.2|342.4|29.5|166.7|20.8|126.2|23.3|
|Equalization Transfer<sup>3</sup>|74.5|5.0|38.0|3.3|39.6|4.9|30.5|5.6|
|Subtotal|949.0|63.8|||||||
|**Specific Purpose Transfers**|||||||||
|Grants for Increasing Wages|91.9|6.2|89.9|7.7|79.2|9.9|68.5|12.7|
|Grants for Rural Tax Reform|52.3|3.5|30.5|2.6|33.0|4.1|33.8|6.3|
|Grants for Minority Regions|7.7|0.5|5.8|0.5|1.7|0.2|1.7|0.3|
|Prio-1994 Subsidies<br>|12.6|0.8|12.4|1.1|18.2|2.3|16.5|3.0|
|Ad Hoc Transfers<sup>4</sup>|322.3|21.7|242.6|20.9|149.3|18.6|98.2|18.1|
|Others<sup>5</sup>|51.4|3.5|43.7|3.8|84.2|10.4|54.4|10.1|
|Subtotal|538.2|36.2|||||||
|**Total**|1487.2|100.0|1160.8|100.0|802.7|100.0|541.2|100.0|
|Percentage of Local<br>Expenditure<br>Notes:||||67||57||66|

1. Whether counties get transfers from provincial or prefectural level government is dependent upon whether the province has implemented “province managing county” reform. 2. Revenue sharing transfers from 2002 include the following shared taxes: VAT, Enterprise Income Tax, and Personal Income Tax; before 2002, the revenue sharing transfers only reflected the amount of local VAT revenue.

3. The Equalization Transfer refers to the Transitory Period Grant established since 1995.

4. Ad Hoc Transfers refer to the earmarked grants ( _Zhuanxiang Zhuanyi Zhifu_ ) as categorized in the MOF dataset.

|5. Others include various accountingclosingtransfers,civil service unit reform subsidies,and the non-categorized transfers in the MOF dataset.|
|---|

6
levels), and long-run implications through their impact on productivity, economic inequality and poverty. At the micro level, early sociological studies emphasized that, for the most part, remittances were financing consumption and housing expenditures<sup>3</sup> , with limited dynamic effects. More recent research has found that migration is associated with higher levels of entrepreneurship and educational attainment of the children of migrants.<sup>4</sup> This paper investigates further the impact of migration on human capital accumulation, focusing on child health outcomes. Child health outcomes are one important aspect of well-being and a key determinant of future productivity. In addition, our results provide evidence that the impact of migration exceeds just the direct effects of remittances, suggesting that consideration of the additional indirect benefits and costs of migration should be incorporated into the design of optimal migration policy.

Identification of the health effects of migration is complicated by the fact that migrants are not randomly drawn from the general population. Individuals in poor health are unlikely to be able to endure the process of crossing the border into the U.S., while the most prosperous and healthy rural Mexicans are less likely to find the benefits of illegal migration outweigh their other options in Mexico. Secondly, unobserved shocks such as crop failures or natural disasters may be both an impetus for migration and also result in worsening health conditions in the community. As a result, simple comparison of the health of migrants and non-migrants and their children will not provide reliable estimates of the effects of migration.

In this paper we use instrumental variables methods in order to account for these difficulties and provide credible estimates of the impact of migration on child health, allowing quantification of some of the benefits of migration for human capital accumulation in the sending country. A nationally representative demographic survey, the ENADID 1997 is used to provide a broad sample of migrants in rural communities with varying levels of migrant experience. Historic migration networks formed as a result of U.S. demand conditions and the pattern of development of the railroad system in the early 1900s are used as instruments for current levels of migration. Infant mortality rates and birth weights are the two child health outcomes considered in this

> 3 See the literature review in Durand, Parrado and Massey (1996).

> 4 See, for example, Hanson and Woodruff (2003) on education and Woodruff and Zenteno (2001) on microenterprises.

- 2 -
36. Meier P. Digital humanitarians: How big data is changing the face of humanitarian response. Routledge; 2015.

37. Dhavase N, Bagade AM. Location identification for crime disaster events by geoparsing Twitter. In: International Conference for Convergence for Technology-2014; 2014. p. 1–3.

38. Aggarwal CC, Zhai CX. Mining Text Data. Boston, MA: Springer; 2012.

39. Yin J, Karimi S, Lampert A, Cameron MA, Robinson B, Power R. Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI); 2015.

40. Al-Olimat H, Thirunarayan K, Shalin V, Sheth A. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models. In: Proceedings of the 27th International Conference on Computational Linguistics; 2018.

41. Premamayudu B, Subbarao P, Koduganti VR. Identification of Natural Disaster Affected Area Precise Location Based on Tweets. International Journal of Innovative Technology and Exploring Engineering. 2019;8(6).

42. Sangameswar MV, Nagabhushana Rao M, Satyanarayana S. An algorithm for identification of natural disaster affected area. Journal of Big Data. 2017;4(39).

43. de Bruijn JA, de Moel H, Jongman B, de Ruiter MC, Wagemaker J, Aerts J. A global database of historic and real-time flood events based on social media. Scientific Data. 2019;6(311).

44. Ristea A, Boni MA, Resch B, Gerber MS, Leitner M. Spatial crime distribution and prediction for sporting events using social media. International Journal of Geographical Information Science. 2020;0(0):1–32. doi:10.1080/13658816.2020.1719495.

45. Gerber MS. Predicting crime using Twitter and kernel density estimation. Decision Support Systems. 2014;61:115 – 125. doi:https://doi.org/10.1016/j.dss.2014.02.003.

46. Yang D, Heaney T, Tonon A, Wang L, Cudr ́e-Mauroux P. CrimeTelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web. 2018;21(5):1323–1347.

47. Karimzadeh M, Pezanowski S, MacEachren AM, Wallgr `[U+FFFD]` n JO. GeoTxt: A scalable geoparsing system for unstructured text geolocation. Transactions in GIS. 2019;23(1):118–136. doi:10.1111/tgis.12510.

48. Austroads. Guide to roadsafety part 8: Treatment of crash locations; 2015.

14
Table 3. Responsiveness of Catch to Price: Intensive Margin

||Dep. varia<br>Pichilingue<br>|ble: log catch<br> <br>|<br>|Abreojos<br>|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Log coop price|0.563**|0.563|0.782**|0.670***|0.670***|0.704***|
||(0.220)|(0.345)|(0.352)|(0.029)|(0.051)|(0.046)|
|Log mkt price|-0.599|-0.599|0.364|0.310|0.310|1.124**|
||(1.319)|(1.244)|(1.335)|(0.221)|(0.274)|(0.505)|
|Obs|2618|2618|2618|23586|23586|23586|
|Fixed effects|Sp-M-FT|Sp-M-FT|Sp-wk-FT|Sp-M-FT|Sp-M-FT|Sp-wk-FT|
|Clustering|none|Sp-M-FT|Sp-wk-FT|none|Sp-M-FT|Sp-wk-FT|
|Num. groups|1493|1493|2110|3169|3169|7153|
|Within-R2|0.006|0.006|0.018|0.025|0.025|0.041|

Note: All specifications use linear fixed effects estimation. First three columns use only Pichilingue observations, and next three columns use only Abreojos observations. Columns 1, 2, 4, and 5 include fixed effects at the species-month-fishing team level, while columns 3 and 6 include fixed effects at the species-week-fishing team level. “Num. groups” indicates the number of unique combinations at each level. Standard levels clustered at the level indicated in the “Clustering” row.

4
We also document a novel channel not previously examined in the literature. The channel is referred to as “the regulatory channel”. In particular, we find that when government debt is higher, firms are more likely to receive visits from tax officials and spend more time dealing with government regulations requirements (see Table 6). This is a channel that can potentially hurt firm operations. Again, the effects are much stronger and more statistically significant for small and medium-size firms. This disproportionate effect can exacerbate the unequal playing field against small and medium-size firms. We do not have further evidence regarding the motivation of regulatory enforcement. However, we speculate that government officials face more pressure to raise taxes and other revenue as government debt rises.

## **Table** **<u>6: The Regulatory Channel</u>**

|**Model**<br>**Outcome Variable**|**Senior ma**<br>**in dealin**<br>**of govern**|**OL**<br>**nagement t**<br>**g with requ**<br>**ment regula**|**S with Firm**<br>**ime spent**<br>**irements**<br>**tions (%)**|**and Year**<br>**Average**<br>**required**|**FE**<br>**number of**<br>**meetings**<br>**officials**|**visits or**<br>**with tax**|
|---|---|---|---|---|---|---|
|**Sample**|**All**|**SME**|**Large **|**All**|**SME**|**Large **|
|Central government debt, % of GDP,<br>lagged (t-1)|0.019|0.025*|0.014|0.008***|0.007**|0.004|
||(0.017)|(0.014)|(0.055)|(0.003)|(0.003)|(0.008)|
|Constant|YES|YES|YES|YES|YES|YES|
|Firm Fixed effects|YES|YES|YES|YES|YES|YES|
|Year Fixed effects|YES|YES|YES|YES|YES|YES|
|Other controls|YES|YES|YES|YES|YES|YES|
|Number of observations|24,789|20,156|4,633|26,268|21,463|4,805|
|Adjusted R2|0.360|0.338|0.498|0.444|0.449|0.488|

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. Log of real GDP per capita, Log of age of firm, 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
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# 1 Introduction

The degree of mobility in income is often seen as a measure of the equality of opportunity in a society, and of the flexibility and freedom of movement in the labor market (Atkinson, Bourguignon and Morrisson, 1992). Greater mobility makes the distribution of lifetime incomes more equal for a given level of single period income inequality. On the other hand, Jarvis and Jenkins (1998) note that too much mobility may represent income fluctuations and economic insecurity. Gottschalk and Spolaore (2002) formalize this trade-off in a model with both aversion to inequality and aversion to unpredictability of incomes, finding the socially desirable level of mobility will be less than a level at which there is full reversal of ranks over time. Nevertheless, in many developing countries, the concern is more likely to be that there is too little, rather than too much, mobility. In particular, Piketty (2000) surveys recent theoretical work which finds that the presence of credit constraints can give rise to the possibility of “lowmobility traps”, whereby households who need to borrow to finance investment can take a long time to build up wealth.

Measurement of the degree of mobility using panel data on earnings is complicated by the presence of measurement error, and by non-random attrition from the panel. A simple measure of mobility is the slope coefficient from a regression of current period earnings on lagged earnings (e.g. Jarvis and Jenkins, 1998; Fields et al. (2003)). Classical measurement error causes the well-known attenuation bias towards zero in the estimated slope coefficient, leading one to overstate the degree of mobility. The existing literature has attempted to overcome the measurement error problem through the use of instrumental variable methods.<sup>1</sup> Instruments for lagged income have included lagged expenditure (e.g. McCulloch and Baulch, 2000), subjective measures of well-being (Luttmer,

> 1 An alternative method used in developed countries has been the use of administrative data from payroll records, where measurement error is likely to be much less (e.g. Aaberge et al., 2002). Such data is much less common in developing countries, and does not allow analysis on the dynamics of earnings of the self-employed, or of total household income.

2
> - _11_ -

# TABLE 1: STRUCTURE OF PUBLIC SECTOR DEBT (contt...) (Percentage of GDP)

|luRt;mY:<br>^....<br>...|1980-81|1982-84|1985-89|
|---|---|---|---|
|1. TOTAL NET DOMESTIC DEBT PLUS<br>CEN rRAL BANK FINANCE (A+B)<br>||||
|A. Total Do-esic Debt|6.0|3.3|6.3|
|Bonds and Bills<br>|_3.5_<br>|3.4<br>|5.6|
|NetBank Credit<br>EBF|3.1<br>|1.9<br>|1.8<br>|
|s|-0.5|-2.0|-1.1|
|Borrowers<br>Cl||||
|entra GO ernment<br>Nfil|2.4<br>|2.9<br>|5.3<br>|
|onunaca SEEs<br>|4.1<br>|2.4<br>|2.1<br>|
|EBFs|-0.5|-2.0|-1.1|
|B. Central Bank Finance|4.3|6.7|6.6|
|II. TOTAL NET FOREIGN DEBT<br>(Excl. K Loswa/Gains)|19.5|21.0|26.7|
|Ila. TOTAL NET FOREIGN DEBT<br>(Incl. K Loscs/Gains)|^.!.2|29.0|43.0|
|MEMO: ACUMULATED FOREIGN<br>EXCH. LOSSES (_ a-Il)|1.7|8.0|16.4|

y..G .0. S,,,> As,,, V-.....

||1980-81|1982-84|1985-87|
|---|---|---|---|
|Total Net Foreign Debt<br>|33.3|47.0|37.4|
|(Lacl. K losaes/Gamns)||||
|Total Net Foreign Debt<br>(cxcl. K loswa/Gains)|34.3|43.6|29.3|

SOURCE: See statistical annexes.

II Percentage of GNP 2/ Percentage of GSP
Table A15: Results: Mechanisms - Interpersonal Socio-Emotional Skills

||(1)<br>Listening|(2)<br>Empathy|(3)<br>Expresiveness|(4)<br>Relatedness|(5)<br>Infuence|(6)<br>Negotiation|(7)<br>Collaboration|
|---|---|---|---|---|---|---|---|
|Offered Training|0.08<sup>_∗_</sup>|-0.01|0.06|0.03|0.06|0.03|0.04|
||(0.04)|(0.04)|(0.05)|(0.04)|(0.04)|(0.05)|(0.05)|
|Listening|0.34<sup>_∗∗∗_</sup><br>(0.04)|||||||
|Empathy||0.34<sup>_∗∗∗_</sup><br>(0.05)||||||
|Expressiveness|||0.36<sup>_∗∗∗_</sup><br>(0.04)|||||
|Relatedness||||0.41<sup>_∗∗∗_</sup><br>(0.05)||||
|Infuence|||||0.41<sup>_∗∗∗_</sup><br>(0.04)|||
|Negotiation||||||0.31<sup>_∗∗∗_</sup><br>(0.04)||
|Collaboration|||||||0.41<sup>_∗∗∗_</sup><br>(0.04)|
|Control Mean|-0.04|0.00|-0.03|-0.01|-0.03|-0.01|-0.03|
|Observations|849|849|849|849|849|849|849|

Notes: OLS regressions controlling for strata dummies used in the randomization and baseline value of the outcome. Soft skills were only measured at baseline and endline. Robust standard errors shown in parenthesis. The controls presented in the table are the baseline measures of each index.

Table A16: Gender and Socio-Emotional Skills

||(1)<br>Aggregate<br>SES index|(2)<br>Intrapersonal<br>SES index|(3)<br>Interpersonal<br>SES index|
|---|---|---|---|
|Gender gap (female)|-0.08<sup>_∗∗∗_</sup>|-0.06<sup>_∗_</sup>|-0.10<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.03)|
|Observations|895|895|895|

Notes: Gender gap is an indicator variable for women. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Robust standard errors reported.

37
low interest rates: Kenya as a regional safe haven for financial assets.<sup>32</sup> Such investors are not likely to be fussy about the rate of return.

Table 2. Interest Rate Decomposition

|||(In pe|rcent)|||||||
|---|---|---|---|---|---|---|---|---|---|
||1999|2000|2001|2002|2003|2004|2005|2006|2007|
|91-Day Tbill|13.3|12.1|12.7|8.9|3.7|3.0|8.4|6.8|6.8|
|U.S. Treasury bill|4.7|5.8|3.5|1.6|1.0|1.4|3.2|4.7|4.4|
|Diference|8.6|6.2|9.3|7.3|2.7|1.6|5.3|2.1|2.4|
|Actual KSh./US$ depreciation|17.8|7.0|0.7|-1.9|-1.2|1.6|-6.4|-4.1|-9.7|
|EMBI Africa (in basis points)|..|..|..|529|232|177|145|74|166|
|Nigeria|1338|2037|1426|2276|732|667|523|481|..|
|South Africa|..|..|..|238|141|95|85|85|164|
|EMBI+ Spread (in basis points)|824|756|731|765|418|356|245|169|239|

Source: Central Bank of Kenya; International Financial Statistics; JPMorgan; and Staff estimates.

In Table 2, the difference between US T-bill and Kenyan T-bill rates has fallen sharply starting in 2003, indicating declining devaluation and default risk; the average difference during 1999-2002 was 7.9 percentage points but went down to 2.8 percentage points during the 20032007 period. Unfortunately, there is not a way of disentangling the DRP and the SRP. A standard way of doing so is to assign the same SRP to Kenyan T-bills as that on an instrument of similar maturity issued in the international capital markets (with SRP given by the spread on such an instrument relative to an identical currency and maturity borrowing by a benchmark country, such as the US) and then obtain the DRP as a residual; but Kenya’s external debt is mainly on concessional terms and it has not had access to the international capital markets. Ex post, the Kenyan shilling has been on an appreciating trend since 2002, as can be seen from the table.

In October 2006, S&P rated Kenya’s capacity to repay long term sovereign obligations denominated in foreign currency as B+, which indicates that the sovereign has the capacity to meet its financial commitment, but adverse business, financial, or economic conditions could impair its capacity. Countries that received the same S&P rating of B+ in 2006 and are tracked by the EMBI+ index exhibited a spread of over 200 basis points (bps) at end-2006 over the U.S.

> 32 For example, for oil money from southern Sudan.  Anecdotal evidence suggests that the Kenyan shilling is widely tendered in the region, including in troubled areas like Somalia.

24
They then argue that they identify a bias in the 2005 data in terms of the product list for ring countries. Their adjustment for this bias further reduces the difference to about 9 to 12 percent. Applying both the harmonized methodology and the bias correction simultaneously reduces the 24 percent difference in mean consumption to somewhere between 4 and 10 percent (depending on the measure considered). The latter bias correction can be viewed as a mix of data quality and methodology issues, but their analysis reveals that the lack of a harmonized method across the two PPP indices is one significant explanation for the difference. In this light, Inklaar and Rao can be interpreted as viewing the 2011 PPP index as an improvement over the 2005 index.<sup>6</sup>

# **_B. Initial Poverty Estimates Based on the 2011 PPP Data_**

That there is now much attention to the 2011 PPP index and, more generally, the importance of price adjustments to global poverty measurement can be largely attributed to the provocative and useful blogs by Chandy and Kharas (2014) and Dykstra, Kenny, and Sandefur (2014).

Immediately after the release of the 2011 PPP data, Dykstra, Kenny and Sandefur estimated that in 2010, the share of people in the developing world living below the $1.25 per day poverty line declined from 19.7 percent to 8.9 percent “overnight”.<sup>7</sup> Chandy and Kharas (2014) follow an approach similar in spirit (though differing in the details) to the methodology used in Chen and Ravallion (2010) and re-estimate the poverty line accounting for changes in local price levels. They provide several candidate estimates of the count of the poor, but hone in on an estimate

> 6 It is useful to also note that the question is not whether the 2005 PPP index is of better or worse quality than the 2011 PPP index, but rather whether the combination of the 2005 PPP index and the extrapolation methodology (based on inflation rates) outperforms the new 2011 PPP data.

> 7 Their methodological approach is to adjust the $1.25 poverty line, which is denominated in 2005 USD, to 2011 by using the U.S. inflation rate. This adjustment brings their poverty line to a value of $1.44 in 2011 USD. This methodology differs significantly from the approach followed by Chen and Ravallion (2010) and described in World Bank (2014b) for setting an international poverty line.

8