rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Digital technologies can provide opportunities for developing economies to leapfrog into the future. They can change the nature of businesses, create better employment opportunities, and potentially transform lives (World Bank, 2019a). Internet upgrades have been found to increase employment into higher skilled occupations in Africa (Hjort and Poulsen, 2019), showing the potentially inclusive nature of such technologies.<sup>3</sup> Mobile money is a digital technology success story that, originally adopted mainly in East Africa, then spread to the rest of the continent and beyond. Mobile money penetration has been well-documented, with studies showing several benefits for individuals and households (Aker and Mbiti, 2010; Suri and Jack, 2016) through reduction of transaction costs (Jack and Suri, 2014), lowering of travel costs (Aker et al. 2013; Bångens and Björn Söderberg 2011), increases in welfare by smoothing unexpected income shocks (Jack and Suri 2014), increasing security (Wright et al. 2014), and facilitating remittances (Munyegera and Matsumoto 2016).

Given the potentially inclusive effects of digital technology, a key question is whether mobile money adoption and diffusion has benefited women, particularly in Africa where gender disparities are still notable. Young women in Africa are less likely to be employed than young men due to challenges in obtaining skills, distorted time allocation, and limited access to capital (World Bank, 2020). Evidence at the household and individual levels indicates that mobile money may have helped women. In Niger, cash transfers through a mobile money system in response to a drought led households to diversify the crops grown, especially for women who in response grew cash crops (Aker et al., 2013). In Kenya, mobile money alleviated poverty, particularly for femaleheaded households where women moved out of subsistence farming and changed their main occupation to business or retail (Suri and Jack, 2016).

Persistent gender disparities are observed also for women entrepreneurs. Female-owned businesses in Africa tend to have fewer employees, lower value-added, and lower productivity than their male counterparts (World Bank, 2019b). Part of the observed productivity gaps can be attributed to outward orientation (export status and foreign ownership), the ability to protect

> 3 See similar findings between internet and female labor force participation in Jordan (Viollaz and Winkler, 2020).

2
parental leave days are 50 days, the total number of leave days available to the mother would be the sum of maternity leave, parental leave days reserved for the mother, plus 35 days (50 days multiplied by 70%). Also in Table 3, we show the correlation between family leave and the female labor force participation rate under these different assumptions in columns (2) and (3). As with the first robustness test, the other results are recorded in tables A2 and A3 of the appendix.

The correlations displayed below once again confirm those from column (1) of Table 2a—where the leave gap between mothers and fathers is larger, a smaller percentage of the female working age population participates in the labor force.

_Table 3: Robustness checks on additional controls and alternative assumptions on mothers’ leave and female labor force participation rate_

||**Adding**<br>**additional**<br>**control**<br>**variables**<br>**(1)**|**Excluding**<br>**unallocated**<br>**leave days**<br>**(2)**|**Multiplying**<br>**unallocated**<br>**leave days by %**<br>**remuneration**<br>**(3)**|
|---|---|---|---|
|**_Y = Female labor force participation rate_**||||
|**Log of gap between mothers and fathers leave at t-3**|-0.1654*|-0.2164***|-0.2446***|
||(0.0979)|(0.0583)|(0.0643)|
|**Log of mothers' leave days at t-3**|-0.0017**|-0.0016|-0.0038***|
||(0.0008)|(0.0015)|(0.0007)|
|**Residual WBL Index**|2.4236**|5.1749***|5.3596***|
||(1.1497)|(0.8761)|(0.8733)|
|**Log of GDPper capita at t-3**|-42.3806***|-39.1594***|-39.4245***|
||(2.4079)|(1.4145)|(1.3998)|
|**Log of GDPper capita at t-3 squared**|2.5451***|2.5148***|2.5338***|
||(0.1505)|(0.0914)|(0.0905)|
|**% Female at childbearing age **|-0.1218**|0.0811**|0.0864**|
||(0.0499)|(0.0363)|(0.0360)|
|**Fertility rate, total(birthsper woman)**|-0.6323*|-0.3094|-0.2148|
||(0.3249)|(0.2190)|(0.2183)|
|**Proportion of seats held by women in nationalparliaments(%)**|0.0247**|||
||(0.0123)|||
|**School enrollment, secondary, female(% gross)**|-0.0195***|||
||(0.0075)|||
|**Number of observations**|2,492|4,897|4,897|
|**R2**|0.979|0.967|0.967|
|**Adjusted R2**|0.977|0.966|0.966|
|<br>Note: * = 10 percent, ** = 5 percent, *** = 1 percent. Standard erro<br>andyear fixed effects.|<br>rs are in parenthe|<br>ses. Regressions als|<br>o include country|

_4.4 Results by region_
INFRASTRUCTURE AND GROWTH IN AFRICA

**Figure 5. Changes in growth per capita due to changes in growth fundamentals across regions, 2001–5 versus 1991–5, Using [IK1] index of aggregate infrastructure services**

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>Western Europe EAP7 North Africa West Africa East Africa Southern Africa Central Africa AFRICA<br>Stabilization policies Structural policies Infrastructure stock Infrastructure quality<br>Percent<br><!-- End of picture text --> Note: The aggregate index of infrastructure stocks [IK1] summarizes information on telephone main lines and mobile phones (per 1000 workers), electricity generating capacity (in MW per 1000 workers, and the length of the total road network (in km. per sq. km. of arable land).

32
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE connections per employee, which is only a third of the value for the peer group. Water tariffs appear to cover operating costs, but do not make any allowance for capital costs. Overall, the financial value of these inefficiencies adds up to 124 percent of utility revenues, meaning that the utility loses about as much money as it collects. This level of inefficiency is quite typical of water utilities in neighboring countries and is somewhat better than the performance in some other post-conflict countries, such as Democratic Republic of Congo and Rwanda (figure 7).

**Figure 7. Benchmarking hidden costs of water utility inefficiencies**

<!-- Start of picture text --> unaccounted losses % of revenues underpricing collection inefficiencies<br><!-- End of picture text --> Liberia is not currently on track to meet the MDG targets. Reaching the MDG targets would require sustained increased levels of spending on the water and sanitation sector (table 7). Under the assumption that Liberia keeps its existing mix of water and sanitation technologies, the cost of meeting the target would be around $120 million per year for a decade.

**Table 7. Liberia’s spending needs to reach Water and Sanitation MDGs**

||Ca<br>|pital expenditure, of which<br>|:<br>|||
|---|---|---|---|---|---|
||Expansion|Rehabilitation|Total|O&M|Total|
|_Base scenario_||||||
|Water|33.6|18.2|51.7|22.7|74.4|
|Sanitation|23.6|6.6|30.1|17.1|47.2|
|Total|57.1|24.7|81.8|39.8|121.6|
|_Pragmatic scenario_||||||
|Water|10.5|18.2|28.6|16.3|45.0|
|Sanitation|16.2|6.6|22.7|9.7|32.4|
|Total|26.6|24.7|51.4|26.0|77.4|

Source: Banerjee and others 2010.

If services are instead expanded exclusively using lower end technologies, such as stand posts and improved latrines, costs could be as low as $80 million annually. In either case, spending of $25 million a year would be required to redress the rehabilitation backlog, which is mainly in the water sector. Ongoing operations and maintenance requirements are around $30 million a year.

16
|**Table 2: Det**|**erminants of**<br> <br>In school|**youth activ**<br>Male Youth|**ity decision**<br>|**s**<br>F<br>in school|emale You|th|
|---|---|---|---|---|---|---|
||<br>and LF|in LF|inactive|<br>and LF|in LF|inactive|
|Age|0.94|2.19***|0.98|1.78|2.11***|1.77***|
||[0.23]|[0.27]|[0.22]|[0.96]|[0.38]|[0.24]|
|Married|1.5|3.61***|5.54***|1.51|11.26***|26.37***|
||[0.39]|[0.52]|[1.05]|[0.86]|[2.19]|[4.86]|
|Household Head:|||||||
|Educ. (Middle and below)|0.93|0.56***|0.65***|1.29|0.43***|0.39***|
||[0.10]|[0.03]|[0.07]|[0.32]|[0.03]|[0.02]|
|Educ. (Matric and above)|1.14|0.17***|0.28***|0.73|0.18***|0.13***|
||[0.12]|[0.01]|[0.03]|[0.19]|[0.02]|[0.01]|
|Married|1.57**|1.15*|0.97|1.21|1.31**|0.87|
||[0.28]|[0.10]|[0.15]|[0.43]|[0.17]|[0.08]|
|Female|0.89|0.43***|0.73|0.6|0.46***|0.44***|
||[0.28]|[0.06]|[0.21]|[0.31]|[0.10]|[0.07]|
|Female and Married|0.71|0.52***|0.72|0.43|0.78|0.82|
||[0.26]|[0.09]|[0.25]|[0.31]|[0.19]|[0.15]|
|Employed (in agricultural sector)|0.66***|0.44***|0.57***|0.23***|0.65***|1.07|
||[0.10]|[0.03]|[0.08]|[0.09]|[0.07]|[0.09]|
|Employed (in non-agricultural sector)|0.56***|0.52***|0.68***|0.47***|0.43***|0.87**|
||[0.08]|[0.03]|[0.08]|[0.13]|[0.04]|[0.06]|
|Migration within past 10 years|0.83|1.13|1.08|0.82|0.82|1.1|
||[0.19]|[0.12]|[0.24]|[0.43]|[0.12]|[0.13]|
|# of members, 0-5, female|1.34***|1.39***|1.19***|1.37*|1.80***|1.32***|
||[0.07]|[0.04]|[0.06]|[0.23]|[0.08]|[0.05]|
|# of members, 0-5, male|<br>1.53***|<br>1.41***|<br>1.22***|<br>1.54***|<br>1.78***|<br>1.33***|
||[0.08]|[0.04]|[0.06]|[0.24]|[0.08]|[0.05]|
|# of members, 6-14, female|<br>1.47***|<br>1.35***|<br>0.99|<br>1.50***|<br>1.46***|<br>1.07***|
||[0.06]|[0.03]|[0.04]|[0.14]|[0.04]|[0.02]|
|# of members, 6-14, male|1.27***|1.33***|1.19***|1.15|1.44***|1.19***|
||[0.05]|[0.03]|[0.05]|[0.12]|[0.04]|[0.03]|
|# of members, 15-24, female|1.23***|1.14***|0.85***|1.17|1.14***|0.92***|
||[0.05]|[0.02]|[0.04]|[0.11]|[0.04]|[0.02]|
|# of members, 15-24, male|0.71***|0.80***|1.11***|0.67***|0.68***|1.05**|
||[0.03]|[0.02]|[0.04]|[0.07]|[0.02]|[0.02]|
|# of members, 25-65, female|1|0.87***|0.86**|0.91|0.76***|0.82***|
||[0.07]|[0.03]|[0.06]|[0.15]|[0.04]|[0.03]|
|# of members, 25-65, male|0.56***|0.58***|0.96|0.39***|0.54***|0.92**|
||[0.04]|[0.02]|[0.05]|[0.07]|[0.03]|[0.03]|
|# of members 66+|<br>0.84|<br>0.71***|<br>0.97|<br>0.89|<br>0.79***|<br>0.85***|
|,|[0.09]|[0.04]|[0.09]|[0.20]|[0.06]|[0.05]|
|# work / # non work|<br>4422***|<br>3494***|<br>382***|<br>2099***|<br>2204***|<br>205***|
||.<br>[3.93]|.<br>[2.86]|.<br>[0.57]|.<br>[2.46]|.<br>[1.97]|.<br>[0.17]|
|Urban|0.78***|0.70***|0.53***|0.55***|0.44***|0.38***|
|Number of Observations|22392|22392|22392|21262|21262|21262|

Note: 1 Variables included but not reported: age squared, province dummies; 2 standard errors are in brackets; 3. *** p<0.01, ** p<0.05, * p<0.1 23
# **B BIS Data**

## **B.1 International Debt Securities**

The Bank for International Settlements (BIS) produces datasets on international bond issuance and bonds outstanding, by sector and by residence or nationality of the issuer. International debt securities (IDS) are defined as those issued in a market other than that of the country where the borrower resides (Grui ́c & Wooldridge, 2012). This does not necessarily imply that the securities are held by foreigners, but can be taken as an approximation for external of debt securities.<sup>71</sup> Since the IDS data are on a holdings compiled securityby-security basis, granular sectoral splits are easy to obtain, unlike the data on debt from international bank creditors which requires some construction to obtain the split.

The IDS data are important for our exercise. While the BOP data relies on reporting by national statistical offices (which can result in incomplete coverage of portfolio debt securities by sector), the IDS data are compiled directly on a security-by-security basis, which can result in much better coverage. The IDS data can also be presented on a residency basis or by the nationality of the issuing institution. See Avdjiev, Chui, and Shin (2014) and Shin (2013) for a more detailed discussion of this issue.

There are several options for how we allocate international debt securities to each sector. As noted earlier, bonds can be classified based on the residence of the issuer or the nationality of the issuer. Further, the BIS classifies IDS according to sector with several subsectors

> 71While this is a reasonable assumption for most borrowing sectors and countries in the world, there are some exceptions. Most notably, the gap between the set of IDS and the set of externally-held debt securities tends to be considerable in the case of government bonds issued by reserve currency countries, since these countries often issue large amounts of government debt in domestic markets, which are then traded abroad. Lately, this has also been the case for the government bonds of several large EMEs (e.g. Brazil, Mexico, and Poland), albeit to a lesser degree than for government bonds issued by reserve currency countries. For most of these cases, BOP data is available and used. Otherwise, we rely on other data sources first to avoid this issue.

84
Table 4: The choice of how much to export

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Subj. fnancial|-2.659**|||-1.884*||||
|constraint|(1.190)|||(1.059)||||
|Unable to obtain a||-3.777*|||-3.566*|-82.438**|-19.942|
|loan? (Yes=1)||(2.153)|||(1.956)|(39.184)|(30.848)|
|Foreign shock|||0.127***|0.127***|0.129***||0.135***|
|(%∆GDP _×_ %Export)|||(0.026)|(0.025)|(0.025)||(0.024)|
|Domestic shock|||-0.045***|-0.045***|-0.045***||-0.042***|
|(%∆GDP _×_ (100-%Export))|||(0.004)|(0.004)|(0.004)||(0.006)|
|Log(Labor|-2.847***|-2.869***|-2.364***|-2.429***|-2.452***|-5.413***|-2.858***|
|Productivity)|(0.677)|(0.686)|(0.627)|(0.632)|(0.637)|(1.701)|(1.081)|
|Firm Age|-0.241***|-0.241***|-0.125**|-0.122**|-0.120**|-0.130|-0.098|
||(0.056)|(0.056)|(0.055)|(0.055)|(0.055)|(0.105)|(0.076)|
|Small Firm? (Yes=1)|-4.414**|-4.603**|-3.141*|-3.144*|-3.322*|-8.324**|-4.153*|
||(2.000)|(2.023)|(1.837)|(1.832)|(1.836)|(3.656)|(2.339)|
|Foreign Ownership?|10.042***|10.077***|10.197***|10.043***|10.027***|6.253|9.245***|
|(1=Yes)|(2.485)|(2.471)|(2.362)|(2.364)|(2.334)|(3.818)|(2.694)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|41.796|41.796|41.796|41.796|41.796|41.796|41.796|
|F stat||||||5|27|
|Kleibergen-Paap LM test||||||0.081|0.260|
|Hansen J stat||||||0.138|0.007|
|Endog. test||||||0.005|0.586|
|N|6275|6275|6275|6275|6275|6275|6275|

The dependent variable is the _Share_ ( _ExportedSalesit_ ). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

18
exogenous transfer measure as a share of income, _μi_ and _μt_ are state and quarter FEs, respectively and _eit_ is the error term. _Xit_ is vector of controls (and _δ_ is vector of associated coecients) that varies across specications. The transfer measure is scaled by lagged income to generate a multiplier interpretation of coecients (i.e., the dollar value of extra non-transfer income produced in a state when its residents receive an extra dollar of transfers). This specication of growth rates regressed on a scaled scal shock is standard in the multiplier literature.<sup>12</sup> _β_ 0 in Equation 2 is known in the literature as the impact multiplier and is the main coecient of interest in this paper (I also calculate cumulative multipliers below, which turn out to be similar). Relative to cumulative multipliers estimated over longer horizons, impact multipliers have two advantages: rst, they require weaker identication assumptions, as there is only a short window between cause and eect that reduces the number of possible omitted variables; and second they tend to be estimated more precisely (smaller standard errors).

In the spirit of Romer and Romer (2010, 2016), Equation 2 is estimated as a reduced form (by ordinary least squares (OLS)) rather than by instrumental variables (IV) (I also produce IV estimates, which turn out to be similar). The reduced form is appropriate as my exogenous variation is the variable of economic interest (the state-level transfer shock induced by an aggregate policy change), rather than being correlated with the variable of interest, as is typically the case.

I use two measures of non-transfer income as left hand side variables: labor income per capita _Yit_ = ( _WL_ )<sup>_pc_</sup> _i,t_<sup>(equivalent to the wage bill), which is available</sup> for the whole sample, or quarterly GDP per capita _Yit_ = _GDP_<sup>_pc_</sup> _i,t_<sup>from2005.13</sup> All variables are deated by the national quarterly Personal Consumption

> 12For example, a specication of GDP growth rates regressed on the change in expenditure as share of GDP (or similar) is used in Barro and Redlick (2011), Nakamura and Steinsson (2014), Miyamoto, Nguyen, and Sergeyev (2018), Kraay (2014), among others. ∆ _tri,t/Yi,t−_ 1 is equivalent to the change in transfers per capita as a share of income per capita.

> 13Labor income data come from the Bureau of Economic Analysis (BEA) under the ocial title Earnings by place of work and mostly consist of wage and salary disbursements (70%). Labor income excludes income from transfers and is before income taxes. I focus on these dependent variables as other potential outcome variables are missing at the state level (consumption), are not available quarterly (GDP before 2005), are not seasonally adjusted, or are not available for both temporary and permanent transfer samples.

12
# Table 6: Simulated changes in predicted per capita consumption for different <u>counterfactual scenarios by household characteristics (NPR 10,000)</u>

|||E|xpected consu<br>**Level of**|mption<br>**+10% point**|<br>**+10% point**|
|---|---|---|---|---|---|
|Conditionalon:|**Actual**|**No**<br>**migration**|<br>**migration**<br>**as of**<br>**1995/96**|<br>**increase in**<br>**domestic**<br>**migration**|<br> <br>**increase in**<br>**international**<br>**migration**|
|_Ethnicity_||||||
|Brahman/Chhetri|1.850|1.752|1.796|1.934|1.872|
|Dalit|1.052|1.033|1.030|1.088|1.059|
|Newar|2.670|2.557|2.611|2.770|2.696|
|Terai-Hill Janajatis|1.174|1.068|1.123|1.232|1.205|
|Muslim \ Other Minorities|1.254|1.182|1.213|1.316|1.269|
|_Land holdings a year ago_||||||
|Landless households|1.862|1.737|1.804|1.937|1.891|
|Farm plot < 1 ha|1.296|1.224|1.255|1.362|1.314|
|Farm plot: 1-2 ha|1.549|1.465|1.501|1.619|1.571|
|Farm plot > 2 ha|1.876|1.762|1.810|1.943|1.912|
|_Lagged durable asset index_||||||
|No Assets|1.021|0.963|0.989|1.068|1.035|
|Asset poor (1 - 33<sup>th</sup>percentile)<br>|1.076|0.988|1.035|1.142|1.093|
|(33<sup>th</sup>- 66<sup>th</sup>percentile)|1.471|1.380|1.422|1.550|1.491|
|Asset rich (66<sup>th</sup>- 100<sup>th</sup>)|3.104|2.949|3.015|3.210|3.157|
|_Geography dummies_||||||
|Katmandu|3.495|3.334|3.418|3.591|3.541|
|Other urban areas|2.476|2.291|2.391|2.572|2.527|
|Rural western mount/hills|1.187|1.154|1.157|1.241|1.203|
|Rural eastern mount/hills|1.137|1.064|1.105|1.196|1.150|
|Rural western Terai|1.269|1.177|1.223|1.368|1.280|
|Rural eastern Terai|1.388|1.300|1.336|1.443|1.414|
|**Total**|1.493|1.405|1.446|1.561|1.515|

37
category), 2, 3, 4, 5, and ≥ 6. As shown in column 5, in line with the baseline result, over-reporting generally follows an upward trend (except for the 4th year) within the first term of the party secretaries, and peaks in the 5th year. Importantly, the effect is small and statistically indistinguishable from 0 beyond the 5th year, consistent with the finding of Figure 1. In contrast, none of the years-in-office dummies for the mayors is large and statistically significant.

# **5.2 Further robustness checks**

# **5.2.1 Omitted political connections**

Although we have considered the role of political connections when examining the political cycle effects in GDP manipulation, our specific measure based on Jiang (2018) could be insufficient to capture all forms of potential connections. Yet showing that our key results are not driven by omitting political connections is important. Connections with the upper-level officials, for instance, may determine a PS’s ability to fabricate: he may be implicitly allowed to engage in more manipulation. Political connections could also put him on a fast track for promotion and thus affect the length of his tenure. We thus address this concern by including alternative measures of connections in Table 4.

We replicate our baseline result in column 1 for comparison. In column 2, we include the PS’s other important prior working experience, namely, being the city mayor, the vice governor, and a member of the standing committee of the provincial party, to capture political connections formed during the tenures. Since local favoritism by the upper-level officials may play an important role in shaping the local political economy (Chu et al. 2021), in column 3, we control two dummy variables indicating whether the city is the provincial party secretary’s hometown and was the location of his previous workplace respectively. In column 4, we investigate the role of connections that may stem from shared demographic characteristics between the city PS and the provincial party secretary (PPS). We use two variables to capture the potential connection: a dummy indicating that both the city PS and provincial PS are female, and a dummy for hometown ties (i.e., the city PS and provincial PS are from the same prefecture), which have been documented as being important in the political selection in China (Fisman et al. 2020). In column 5, we examine the connections between the city PS and the provincial PS that could be formed due to their shared local working experience, namely having worked in the same provincial government branches and in the same city. Finally, we include all of the above measures of connection in column 6.

Based on columns 2 to 6, a PS with experience as a mayor exaggerates GDP more, while a city that is the hometown of the provincial PS exhibits less manipulation. The coefficients on the other measures of connections are small and statistically insignificant. Most importantly, the coefficients on _YearsInOffice_ and its squared term remain similar to the baseline results in Table 2. Our result on the 19