rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Alternative PTA specification estimation results: net FDI inflows

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|**Variable**|||**Coef**|**cient**|||
|GDP|0.899<sup>_∗∗_</sup>|0.906<sup>_∗∗_</sup>|0.900<sup>_∗∗_</sup>|0.879<sup>_∗∗_</sup>|0.898<sup>_∗∗_</sup>|0.910<sup>_∗∗_</sup>|
||(0.017)|(0.018)|(0.017)|(0.018)|(0.017)|(0.019)|
|OPEN|0.941<sup>_∗∗_</sup>|0.905<sup>_∗∗_</sup>|0.904<sup>_∗∗_</sup>|0.899<sup>_∗∗_</sup>|0.945<sup>_∗∗_</sup>|0.785<sup>_∗∗_</sup>|
||(0.083)|(0.080)|(0.079)|(0.080)|(0.083)|(0.107)|
|GDPGRO|0.256<sup>_†_</sup>|0.263<sup>_†_</sup>|0.261<sup>_†_</sup>|0.271<sup>_†_</sup>|0.260<sup>_†_</sup>|0.342<sup>_∗_</sup>|
||(0.142)|(0.143)|(0.143)|(0.142)|(0.143)|(0.153)|
|CPIGRO|-0.008<sup>_†_</sup>|-0.009<sup>_†_</sup>|-0.008<sup>_†_</sup>|-0.008<sup>_†_</sup>|-0.008|-0.009<sup>_†_</sup>|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|RERGRO|-0.336<sup>_∗_</sup>|-0.322<sup>_∗_</sup>|-0.315<sup>_∗_</sup>|-0.326<sup>_∗_</sup>|-0.338<sup>_∗_</sup>|-0.329<sup>_∗_</sup>|
||(0.146)|(0.146)|(0.147)|(0.147)|(0.146)|(0.155)|
|WLDGRO|-0.677<sup>_†_</sup>|-0.633|- 0.638|-0.635|-0.643|-0.810<sup>_∗_</sup>|
||(0.391)|(0.393)|(0.393)|(0.390)|(0.391)|(0.408)|
|WLDFDIadj|0.373<sup>_∗∗_</sup>|0.404<sup>_∗∗_</sup>|0.412<sup>_∗∗_</sup>|0.391<sup>_∗∗_</sup>|0.372<sup>_∗∗_</sup>|0.390<sup>_∗∗_</sup>|
||(0.039)|(0.038)|(0.037)|(0.036)|(0.039)|(0.039)|
|DISTA|-0.155<sup>_∗∗_</sup>|-0.112||0.026<sup>_†_</sup>|-0.156<sup>_∗∗_</sup>|-0.189<sup>_∗_</sup>|
||(0.058)|(0.095)||(0.015)|(0.059)|(0.093)|
|PTAGDP|0.060<sup>_∗∗_</sup>||||0.061<sup>_∗∗_</sup>|0.052<sup>_∗∗_</sup>|
||(0.018)||||(0.018)|(0.020)|
|PTAGDPm||-1.186<br>(0.770)|||||
|PTAd|||0.276<sup>_∗_</sup><br>(0.117)||||
|PTAnum||||0.035<sup>_∗∗_</sup><br>(0.009)|||
|PTAEXP|||||0.058<br>(0.052)||
|N|1541|1541|1541|1541|1541|1331|
|Log-lhd|-1716.811|-1722.399|-1721.352|-1716.134|-1716.007|-1423.608|
|_χ_<sup>2</sup>|3146.643|3185.418|3170.289|3473.614|3141.041|4080.322|
|Signifcance level<br>_Note:_ Standard <br>logarithms.|s:<br>_†_ 10%<br> errors are in|_∗_5%<br>_∗∗_<br> parentheses.|1%<br> The dependen|t variable is e|xpressed in n|atural|

way to interpret this result is to conclude that the positive impact of the extended market variable is mainly driven by PTAs between large and small countries, rather than partners of equal size. However, this statement must be very heavily qualified due to the complexities in construction of the multiplicative version of _PTAGDP_ .

The second alternative specification focuses on estimating the threshold effect of signing a PTA. Section 4.2 has argued that the _PTAGDP_ variable captures two effects: the initial (threshold) impact of becoming a part of a larger market, and the continued effect of expanding that market through growth and ascension to new PTAs. In order to establish the relative importance of the 34
mercial units account for about 90% of the non-residential in our sample study. Figure 2b also indicate that hotels (tourism sector) were heavily affected by the pandemic as electricity consumption declined significantly during the three months lockdown period. While consumption improved after the lockdown was lifted, it still remained lower than pre-pandemic levels. This result is in line with economic data from several countries indicating the devastating impact of the pandemic on the tourism sector’s contribution to GDP<sup>19</sup> due to SAH and restrictions on international travels (Mulder, 2020; Abbas et al., 2021).

## _Rural-Urban differences_

Finally, we explore the differences in the impact of COVID on electricity consumption between rural and urban households. The average effects are shown in Table 4. The DID estimates suggest electricity consumption among urban households increased by about 5.3% (column 2) compared to a 3.2% (column 4) increase among rural households. To trace the dynamics in the effects, we show estimates of the monthly changes in electricity consumption in Figure 3 (see Table 5 for details<sup>20</sup> ). The results in Figure 3 show an increased level of consumption among both rural and urban households, albeit the rate of increase was consistently higher among urban households for the majority of the period.

Taken together, the analysis of the Rwandan electricity data suggests a modest increase in electricity consumption associated with the COVID-19 pandemic, albeit, non-residential electricity consumption experienced large declines. This partly reflects the differing responses to the impact of the pandemic across various sectors.

# **5.2 Ghana**

We now turn our attention to the Ghana data. Similar to the analysis in Section 5.1, we begin by looking at the average effects of the pandemic on electricity consumption in Ghana, estimated separately for the various user groups, as shown in Table 6. The DID estimate in column 2 suggests that overall, relative to the pre-COVID lockdown period, electricity consumption increased by about 20.8%.<sup>21</sup> This increase appears to be largely driven by residential electricity consumption, which increased by 22.7% during the period (column 4), while consumption by non-residential customers increased only marginally by

> 19https://www.imf.org/en/Publications/fandd/issues/2020/12/impact-of-the-pandemic-on-tourism-behsudi 20See Table A3 for the corresponding first difference estimates. 21 _._ 189 ( _exp −_ 1) _×_ 100 = 20 _._ 8%

11
Table 2: Model Parameters and Baseline Values

|**Sym. Defnition**|**Determination**|**Values**|
|---|---|---|
|**_Panel A. Preference Parameters_**|||
|_σ_<br>Elasticity of substitution across _j_|Comin et al. (2021)|0.25|
|_εj_<br>Income efect non-homoth. CES|Comin et al. (2021)|_{_0_._01_,_1_,_1_._3_}_|
|_θj_<br>Sectoral preference shifters|Calibrated across countries||
|_σν_<br>Dispersion of pref. shocks over _oj_|Hsieh et al. (2019)|2|
|_σs_<br>Elasticity of substitution b/w home<br>and market services|Ngai and Petrongolo (2017)|2.3|
|_αs_<br>Preference weight on home services|Ngai and Petrongolo (2017)|0.2|
|**_Panel B. Ability Distribution and Re_**|**_turns to Ability_**||
|_μgz_<br>Avg. ability _z_ by gender<br>_σgz_<br>S.D. of ability _z_ by gender<br>_κoj_<br>Returns to ability in _oj_|Avg. Years of Schooling<br>S.D. Schooling Yrs<br>Mincer Regressions||
|**_Panel C. Production Technology_**|||
|_γoj_<br>Expenditure shares on _Hoj_<br>_Bj_<br>Sectoral productivity of sector _j_|Wage Expenditure by _oj_<br>Zero proft conditions||
|**_Panel D. Gender Barriers_**|||
|_λoj_<br>Gender norms/excess utility cost|Gender Emp. Ratios||
|_τoj_<br>Female wage discrimination in _oj_|Gender Wage Ratios||

_<u>Notes:</u>_ This table defines all model parameters and describes how we determine them in our quantification exercise. The preference parameters listed in Panel A are constant across countries and time, but sectoral preference shifters _θj_ can vary across countries. All other parameters can vary across countries and years.

# **4.1 Externally Determined Parameters**

**Preference Parameters:** We set the dispersion of workers’ preference shocks across occupation-sector pairs to _σν_ = 2, following Hsieh et al. (2019) and Cassan et al. (2024). For the CES preferences over home and market services, we set the elasticity of substitution to _σs_ = 2 _._ 3, and the preference weight on home services to _αs_ = 0 _._ 2, following Ngai and Petrongolo (2017). For the non-homothetic CES preferences, we use estimates from Comin et al. (2021), setting the elasticity of substitution to _σ_ = 0 _._ 25 and the parameters that discipline income effects to _εj_ = _{_ 0 _._ 01 _,_ 1 _,_ 1 _._ 3 _}_ for _j_ = _{A, M, S}_ .<sup>17</sup> We assume preference parameters to be the same across countries and over time. Sectoral preference shifters _θj_

> 17As in Comin et al. (2021), we can only identify _εj_ relative to a base sector, so we normalize _εj_ for manufacturing to 1.

21
Table 11 OLS estimation<sup>of the employmentequation</sup>

|Explanatoryvariables|(1)|Regressi<br>(2)|onmo|dels<br>(3)||(4)|
|---|---|---|---|---|---|---|
|emplOO|0.946<br>_223.80_|0.939<br>_211.28_||0.941<br>_213.54_||0.950<br>_209.84_|
|_Ownership(public)_<br>||||||0005|
|private|-0.016<br>|0.003<br>||-0.010<br>033||.<br>_018_|
||_0.64_<br>|_0.11_<br>||.<br>||_._<br>|
|mixed|0.035<br>|0.040<br>||0.036<br>_090_||0.037<br>_084_|
||_0.93_<br>|_1.00_<br>||_._<br>||_._<br>|
|export|0.059<br>|0.036<br>||0.047<br> <br>_407_|***<br>_***_|0.041<br>_351_|
||_5.49_<br>|_3.12_<br>||_._<br>||_._<br>|
|credit|0.019<br>|-0.019<br>_163_||-0.019<br>_165_||0.000<br>_0_|
||_1.66_<br>|_._<br>||_._<br>|||
|invest|0.074***<br>|0.075<br>||0.073 <br>_1204_|***|0.072<br>_1084_|
||_11.88_|_12.15_<br>||_._<br>||_._<br>|
|ktol||0.020 <br>|**|0.025<br>_751_|***|0.035<br>_991_|
|||_6.17_<br>||_._||_._|
|wage||0.021<br>_1.69_|*||||
|productive||0.043<br>_8.59_|||||
|labcost||||-0.043<br>_8.57_|||
|profitab<br>||||||0.008<br>_2.24_<br>|
|industry|no|yes||yes||yes|
|constant|0.114|-0.613||-0.040||-0.090|
|No.ofobs.<br><sup>2</sup>|8961<br>7|8822<br>0911||8822<br>0910||6923<br>0917|
|R|0.90|.||.||.|

_Dependent variable:_ log of<sup>average employmentin 2001</sup> _Definitions of explanatory_<sup>_variables:_</sup> emplOO = log of average employment<sup>in 2000</sup> export = 1 if a firm sells<sup>its products aborad,0 otherwise</sup> credit = 1 if a firm has a debt<sup>towards financial institutions, 0 otherwise</sup> capform = log of the index<sup>of fixed-capital growthover one year.</sup> ktol = log of capital-to-labor<sup>ratio</sup> wage = log of average monthly<sup>wage</sup> labcost= log of labor costs<sup>as</sup> a<sup>share of sales</sup> productiv=log productivty<sup>(salesper employee).</sup> profitab log of the ratio of operating profit<sup>to sales</sup> industry = 2-digit NACE classification All explanatory variables<sup>referto 2000,i.e.are lagged one year.</sup> Absolute values of the t-statistics<sup>are in italics</sup> t-statistics were caluclated<sup>usingHuber-Whiterobust standarderrors.</sup>

- significant at the I percent<sup>level,** - significant at the 5 percent level,</sup>

* - signifcantat the 10 percent<sup>level.</sup>

_-33-_
much as appeared to be the case in column 1. Once the interaction of population density and open defecation is accounted for, there is no longer an apparent interaction between urban place and sanitation – which is consistent with a large effect of sanitation on infant mortality even in densely populated rural places. Moreover, population density _per se_ appears neither associated with greater nor lesser mortality.

Table 2 suggests that in developing countries, an interaction between sanitation and population density importantly moderates the relationship between place and early-life health and mortality. The following analyses sharpen our understanding of this interaction, and investigate its external and internal validity.

# **3.3 The interaction of sanitation and population density in 172 DHS**

We have seen that the relationship between urban place and health depends importantly on population density and on open defecation. In this section we focus directly on establishing and interaction between population density and sanitation, and assess the robustness of the estimate.

## **3.3.1 Empirical strategy**

For each dependent variable, we regress health on a linear interaction of local sanitation and population density, controlling for household sanitation and one of three levels of fixed _α_ :

- _country_ : for example, a fixed effect for India, pooling over the 1992, 1998, and 2005 DHS surveys

- _survey_ : a partition of _country_ , for example, a fixed effect for India in each surveyed year

12
75

|**Table 31 Differences of Insurance and Ban **<br> <br>|**k Products**<br>|
|---|---|
|Characteristics<br>Insurance<br>Company|Bank|
|Conditional<br>Saving<br>(Pillar 3a)<br>Risk-Coverage|only Saving|
|Unconditional<br>Saving<br>(Pillar 3b)<br>Risk-Coverage<br>Source: Allianz Suisse|Saving<br>Investment|

# **Table** **<u>32  Differences of conditional and unconditional precautionary capital</u>**

<!-- Start of picture text --> Differences 3a 3b<br>Individuals Employed and aged below the all<br>AHV-age<br>and having a Swiss Tax-Domicile<br>Products limited unlimited<br>Maturity Limited to AHV-age, no limitations<br>but it cannot be paid out earlier<br>than 5 years before the AHV-age<br>Contributions Limited to the maximum amount no limitations<br>that is tax deductible:<br>with a 2nd pillar: 6,192 CHF<br>without a 2nd pillar: 30,960<br>or 20% of total earned income<br>Beneficiary in case of death specified by law free<br>Repurchase under certain conditions possible<br>Pledging possible for the use of own real possible for every reason<br>estate<br>Tax deductible Premiums can be fully deducted Lump–sum deduction possible<br>contributions from taxable income<br>Taxation during the None Repurchase Value<br>contract period and Profit Shares<br>Taxation at the redemption Taxation as income No taxation of insurances<br>applying a reduced rate that are based on premiums<br><!-- End of picture text --> <!-- Start of picture text --> Source: Allianz Suisse<br><!-- End of picture text --> The basic third pillar instruments offered by life insurance companies are listed below. Only products that contribute in some aspects to old-age insurance are mentioned. Thus the detailed terms are negotiated bilaterally.

- _Private Annuity_ : A private annuity provides a lifelong annuity. Some features can be added to the standard case. Hence, it is possible to include the spouse as beneficiary or to agree that the outstanding balance is repaid to the spouse in case the main beneficiary dies.

- _Endowment Insurance_ : This contract provides insurance in case of death. Otherwise
are close to the national average value, and disjoint with confidence intervals among the poorer clusters.

**Table 3. Summary statistics on CVs at PSU level by Governorate**

|Governorate|Minimum|Median|Maximum|
|---|---|---|---|
|**TUNIS**|0.021|0.054|0.396|
|**ARIANA**|0.021|0.046|0.448|
|**BEN AROUS**|0.017|0.055|0.526|
|**MANOUBA**|0.021|0.075|0.407|
|**NABEUL**|0.022|0.060|0.459|
|**ZAGHOUAN**|0.026|0.073|0.579|
|**BIZERTE**|0.027|0.073|0.539|
|**BEJA**|0.035|0.082|0.500|
|**JENDOUBA**|0.023|0.065|0.610|
|**LE KEF**|0.024|0.078|0.453|
|**SILIANA**|0.027|0.081|0.455|
|**SOUSSE**|0.027|0.070|0.566|
|**MONASTIR**|0.024|0.055|0.379|
|**MAHDIA**|0.029|0.075|0.591|
|**SFAX**|0.022|0.063|0.513|
|**KAIROUAN**|0.026|0.062|0.481|
|**KASSERINE**|0.021|0.074|0.583|
|**SIDI BOUZIDE**|0.031|0.069|0.513|
|**GABES**|0.021|0.077|0.499|
|**MEDNINE**|0.023|0.071|0.540|
|**TATAOUINE**|0.030|0.087|0.649|
|**GAFSA**|0.024|0.085|0.570|
|**TOZEUR**|0.028|0.084|0.600|
|**KEBILI**|0.037|0.079|0.428|

Source: our elaborations from INS (2020).

In order to better highlight the findings, in the Annex we have reported four graphs corresponding to the 1,800 clusters selected for the new EBCNV 2021 survey (see Section 4). In particular, Figure A1 reports confidence intervals for all 1,800 clusters, whereas Figures A2, A3 and A4 report confidence intervals for the bottom of the distribution, the distribution near the national mean consumption level, and the top of the distribution, respectively.

14
appearing to die in ways consistent with each of these three theories. We characterize which types of firms are most likely to die for each of these three reasons.

The remainder of the paper is structured as follows: Section 2 discusses the different approaches to measuring firm death that have been used in the existing literature; Section 3 outlines our approach to firm death and the sample we have put together; Section 4 examines the prevalence of firm death; Section 5 which firms are more likely to die; Section 6 why firms die; and Section 7 concludes.

# **2. Measurement of Firm Death in the Existing Literature**

At least four different approaches have been used to measure firm death in the existing literature, each with particular strengths and drawbacks.<sup>3</sup> A first approach is seen in some of the earliest empirical work on the topic, undertaken by Carl Liedholm and Donald Mead in the early 1990s in several sub-Saharan African countries (Liedholm and Mead, 1995; Mead and Liedholm, 1998). They carried out cross-sectional “closed firm” surveys by asking a random sample of households about enterprises they previously ran, but no longer operate, in addition to those they currently run. Based on this data, they report an average closure rate of 12.9 percent per year; although using the same data McPherson (1995) reports annual hazard rates of firm death of 3 to 4 percent per year. This approach has the advantage of allowing for large samples, and for representativeness of the areas in which sampling occurs. However, as the authors themselves acknowledge, it is likely to be much less accurate than panel surveys which track businesses over time, since people may forget or not want to talk about businesses that failed, and there is likely to be substantial recall error as to exactly when the business closed. Moreover, it will not capture people who close their businesses and then migrate out of the area.

A second approach has been to use manufacturing censuses or company registers. Examples include Roberts and Tybout (1996) who provide annual firm exit rates of 10.8 percent in Chile, 11.1 percent in Colombia, and 6.0 percent in Morocco; Bartelsman et al. (2009) who graphically show annual exit rates of approximately 4 to 5 percent in 10 Latin American and Eastern European economies; Klapper and Richmond (2011) who use a firm census of formal firms in Côte d’Ivoire and find annual exit rates of around 10 percent; and Shiferaw (2009) who reports

> 3 A fifth, related, approach has been to use panel labor force surveys to track movements into and out of selfemployment status (e.g. Demirgüc–Kunt et al. 2011; Fajnzylber et al, 2006). This does not enable examination of whether a particular firm survives, since individuals can shut down one firm and start another one, whilst remaining self-employed. Furthermore, labor force surveys typically do not contain information on the firm such as firm age, number of employees, or profitability.

4
In their paper on the determinants of financial system and stock market development, Beck, Demirguc-Kunt, and Levine (2003) also discuss property rights protection. Our methodology and findings differ from theirs in several respects. First, while we use firm-level data on property rights protection, they use a country-level index, compiled by the Heritage Foundation from several private and public sources, and cannot address the issue of whether firm characteristics affect property rights protection. Second, while we consider each institutional theory separately and treat all of them equally, Beck et al (2003) focus on legal origin and settler mortality and use the proxies for other theories simply as control variables. Finally, their sample of 70 countries does not include any of the former Socialist countries that are in our sample. These factors are likely to explain why we find a clear difference in the explanatory power of the legal origin and endowment views for property rights protection and for several related variables, and their paper does not.

The rest of the paper is organized as follows: Section 2 discusses the various institutional determinants of property rights that we investigate in this paper. Section 3 presents the empirical methodology. Section 4 presents the data and the results of the variance decomposition analysis. Section 5 presents additional robustness tests and we conclude with suggestions for future research in section 6.

# **2. Institutional Determinants of Property Rights Protection**

In order to understand better the determinants of firm’s perceptions of property rights protection, we present a one-to-one comparison of four different institutional theories that have been shown by current literature as being important predictors of property rights - Legal Origin, Endowments, Culture and Ethnic Fractionalization.

LLSV (1998) argue that legal systems differ in how much they protect the rights of private investors vis-à-vis the state and minority shareholders. They argue that legal systems that evolved from common law traditions have tended to be supportive of private property rights. By contrast, civil law systems were established by states as acts of policy. They tend to be designed to state administration, are more predictable, and are less likely to favor individuals over the state or to tailor decisions in ways that safeguard individual 5
# **References**

Courtenay Cabot Venton, Catherine Fitzgibbon, Tenna Shitarek, Lorraine Coulter, and Olivia Dooley (2012). _The Economics of Early Response and Disaster Resilience: Lessons from Kenya and Ethiopia_ , Economics of Resilience Final Report.

Clarke, D., Mahul, O., Poulter, R., Teh, T.L. (2016). _Evaluating Sovereign Disaster Risk Finance Strategies: A Framework._ World Bank Policy Research Working Paper.

Clarke, D.J. and R.V. Hill, (2012). Cost Benefit Analysis of the African Risk Capacity Facility, IFPRI.

Clarke, D. and Wren-Lewis, L. (2016). _Solving commitment problems in disaster risk finance._ World Bank Policy Research Working Paper.

Drechsler, M. (2016). Early warning, early action: The use of predictive tools in drought response through Ethiopia’s Productive Safety Net Programme. World Bank Policy Research Working Paper. World Bank (2014). _Ethiopia - Fourth Productive Safety Nets Project : Ethiopia - Productive Safety Nets Project four (English)_ , World Bank.

Government of Ethiopia (2016). _2016 Ethiopia Humanitarian Requirements Document_ .

16
# **C: Olley-Pakes Decomposition of lnTFP, by Year and Select Industries**

<!-- Start of picture text --> 6.00<br>5.46<br>Aggregate<br>5.00<br>Avg. Technical<br>4.08<br>3.73 Allocative<br>4.00<br>3.30<br>2.99 2.90<br>3.00 2.52 2.47 2.75 2.62 2.56 2.63 2.70<br>2.00<br>1.17 1.17<br>0.78<br>1.00<br>0.13<br>0.00<br>-0.07<br>-1.00<br>D: Olley-Pakes Decomposition of lnTFP, by Year and Size<br>6.00 Aggregate<br>5.19<br>Avg. Technical<br>5.00<br>Allocative 4.46<br>4.08<br>4.00 3.57<br>3.33<br>3.11 3.17<br>2.94 2.98 2.97 2.92<br>2.81<br>3.00 2.63 2.59<br>2.44<br>2.34<br>1.86<br>2.00<br>1.48<br>1.10<br>1.00 0.60 0.59 0.75<br>0.48 0.48<br>0.00<br>ln(TFP)<br><!-- End of picture text --> 33