rafmacalaba/datause-encoder-data huggingface dataset:
_Table 1: Internet access in favor of:_

||**2018**|**2019**|**2020**|**2021**|
|---|---|---|---|---|
|**Algeria**|male|male|male|male|
|**Bahrain**|male|female|female|female|
|**Egypt, Arab Rep. **|male|male|male|male|
|**Iran, Islamic Rep. **|female|male|equal|male|
|**Jordan**|male|male|female|male|
|**Kuwait**|equal|equal|male|male|
|**Lebanon**|male|male|male|male|
|**Morocco**|male|male|male|male|
|**Oman**|male|male|male|female|
|**Qatar**|male|male|male|male|
|**Saudi Arabia**|female|equal|equal|equal|
|**Tunisia**|male|male|male|male|
|**United Arab Emirates**|male|female|female|female|

Source: Constructed by the author using the gender gap access indicator of the Internet Inclusion Index constructed by the EIU.

_Note: The measure of gender gap in internet access is computed as (% male access - % female access / % male access). Positive values indicate that male access exceeds female access. A smaller or negative gap indicates greater female connectivity (The Economist Intelligence Unit, 2021a)._ _Figure 2: Gender Economic Gap and Gender Gap in internet access (2018-2021)_ Source: Constructed by the author using gender gap access indicator of the Internet Inclusion Index constructed by the EIU and female-to-male labor force ratio from the World Development Indicators.

7
**Table 11: Observed and simulated mean accepted wages**

||Benchmar|k<br>Same_gend<br>er|frictions|wage_dist|H_prod|preferences|cost|
|---|---|---|---|---|---|---|---|
|pt_m|1.3|1.3|1.3|1.3|1.3|1.3|1.3|
|ft_m|0.7|0.7|0.7|0.7|0.7|0.7|0.7|
|pt_w|1.2|1.3|1.2|1.3|1.2|0.0|0.0|
|ft_w|0.6|0.7|0.6|0.7|0.6|0.0|0.0|

_Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey. _Note:_ Abbreviations: FT= employed full-time; PT= employed part-time. This table presents the converted values in 2011 USD PPP dollars.

**To understand the results of these counterfactual experiments, it is important to recall findings from the estimated parameters.** For example, the parameters indicated that husbands have a labor market advantage in wages offered, as well as a higher probability of finding or losing jobs. Moreover, the leisure preferences of husbands are weighted more heavily than for wives in the instantaneous utility, husbands’ search costs are higher, and wives are on average more productive in home production.

**Figure 8: Mean time in home production by labor market state (%)**

<!-- Start of picture text --> Males without children Males with children<br>60 60<br>50 50<br>40 40<br>30 NP 30 NP<br>UE UE<br>20 20<br>PT PT<br>10 FT 10 FT<br>0 0<br>Females without children Females with children<br>60 60<br>50 50<br>40 40<br>30 NP 30 NP<br>UE UE<br>20 20<br>PT PT<br>10 FT 10 FT<br>0 0<br><!-- End of picture text --> _Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey. _Note:_ Each bar represents the average time allocation in home production of husbands and wives on each labor market state.

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- 21 -

significantly, as shown in Table 1. Though, the appliance ownership is still an important determinant of the residential demand for electricity. Thus, a number of dummy variables for various household appliances are included in _X_ 1 as independent variables.

**Alternative energy choice.** Fourth, if there are any important alternative energy sources, such as liquefied petroleum gas (LPG) and firewood, those substitutes should be incorporated in the estimation model. Ideally, with sufficient data the model should be estimated as a system of multiple demand functions (e.g., Nauges and van den Berg, 2009). However, data are not always available. Even in a single equation model, the demand function can also involve prices of alternative sources (Filippini and Pachauri, 2004). An alternative approach may be to introduce another equation for deciding whether or not to connect the service (Basani _et al_ ., 2008). Behind this approach, it is implicitly assumed that some alternatives exist. Given this specification, the conventional Hausman self-selection model can be used for estimation.

In the case of Albania, there are few important alternative energy choices for households. The country has already achieved almost universal access to electricity network, though the quality of supply still remains problematic. The access rate was 99.97 percent in 2005. People may use LPG for heating and especially for cooking, but the use of gas may be relatively limited, in particular in urban areas.<sup>14</sup> By contrast, many firms are equipped with their own private backup facilities.<sup>15</sup> Therefore, no alternative choice is explicitly considered in our empirical model. The paper takes into account whether households are using electricity or LPG for major household activities, such as heating and cooking. This is expected to

> 14 There is some difference between the LSMS data and people’s perception about the energy alternative. While the LSMS data indicate that some amount of household expenditure was spent for LPG and firewood, people perceive that they are largely dependent on electricity for energy. This is partly because there is a considerable difference in living conditions between urban and rural areas. In rural areas, firewood is often an alternative energy source. According to the 2005 LSMS, some 70 percent of rural households used some firewood for heating or lighting, while only 30 percent of urban residents used.

> 15 According to the 2005 LSMS, only about 3 percent of households answered that an alternative energy source for lighting was power generator. On the other hand, about 80 percent of firms in the country have their own backup generators, which are estimated to have generated some 30 percent of firm electricity consumption in 2006 (Iimi, 2010).
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labor and non-labor resources in agriculture. Given the multiple paths through which migration is bound to affect these decisions, it is difficult to sign these relationships a priori. The available empirical literature on the topic is ambiguous; relatively few empirical studies have been carried out, and for those that do, findings do not provide a consistent pattern.

Only recently has quantitative research begun to relate migration and farm production in Albania. Germenji and Swinnen (2004) find that remittances encumber farm efficiency in rural Albania due to reduced labor efforts. Azzarri et al. (2006) find that international migration networks are associated with a significantly lower probability of participation in the labor force by the remaining household members. However, while McCarthy et al. (2006) document a drop in the quantity of agricultural labor effort as well, they find a reallocation of land use towards less labor intensive production systems, particularly livestock, ultimately resulting in greater agricultural and total household income.

In analyzing the impact of migration on family farming decisions, this paper extends these earlier studies in two directions. First, we explore the linkages between migration and family labor allocation, testing for the hypothesis of an increasing feminization of agriculture as a result of international migration being male dominated. Second, we analyze the relationship between migration and investments in productivityenhancing and time-saving technologies, testing the hypothesis that households are compensating for less agricultural labor effort with investments in capital intensive technologies.

The analysis in this paper is primarily based on data from the 2005 Albania Living Standards Measurement Study survey (ALSMS05), carried out by the Albanian Institute of Statistics (INSTAT) in collaboration with the World Bank. The remainder of the paper is organized as follows. In the next two sections, we first briefly introduce the Albanian context and then summarize some of the empirical literature on the topic. In Section IV, we describe the data and present some descriptive statistics. We continue in Section V by outlining the estimation strategy adopted and describe the empirical model. The estimation findings are presented in Section VI, before concluding in the last section.

3
# **Tables**

||**Table 1: Summary Statistics**|||
|---|---|---|---|
|Variable|Description|Treated|Control|
|Agriculture|Employment rate in Agriculture|0.19<br>(0.13)|0.18<br>(0.03)|
|Other Sectors|Employment rate in other sectors (manufacturing and construction)|0.54<br>(0.11)|0.52<br>(0.12)|
|Unemployment|Unemployment rate|0.16<br>(0.006)|0.11<br>(0.04)|
|Females|Share of active females|0.26|0.27|
|||(0.04)|(0.04)|
|Age 15-17|Share of population ages 15-17|0.01|0.01|
|||(0.03)|(0.01)|
|Age 18-50|Share of population ages 18-50|0.26<br>(0.04)|0.27<br>(0.02)|
|Age 51-65|Share of population ages 51-65|0.04<br>(0.01)|0.01<br>(0.01)|
|No education|Share of working age population with no education|0.08<br>(0.06)|0.07<br>(0.02)|
|Low education|Share of working age population with at least<br>secondary education|0.33|0.35|
|||(0.07)|(0.04)|
|High education|Share of working age population with at<br>least tertiary education|0.57|0.57|
|||(0.11)|(0.06)|
|Urban|Share of working age population in urban areas|0.61<br>(0.22)|0.62<br>(0.04)|
|SPEI at 12 months|Average value of SPEI at 12 months|-0.21<br>(0.68)|0.37<br>(0.58)|

_Notes_ : Sample size is 480 (24 governorate × 20 year cells). Standard deviation in parenthesis.

30
improvement in efficiency and resource allocation that may follow from MNC entry into monopolistic host country industries. Another problem is that there is some confusion regarding effects that are endemic to MNCs and those that are only speeded up by MNC presence. Few authors have been able to make a proper distinction between these two effects, but it may not be a crucial issue in the present context. What matters is the impact made by MNCs, and not the question of whether it is caused by foreign ownership or some other of the MNCs' characteristics. Yet another (perhaps more important) complication is that there is no simple relationship between competition and efficiency, on the one hand, and concentration, on the other hand. This will warrant some further comments later.

Moving to the empirical findings, it is clear that the overwhelming majority of studies are able to establish a positive correlation between foreign entry and presence and seller concentration in host country industries (see e.g. Dunning, 1993 and Caves, 1996, for surveys). However, the causal links are more difficult to establish. One finding is that the correlation disappears once other determinants of concentration are taken into account, and that MNCs do not cause concentration but are drawn to concentrated industries (Fishwick, 1981; Globerman, 1979b). Knickerbocker (1976) shows that entries by MNCs into the US market in the 1960s led to lower concentration, and that the same pattern was evident also for Canada, Italy, France, and West Germany. Commenting on these and other studies, Caves (1996, p. 89) concludes that the "'correlations do not themselves prove that any direct causal relationships exist between foreign investment and concentration". It should, however, be noted that most of the studies look at effects of MNC entry, and it is possible that the concentration-reducing impact does not hold for already established affiliates, who may instead be interested in building barriers to entry.

Regarding the studies of developing countries, most authors have not been able to - or have not even tried to - determine whether the high degrees of concentration in the industries where foreign affiliates are present have been caused by MNCs or whether MNCs have just been attracted to these industries by good profit opportunities. Two (seemingly contradictory) exceptions are Evans (1977), who claims that MNCs tended to reduce concentration in the oligopolistic Brazilian pharmaceutical industry, and Newfarmer (1979), who argues for the 30
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Table 3: Correlation between migration and land tenure arrangements (OLS)

||(1)|(2)|(3)|(4)|(5)|(6)<br>(7)|
|---|---|---|---|---|---|---|
||fxed rent|fxed (money)|fxed (crop)|sharecropping|relatives|contract length<br>written contract|
|migration|0.182*|0.431***|-0.249***|-0.540***|0.291***|0.290***<br>-0.0232|
||(0.103)|(0.0879)|(0.0779)|(0.102)|(0.0673)|(0.0978)<br>(0.0740)|
|2012 year|-3.247|-0.307|-2.940|-3.947|7.420***|20.78***<br>3.541**|
||(2.618)|(2.427)|(1.807)|(2.578)|(1.518)|(2.468)<br>(1.702)|
|Constant|33.96***|0.905|33.05***|76.86***|-13.25***|18.82***<br>12.70***|
||(6.622)|(5.469)|(5.071)|(6.603)|(4.111)|(6.238)<br>(4.796)|
|Observations|927|927|927|927|927|927<br>927|
|R-squared|0.004|0.022|0.019|0.036|0.056|0.090<br>0.005|
|Note: This table|uses an O|LS regressio|n to present|the correlation|between|the percentage of rented-in acreage|
|under various te<br>percentage of th<br>percentage of acr|rms and t<br>e total ma<br>eage of re|he number o<br>le out-migra<br>nted in land.|f high-skilled <br>nts between <br> Robust stan|male out-mig<br> the ages of 2<br>dard errors. **|rants bet<br>0-64 years<br>* p_<_0.01,|ween the ages of 20-64 years (as a<br>).<br>The dependent variable is as a<br> ** p_<_0.05, * p_<_0.1.|
Table 7: Systematic Difference between New and Official Growth Measures

||Absolute diference|Underestimation|Overestimation|
|---|---|---|---|
||(1)|(2)<br>diference_>_0|(3)<br>diference_<_0|
|(log) GDP per capita|-0.36*|-0.22|0.54*|
||(0.21)|(0.30)|(0.30)|
|Statistical capacity|-0.0029|0.00093|0.032*|
||(0.0094)|(0.013)|(0.018)|
|Voice and accountability|-0.33*|-0.32|0.44*|
||(0.18)|(0.28)|(0.22)|
|Agriculture share|-0.019|-0.0077|0.062|
||(0.027)|(0.048)|(0.039)|
|Industry share|-0.0080|-0.011|-0.016|
||(0.021)|(0.039)|(0.025)|
|Service share|0.0091|0.042|0.018|
||(0.025)|(0.046)|(0.029)|
|Constant|4.58*|1.21|-9.79**|
||(2.71)|(4.75)|(4.05)|
|Obs (number of countries)|76|32|44|
|Adjusted _R_<sup>2</sup>|0.042|-0.044|0.20|

_Note_ . The sample includes 76 EMDEs between 2013Q2 and 2020Q4. Throughout, standard errors are in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# _Short-Run Differences between National Accounts and Nighttime Lights_

Next, we abstract from long-run differences and focus on short-run differences. While systematic differences between official GDP growth and nighttime light-adjusted GDP growth are revealed in the long run, short-run differences can provide insights into GDP growth measurement errors in extraordinary episodes when official measures might be inadequate, such as during conflicts, social unrest, and epidemics. By allowing for country-specific intercepts in equation (9), long-term differences are filtered out. The nighttime light-adjusted GDP measure fluctuates around GDP as measured in the national accounts, but there are no differences on average over the estimation period. Large absolute differences between the two imply both strong under- and overestimation of GDP.

Our light-adjusted measure derived from equation (10) may be especially insightful 22
The introduction of an autonomous regulator is also significantly positively associated with higher access, confirming that regulators can also play an important role in ensuring that contracts were effectively designed. IPPs sold their generated electricity to state-owned utilities, which entered into long term power purchasing agreements (PPAs) with state-backed guarantees for the off-taking utility’s performance. By signing long term PPAs, IPPs accepted construction and operating risks and shared fuel availability risk with fuel suppliers. In most cases, they were protected from demand risk under the terms of their PPAs for their debt servicing needs and equity returns.

**TABLE 6 FIXED AND RANDOM EFFECT TESTS**

||**Access**|**Operational**<br>**Efficiency**|**Financial Efficiency**|**Emissions**<br>**Index**|
|---|---|---|---|---|
||**HAUSM**|**AN TEST FOR FIXE**|**D EFFECT**||
|**Hausmanχ2 (8)**|7.55|17.49***|98.75***|7.39|
||**PR**<br>|**EFERRED SPECIFIC**|**ATION**||
|Fixed effect|||||
|Random effect|||||
|**BR**<br>|**EUSCH AND P**|**AGAN LAGRANGIAN**|**MULTIPLIER TEST**||
|**BPLMχ2 (1)**||323.91***|31.91***||
||**PR**|**EFERRED SPECIFIC**<br>|**ATION**<br>||
|Random effect|||||
|Pooled|||||

Finally as expected higher income countries are characterized by significantly higher levels of access, as is the case for countries with larger system size. The interacted term between GDP per capita and power system size is negative, revealing decreasing returns to scale by the highest income countries - such as OECD countries - which have already achieved close to universal access and face an exponentially increasing cost in connecting each new consumer as they approach full coverage. The explanatory power of these variables is very high, reaching 90% in the preferred specification of random effects.

# **ii. Labor productivity**

The level of labor productivity (expressed in terms of energy sold per employee) is significantly reduced by both partial and full vertical unbundling. It is instead enhanced by the increase of disaggregation in distribution. More players in distribution are expected to drive efficiency gains through means such as benchmark competition (also known as yardstick competition).

The introduction of private sector participation also helped to significantly enhance operational efficiency and labor productivity in distribution.

14
<!-- Start of picture text --> 70 100,000<br>60<br>50<br>40<br>10,000<br>30<br>20<br>10<br>0 Cambodia<br>1,000<br>100 1,000 10,000 100,000<br>GDP per capita (constant 2000 US$)<br>Brazil Botswana China Hong Kong, China<br>India Indonesia Japan Korea, Rep.<br>Malaysia Malta Oman Singapore<br>Taiwan, China Thailand Vietnam Cambodia<br>EXPY<br>China Thailand India Sri Lanka Malaysia Pakistan Vietnam Lao PDR Bangladesh Cambodia<br><!-- End of picture text --> _<u>Note: EXPY is an indicator of the complexity of a country’s basket of exports. Cf. Hausmann and Klinger (2007). Source: World Bank, based on UN Contrade database and</u> WDI._

# **III. Historical Overview**

14. Before reviewing the key constraint to growth, this section seeks to derive lessons from the recent episode of rapid growth. It is expected that to achieve rapid growth, some key constraints were indeed overcome. A review of rapid growth will also help the comparative advantage on which future growth could build.

15. **Cambodia has established a strong track record of growth and stable macroeconomic conditions** (Figure 3). Growth accelerated in 1999 as the domestic political situation became clearer and the external economic situation improved following the 1997 Asian crisis. Growth averaged 9.8 per annum, with inflation largely remaining below 5 percent throughout the period. A review of this performance suggests three main dimensions to this achievement.

**Figure 3: Growth has been rapid and inflation low**

a/ Growth (%, left) and GDP per capita (US$, right) b/ Inflation (%, left) and US$ rate (riels, right) <!-- Start of picture text --> 14 700<br>593 14 4,200<br>12 600<br>10.2 12 4,000<br>10 500<br>10.8<br>10 3,800<br>8 400<br>285 8 3,600<br>6 300<br>6 3,400<br>4 200<br>4 3,200<br>2 100<br>2 3,000<br>0 -<br>0 2,800<br>1997 1999 2001 2003 2005 2007<br>1997 1999 2001 2003 2005 2007<br>-2 2,600<br><!-- End of picture text --> _<u>Note: the NIS has not yet updated population figures. Hence GDP per capita based on projections. Source: NBC and NIS.</u>_

# **History and Geography**

16. **The first dimension of this growth episode has been the country’s favorable geography and its recovery from conflict and instability** . Cambodia sits in a dynamic region, with access to international markets and dynamic neighbors such as Vietnam and Thailand.

7
over time. Ducruet and Notteboom (2012) tackle the evolution of shipping networks using Lloyd’s Marine Intelligence Unit data from 1996 and 2006. They argue that, over time, the shipping network converges to a structure with densely concentrated and connected neighborhoods of ports. These neighborhoods are distant from one another and are connected via hubs (see Appendix 6.1 for more details). Hoffmann and Hoffmann (2020) compare the global 2020 network structure of ports to that of 2006. One of their main findings is that the network center has shifted from Europe to Asia with the major shifts being associated with Chinese ports (e.g., Nansha, Ningbo, and Shekou).

The amount of market power affects the freight rates in two ways. First, the markup over the marginal cost of transportation increases in market power: According to Hummels et al. (2009) it is lower on thick routes with more carriers and greater traffic. Second, larger traffic prompts carriers to use larger vessels, which minimizes the average cost of transportation (Asturias, 2020; Ganapati et al., 2020). Both effects work in the same direction lowering the observed freight rates on thicker routes with larger traffic. Potentially, over-reliance on larger ports may increase the risk of the lack of resilience of global transportation system to various shocks, which should become a topic of future research explorations.

Containerized maritime transportation is very concentrated. In 2018, the top 9 carriers (Maersk, MSC, Cosco-OOCL, GMA CGM, Evergreen, Hapag-Lloyd, ONE, and Yang Ming) organized in three alliances controlled 80% of the market (Merk et al., 2018). Furthermore, there are many “thin” routes, i.e., routes served by either a single or very few carriers. Hummels et al. (2009) reported a high degree of the within-route concentration, with the majority of importer-exporter pairs served by three or fewer ships, often owned by a single carrier. Using BlueWater Research data for years 20122021, Ardelean and Lugovskyy (2021), reported that up to 60% of rotations<sup>26</sup> connecting to the US were served by 4 carriers or less. Naturally, we can expect relatively high markups charged by the carriers, especially on the thin routes (see Hummels et al., 2009, for a discussion of the variation in route-specific markups).

> 26 Rotations are defined as ports on a given schedule, similar to having bus stops on a bus schedule. Rotations often include over two ports in multiple countries.

28