rafmacalaba/datause-encoder-data huggingface dataset:
(construction) was more than three-fold. In the same year, the manufacturing sector was 80 percent more productive than the trade services sector, and the business services sector was 55.9 percent more productive than the manufacturing sector. Excluding the mining sector, the sectors with the consistently highest labor productivity are the business services, transport services, and manufacturing sectors. Figure 11 shows that these are also the only three sectors with labor productivity higher than that of the overall economy. Due to the large productivity gaps between sectors, at least in principle structural change toward more productive sectors can greatly improve the aggregate productivity of the Malaysian economy – but only if structural change indeed strengthens the relative importance of more productive sectors.

**Table 9: Average annual growth in output per worker, 1987-2018**

||**Average**|**annual growt**<br>**(**|**h in output p**<br>**%)**|**er worker**|**Average a**<br>**(e**<br>**1987**|**nnual growt**<br>**xcluding cri**<br>**1997**|**h in output**<br>**sisyears) (%**<br>**2007**|**per worker**<br>**)**<br>1987|
|---|---|---|---|---|---|---|---|---|
||**1987-1997**|**1997-2007**|**2007-2018**|1987-2018|**-**<br>**1997**|**-**<br>**2007**|**-**<br>**2018**|-<br>2018|
|Agriculture|2.9|2.3|2.2|_2.4_|1.4|3.0|2.6|_2.4_|
|Mining|-4.8|3.3|-6.6|_-2.5_|-4.3|3.0|-4.2|_-2.2_|
|Manufacturing|6.1|5.1|1.7|_3.9_|6.3|8.4|2.8|_5.3_|
|Construction|1.5|-3.1|5.6|_1.5_|1.8|0.7|7.0|_3.8_|
|Trade|8.4|0.7|2.3|_3.1_|8.6|5.3|1.9|_4.7_|
|Transport|3.5|3.3|2.1|_2.8_|3.0|4.8|4.6|_4.2_|
|Business|9.1|-0.1|6.7|_4.7_|8.9|1.5|2.8|_4.1_|
|Other|1.2|3.3|0.9|_1.8_|1.2|2.6|2.7|_2.3_|
|_Aggregate_|_2.9_|_2.0_|_1.5_|_2.0_|_2.4_|_3.8_|_2.4_|_2.8_|
|_Aggregate excluding_<br>_mining_|_4.8_|_2.4_|_2.1_|_2.8_|_4.5_|_4.7_|_2.8_|_3.8_|

Source: Authors’ calculations based on data from DOSM Note: The excluded years are 1997-1999, 2001-2003, and 2008-2010.

**Figure 10: Output per worker by sector, 19872018**

<!-- Start of picture text --> 200,000.00<br> 150,000.00<br> 100,000.00<br> 50,000.00<br> -<br>1987 1997 2007 2018<br>Construction Other Agriculture<br>Trade Manufacturing Transport<br>Business<br>Output per worker (2015 RM)<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM

**Figure 11: Ratio of sectoral productivity to aggregate productivity, 2018**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Source: Authors’ calculations based on data from DOSM<br>Note: A value higher than 1 indicates that a sector is more<br>productive than the economy as a whole, vice versa.<br>productivity<br>Other Trade Business<br>Ratio of sector productivity to aggregate Construction Agriculture Manufacturing Transport<br><!-- End of picture text --> Between 1987 and 1997 and between 1997 and 2007, the correlation between initial labor productivity and subsequent labor reallocation was positive (see Figures 12 and 13). However, this correlation was negative 15
# **1 Introduction**

Climate change is generating unprecedented pressure on the agriculture sector as the increased variability of weather translates into extreme events that are largely responsible for short- and medium-term impacts (Day et al., 2019; Stevanović et al., 2016; Key and Sneeringer, 2014). The frequency, duration, and intensity of these events, in particular droughts, are expected to dramatically intensify in the future (Fischer et al., 2021; Chiang et al., 2021). Recent estimates point at a reduction in the global agricultural total factor productivity by 22 percent (Ortiz-Bobea et al., 2021). However, the regional distribution of these impacts is largely heterogeneous. In the Middle East and North Africa (MENA) region, it is projected that surface water availability will be reduced by 5-40 percent during 2030-2065 compared to the availability during 1976-2005, with decreases in runoff of 10-63 percent by mid-century in Morocco and Tunisia (Pörtner et al., 2022). In addition, climate conditions strongly interact with the local socioeconomic context; therefore, economies that are heavily dependent on the agriculture sector, with weaker institutions and poor safety nets, are those paying the highest toll (Hallegatte et al., 2018). These interactions are already manifesting with a greater intensity on the African continent, an area that is considered a climate hotspot (Blunden and Arndt, 2020; Niang et al., 2014). While the direct impacts of weather variability on land productivity are well documented (Schleussner et al., 2018; Dell et al., 2014; Deschênes and Greenstone, 2007; Di Falco et al., 2011, among others), we know much less about how these impacts spill over into the labor markets. This gap is particularly evident in MENA countries, where the limited availability of data leaves policy makers with little guidance (Cramer et al., 2018).

In this paper we contribute to address this gap by investigating the effects of severe drought shocks on the Moroccan labor market using individual labor-force surveys aligned with granular weather data at the provincial level. In a staggered difference-in-difference setting, we 2
ecosystems, which are critical to the livelihoods of many rural communities, as detailed in section 3. Soil erosion, deforestation, and water scarcity can reduce agricultural productivity and biodiversity, undermining the sustainability of local economies and food systems.

In addition, repeated weather shocks can lead to chronic poverty. Households that are frequently affected by employment and income losses may need to deplete their savings or assets, making it increasingly difficult to recover between shocks. As a consequence, households may be trapped in a state of persistent poverty. Finally, as weather shocks become more frequent and severe, households may be forced to migrate in search of better living conditions and economic opportunities. This can lead to further urbanization in Paraguay, and put additional strain on its urban infrastructure and services. Internal displacement can also lead to social tensions and deteriorate the vulnerability of both displaced and host communities.

# _Estimation challenges and limitations_

Our results are consistent with previous literature that has established association between weather shocks and poverty (Hallegatte & Rozenberg, 2017). Still, some noise in the estimates remain, which we attribute mostly to the timing of the weather shocks, the timing of data collection, and the cyclical nature of economic activities. First, EPH data is collected over a time period of three (and in some years slightly more) months, between October and December (or January), corresponding to the spring and summer months in Paraguay. As described in Section 3, extreme weather events are most common during the summer months, so there may be a variation in the effects we are able to observe depending on whether the household is interviewed in, say, the beginning of October, versus the end of December. As such, the time periods we use to construct our weather variables, which we match with survey data based on the date of the interview, cover different parts of the year, and in the case of households involved in agriculture, the agricultural calendar. We control for seasonal variation by including dummies based on the month of data collection, but there may be unobserved variation we fail to capture. Against this backdrop, we expect the true impacts of extreme weather events to be larger in magnitude than what we can estimate with our data, given that the impacts will be most harshly felt only after the summer, or most severely if weather extremes occur during crop growing cycles. Furthermore, temporal fluctuation in our main variable of interest may persist, especially in rural settings where growing and harvest seasons add to seasonal variation.

There are a few limitations to our study. First, since we only have cross-sectional data, we are unable to track households over time and estimate relationships between weather shocks and poverty over time. Furthermore, these results may underestimate the true impact of weather 26
# **8. References**

- Bandiera, Luca, Nina Budina, Michel Klijn, and Sweder van Wijnbergen (2007) “The ‘How To’ of Fiscal Sustainability: A Technical Manual for Using the Fiscal Sustainability Tool” World Bank Policy Research Working Paper No 4170

- Budina, Nina, and Sweder van Wijnbergen (2007) “Quantitative Approaches to Fiscal Sustainability Analysis: A New WorldBank Tool Applied to Turkey” World Bank Policy Research Working Paper No 4169

- Celasun, Oya , Xavier Debrun, and Jonathan D. Ostry (2007) “Primary Surplus Behavior and Risks to Fiscal Sustainability in Emerging Market Countries: A Fan-Chart Approach,” _IMF Staff Papers_ , 53(3): 401–425.

- Hamilton, J. D. (1989) “A new approach to the economic analysis of nonstationary time series and the business cycle,” _Econometrica_ 57, 357–384.

- Krolzig, H.-M. (1997), _Markov Switching Vector Autoregressions: Modelling, Statistical Inference and Application to Business Cycle Analysis_ , Lecture Notes in Economics and Mathematical Systems, 454, Springer-Verlag, Berlin.

- Ley, E. (2006) “Fiscal (and External) Sustainability,” (Unpublished, The World Bank), available at http://129.3.20.41/eps/pe/papers/0310/0310007.pdf

- Politis, D.N. and Romano, J. (1992) “A circular block-resampling procedure for stationary data,” in _Exploring the Limits of Bootstrap_ , edited by Raoul LePage and Lynne Billard, John Wiley, 263–270.

- Politis, D. N. and H. White (2004) “Automatic block-length selection for the dependent bootstrap,” _Econometric Reviews_ 23(1): 53–70. (See also: A. Patton, D.N. Politis, and H. White (2008) “Correction to: ‘Automatic Block-Length Selection for the Dependent Bootstrap’ by D. Politis and H. White.”)

11
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# **Table 4.3. GSM roaming in ECCAS**

||Angola|Burundi|Cameroon|Central African Republic|Chad|Congo, Rep. of|Congo, Dem. Rep. of|Equatorial Guinea|Gabon|São Tomé and Príncipe|
|---|---|---|---|---|---|---|---|---|---|---|
|Angola||X||X||X|X|X|X||
|Burundi|||||||||||
|Cameroon||||X|X|X|X|X|X||
|Central African Republic|X||X||X|X|X|X|X||
|Chad|||X|X||X|X||X||
|Congo,Rep.of|||X||X||X|X|X||
|Congo,Dem. Rep. of|X|X|X||X|X||X|X||
|Equatorial Guinea|X|X|X|X|X|X|X||X||
|Gabon|X||X|X|X|X|X|X|||
|São Tomé and Príncipe|||||||||||

_Source:_ Derived from Ampah and others 2009.

_Note_ : P = preferential (cheaper tariffs and free incoming calls when using same group network—typically available for prepaid subscribers); X = available but with regulator roaming charges and typically limited to postpaid subscribers; GSM = global system for mobile communications.

A few large operators are beginning to develop a regional presence. All ECCAS countries allow foreign investment in the telecommunications sector. Most countries have two to three foreign operators (table 4.4). There are over a dozen different strategic investors in the region. Zain of Kuwait has the most investments (four countries), followed by France Telecom (three countries). Incumbent operators are partly privatized in three of the countries through sales to strategic investors in Equatorial Guinea (France Telecom), Gabon (Maroc Telecom), and São Tomé and Príncipe (Portugal Telecom). France Telecom once owned part of the incumbent in the Central African Republic but the status of this shareholding is presently unclear. No shares in any telecommunications operator in the region are listed on local stock exchanges.

Relative to other regions, Central Africa suffers from a lack of connectivity to submarine fiber-optic cables. Only Angola, Cameroon, and Gabon have direct access to the undersea fiber-optic South Atlantic 2/West Africa Submarine Cable (SAT-2/WASC). This cable extends from Malaysia to South Africa and then up the west coast of Africa to Portugal and Spain. At present, only three coastal ECCAS countries have landing stations for SAT-3: Angola, Cameroon, and Gabon. The remaining coastal and landlocked countries are bypassed at present—and also lack terrestrial fiber-optic connections with the regional network, which would at least provide some form of indirect access.

48
# **4. Policy implications for Turkey**

As discussed in section 2, Turkey traditionally struggled to attract foreign direct investment, until the country saw a change in fortune in 2005. As discussed in more detail in Sánchez-Martín et al. (2014), the jump in FDI inflows observed in 2005-08 is likely to have to do with the signaling caused by the official launch of _EU_ accession negotiations, among other factors. This seems to be corroborated by the significant coefficient of the EU membership dummy variable present in the cross-country empirical analysis conducted for this paper.

However, in the context of the international economic crisis and the crunch in Southern EU members, starting in 2009, foreign direct investment slowed down. Net FDI inflows to Turkey halved to 1.6 percent of GDP in 2009-2013, from 3 percent of GDP in 2005-2008. This compares to a regional median for countries in Eastern Europe of 3 and 6.2 percent of GDP, respectively, over the same periods. Interestingly, other developing countries (e.g. large Latin American economies) have not experienced a significant decline in FDI inflows over the past five years, which seems to suggest that new EU member countries and other economies with strong ties with the European Union may be suffering from the convulsed economic and political situation traditional investor or partner countries are going through.

In the case of Turkey, EU27 originated FDI inflows decreased from a value of close to US$14 billion in 2006-08 to roughly US$5.5 billion in 2009-10. Foreign direct investment inflows after the 2009 crisis have been mainly sustained thanks to capital accruing to the energy and utilities sector in a context of liberalization. The empirical analysis has shown that in fact privatization seems to have played a significant role in FDI attraction in Eastern European countries. However, as _privatization_ and liberalization processes in Turkey are completed in most sectors, this will no longer be a driving force for FDI inflows.

Drawing from the cross-country empirical results presented in the previous section, we try to understand which other factors, apart from the EU crisis and low-spirited prospects for EU accession, may have contributed to the slowdown in FDI inflows to Turkey. We also discuss which policies could be designed to try to correct this trend. This is especially important for the economic development of Turkey, as we have found empirical firm-level evidence pointing to a large and positive contribution of FDI to the productivity of local companies through knowledge spillovers.

Firstly, cross-country regressions identify the existing stock of FDI in a given country as a significant determinant of FDI inflows, since there seem to be some sort of agglomeration and/or 23
**15**

with the ‘new world’ and economic recovery encourages new investment and a virtuous circle of further improvements in confidence.

- This scenario is plausible in the opinion of the authors. The world will recover but the size and speed of the drop in economic activity has been a salutary lesson for investors who are not likely to forget that quickly. So, to capture the collapse of commodity prices and the financial sector, an initial rise in the equity risk premium of 8 percentage points for the United States is taken for the six sectors in the model: the energy, mining, agriculture, durable and non durable manufacturing and services sectors in 2009 and then dissipates in equal steps over the next four years but staying permanently higher by 2 percent from 2012. The permanent rise in the risk premium reflects the baseline risk premium which is assumed to be close to zero in the projection based on the experience from 2003. Thus there is an overshoot in the return to “normal”.

# _Shock 3: A rise in household risk_

The reappraisal of risk by firms as a result of the crisis also applies to households. As households view the future as being more risky, so they discount their future earnings and that affects their savings and spending decisions. The increase in household risk in the United States is assumed to be 3 percentage points in the ‘plausible’ scenario in 2009, half that in 2010 and back to ‘normal’ in 2011 and thereafter.

# _Summary of three crisis shocks and country differences_

The three shocks by sector the United States are shown in table 3.4.

# 3.4 **Equity risk premium, household risk and housing productivity for the United States under the plausible scenario**

||**_2009_**|**_2010_**|**_2011_**|**_2012_**|**_2013_**<br>|**_...beyond_**<br>**_2014_**|
|---|---|---|---|---|---|---|
|**Plausible scenario**|||||||
|Equity risk premium by<br>sector:|||||||
|–Energy|8|6|4|2|2|2|
|–Mining|8|6|4|2|2|2|
|–Agriculture|8|6|4|2|2|2|
|–durable manufacturing|8|6|4|2|2|2|
|–non durable|||||||
|manufacturing|8|6|4|2|2|2|
|–services|8|6|4|2|2|2|
|Household risk|3|1.5|0|0|0|0|
|Housing productivity|-10|-8|-6|-4|-4|-4|
|_Source:_authors’ calculations.|||||||
35

# **II. Social Advance and Regress**

The Chinese authorities have been much less active, and much less successful, in the social field than in the case of growth-promoting economic reforms. This contrasts with the pre-reform period. While aggregate output growth then was modes, and total factor productivity hardly increased at all, there were important achievements in some social fields. The main examples are basic health and elementary education – although the authorities during this period also were responsible for the devastating famine in connection with the “Great Leap Forward” in the late 1950s and early 1960s and the educational regress during the “Cultural Revolution” in the late 1960s and early 1970s.

What, then, have been the most important social developments in China during the period of economic reform? And what are the consequences of the economic reforms for the functioning of previously established (“inherited”) social arrangements? I start with the second question.

# **II:1. Consequences for Inherited Social Arrangements**

Even to a larger extent than in the Soviet Union and the socialist countries in Eastern Europe, social arrangements in China during the first decades after World War II were organized by work units ( _danwei_ ).<sup>60</sup> For the workforce, the job guarantee was probably the most important component of income security. The compressed distribution of wages, although largely motivated on ideological grounds, added to income security, since not only the economic reward for success but also the economic punishment for failure was quite limited. In urban areas, work units also provided pensions and various cash benefits, as well as “human services”, such as basic education, health care, housing, kindergartens, and recreation facilities – indeed even barber shops. In other words, in addition to serving as executors of centrally assigned production and investment tasks, work units functioned as mini-welfare states. In a typical Chinese formulation, they were “enterprises running

> 60 For a brief survey of social arrangements in China prior to the economic reforms see, for instance, Guan (2000).
# <u>Panel A: Overall Score Nominal (cont.)</u>

||Robust Reg w/o<br>|OLS Dropping top<br>5% of dep var w/o|Dep var:|<br>Average loan size / per|<br>capita GNI|Il|Il|
|---|---|---|---|---|---|---|---|
||Lending<br>methodology<br>dummy|<br>Lending<br>methodology<br>dummy|Robust Reg|OLS Dropping top<br>5% of dep var|OLS w/ the same<br>observations to IV|nstrumenta<br>Variable 1*<sup>1</sup>|nstrumenta<br>Variable 2*<sup>2</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Bank dummy|-0.1762***|-0.1535|-0.0965***|-0.0319|0.1864|0.1475|0.1186|
||[0.000]|[0.118]|[0.004]|[0.763]|[0.535]|[0.610]|[0.705]|
|Credit Union & coop dummy|-0.1344***|-0.1317*|-0.1032***|-0.0418|0.004|0.0407|0.0673|
||[0.000]|[0.084]|[0.001]|[0.568]|[0.980]|[0.804]|[0.695]|
|NGO dummy|-0.2479***|-0.3371***|-0.1949***|-0.2077**|-0.2878***|-0.2881***|-0.3027***|
||[0.000]|[0.000]|[0.000]|[0.011]|[0.001]|[0.000]|[0.000]|
|NBFI dummy|-0.1982***|-0.2034**|-0.1417***|-0.0883|-0.3402***|-0.3791***|-0.3890***|
||[0.000]|[0.028]|[0.000]|[0.425]|[0.003]|[0.002]|[0.002]|
|Preferring individual lending dummy|||0.1007***|0.1609***|0.2877***|0.3747***|0.3637***|
||||[0.000]|[0.003]|[0.002]|[0.000]|[0.003]|
|Preferring solidarity lending dummy|||0.0192|-0.0289|-0.0228|-0.046|-0.0519|
||||[0.355]|[0.447]|[0.797]|[0.727]|[0.684]|
|Log of total assets (PPP$)|0.0269***|0.0436***|0.0243***|0.0332***|0.0829**|0.0727**|0.0733**|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.014]|[0.011]|[0.010]|
|Constant|-0.2417***|-0.8441**|-0.3873***|-0.8500**|-2.9340***|-4.3329***|-4.0163***|
||[0.004]|[0.011]|[0.000]|[0.031]|[0.007]|[0.000]|[0.004]|
|Observations|770|737|641|616|558|558|536|
|Number of countries|41|41|39|39|29|29|28|
|R-squared|0.500|0.378|0.547|0.414|0.304|0.248|0.240|
|Adjusted R-squared|0.489|0.364|0.534|0.396|0.279|0.222|0.212|
|F statistic for weak identification||||||3.893|4.340|
|Shea partial R-squared||||||0.182|0.229|
|Hansen J statistics||||||0.518|0.448|

22
# **Table 7. Effects of fiscal policy volatility on GDP per capita growth**

||<br>|GDPper capit<br>|agrowth<br>|
|---|---|---|---|
|||OLS|IV|
|VARIABLES|(1)|(2)|(3)|
|Fiscal policy volatility|-0.022|-0.116***|-0.110***|
||(0.060)|(0.022)|(0.036)|
|Government size||0.013|0.020|
|||(0.012)|(0.013)|
|Initial GDP per capita||-0.659***|-0.767***|
|||(0.138)|(0.147)|
|Capital account||1.210***|1.230***|
|openness||||
|||(0.302)|(0.314)|
|Investment price||-1.481***|-1.548***|
|||(0.392)|(0.432)|
|Constant|1.986***|8.416***|9.202***|
||(0.533)|(1.157)|(1.332)|
|Observations|177|161|128|
|R-squared|0.006|0.356|0.403|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Fiscal policy volatility in all specifications is measured using primary expenditures. Instruments for (3): Political constraints; control of corruption.

37
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