rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: Marginal determinants of agricultural employment including inheritance, schooling and parental characteristics**

||(1)<br>All|(2)<br>Rural|(3)<br>Urban|(4)<br>Men|(5)<br>Women|(6)<br>Rural men|(7)<br>Rural<br>women|(8)<br>Urban men|(9)<br>Urban<br>women|
|---|---|---|---|---|---|---|---|---|---|
|Father in farming|0.0181|0.0219|0.00746|0.0120|0.0188|0.00892|0.0334|0.0158|-0.00185|
||(0.0167)|(0.0326)|(0.0102)|(0.0262)|(0.0166)|(0.0494)|(0.0403)|(0.0197)|(0.00154)|
|Mother in farm.|0.0845***|0.145***|0.00958|0.139***|0.0446**|0.240***|0.0779*|-0.0226*|0.0204|
||(0.0242)|(0.0397)|(0.0172)|(0.0376)|(0.0218)|(0.0570)|(0.0432)|(0.0118)|(0.0186)|
|Father in non-farm|-0.0355*|-0.0799*|-0.00620|-0.0471|-0.0438**|-0.150**|-0.0516|0.00564|-0.0119|
||(0.0197)|(0.0468)|(0.0103)|(0.0310)|(0.0207)|(0.0687)|(0.0618)|(0.0171)|(0.00737)|
|Mother in non-farm|-0.0414*|-0.107*|-0.00494|-0.0656*|-0.0226|-0.161*|-0.0878|-0.0138|-0.000187|
||(0.0224)|(0.0557)|(0.00936)|(0.0356)|(0.0219)|(0.0838)|(0.0625)|(0.0136)|(0.00154)|
|Father‘s schooling|0.0371|0.150*|-0.00126|0.0260|0.0282|0.0285|0.211**|0.00739|-0.000617|
||(0.0305)|(0.0855)|(0.0103)|(0.0514)|(0.0313)|(0.131)|(0.107)|(0.0223)|(0.00149)|
|Mother‘s schooling|-0.0186|-0.104|-0.000787|-0.0228|-0.0171|-0.127|-0.136*|-0.0194|0.0376|
||(0.0396)|(0.0803)|(0.0163)|(0.0618)|(0.0357)|(0.117)|(0.0725)|(0.0187)|(0.0343)|
|Own schooling|-0.0114|-0.0452|-0.00598|-0.0504*|0.0435*|-0.0543|0.0230|-0.0604***|0.00654|
||(0.0187)|(0.0455)|(0.00835)|(0.0282)|(0.0242)|(0.0669)|(0.0620)|(0.0193)|(0.00492)|
|Inherited land|0.0623***<br>(0.0230)|0.0916**<br>(0.0426)|0.0117<br>(0.0137)|0.0201<br>(0.0304)|0.0992***<br>(0.0331)|0.00354<br>(0.0585)|0.168***<br>(0.0653)|0.00841<br>(0.0198)|0.0115<br>(0.00955)|
|Inherited house|0.00736|0.0175|0.0137|-0.0111|0.0209|-0.0305|0.0842|0.0120|-0.000296|
||(0.0188)|(0.0388)|(0.0124)|(0.0265)|(0.0209)|(0.0533)|(0.0548)|(0.0152)|(0.00207)|
|Inherited other|-<br>0.0432***|-0.0760**|-0.0149**|-0.0331|-0.0289*|-0.0596|-0.0573|-0.0149|-0.00198|
||(0.0159)|(0.0351)|(0.00754)|(0.0250)|(0.0155)|(0.0496)|(0.0435)|(0.0139)|(0.00156)|
|Observations<br>|3,190|1,652|1,433|1,422|1,702|731|866|627|554|
|Pseudo R<sup>2</sup>|0.305|0.213|0.234|0.305|0.365|0.218|0.284|0.298|0.479|

Notes: Robust standard errors in parentheses, clustered at the household level; *** p<0.01, ** p<0.05, * p<0.1. Marginal effects are reported. The regressions include controls listed in Table 5.

34
# **Figure 2.8. Labor force participation has remained high and stable.**

Labor Force Participation Rate (Percent of population aged 15-64), 2009–2020, Women, Men and Total.

<!-- Start of picture text --> 95<br>Male Female<br>Cambodia Urban<br>Rural<br>90<br>85<br>80<br>75<br>70<br>2009 2014 2019<br>Labor force participation<br><!-- End of picture text -->

**Source:** Authors’ elaboration using the CSES household survey.

**Figure 2.9. Non-farm employment has pushed participation in urban areas.**

Labor Force Participation Rate (Percent of population aged 15-64 Urban vs Rural by Gender), 2009–2020 <!-- Start of picture text --> Urban Male<br>95<br>Rural Male<br>90<br>85<br>80<br>75<br>70<br>65<br>2009 2014 2019<br>Source: Authors’ elaboration using the<br>CSES household survey.<br>Labor force participation<br><!-- End of picture text --> The massive structural transformation Cambodia experienced between 2009 and 2019 explains the urban-rural labor dynamics (Karamba et al., 2022). During these years, the agriculture sector lost approximately 1,100,000 million jobs, which is the main employment sector in rural areas. At the same time, more than 2 million jobs were created in industries such as sales, apparel, construction, and other services that are more prominent in urban areas.

FLFP, among the highest in the region, remained stable between 2009 and 2019, a result of Cambodia’s organized structural transformation. While rural FLFP remained stable, urban areas attracted female workers to the labor force.

Cambodia has also achieved high employment. In cities, employment increased by 9 percentage points between 2009 and 2020 from 72.6 percent to 81.5 percent with employment for men and women showing relatively similar increases of 8 percentage points for men and 9 percentage points for women (Figure 2.10). Throughout the decade, unemployment stood at very low levels; the combination of high labor force participation and low unemployment also points to a relatively healthy labor market with very few discouraged and disengaged workers (Figure 2.11).

7 | P a g e
(dummies for each level of education, where illiterate is omitted), male dummy, regional dummies, age, age squared and a dummy for internal migrants.

The results, summarized in Table 22, confirm previous conjectures in the presentation of the stylized facts. The probability of being employed decreases with higher educational attainment, which concurs with higher unemployment rates found among highly-educated individuals. Males are more likely to find employment than females. Unemployment in rural areas is lower than urban areas. Probability of employment increases with age. Most importantly, migrants have a higher probability of being employed than non-migrants even after controlling for education, regions, age and gender (see Table 22). Table 22 – Probit Model – Employment and Migration

|VARIABLES<br>Employed|
|---|
|Read& write<br>0.0996<br>|
|(0.0787)<br> <br>|
|Below Intermediate<br>-0.168**|
|(0.0662)<br> <br>|
|Gen Secondary<br>-0.739***<br>(0.101)|
|Tech. Secondary<br>-0.933***|
|(0.0456)|
|Above Intermediate<br>-1.039***<br>|
|(0.0644)|
|University<br>-1.090***<br>(0.0484)|
|Above University<br>-0.819***<br>(0.166)|
|male<br>0.916***|
|(0.0251)|
|Urban area<br>-0.293***<br>(0.0288)|
|Lower Egypt<br>0.0843**<br>(0.0343)|
|Upper Egypt<br>0.186***<br>|
|(0.0361)|
|frontier<br>0.419***<br>(0.102)|
|age<br>0.126***<br>(0.00629)|
|age2<br>-0.00118***|
|(8.56e-05)|
|mig<br>0.152***<br>(0.0501)|
|Constant<br>-1.160***|
|(0.113)|
|Observations<br>29,475|
|Standard errors in parentheses|
|*** p<0.01, ** p<0.05, * p<0.1|

23
> <sup>37</sup>

As in Kenya, ethnic conflict in Nigeria is endemic and parties are fragile. The largest governing party was only seven years old in 2005. Rates of partisan indifference are correspondingly greater than 50 percent among clustered and unclustered groups. However, unlike Kenya, clustered groups are not more likely to express a partisan preference. Instead, gift-giving plays a large role. In fact, 37 percent of Hausas (one of the clustered groups) and more than half the 59 percent of Hausas who express a partisan preference indicated that they had been offered a gift. Vote-buying has a correspondingly strong effect across all definitions of clustering: the odds that a respondent who had been offered a gift by a politician expresses a partisan preference are more than twice those of respondents who had not, substantially higher than the aggregate effects.

Religious polarization is another characteristic of Nigeria and could bias the results on ethnic polarization. Specifically, if parties make strong appeals to religious groups, members of those religious groups could exhibit very low rates of partisan indifference, such that rates of partisan indifference among clustered ethnic groups look high by comparison. However, the specifications in Table 4, replicated for Nigeria in Table 5, control for religious affiliation, eliminating this source of bias. Moreover, though Sunni and other non-Shiite Muslims are more likely to express a partisan preference, they do not appear to cluster their support. The two groups disproportionately expressed support for two parties, the PDP and the ANPP, and one of those parties, the PDP, is also disproportionately supported by Catholics and mainstream Protestants.

Zambia is the focus of many studies of ethnicity in African politics, with pathbreaking work by Posner (2004) demonstrating the shifting efforts of politicians to target particular ethnic groups depending on the political exigencies and institutional arrangements that they confronted. The very fact that their focus shifted from one type of
Hoselitz, B.F. 1953. "The Role of Cities in the Economic Growth of Underdeveloped Countries." _Joumal of Political Economy_ 5-61:195-208.

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Kaufmann, D. 1991. "The Forgotten Rationale for Policy Reform: The Productivity of Investment Projects." World Bank. Processed.

Keynes, John Maynard. 1923. "Preface." In H. Wright, eds, _Population._ Cambridge, Cambridge University Press.

Komai, Janos. 1990. _The Road to a Free Society._ New York: Norton.

Krugman, Paul. 1994. "The Myth of Asia's Miracle." _Foreign Affairs_ 62-78 (NovemberDecember): 167-88. (Reprinted in _Pop Intemationalism._ Cambridge Mass.: MIT Press).

Krugman, Paul. 1995. In P. Stoneman, ed., _Handbook of Economic Innovation and Technical Change_ (p. 360). Oxford: Blackwell:342-36.

Kuhn, Thomas S. 1962. _The Structure of Scientific Revolutions_ (2nd edition). Chicago: Chicago University Press. (French edition La Structure des Revolutions Scientifiques, Paris; Flammarion 1972.) Lall, S., and Paul Streeten. 1976. _Foreign Investment, Transnationals and Developing Countries._ London: Macmillan.

Lewis, W. Arthur. 1969. _Aspects of Tropical Trade 1883-1965._ Stockholm: Almqvist and Wiksel.

Little, l.M.D., Tibor Scitovsky, and Maurice Scott. 1970. _Industry and Trade in Some Developing Countries._ London: Oxford University Press.

Malinvaud, Edmond. 1977. _The Theory of Unemployment Reconsidered._ Oxford: Basil Blackwell.

Malthus. 1798. In G. Himmelfarb, ed., _Essay on the Principle of Population_ 1960. New York: Modem Library.

Modigliani, Franco. 1970. "The Life Cycle of Saving and Inter Country Differences in the Saving Ratio." In Eltis, Scott, and Wolfe, eds., _Induction, Growth, and Trade: Essays in Honor of Sir Roy Harrod._ Oxford: Clarendon Press.

Morande (forthcoming). "Chilean Saving Growth and Pension Scheme." _World Bank Research Observer._ Morawetz, David. 1978. _Twenty Five Years of Economic Development, 1950-1975._ Baltimore: Johns Hopkins University Press.

47
|(9)<br>0.061_∗∗∗_<br>(0.001)<br>0.025_∗∗∗_<br>(0.007)<br>yes<br>yes<br>yes<br>19,653<br>0.026<br>0.027<br>0.197<br>defned as<br> children,|
|---|
|icited Extension<br>(8)<br>0.062_∗∗∗_<br>(0.001)<br>0.026_∗∗∗_<br>(0.007)<br>0.020_∗∗∗_<br>(0.000)<br>0.010_∗∗∗_<br>(0.001)<br>0.003<br>(0.895)<br>0.001<br>(0.980)<br>0.008_∗_<br>(0.078)<br>-0.000_∗_<br>(0.070)<br>0.013<br>(0.492)<br>yes<br>yes<br>19,653<br>0.017<br>0.024<br>0.197<br>g of 24 clusters <br>ariables: adults,|
|Sol<br>(7)<br>0.065_∗∗∗_<br>(0.001)<br>0.025_∗∗∗_<br>(0.008)<br>yes<br>yes<br>19,653<br>0.011<br>0.037<br>0.197<br> level (consistin<br> the following v|
|(6)<br>0.072_∗∗∗_<br>(0.002)<br>0.030_∗∗_<br>(0.029)<br>yes<br>yes<br>yes<br>20,392<br>0.033<br>0.026<br>0.285<br> the stratum <br> included for|
|Extension<br>(5)<br>0.074_∗∗∗_<br>(0.002)<br>0.032_∗∗_<br>(0.025)<br>0.022_∗∗∗_<br>(0.001)<br>0.010_∗∗∗_<br>(0.003)<br>0.015<br>(0.374)<br>-0.003<br>(0.897)<br>0.009<br>(0.106)<br>-0.000<br>(0.130)<br>0.012<br>(0.666)<br>yes<br>yes<br>20,392<br>0.024<br>0.025<br>0.285<br>nk, 2018).<br> are clustered at<br>rol dummies are|
|(4)<br>0.079_∗∗∗_<br>(0.002)<br>0.031_∗∗_<br>(0.030)<br>yes<br>yes<br>20,392<br>0.020<br>0.049<br>0.285<br>ase (World Ba<br>tandard errors <br>ull sets of cont|
|(3)<br>0.087_∗∗∗_<br>(0.005)<br>0.033_∗∗_<br>(0.022)<br>yes<br>yes<br>yes<br>20,392<br>0.039<br>0.031<br>0.335<br>MS-ISA datab<br>parentheses. S<br> schooling”. F|
|Advice<br>(2)<br>0.089_∗∗∗_<br>(0.005)<br>0.033_∗∗_<br>(0.024)<br>0.020_∗∗∗_<br>(0.002)<br>0.013_∗∗∗_<br>(0.000)<br>0.012<br>(0.576)<br>-0.008<br>(0.767)<br>0.012_∗∗_<br>(0.015)<br>-0.000_∗∗_<br>(0.015)<br>0.027<br>(0.363)<br>yes<br>yes<br>20,392<br>0.030<br>0.031<br>0.335<br>ta from the LS<br>. _p_-values in <br>ucation is “no <br>d.|
|(1)<br>0.094_∗∗∗_<br>(0.005)<br>0.033_∗∗_<br>(0.030)<br>yes<br>yes<br>20,392<br>0.024<br>0.074<br>0.335<br>sed on survey da<br>05, _∗∗∗p <_ 0_._01<br> category for ed<br> head, male hea|
|AgShock<br>NonAgShock<br>Adults<br>Children<br>Primary educ. (head)<br>Secondary educ. (head)<br>Age head<br>Age head sq.<br>Male head<br>HH FE<br>Wave FE<br>Full set of control dummies<br>Observations<br>R-squared (within)<br>R-squared (total)<br>Mean dep. var.<br>_Source:_ Authors’ analysis ba<br>_Notes:_<br>_∗p <_ 0_._10, _∗∗p <_ 0_._<br>region_×_rural). The omitted <br>education of HH head, age of|

21
# **1. Introduction**

In recent years, the improved policy environment, increased donor assistance, and macroeconomic stability have combined to raise Ghana’s growth performance significantly above historical averages. After two and a half decades of independence<sup>1</sup> , Ghana’s per capita income has hardly changed, in large part due to political instability and frequent shifts in the policy paradigms that hurt entrepreneurship, investments and growth. Real per capita income has only started growing slowly after the return of political stability in 1983, which also coincides with the beginning of structural reforms. Since 1990, however, Ghana’s per capita growth accelerated visibly to about 2 percent and then even more during the first years of the new century (Figure 1).

**Figure 1 Ghana’s Long-Term and Short-Term Growth Trends, 1990-2005 (percent)**

<!-- Start of picture text --> 4.00<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Real Per Capita Growth Trend Growth<br><!-- End of picture text --> _Source_ : The authors’ calculation using World Development Indicators data. Trend growth was calculated as the HP-filter for the period 1990-2005.

These recent developments bode well for the government’s objective of doubling the country’s per capita income in the next decade. At the same time, numerous risks remain. Despite the strong growth performance, resource and export base of the economy is narrow and Ghana is highly vulnerable to external shocks. In addition to a looming energy crisis, the country faces major infrastructure gaps in water, sanitation, and roads. Finally, the focus on growth––which is vital for meeting social objectives and the Millennium Development Goals (MDGs)––must not detract from the remaining, critical social agenda in improving health and education outcomes in a low-income country.

> 1 Ghana was the first Sub-Saharan African country to earn independence from Great Britain on March 6, 1957.

2
recent patterns in use of antidumping and other TTBs raise the question of how earlier research on antidumping and conditions of market competition may apply to emerging economies, both as users and as targets.

# **4.2 Fragmentation of production and multinational activity**

Fragmentation of the value chain in the production of many traded goods has been increasing (Hummels, Ishii, and Yi 2001), and the share of trade mediated by affiliates (and/or) parents of the same multinational firm rather than through arm’s-length transactions is large. For example, nearly half of total U.S. imports in 2000 were intra-firm transactions.<sup>22</sup> Increased fragmentation of production and trade between related parties complicate both the political economy of antidumping and its potential impact on conditions of market competition.

Antidumping measures affecting intermediate inputs raise the costs of firms further downstream in the supply chain, thereby hurting overall competitiveness relative to firms and production locations unencumbered by antidumping.<sup>23</sup> Thus, to the extent that international transactions occur between affiliates of the same multinational firm, exporters and importers have a shared incentive to keep markets free from new trade barriers.<sup>24</sup> Global supply chains and a significant 22 In their study of the determinants of intra-firm trade, Bernard, Jensen, Redding, and Schott (2010) report that 46 percent of total U.S. imports in 2000 occurred between related parties. The importance of intra-firm transactions varied significantly by trading partner, ranging from close to zero to nearly 100 percent, and also by product.

23 Krupp and Skeath (2002) provide empirical evidence of a negative impact on production by downstream users of intermediate products subject to antidumping duties. The authors use a panel of 13 upstream/downstream product pairs, e.g., methanol and formaldehyde, for 1977-1992 to identify the effects of antidumping duties in the upstream industry on production and sales in the downstream industry. Antidumping duties upstream are confirmed to have a negative effect on downstream production. However, the authors do not find a significant negative effect on downstream value, perhaps because the direction of the effect on total revenue of lower quantity supplied depends on the elasticity of demand for the downstream product. A challenge for this type of research is finding upstream products tied significantly to specific downstream activities. Results are likely weaker to the extent that downstream producers have access to good substitutes for the input affected by antidumping.

> 24 Gawande, Hoekman, and Cui (2012) find evidence for seven large emerging market economies that participation in global supply chains helped  to keep protectionism in check during the 2008-9 global crisis.

22
The descriptive results on crop yields show clear differences across survey rounds (Table 3). While the results show a significant improvement in both maize and wheat yields in 2019 compared to 2012, yields declined or improved only marginally for most cohorts in 2016. The El Niño drought of 2015-16 may have contributed to the lack of improvements in crop yields in the 2016 survey round. In addition, although wheat yields improved significantly in 2019 compared to 2012 for all cohorts except one, similar improvements were also observed among the control groups, suggesting drivers of yield growth other than DSM. In contrast, maize yield improvements were observed across all treated cohorts in at least one post-treatment survey year, with no similar yield increases in the control group.

**Table 3. Crops' yield (kg/ha) over the survey years and DSM cohorts**

|**Cohorts**|**Statistics**||**Maize**|||**Wheat**||
|---|---|---|---|---|---|---|---|
|||**2012**|**2016**|**2019**|**2012**|**2016**|**2019**|
|Control|Mean|2288.1|2338.4|2187.2|2087.1|2086.7|2396.6***|
||SD|1716.7|1789.3|1661.6|1725.8|1699.1|1725.6|
||Obs.|651|977|712|435|702|946|
|2013|Mean|3646.3|2899.6**|3700.6||||
||SD|2287.9|1900.8|2060.7||||
||Obs.|42|67|59||||
|2014|Mean|3414.6|2949.5*|3661.0|2162.0|2347.2|2603.0**|
||SD|1980.3|2134.6|2032.1|1301.1|1799.1|1787.4|
||Obs.|46|134|96|20|25|22|
|2015|Mean|2972.5|3108.9|3868.0***|2317.9|2232.0|2714.7**|
||SD|2022.6|1740.1|2074.1|1810.1|1742.8|1918.1|
||Obs.|81|110|167|152|149|191|
|2016|Mean|2326.0|2770.1*|3241.2***|1455.0|1658.8|2320.6**|
||SD|1641.3|1620.1|1869.8|1424.7|1343.8|1536.9|
||Obs.|62|78|105|16|66|45|
|2017|Mean|2556.3|2895.8*|2816.9|1800.7|2090.7*|2939.9**|
||SD|1695.2|1815.7|1879.5|1082.5|1645.3|2117.6|
||Obs.|109|158|87|90|149|70|
|2018|Mean|2145.6|2097.0|2692.7***|2524.2|1301.9***|2039.8**|
||SD|1507.3|1642.0|1620.7|1687.5|1282.3|1478.1|
||Obs.|108|181|168|52|112|91|
|2019|Mean|1979.8|2221.8*|2520.1***|1407.7|1601.7|2088.8***|
||SD|1490.4|1813.9|1715.6|999.1|1183.0|1536.1|
||Obs.|160|206|283|21|76|48|
|All DSM|Mean|2503.9|2615.0|3074.4***|2113.2|1871.4**|2509.4***|
||SD|1797.1|1850.9|1924.5|1575.0|1564.5|1817.0|
||Obs.|608|934|965|351|577|467|

_Note_ : *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively, for the difference between 2012 and 2016 and 2012 and 2019, in crop yields. SD refers to standard deviation. 2012 refers to the baseline period before DSM.

11
the identity of the fastest- and the slowest-growing states/provinces may have changed, as is evident in the detailed Indian case study presented below in the next section.

# **Policy Response**

National governments responded with monetary and fiscal policy measures to counter the downturn in aggregate demand. The magnitude of the fiscal response varied according to need as well as capacity. China launched by far the most massive multiyear stimulus, followed by India and Brazil in descending order of magnitude.

The total size of the multiyear stimulus package in China was close to $600 billion (Y4 trillion), about 12.5 percent of 2008 GDP. The projects and programs underpinned by the stimulus were implemented over a period of 27 months.

# **Table 5 Real Annual Changes in Expenditure by National and Provincial/Local Governments in China, 2009**

(billion yuan, except where otherwise indicated)

||Nat|ional Gove|rnment|Provi|ncial/Local|Government|
|---|---|---|---|---|---|---|
||**2008**|**2009**|_Real_|**2008**|**2009**|_Real_|
||_< - - Billion_|_yuan -_||_< - - Billio_|_n yuan -_||
||_- >_||_Growtha/_|_- >_||_Growtha/_|
|National Defence|409.9|482.5|6%|8.0|12.6|42%|
|Public Security|64.9|84.6|17%|341.1|389.8|3%|
|Education|4.9|5.7|4%|851.9|987.0|4%|
|Science & Technology|107.7|143.4|20%|105.2|131.1|12%|
|Culture, Sports & Media|14.1|15.5|-1%|95.5|123.8|17%|
|Safety Net & Employment|34.4|45.4|19%|646.0|715.2|0%|
|Medical & Health Care|4.7|6.4|22%|271.0|393.1|30%|
|Environment|6.6|3.8|-49%|138.5|189.6|23%|
|Community Affairs|1.4|0.4|-76%|419.2|510.4|10%|
|Ag, Water & Forestry|30.8|31.9|-7%|423.6|640.2|36%|
|Transportation|91.3|106.9|5%|144.1|357.8|123%|
|All Others|563.6|599.2|-4%|1480.8|1653.8|0%|
|**Total Gov. Expenditure**<br>_a/ using implicit GDP_<br>_deflator_|**1334.4**|**1525.6**|**3%**|**4924.8**|**6104.4**|**11%**|
|Source: National Bureau of Sta|tistics, China.||||||

_Note:_ Real growth rates were calculated using an implicit GDP deflator.

They included investments of about Y1.2 trillion by the central government and Y2.8 trillion of supporting investment projects and programs by provincial and county governments and nongovernmental entities, financed by loans from domestic banks. Infrastructure 20