rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 27. Total formal employment by sector, 2014- Figure 28. Total formal employment by province of<br>2022 (FTE) employer registration, 2014-2022 (FTE)<br> 600 600<br> 500 500<br> 400 400<br> 300<br> 300<br> 200<br> 200<br> 100<br> 100<br> -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services Lusaka Copperbelt Other<br>Note: FTE defined as 12 monthly returns to account for Note: FTE defined as 12 monthly returns to account for<br>seasonal employment. seasonal employment. Data on province of employment (as<br>Source: World Bank and ZRA staff calculations based on PAYE opposed to registration location of employer) is not sufficiently<br>database complete for analysis. In some cases (most commonly in<br>agriculture), the employer’s province of registration may differ<br>from actual province of operations. Slight differences in totals<br>with other charts are attributable to completeness of provincial<br>data.<br>Source: World Bank and ZRA staff calculations based on PAYE<br>database<br>Figure 29. Total formal employment by size of firm, Figure 30. Year-on-year change in formal<br>2014-2022 (FTE) employment by driver, 2014-2022 (FTE)<br> 700 120<br> 600 100<br>80<br> 500<br>60<br> 400<br>40<br> 300 20<br> 200 0<br>-20<br> 100<br>-40<br> - -60<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>-80<br>Large (>100 employees) Medium (51-100 employees) 2015 2016 2017 2018 2019 2020 2021 2022<br>Micro/small (1-50 employees) Within-firm expansion Within-firm contraction New firm entry Firm exit<br>Note: FTE defined as 12 monthly returns to account for seasonal employment.<br>Source: World Bank and ZRA staff calculations based on PAYE database<br><!-- End of picture text -->

# 5.3. Results of jobs analyses

In line with labor productivity trends, real wages have declined in Zambia across several subsectors. Real wage declines have been driven by within-firm real wage deterioration, pointing to the broad-based nature of wage declines as productivity and pay have struggled to keep pace with inflation (Figure 31). The average Zambian formal worker in the PAYE dataset earned over ZMK 73,000 annually in 2014, which decreased by almost half to about ZMK 39,000 in 2022 (in constant 2010 ZMK terms). This pattern is consistent across a wide array of sectors and subsectors (Figure 32)―real wages for formal workers have declined in mining and quarrying, manufacturing, construction, wholesale and retail trade, financial services, and transport. Real wage declines in the formal sector are also in line with real wage declines in Zambia (i.e., for both formal and informal workers). These are observed in Zambia’s Labour Force Survey data: between 2017 and 2021, real average monthly earnings declined by 24 percent (Zambia MLSS 2022). An exception to this trend is formal workers in agriculture, forestry, and fishing, which is in line with the sector’s relatively stronger labor productivity performance. Although formal workers in this sector 17
Table A.8 in the Appendix presents by province, the predicted per capita expenditure, poverty rate, number of poor, and share in the total number of poor in the country. Lai Chau, Ha Giang and Dien Bien are three poorest provinces with the poverty rate of more than 70%. As expected, Hanoi and Ho Chi Minh city are the richest cities, followed by Da Nang, Hai Phong, Quang Ninh, Binh Duong, Ba Ria-Vung Tau.

Figure 1 shows the spatial distribution of poverty by provinces and districts in 2009. Poverty rates are highest in the mountainous Northern areas and lowest in the Mekong and Red River Deltas. Disaggregating down to the district level reveals a greater degree of heterogeneity in terms of both pockets of extreme poverty and pockets with particularly low levels of poverty. As we shall see below, such heterogeneity across sub-national localities translates into gains from spatial targeting of anti-poverty resources.

Figure 2 graphs the density of the poor across the country. Because of their large populations, the Mekong and Red River Delta regions still account for a significant number of the poor in the country. However, as we shall see below, the picture in 2009 is much less accentuated than at the time of the preceding census, and as such indicates a clear attenuation of the pattern described in earlier studies of poverty in Vietnam (see Minot et al, 2003) where the distribution of the number of poor people was inversely correlated with the spatial distribution communities’ poverty rates. Then, poverty rates were highest in relative sparsely populated localities and these thus accounted for only a modest fraction of the poor. Today, poverty rates remain spatially concentrated but the distribution of poor people is more evenly spread across the country. Consequently Vietnam’s poorest communities account for a larger share of the poor population.

Despite the ongoing urbanization process, poverty in Vietnam is currently still largely a rural phenomenon: 95 percent of the poor live in rural areas. The poverty rate in urban areas is generally low, and there is a large difference in poverty rates between urban and rural areas even within a province and a district; see Figure 3. Figure 4 shows clearly that poverty is markedly higher amongst ethnic minorities than Kinh and Hoa households. Even within the same mountain or delta region, there remains a large gap in poverty between ethnic minority households and Kinh (Hoa) households.

10
land on which the latrine was constructed. Each beneficiary household received a latrine, and these were intended for the household’s exclusive use.<sup>21</sup> The empirical analysis below takes as unit of observation all _parroquias_ in rural Ecuador. Our main variable of interest takes on three different values depending on whether a _parroquia_ received _no project_ , _at least_ one latrine project, or at least one FISE project but no latrine projects. We seek to assess to what extent the value taken by this indicator variable is affected by community-level characteristics, such as poverty and inequality.

# _Poverty and Inequality Estimates at the Community Level_

Poverty and inequality rates were estimated at the level of each _parroquia_ on the basis of a methodology that is described in detail in Elbers, Lanjouw and Lanjouw (2002, 2003). We estimate poverty based on a household per-capita measure of consumption expenditure, _yi._ A model of _yi_ is estimated using 1994 household survey data (INEC’s _Encuesta Sobre Las Condiciones de Vida - ECV_ ), with the set of explanatory variables restricted to those that are also found in, and strictly comparable to, the population census of 1990. We regress log per-capita consumption expenditure for household _i_ on a set of household-level demographic, occupational and educational variables, as well as census variables calculated at the level of the census-tract or other level of aggregation above the household level: ln _yi_ = **x** _i_ β + _ui_ , (6) where β is a vector of _k_ parameters and _ui_ is a disturbance term satisfying _E[ui|xi] = 0_ . The model in (6) is estimated using the survey data. We then use these estimates to calculate the welfare of an area or group in the population census. Letting _W_ represent an indicator of poverty or inequality, we estimate the expected level of _W_ given the observable characteristics in the population census, and the parameter estimates from (6). We denote this expectation as:

> 21   A separate category of FISE projects – designated “public toilets” – are more readily seen as public goods, and are kept separate from the latrines category in Table 1.  These represent around 4% of all FISE projects.

18
**Figure 1. Average Annual Real GDP Growth (Percent)**

<!-- Start of picture text --> 6.0<br>4.0<br>2.0<br>0.0<br>1990-1994 1995-1999 2000-2004 2005-2010<br>-2.0 EU15 North<br>EU15 Continental<br>-4.0<br>EU15 South<br>EU11<br>-6.0<br><!-- End of picture text --> _Source:_ For EU11: World Bank ECACE regional tables; for Western Europe, IMF World Economic Outlook<sup>6</sup>

**Figure 2. GDP per Capita, PPS Percent of EU15 Average**

<!-- Start of picture text --> 73 76<br>68<br>58 55 56 59 59 62<br>49<br>41 43<br><!-- End of picture text --> _Source:_ World Bank ECACE regional tables and IMF WEO _Note:_ Purchasing Power Standard (PPS)

**Table 1. Income Gap Change, EU15 Average and EU11 Economies**

||**1990-**|**1995-**|**2000-**|**2005-**|
|---|---|---|---|---|
||**1994**|**1999**|**2004**|**2010**|
|**EU11**|–**1.36%**|**0.08%**|**0.70%**|**0.42%**|
|Bulgaria|–0.36%|-0.29%|0.34%|0.29%|
|Croatia|–2.45%|0.12%|0.63%|0.23%|
|Czech R.|–1.03%|-0.59%|0.41%|0.57%|
|Estonia|–1.66%|0.46%|1.32%|-0.02%|
|Hungary|–0.97%|-0.10%|0.60%|-0.02|
|Latvia|–2.31%|0.42%|1.06%|-0.05%|
|Lithuania|–2.76%|0.28%|1.05%|0.31%|
|Poland|-0.05%|0.41%|0.31%|1.08%|
|Romania|-0.48%|-0.43%|0.42%|0.30%|
|Slovakia|-1.68%|0.09%|0.71%|1.34%|
|Slovenia|-1.18%|0.45%|0.82%|0.61%|

_Source:_ World Bank ECACE regional tables and IMF WEO _Note:_ Positive values indicate that the gap decreases, negative values indicate that the gap increases 6 EU15 South: Greece, Italy, Portugal, and Spain; EU15 North: Denmark, Finland, Ireland, Sweden, and he United Kingdom; EU15-Continental: Austria, Belgium, France, Germany, Luxemburg, and the Netherlands.

5
the financial institutions to lend to SHGs and to improvements in the organizational systems of the federations.

**Table 3.  Profitability of KVK federations**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|Operational Income<br>|234,196<br>|797,227<br>|2,423,971|
|Financial Expenses<br>|22,720<br>|456,762<br>|1,774,387|
|Operational Expenses<br>|189,908<br>|411,984<br>|637,125|
|DHAN expenses<br>|163,495<br>|362,560<br>|175,992|
|Operational Surplus / Loss<br>|(141,927)<br>|(434,079)<br>|(163,533)|
|Operational Self Sufficiency (%)|62|65|94|

**Table 4.  Profitability of KKVS SHGs**

|_Particulars_|_98-99_|_99-00_|_00-01_|
|---|---|---|---|
|Operational Income<br>|1,087,087<br>|1,911,642|3,815,099|
|Financial Expenses<br>|383,091<br>|950,264<br>|2,102,390|
|Operational Expenses<br>|604,213<br>|1,340,272|1,741,284|
|Operational Surplus / Loss<br>|99,783<br>|(378,894)<br>|(28,575)|
|Adjustments<br>|43,075<br>|19,789<br>|14,932|
|Operational Self Sufficiency (%)|110|83|99|
|Financial Self Sufficiency (%)|106|83|99|

**Table 5.  Profitability of SPMS federation**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|Operational Income<br>|1,024,379<br>|1,339,794<br>|2,438,960|
|Financial Expenses<br>|331,813<br>|563,226|1,283,772|
|Operational Expenses<br>|782,024<br>|840,160<br>|986,110|
|Operational Surplus / Loss<br>|(89,458)<br>|(63,592)<br>|169,078|
|Operational Self Sufficiency (%)|92|95|107|

**Table 6.  Consolidated Profitability of SPMS SHGs**

|_Particulars_|_2000-01_|
|---|---|
|Operational Income<br>|5,997,195|
|Financial Expenses<br>|3,673,235|
|Operational Expenses<br>|1,859,282|
|Operational Surplus / Loss<br>|464,678|
|Adjustments<br>|29,201|
|Operational Self Sufficiency (%)|108|
|Financial Self Sufficiency (%)|108|

- 20 -
reported in the first column of Table 13. The intercepts can therefore be interpreted as estimates of the constraint level in a given country for a benchmark manufacturing firm with 30 employees that is domestically owned, with no foreign ownership or import/export activity, and that is located in a large or capital city. These intercepts are the “country conditional means” that we analyze below.<sup>7</sup> For those SAR countries for which we have surveys in multiple years, we obtain year-specific conditional means from the means by year of the residuals from the firm-level regression.

The implication of the modeling framework set out above is that job-creating firms are likely to benefit most from the success of policy-makers in relaxing the most pressing external environment constraints. For the reasons discussed earlier, we focus on business environment constraints with a public good character and therefore do not include the “Tax Rates” or “Access to Finance” constraints in the main analysis. The latter measures are considered briefly in Appendix 2.

_The ranking of constraints by country_ The conditional country means are reported for each of the SAR countries and for the region as a whole in Table 15. The countries are listed according to GDP per capita. Because the conditional means are sometimes based on surveys from more than one year, GDP per capita used in the table and in the diagrams is a weighted average of GDP per capita, where the weights are the survey sample size in the relevant sample years. For the region as a whole, political instability, electricity and corruption are the three top-ranked constraints. However, there is considerable variation across the countries.

The differences between “finance” and the public good constraints discussed above mean it is not possible to interpret the country conditional mean for finance as comparable with the other constraints. As an example, think of the following experiment. If the economy is affected by a positive productivity shock, we would expect the average firm to report a higher cost of a public good constraint but lower cost of access to finance. Since the responses to the finance question are non-

> 7 The results for the unconditional country means, i.e., when using simple means across firms in a given survey, are very similar.

27
buyer (subsistence farmer) or a net seller (market integrated farmer). Clearly the effect on poverty will be determined by the composition of net food buyers and sellers in rural and urban areas. Barrett & Dorosh (1996) state that majority of the small farmers are net food buyers and hence will be adversely affected from an increase in food prices.

Deaton (1989), examining the effect of a hypothetical change in rice prices on the real income distribution in Thailand using non parametric techniques, finds that higher rice prices will benefit all rural households (the extent of which depends on the region), especially the middle-income ones. A similar study by Budd (1993) for Cote d’Ivoire show that impacts will be different for various regions and the magnitude will be relatively small, concluding that rural poor will not be affected much. Barrett & Dorosh (1996) extend the methodology and examine the effect of a change in rice prices on household welfare in Madagascar. They find that a good portion of the farmers are net rice buyers and will likely suffer from increased rice prices (based on first order effects). As they also mention, this contradictory result (to the previous two similar studies) may be due to the fact that rice is the major staple with very high budget shares, and also that Madagascar is a low income country unlike the wealthier Thailand.

Vietnam has experienced a drastic decline in poverty in the last decade. During this time it also took big unilateral steps in trade liberalization and is now one of the most open economies in Southeast Asia. The reforms for liberalization started in the late 1980’s with the “doi moi” reforms. The aim of “doi moi” was to slowly change Vietnam’s economy into a market economy. Domestic and external trade policy reforms have increased Vietnam’s involvement with the world<sup>5</sup> . One of the most important reforms in trade policy was the liberalization of trade in rice. Rice is the major staple in Vietnam; approximately 80% of the households cultivate rice, 50% of which is for subsistence. Vietnam is one of the major exporters of rice in the world accounting for 9- 17% of world rice exports. In the last decade, it has been the second largest exporter (by volume) of rice (Minot and Goletti, 2000). This paper aims to analyze the impact of the trade reforms (the most attention grabbing of which is the rice liberalization) and consequently the increase in price of rice on the welfare of different groups of households (i.e. net rice buyers, sellers). Panel data is used in providing the income growth for each

> 5 The land titling reform and liberalization of the fertilizer market are such examples. See Niimi et al (2003) for a detailed description of “doi moi” reforms.

3
> <sup>21</sup>

confirm that the colonial heritage dummies reported here, are significant predictors of the political variables. However, the Hansen _J_ -test rejects the assumption that these (or any other of the usual instruments) can be excluded from the second stage regressions.

However, although one cannot reject the hypothesis that the results in Table 2a are driven by the endogeneity of the political variables, once legal origin variables are taken into account, as in tables 5 and 6 below, the same instrumental strategy succeeds in four of six of the regressions and the political variables remain strongly significant.

**Table 2a:  Continuous years of elections, newspaper circulation and lending to the** **<u>private sector</u>**

||Con<br>competi<br>|tinuous yea<br>tive electio<br>|rs of<br>ns(1975)<br>|Average<br>|Newspaper Ci<br>(1975-2000)<br>|rculation<br>|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Growth<br>75 – 00<br>(_OLS_)|Level 2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|
|Political variable|.0006|.005|.013|.00008|.001|.002|
||(_.01_)|(_.09_)|(_0.0_)|(_.01_)|(_0.0_)|(_0.0_)|
|Land (thousands<br>of square miles)|-.000002<br>(_.28_)|-.00002<br>(_.32_)|-.00008<br>(_.66_)|-.0000002<br>(_.88_)|.00002<br>(_.90_)|.0002<br>(_.20_)|
|Average|.000001|.00026|-.00015|-.00004|.006|.004|
|population<br>(millions)|(_.92_)|(_.37_)|(_.71_)|(_.76_)|(.06)|(_.12_)|
|Log real<br>income/capita<br>(1975)||.21<br>(_0.0_)|||.12<br>(_.003_)||
|Private sector|-.02|||-.05|||
|lending (1975)|(_.15_)|||(_.02_)|||
|_R_<sup>_2_</sup>|.08|.36|.29|.11|.45|.43|
|_N_|66|73|88|78|94|115|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Constants not reported.

The ordinary least squares estimates of the effects of both the continuous years of competitive elections and average newspaper circulation indicate large effects that are robust
greater openness durintg this period, while the remaining countries experienced a fall in openness. Note that SADC countries and the SADC region have greater levels of openness than other regions - which is due to the small size of their domestic markets - and that the change **in** openness experienced during this period was greater than most other regions.

However, greater openness has not resulted in deeper integration into the global markets, but quite the opposite. Figure 2 shows the evolution of exports from Eastern and Southern Africa as a share of global exports. As can be observed, ESA region has been increasingly marginalized from world trade. In 1990, total exports from ESA represented around 1.05 per cent of total world exports; one decade later in 2002 they were 0.96 per cent. By RTAs, while SADC has barely maintained its share in world trade, COMESA has seen its exports falling from 0.59 to 0.44 of world exports. The main difference between the two groupings is accounted for by South Africa, which exports increased notably after the change of regime in 1994. Overall, therefore, for Eastern and Southern Africa, the 90s can be easily considered as a _"lost decade"_ in terms of integration into the global economy.

<!-- Start of picture text --> % of Word Figure 2: Eastern and Southern Africa (ESA):<br>Exports share of total world exports during the 90s<br>1.20<br>1.00 _<br>0.80 - - - TOTAL ESA<br>_ +-SADC<br>0.60 - COMESA<br>0.40 -<br>0.20<br>0.00 - l<br>1990 1992 1994 1996 1997 2000 2001<br>Source: IMF Directon of Trade<br><!-- End of picture text --> 3. **Regional Trade Agreements in Eastern and Southern Africa: stepping stones or stumbling blocks to integration into the world economy?** Data from the previous section indicates that the proliferation of preferential trade agreements and the generalized movement towards trade liberalization in most countries during the 90s has proven quite ineffective in substantially increasing the level of intraregional flows. The reality is that most countries in the region may not be natural trading partners because they have similar factor endowments and consequently, tend to export and import similar goodls (Wood and Mayer 2001). Similar factor endowments across 8
41

# RIFEmECS

Akiyama, T. and P. K. Trivedi (1987a) A New Global Tea Model: Specification, Estimation and Simulation. World Bank Starf Commodity Working Papers, Number 17. Washington : The World Bank.

___________________________ (1987b) "Vintage Production Approach to Perennial Crop Supply : An Application to Tea in Major Producing Countries", Journal of Econometrics 36, 133-161.

Binswanger, H., Y. Mundlak, N.C. Yang and A. Bowers (1985), "The Determinants of Cross-Country Aggregate Agricultural Supply". Journal of Econometrics, 36.

Dufour, J-N. (1982) "Recursive Stability Analysis of Linear Regression Relationships", Journal of Econometrics, 19, 31-76.

French, B.C., C. A. King, and D. D. Minami (1985) "Planting and Removal of Perennial Crops", American Journal of Agricultural Economics, 67, 215-223.

Hartley, M. J., M. Nerlove and R. K. Peters (1985) The SupplY ResDonse for Rubber in Sri Lanka : A Preliminary Analysis, World Bank Staff Worklnig Papers, Number 657, Washington, D. C.: The World Bank.

Hayashi, F. (1982) " Tobin's Marginal Q and Average Q : Neoclassical Interpretation", Econometrica, 50, 213-224.

Knight, P. T. (1976) "Economics of Cocoa Production in Brazil", Chapter 10 in Cocoa Production : Economic and Botanical Perspectives , John Simmons (Editor). New York : Praeger Publishers.

Monteiro, A. (1987) "Estmativas de margea bruta de producao de cacau, dos investimentos na implatacao e renovacao de lavouras decadentes nas propriedades cacaueiras de Estado da Bahia. Periodo 1965 a 1986". Unpublished CEPLAC paper.

Nerlove, M. (1979) "The Dynamics of Supply : Retrospect and Prospect", American Journal of Agricultural Economics, 61, 874-888.

Nickell, S. J. (1976) The Investment Decisions of Firms . Cambridge University Press.

Tafani, R. R. (1987) "Estimativas de producao de cacau no Brasil Para **08** proximos anos". Unpublished CEPLAC paper.

____________ (1987) "Juros Subsidiados Pela CEPLAC". Unpublished **memorandum.** Trivedi, P. K. (1986) "A Framework for Studying the Supply Response of Perennial Crops", Division Working Paper No. 1986-1, Commodity Studies and Projections Division, EPD, The World Bank