rafmacalaba/datause-encoder-data huggingface dataset:
the informal sector do not report weekly or monthly earnings; only workers in the agricultural sector both qualify as informal sector workers and produce earnings data in the SAKERNAS survey. Table 9 below describes the quarterly fluctuations of agricultural workers' earnings.

**Table 9. Seasonal Fluctuations in Earnings Among Farm Workers**

||**1st**|**2nd**|**3rd**|**4th**|**Total**|
|---|---|---|---|---|---|
|**Farm Laborers**||||||
|Weekly Earnings|12,054|11,878|12,508|12,191|12,127|
|Monthly Eamings|51,660|50,904|53,605|52,246|51,975|
|**All Farm Workers**||||||
|Weekly Earnings|14,311|14,371|15,965|15,248|14,908|
|Monthly Earnings|61,332|61,589|68,423|65,350|63,893|

Note: All farm workers include farm laborers and workers on plantations, in livestock and fishing, and in forestry and hunting. Farm laborers account for about 2.9 million workers, while all farm workers account for approximately 4.9 million workers.

Somewhat surprisingly, earnings for farm laborers display very little fluctuation across quarters. Both weekly and monthly wages drop only 5 percent from the peak quarter (3) to the lowest quarter (2), declines which are smaller than those shown in full employment levels; recall that the figures in Table 7 demonstrated that underemployment among farm workers declined 10 percent from the peak quarter to the lowest quarter.'<sup>0</sup> Including in the analysis other workers in the agricultural sector produces a rather different picture, as both weekly and monthly earnings fluctuate by over 10 percent from the peak quarter (3) to the lowest quarter (1). The fluctuations are even more striking if only non-farm workers in the agricultural sector are considered; mean earnings for workers on plantations and in livestock, fishery and forestry activities fluctuate by more than 15 percent from peak to low quarters, and variation in individual earnings across quarters may be considerably higher. Moreover, unless variations in earnings across quarters are perfectly correlated, aggregation of data and examination of mean earning levels will understate the amount of variation experienced by agricultural workers.

Looking the data by region demonstrates how lower levels of aggregation reveal higher levels of fluctuation in earnings. Sumatra and Java are characterized by the least seasonality while Kalimantan, Sulawesi and the rest of Eastern Indonesia experience ' These findings are consistent with data provided by BPS concerning average wage rates for farm workers. The BPS Publication, _"Statistik Upah Buruh Tani di Pedesaan 1988-1994"_ reports a steady linear increase in nominal wages (reported monthly) throughout the archipelago with no evidence of seasonality in wages for planting, hoeing, and weeding.

14
Table 5a: Decomposition of change in 8<sup>th</sup> <u>grade Mathematics score at percentiles</u>

||**Characteristics**|**Coefficients**|**Total change**|
|---|---|---|---|
|**P10**||||
|Student and household<br>|1.01<br>|-1.03<br>|-0.02<br>|
|Schools<br>Teachers|-0.50<br>039|4.35<br>-391|3.85<br>-352|
|<br>Constant|.<br>-|.<br>26.16|.<br>26.16|
|Approximation error<br>|2.03<br>|-1.82<br>|0.21<br>|
|**Total**<br>**P20**|**2.94**|**23.75**|**26.69**|
|Student and household|0.99|6.56|7.55|
|Schools<br>Teachers|-0.02<br>0.89|13.63<br>-8.10|13.61<br>-7.21|
|Constant|-|17.47|17.47|
|Approximation error|1.35|-1.10|0.15|
|**Total**<br>|**3.20**|**28.45**|**31.65**|
|**P30**||||
|Student and household|1.32|17.67|18.99|
|Schools|-1.35|21.50|20.15|
|Teachers|2.25|-12.64|-10.39|
|Constant|-|5.22|5.22|
|Approximation error|2.07|-1.86|0.21|
|<br>**Total**<br>**P40**|**4.28**|**29.89**|**34.17**|
|Student and household|0.50|-11.37|-10.87|
|Schools|-1.08|7.29|6.21|
|Teachers<br>|1.32|-5.25<br>|-3.93<br>|
|Constant|-|42.11|41.11|
|Approximation error<br>|2.72<br>|-2.19<br>|0.53<br>|
|**Total **<br>**P50**|**3.46**|**30.60**|**34.06**|
|Student and household|0.88|7.96|8.84|
|Schools<br>|-2.67<br>|18.55<br>|15.88<br>|
|Teachers|3.44|-19.21|-15.77|
|Constant<br>|-<br>|24.58<br>|24.58<br>|
|Approximation error<br>**Total**<br>|0.69<br>**2.33**|-0.43<br>**31.45**|0.26<br>**33.78**|
|**P60**||||
|Student and household|0.23|17.40|17.63|
|Schools|-2.16|3.94|1.78|
|Teachers<br>|2.53|-13.86<br>|-11.33<br>|
|Constant|-|57.93|57.93|
|Approximation error|3.23|-2.73|0.50|
|<br>**Total**|**3.84**|**27.87**|**31.71**|
|**P70**<br>Student and household|-0.06|-8.05|-8.11|
|Schools|-1.45|4.94|3.49|
|Teachers<br>|1.83|-5.84<br>|-4.01<br>|
|Constant<br>Aiti|-<br>372|39.49<br>348|39.49<br>024|
|pproxmaon error<br>**Total**|.<br>**4.04**|-.<br>**27.05**|.<br>**31.09**|
|**P80**<br>||||
|Student and household|-0.35|-18.85|-18.50|
|Schools<br>|-1.03<br>|-2.90<br>|-3.93<br>|
|Teachers<br>Constant|3.46<br>-|-9.21<br>6365|-5.75<br>6365|
|<br>|<br>|.<br>|.<br>|
|Approximation error<br>**Total**|5.24<br>**731**|-5.72<br>**2697**|-0.48<br>**3428**|
|<br>**P90**|**.**|**.**|**.**|
|Student and household|028|-928|-900|
|<br>Schools|.<br>-0.52|.<br>-6.80|.<br>-7.32|
|Teachers<br>|1.41|1.09<br>|2.50<br>|
|Constant|-|51.40|51.40|
|Approximation error|3.88|-4.35|-0.47|
|<br>**Total **|<br>**5.05**|<br>**32.06**|<br>**37.11**|
|N: 2003<br>||5,100<br>||
|N: 2007||6,792||

26
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48
# 1. Inflation in **1985-1989**

# 1.1 **"Types"** of **Inflation**

As numerous Czechoslovak studies show, inflationary parameters were "built in" and they were a generic feature of the tradidonal centrally planned economy. They manifested themselves as a continuous aggregate demand overhang, as a gap between demand for economic resources on the part of economic subjects, and the available disposable resources.<sup>2</sup> In addition to systemic causes, two -ther factors contributed to inflation in Czechoslovakia: the type of economic policy and external causes (such as rising import p.ices and other external shocks). All the inflationary factors may have intertwined but systemic causes were decisive for inflationary trends in Czechoslovakia in the period analyzed here, i.e., in the second half of the 1980s.

As shown in Section 2, all three types of inflation coexisted in the Czechoslovak economy: first, there was open inflation, seen in the growth of consumer prices (in the Consumer Price Index, CPI). The price level increased as a result of autonomous pressures, such as the existence of persistent excess demand in dile (partial) consumer goods market, which had to be accommodated by decisions of central authorities to raise prices. But this was only one of several ways in which disequilibrium demonstrated itself in a centrally planned economy.

The second type of macroeconomic disequilibrium was suppressed inflation. Rigidity of prices, exchange rates, wages, and interest rates meant that prices in markets for goods, services, foreign currency and labor did not usually balance supply and demand; this resulted in demand overhang for goods and services on the one hand and excess money holdings on the other. Together with administrative allocations of inputs, price rigidity was another important factor that brought about a highly inelastic supply response. The demand overhang also resulted in searches for goods in short supply, "forced" substitution and "forced" savings, queuing, rationing systems, waiting lists, and other alternative mechanisms of allocation. Typical in Czechoslovakia were waiting lists for state and cooperative flats, and, at the end of the 1980s, for cars. In that case, disequilibriur was not accompanied by a corresponding increase in prices

> **2Ir** the view of most Czechoslovak economists, the primary cause of disequilibrium at the macroeconomic level lies in a centrally planned economy on the demand side. The principal role of demand inflation, however, does not mean that cost inflation plays no role, and that there is no feedback between cost and demand inflation. The enduring demand overhang in the investment sector, and in some parts of the sector of materials and intermediary products, creates a favorable climate for cost-push inflation. However, the transmission mechanism of inflationary pressures was not the same as in market economics and has not been fully understood.

3
<!-- Start of picture text --> Public NSO<br>9° - ors-<br>@<br>N iN<br>11°S- = 11°S- :<br>? yy [-] Protected Area<br>Ye 8s<br>Poverty<br>all as (0.682,0.973]<br>13°S - /; Li Ki 13°S - b Fil i (0-619.0-682]<br>& g t (0.554,0.619]<br>< a). 3S US (0.504,0.554]<br>Z —. : : (0.445,0.504]<br>Yi éA sy éi N (0.399,0.445]lo0.321,0.399 rvooson<br>: % ] : > ] (0.092,0.172]<br>15°S- - 15°S- . [0,0.092]<br>qf Cw dt % A<br>17°S- 47°S-<br>33.5°E 35.0°E 33.5°E 35.0°E<br><!-- End of picture text -->
lower than in countries at a similar level of development, including Bulgaria, Colombia, Ecuador, Peru and South Africa (Figure 18 ).

# **Figure 18 Herfindahl index and share of top 1%, 5% and 10% of exporters**

<!-- Start of picture text --> Herfindahl Index Share of Top 1% firms in Total Exports<br>1<br>0.2<br>0.9<br>0.18<br>0.16 0.8<br>0.14 0.7<br>0.12 0.6<br>0.1 0.5<br>0.08 0.4<br>0.06 0.3<br>0.04 0.2<br>0.02 0.1<br>0 0<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br>Share of Top 5% firms in Total Exports Share of Top 10% firms in Total Exports<br>1 1<br>0.95 0.95<br>0.9 0.9<br>0.85 0.85<br>0.8 0.8<br>0.75 0.75<br>0.7<br>0.7<br>0.65<br>0.65<br>0.6<br>0.6<br>0.55<br>0.55<br>0.5<br>0.5<br>2004 2005 2006 2007 2008 2009<br>2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br>Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).<br><!-- End of picture text --> It appears that the lower concentration of exports in Pakistan is due to a very large presence of small exporters. Indeed, while the average size of exporters in Pakistan is lower than in countries such as Bulgaria, Colombia, Ecuador, Peru and South Africa the median is higher, suggesting that there are more important non-linearities in the distribution of exports in Pakistan than in the distribution of exports in comparable countries, with large exporters determining the bulk of exports (see Figure 1 9 ).

25
**Figure 12: Brazil   (1,-1,*,*) versus (1,-1,-1,*) restrictions**

<!-- Start of picture text --> Test of known beta eq. to beta(t)<br>2<br>1.8 (1,-1,*,*)<br>1.6 (1,-1,-1,*)<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1<br><!-- End of picture text -->

**Figure 13: Brazil   (1,-1,*,*) versus (1,-1,-1,*) restrictions [without dummies]**

<!-- Start of picture text --> Test of known beta eq. to beta(t)<br>1.8<br>1.6 (1,-1,*,*)<br>1.4 (1,-1,-1,*)<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1<br><!-- End of picture text --> Note: Difference between Figures 12 and 13 is that underlying model in Figure 13 is without dummies. Since critical values of the Johansen test are not strictly valid in the presence of dummies, this is a common sensitivity analysis.

38
**Figure 8: Cumulative Share of Exports by Rank**

<!-- Start of picture text --> 0 1000 2000 3000 4000 5000<br>Rank of Product<br>Cumulative share of trade, 1992<br>Cumulative share of trade, 2005<br>Note: Data uses HS 6-digit classifications. Rank is largest to smallest by value.<br>100<br>80<br>60<br>40<br>Cululative Share of Trade 20<br>0<br><!-- End of picture text -->

**Figure 9: Cumulative Share of Exports by Rank, Top 500 Products**

<!-- Start of picture text --> 0 100 200 300 400 500<br>Rank of Product<br>Cumulative share of trade, 1992<br>Cumulative share of trade, 2005<br>Note: Data uses HS 6-digit classifications. Rank is largest to smallest by value.<br>100<br>80<br>60<br>40<br>Cululative Share of Trade<br>20<br><!-- End of picture text --> 24
# **Table A8: Contribution of Each Explanatory Factor to Wage Differentials, Using Dummies for the Highest Degree Completed by Region 2003-2007**

||**#1**|**#2**<br>**NCR**|**#3**|**#4**|**#5**<br>**Luzon**|**#6**|**#7**|**#8**<br>**Visayas**|**#9**|**#10**|**#11**<br>**Mindanao**|**#12**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|
|**Residual**|**56.0**|**64.2**|**50.7**|**50.0**|**52.2**|**46.1**|**46.7**|**51.9**|**40.3**|**46.0**|**51.9**|**37.9**|
|**Personal Attributes**|**32.1**|**24.0**|**36.3**|**30.3**|**23.9**|**35.8**|**34.8**|**28.9**|**39.8**|**38.9**|**33.3**|**44.2**|
|_Gender_|_3.2_|_--_|_--_|_1.6_|||_1.4_|||_0.9_|||
|_Experience_|_1.3_|_1.7_|_0.5_|_1.7_|_3.3_|_0.2_|_2.1_|_3.4_|_1.1_|_3.9_|_4.2_|_3.3_|
|_Highest Degree Completed_|_27.6_|_22.4_|_35.7_|_27.0_|_20.6_|_35.5_|_31.3_|_25.4_|_38.7_|_34.1_|_29.1_|_40.9_|
|**Sector**|**0.6**|**-0.3**|**1.2**|**6.8**|**14.0**|**1.6**|**4.7**|**10.2**|**1.2**|**1.8**|**6.0**|**-0.7**|
|**Occupation**|**11.1**|**11.8**|**11.8**|**12.8**|**9.6**|**16.4**|**13.5**|**9.0**|**17.8**|**12.8**|**8.6**|**17.6**|
|**Year**|**0.1**|**0.2**|**0.1**|**0.1**|**0.2**|**0.1**|**0.4**|**0.1**|**0.9**|**0.4**|**0.1**|**1.1**|

22
Unconditional Cash Transfers in China: An Analysis of the Rural Minimum Living Standard Guarantee Program<sup>1</sup>

**Jennifer Golan Terry Sicular Nithin Umapathi** The University of Manchester The University of Western Ontario The World Bank

JEL classifications: I38, O15 Keywords: Rural poverty, cash transfers, targeting, China

> 1We are grateful to Luo Chuliang, Wang Dewen, Philip O’Keefe, Song Jin, and Reena Badiani for their suggestions and input.
Next, we attempt to explain changes in welfare in terms of income and expenditure components. Suppose the average jth income component changes from 8 to g; and its concentraton index from _CJ_ to C;, then the total change in welfare may be written as where use has been made of (4), which shows that i&;(1-Cj-)-t&,1-cj) is the contribution of the jth income component to the total change in welfare. This contribution is further decomposed into two components; one due to a change in the mean of the jth income component and second due to a change in the distribution of the jth income component across total family income. Thus, Substituting (20) into (19) gives The calculations based on (21) are presented in Tables 16 and 17.

It can be see that real per capita welfare increased by 18.6 rubles (at 1980 prices) between 1989 arnd 1991. The major share of this increase came from government cash transfers which improved the per capita welfare by 12.2 rubles. Most of this increase came from the increase in the real value of govemmen **t** ransfers (12.3 rubles). This means that the distribution of governmen **t** ansfrs did not change significantly during this period.

It is intresting to note that wage and salary income (of the blue and white collar workers) added only 3.6 rubles to total welfare. And all of this increase came from the redistribution of wage and salary income in favor of the poorer families. The collective farm income led to a fall in total per capita welfare by 1.9 rubles between 1989 and 1991. Of this 1.9 rubles reduction, a fall of 3.2 rubles was due to a sharp decline in average income- from farm income, but the redistribution of this income component in favor of the poorer families led to an inrase in wdfare of 1.3 rubles. Small holding income contributed to an increase in welfare of 2.4 rubles in the 1989-91 peiod.

Trends changed damaticaUly between 1991 and 1992. Per capita welfire declined by 32.1 rubles. A lare proporton of this reduction is explained in terms of movements in salary income and government cash transfers. The average levels of both these components declined sharply and at the same time, both these components redistributed income away from the poorer families.

33
where _Yj_ , _t_ is locality _j_ ’s total income (in real terms)<sup>12</sup> at time _t_ , _TCij_ , _t_ is a transport cost function between localities _i_ and _j_ , and _σ_ is an elasticity term. Since the market potential formula has already been calibrated for Mexico, we use the same iceberg transport cost specification as the one estimated by Pérez Cervantes and Sandoval Hernández (2015), with _TCij_ , _t_ = _e_<sup>(.0557+.0024</sup><sup>_τij_,</sup><sup>_t_)</sup> for _j_ = _i_ and _σ_ = 9. Similarly to formula (1), formula (2) measures the potential demand for goods traded from location _i_ but in terms of income.

Observe that in both formulas (1) and (2), we exclude ’own locality’ to reduce endogeneity concerns.<sup>13</sup> Table 2 below details the mean of the market access and market potential indicators at the locality level over the study period. As one can see, there were large increases in both market access and market potential over the studied period.

Table 2: Mean and median market access and market potential of localities (1986 – 2014)

||M|arket acc|ess|M|arket potent|ial|
|---|---|---|---|---|---|---|
|Year|Mean|Median|sd|Mean|Median|sd|
|1986|24.41|0.00|389.89|–|–|–|
|1994|23.11|0.00|376.21|16,713.55|1,444.03|25,204.14|
|1999|24.90|0.00|393.58|445,492.00|55,543.83|617,044.60|
|2004|48.57|0.01|1,000.78|753,410.80|111,578.70|1,010,617.00|
|2009|53.09|0.01|1,093.79|1,271,577.00|220,128.80|1,680,713.00|
|2014|56.19|0.01|1,152.08|1,364,670.00|257,035.20|1,814,512.00|

_Sources_ : DeLorme, aaa and Economic Censuses (inegi). _Notes:_ Mean and median market access and market potential values are calculated over the sample of localities in Mexico.

On Figure 3, we represent market access and market potential at the locality level (overlayed on the road network) in 1986 and 2016. For comparison purposes, on both maps, the four categories correspond to the 1986 municipality quartiles of market access or market potential respectively. Although both maps show improvements along our two indicators, the contrast is starker with the market potential measure. This is understandably due to larger increases in real per capita income than in population.

> 12We use the real gross production provided by the economic census at the municipality level to proxy for local income.

> 13Also note that to increase accuracy of our measures, for metropolitan areas, we calculate market access and market potential by first calculating municipality level formulas (1) and (2) excluding all localities _j_ that belong to the same metropolitan area as municipality _i_ . We then compute a metropolitan area weighted average of these municipality indexes using municipality area as the weight.

10
In other respects, the reforms were less ambitious. First, although the Policy advocated partial privatization of MTL through the identification of a strategic partner, the policy<sup>stopped</sup> short of advocating full privatization (i.e., the sale of a controlling stake in the enterprise). The description of a 'strategic partner' could describe arrangements ranging from between<sup>a</sup> management contract combined with a small equity holding to full privatization. During<sup>field</sup> interviews, it was clear that some disagreement about the precise meaning of a 'strategic partner' remained. Second, although the policy says that a second cellular license would be issued, it was unclear how much competition would be introduced in the cellular market. During field interviews, Celtel, the company that was awarded the second cellular license, claimed that the Government had agreed not to issue any additional cellular licenses for at least five years after it started operating.<sup>23</sup> Third, the incumbent operator was allowed to retain monopolies over international and fixed-line services for at least five years after the separation of postal<sup>and</sup> telecommunications services and internet service providers were required to lease lines from the incumbent operator. Fourth, MTL was not required to divest its holdings in MalawiNet or TNM. Finally, MTL was awarded a five-year exclusivity agreement, from the date of its incorporation, apparently ruling out competition in local fixed-line, long distance and international services over this period. The Policy and Act did not oblige the Government to open these sectors to competition after the five-year period was complete.

# IIV. POLITICAL CIRCUMSTANCES SURROUNDING IREFoRM

In many ways, the reform, as envisaged in the 1998 Communications Sector Policy Statement and Law, was quite ambitious. Further, it was accomplished as part of a larger reform program covering all areas of communications including politically sensitive areas such as radio and television. However, the plans were modest compared to the more ambitious reforms attempted in other case-study countries (e.g., in Ghana and Uganda). Further, the reform was enacted later than reforms in other countries in SSA, including most of the other case-study countries. In this section, we discuss what motivated reform, how the government overcame opposition, and why the planned reform was less extensive than in the other case study countries.

> 23 These claims were later repeated in the intemational and local press (see, for example, Economist Intelligence Unit, 2001a, p. 38)

22